diff --git a/.flake8 b/.flake8
deleted file mode 100644
index 2b3720f9..00000000
--- a/.flake8
+++ /dev/null
@@ -1,11 +0,0 @@
-# Taken directly from https://github.com/ambv/black/blob/master/.flake8
-[flake8]
-ignore = E203, E266, E501, W503, C901, D104, D100, D101, D102, D103, D105, D401
-max-line-length = 88
-max-complexity = 18
-select = B,C,E,F,W,T4,B9,D
-enable-extensions = flake8-docstrings
-per-file-ignores =
-    tests/**:D101,D102,D103
-    src/datajudge/__init__.py:F401
-docstring-convention = numpy
diff --git a/.github/.pull_request_template.md b/.github/.pull_request_template.md
index f81b1bee..0e11397f 100644
--- a/.github/.pull_request_template.md
+++ b/.github/.pull_request_template.md
@@ -1,5 +1,5 @@
 # CI
 
-<!-- 
+<!--
 Note: In order for the CI to test against the correct database backend, please label this pull request with the name of the backend you'd like to test against, e.g. "postgres" for running the postgres tests on each commit.
--->
\ No newline at end of file
+-->
diff --git a/.github/actions/pytest/action.yml b/.github/actions/pytest/action.yml
deleted file mode 100644
index c0d24adb..00000000
--- a/.github/actions/pytest/action.yml
+++ /dev/null
@@ -1,27 +0,0 @@
-name: pytest
-description: installs package, runs pytest and creates coverage report.
-
-inputs:
-  backend:
-    description: which database backend to use
-    required: false
-  args:
-    description: arguments that are passed to pytest, e.g. "-k filter" or "tests/integration"
-    required: false
-
-runs:
-  using: composite
-  steps:
-    - name: Run Integration Tests
-      shell: bash -l {0}
-      run: |
-        flit install -s
-        if [[ "${{ inputs.backend }}" != "" ]]; then
-          pytest --cov=datajudge --cov-report=xml --cov-append --backend=${{ inputs.backend }} ${{ inputs.args }}
-        else
-          pytest --cov=datajudge --cov-report=xml --cov-append ${{ inputs.args }}
-        fi
-    - name: Generate code coverage report
-      uses: codecov/codecov-action@v3.1.3
-      with:
-        file: ./coverage.xml
diff --git a/.github/workflows/build_and_publish.yaml b/.github/workflows/build_and_publish.yaml
index c7146e97..f537ee30 100644
--- a/.github/workflows/build_and_publish.yaml
+++ b/.github/workflows/build_and_publish.yaml
@@ -3,9 +3,9 @@ on:
   pull_request:
   push:
     branches:
-      - 'main'
+      - "main"
     tags:
-      - 'v*'
+      - "v*"
   release:
     types:
       - published
@@ -17,16 +17,16 @@ jobs:
     strategy:
       fail-fast: true
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
       - name: Build
         # It seems as though flit publish expects a .pypirc file as compared to
         # only a token passed as an argument.
         # See https://flit.pypa.io/en/latest/cmdline.html#flit-publish
         run: |
           pixi run flit build
-      - uses: actions/upload-artifact@v4
+      - uses: actions/upload-artifact@v6
         with:
           path: dist/*
 
@@ -34,13 +34,14 @@ jobs:
     name: Upload to PyPI
     needs: [build_artifacts]
     runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: write
+    environment: pypi
     if: github.event_name == 'release' && github.event.action == 'published'
     steps:
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v7
         with:
           name: artifact
           path: dist
-      - uses: pypa/gh-action-pypi-publish@v1.9.0
-        with:
-          user: __token__
-          password: ${{ secrets.PYPI_TOKEN }}
+      - uses: pypa/gh-action-pypi-publish@v1.13.0
diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index 1bbdb566..1a44c4a6 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -8,53 +8,36 @@ on:
   # to the main branches and tags.
   push:
     branches:
-      - 'main'
-      - 'pre-commit-autoupdate'
+      - "main"
+      - "pre-commit-autoupdate"
     tags:
-      - 'v*'
-
+      - "v*"
 
 jobs:
-
   pre-commit-checks:
     name: Pre-commit Checks
     runs-on: ubuntu-latest
     steps:
       - name: Checkout branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
         with:
           environments: default lint
       - name: pre-commit
         run: pixi run pre-commit-run --color=always --show-diff-on-failure
 
-  mypy-type-checks:
-    name: Mypy Type Checks
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout branch
-        uses: actions/checkout@v4
-      - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
-        with:
-          environments: default lint
-      - name: mypy
-        run: |
-          pixi run -e mypy postinstall
-          pixi run -e mypy mypy .
-
   unit-tests:
     name: "unit tests"
     strategy:
       fail-fast: true
       matrix:
         env:
-          - py38
-          - py39
           - py310
           - py311
           - py312
+          - py313
+          - py314
           - sa1
           - sa2
         os:
@@ -64,18 +47,19 @@ jobs:
     runs-on: ${{ matrix.os }}
     steps:
       - name: Checkout branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
         with:
           environments: ${{ matrix.env }}
       - run: |
           pixi run -e ${{ matrix.env }} postinstall
           pixi run -e ${{ matrix.env }} coverage --color=yes
       - name: Generate code coverage report
-        uses: codecov/codecov-action@v3.1.3
+        uses: codecov/codecov-action@v5.5.2
         with:
           file: ./coverage.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
 
   mssql:
     if: ${{ contains(github.event.pull_request.labels.*.name, 'sqlserver') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
@@ -85,11 +69,11 @@ jobs:
       fail-fast: false
       matrix:
         env:
-          - mssql-py38
-          - mssql-py39
           - mssql-py310
           - mssql-py311
           - mssql-py312
+          - mssql-py313
+          - mssql-py314
           - mssql-sa1
           - mssql-sa2
     services:
@@ -102,16 +86,11 @@ jobs:
           - 1433:1433
     steps:
       - name: Checkout branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
         with:
           environments: ${{ matrix.env }}
-      # TODO: Consider removing this manual installation since the docker image already comes with a msodbc driver.
-      - name: Install msodbcsql17 driver
-        run: |
-          wget https://packages.microsoft.com/ubuntu/20.04/prod/pool/main/m/msodbcsql17/msodbcsql17_17.9.1.1-1_amd64.deb
-          ACCEPT_EULA=Y sudo apt install ./msodbcsql17_17.9.1.1-1_amd64.deb --allow-downgrades
       - name: Wait for SQL Server
         timeout-minutes: 1
         run: until docker logs "${{ job.services.db.id }}" 2>&1 | grep -q "SQL Server is now ready"; do sleep 10; done
@@ -119,9 +98,10 @@ jobs:
           pixi run -e ${{ matrix.env }} postinstall
           pixi run -e ${{ matrix.env }} coverage --color=yes
       - name: Generate code coverage report
-        uses: codecov/codecov-action@v3.1.3
+        uses: codecov/codecov-action@v5.5.2
         with:
           file: ./coverage.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
 
   postgres:
     if: ${{ contains(github.event.pull_request.labels.*.name, 'postgres') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
@@ -134,11 +114,11 @@ jobs:
           - latest
           - 11
         env:
-          - postgres-py38
-          - postgres-py39
           - postgres-py310
           - postgres-py311
           - postgres-py312
+          - postgres-py313
+          - postgres-py314
           - postgres-sa1
           - postgres-sa2
     services:
@@ -152,19 +132,19 @@ jobs:
           - 5432:5432
     steps:
       - name: Checkout branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
         with:
           environments: ${{ matrix.env }}
       - run: |
           pixi run -e ${{ matrix.env }} postinstall
           pixi run -e ${{ matrix.env }} coverage --color=yes
       - name: Generate code coverage report
-        uses: codecov/codecov-action@v3.1.3
+        uses: codecov/codecov-action@v5.5.2
         with:
           file: ./coverage.xml
-
+          token: ${{ secrets.CODECOV_TOKEN }}
 
   db2:
     if: ${{ contains(github.event.pull_request.labels.*.name, 'db2') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
@@ -174,10 +154,13 @@ jobs:
       fail-fast: false
       matrix:
         env:
-          - db2-py38
-          - db2-py39
           - db2-py310
-          - db2-sa1
+          # Environment can't be solved.
+          # - db2-py311
+          - db2-py312
+          - db2-py313
+          # Environment can't be solved.
+          # - db2-py314
     services:
       DB:
         image: ibmcom/db2:11.5.5.1
@@ -193,9 +176,9 @@ jobs:
 
     steps:
       - name: Checkout branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
         with:
           environments: ${{ matrix.env }}
       - name: Wait for db2 to load
@@ -205,9 +188,10 @@ jobs:
           pixi run -e ${{ matrix.env }} postinstall
           pixi run -e ${{ matrix.env }} coverage --color=yes
       - name: Generate code coverage report
-        uses: codecov/codecov-action@v3.1.3
+        uses: codecov/codecov-action@v5.5.2
         with:
           file: ./coverage.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
 
   snowflake:
     if: ${{ contains(github.event.pull_request.labels.*.name, 'snowflake') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
@@ -215,84 +199,83 @@ jobs:
     runs-on: ubuntu-latest
     env:
       SNOWFLAKE_USER: DATAJUDGE
-      SNOWFLAKE_PASSWORD: ${{ secrets.SNOWFLAKE_PASSWORD }}
+      SNOWFLAKE_PRIVATE_KEY: ${{ secrets.SNOWFLAKE_PRIVATE_KEY }}
       SNOWFLAKE_ACCOUNT: ${{ secrets.SNOWFLAKE_ACCOUNT }}
     strategy:
       fail-fast: false
       matrix:
         env:
-          - snowflake-py38
-          - snowflake-sa1
+          - snowflake-py310
     steps:
       - name: Checkout branch
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
         with:
           environments: ${{ matrix.env }}
       - run: |
           pixi run -e ${{ matrix.env }} postinstall
           pixi run -e ${{ matrix.env }} coverage --color=yes
       - name: Generate code coverage report
-        uses: codecov/codecov-action@v3.1.3
+        uses: codecov/codecov-action@v5.5.2
         with:
           file: ./coverage.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
 
-  # The authentication of these tests is currently failing.
-  # These tests should be reenabled asap.
-
-  # bigquery:
-  #   if: ${{ contains(github.event.pull_request.labels.*.name, 'bigquery') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
-  #   name: "BigQuery"
-  #   runs-on: ubuntu-latest
-  #   strategy:
-  #     fail-fast: false
-  #     matrix:
-  #       env:
-  #         - bigquery-py38
-  #         - bigquery-sa1
-  #   steps:
-  #     - name: 'Authenticate to Google Cloud'
-  #       uses: 'google-github-actions/auth@71fee32a0bb7e97b4d33d548e7d957010649d8fa'
-  #       with:
-  #         credentials_json: '${{ secrets.GCP_KEY }}'
-  #     - name: Checkout branch
-  #       uses: actions/checkout@v4
-  #     - name: Set up pixi
-  #       uses: prefix-dev/setup-pixi@v0.8.1
-  #       with:
-  #         environments: ${{ matrix.env }}
-  #     - run: |
-  #         pixi run -e ${{ matrix.env }} postinstall
-  #         pixi run -e ${{ matrix.env }} coverage
-  #     - name: Generate code coverage report
-  #       uses: codecov/codecov-action@v3.1.3
-  #       with:
-  #         file: ./coverage.xml
+  duckdb:
+    if: ${{ contains(github.event.pull_request.labels.*.name, 'duckdb') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
+    name: "DuckDB"
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        env:
+          - duckdb-py310
+          - duckdb-py314
+          - duckdb-sa2
+    steps:
+      - name: Checkout branch
+        uses: actions/checkout@v6
+      - name: Set up pixi
+        uses: prefix-dev/setup-pixi@v0.9.3
+        with:
+          environments: ${{ matrix.env }}
+      - run: |
+          pixi run -e ${{ matrix.env }} postinstall
+          pixi run -e ${{ matrix.env }} coverage --color=yes
+      - name: Generate code coverage report
+        uses: codecov/codecov-action@v5.5.2
+        with:
+          file: ./coverage.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
 
-  impala:
-    if: ${{ contains(github.event.pull_request.labels.*.name, 'impala') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
-    name: "Impala"
+  bigquery:
+    if: ${{ contains(github.event.pull_request.labels.*.name, 'bigquery') || contains(github.event.pull_request.labels.*.name, 'ready') || github.ref == 'refs/heads/main' }}
+    name: "BigQuery"
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
         env:
-          - impala-py38
-          - impala-sa1
+          - bigquery-py310
+          - bigquery-sa1
+          - bigquery-sa2
     steps:
       - name: Checkout branch
-        uses: actions/checkout@v4
-      - name: Set up container
-        run: docker compose up --wait impala
+        uses: actions/checkout@v6
+      - name: "Authenticate to Google Cloud"
+        uses: "google-github-actions/auth@71fee32a0bb7e97b4d33d548e7d957010649d8fa"
+        with:
+          credentials_json: "${{ secrets.GCP_KEY }}"
       - name: Set up pixi
-        uses: prefix-dev/setup-pixi@v0.8.1
+        uses: prefix-dev/setup-pixi@v0.9.3
         with:
           environments: ${{ matrix.env }}
       - run: |
           pixi run -e ${{ matrix.env }} postinstall
           pixi run -e ${{ matrix.env }} coverage
       - name: Generate code coverage report
-        uses: codecov/codecov-action@v3.1.3
+        uses: codecov/codecov-action@v5.5.2
         with:
           file: ./coverage.xml
+          token: ${{ secrets.CODECOV_TOKEN }}
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 139907a3..ec66b6d9 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -13,12 +13,12 @@ name: "CodeQL"
 
 on:
   push:
-    branches: [ "main", main_protection ]
+    branches: ["main", main_protection]
   pull_request:
     # The branches below must be a subset of the branches above
-    branches: [ "main" ]
+    branches: ["main"]
   schedule:
-    - cron: '16 4 * * 4'
+    - cron: "16 4 * * 4"
 
 jobs:
   analyze:
@@ -32,45 +32,44 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        language: [ 'python' ]
+        language: ["python"]
         # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
         # Use only 'java' to analyze code written in Java, Kotlin or both
         # Use only 'javascript' to analyze code written in JavaScript, TypeScript or both
         # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support
 
     steps:
-    - name: Checkout repository
-      uses: actions/checkout@v4
+      - name: Checkout repository
+        uses: actions/checkout@v6
 
-    # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
-      uses: github/codeql-action/init@v3
-      with:
-        languages: ${{ matrix.language }}
-        # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
+      # Initializes the CodeQL tools for scanning.
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@v4
+        with:
+          languages: ${{ matrix.language }}
+          # If you wish to specify custom queries, you can do so here or in a config file.
+          # By default, queries listed here will override any specified in a config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
 
-        # Details on CodeQL's query packs refer to : https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
-        # queries: security-extended,security-and-quality
+          # Details on CodeQL's query packs refer to : https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+          # queries: security-extended,security-and-quality
 
+      # Autobuild attempts to build any compiled languages  (C/C++, C#, Go, or Java).
+      # If this step fails, then you should remove it and run the build manually (see below)
+      - name: Autobuild
+        uses: github/codeql-action/autobuild@v4
 
-    # Autobuild attempts to build any compiled languages  (C/C++, C#, Go, or Java).
-    # If this step fails, then you should remove it and run the build manually (see below)
-    - name: Autobuild
-      uses: github/codeql-action/autobuild@v3
+      # ℹ️ Command-line programs to run using the OS shell.
+      # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
 
-    # ℹ️ Command-line programs to run using the OS shell.
-    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+      #   If the Autobuild fails above, remove it and uncomment the following three lines.
+      #   modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance.
 
-    #   If the Autobuild fails above, remove it and uncomment the following three lines.
-    #   modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance.
+      # - run: |
+      #     echo "Run, Build Application using script"
+      #     ./location_of_script_within_repo/buildscript.sh
 
-    # - run: |
-    #     echo "Run, Build Application using script"
-    #     ./location_of_script_within_repo/buildscript.sh
-
-    - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v3
-      with:
-        category: "/language:${{matrix.language}}"
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@v4
+        with:
+          category: "/language:${{matrix.language}}"
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 00000000..527937cb
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,23 @@
+name: Docs
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+permissions:
+  contents: write
+jobs:
+  docs:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout branch
+        uses: actions/checkout@v4
+      - name: Set up pixi
+        uses: prefix-dev/setup-pixi@ba3bb36eb2066252b2363392b7739741bb777659
+        with:
+          environments: docs
+      - name: Build docs
+        run: pixi run -e docs docs-build
+      - name: Deploy docs
+        if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+        run: pixi run -e docs mkdocs gh-deploy --force
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4a02354a..aae87298 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -11,37 +11,62 @@ repos:
         always_run: true
         require_serial: true
         pass_filenames: false
-      - id: black
-        name: black
-        entry: pixi run -e lint black
+      # ruff
+      - id: ruff
+        name: ruff
+        entry: pixi run -e lint ruff check --fix --exit-non-zero-on-fix --force-exclude
         language: system
         types_or: [python, pyi]
         require_serial: true
-      - id: flake8
-        name: flake8
-        entry: pixi run -e lint flake8
+      - id: ruff-format
+        name: ruff-format
+        entry: pixi run -e lint ruff format --force-exclude
         language: system
         types_or: [python, pyi]
         require_serial: true
-      - id: pyupgrade
-        name: pyupgrade
-        entry: pixi run -e lint pyupgrade
+      # mypy
+      - id: mypy
+        name: mypy
+        entry: pixi run -e default mypy
         language: system
-        types: [python, pyi]
-      - id: isort
-        name: isort
-        entry: pixi run -e lint isort
+        types: [python]
+        require_serial: true
+      # ty
+      - id: ty
+        name: ty
+        entry: pixi run -e default ty check --no-progress
         language: system
-        types: [python, pyi]
-      - id: typos
-        name: typos
-        entry: pixi run -e lint typos --force-exclude
+        types: [python]
+        require_serial: true
+      # prettier
+      - id: prettier
+        name: prettier
+        entry: pixi run -e lint prettier --write --list-different --ignore-unknown
         language: system
         types: [text]
-        exclude: "\\.csv$"
-        require_serial: true
+        files: \.(md|yml|yaml)$
+      # taplo
       - id: taplo
         name: taplo
         entry: pixi run -e lint taplo format
         language: system
         types: [toml]
+      # pre-commit-hooks
+      - id: trailing-whitespace-fixer
+        name: trailing-whitespace-fixer
+        entry: pixi run -e lint trailing-whitespace-fixer
+        language: system
+        types: [text]
+      - id: end-of-file-fixer
+        name: end-of-file-fixer
+        entry: pixi run -e lint end-of-file-fixer
+        language: system
+        types: [text]
+      # typos
+      - id: typos
+        name: typos
+        entry: pixi run -e lint typos --force-exclude
+        language: system
+        types: [text]
+        exclude: "\\.csv$"
+        require_serial: true
diff --git a/.prettierrc b/.prettierrc
new file mode 100644
index 00000000..cd78592c
--- /dev/null
+++ b/.prettierrc
@@ -0,0 +1,15 @@
+{
+  "singleQuote": false,
+  "bracketSpacing": true,
+  "printWidth": 200,
+  "endOfLine": "auto",
+  "tabWidth": 2,
+  "overrides": [
+    {
+      "files": "*.md",
+      "options": {
+        "tabWidth": 4
+      }
+    }
+  ]
+}
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 583f0722..755c5e72 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -6,10 +6,7 @@ build:
   commands:
     - mamba install -c conda-forge -c nodefaults pixi
     - pixi run -e docs postinstall
-    - pixi run -e docs docs
+    - pixi run -e docs docs-build
     - pixi run -e docs readthedocs
-sphinx:
-  configuration: docs/source/conf.py
 formats:
-- pdf
-
+  - pdf
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 00000000..c86b721c
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,170 @@
+# Changelog
+
+## 1.13.0 - 2026.01.21
+
+- Deprecate `sqlalchemy` <2.0.0.
+
+## 1.12.0 - 2026.01.12
+
+- Drop support impala as a backend.
+- Add support for DuckDB as a backend.
+
+## 1.11.0 - 2025.12.17
+
+- Drop support for Python 3.8 and Python 3.9.
+
+## 1.10.0 - 2025.02.13
+
+- Address deprecation warnings from `sqlalchemy`.
+- Provide more type hints.
+
+## 1.9.3 - 2025.01.13
+
+**Bug fixes**
+
+- Fix a bug in
+  [`datajudge.WithinRequirement.add_date_no_overlap_constraint`][datajudge.requirements.WithinRequirement.add_date_no_overlap_constraint] and
+  [`datajudge.WithinRequirement.add_date_no_overlap_2d_constraint`][datajudge.requirements.WithinRequirement.add_date_no_overlap_2d_constraint] and
+  [`datajudge.WithinRequirement.add_numeric_no_overlap_constraint`][datajudge.requirements.WithinRequirement.add_numeric_no_overlap_constraint] in
+  which some overlaps were not detected due to equality of their
+  leftmost bounds.
+
+## 1.9.2 - 2024.09.05
+
+**Bug fixes**
+
+- Fix a bug in [`datajudge.constraints.numeric.NumericPercentile`][datajudge.constraints.numeric.NumericPercentile] which
+  could lead to off-by-one errors in retrieving a percentile value.
+
+## 1.9.0 - 2024.06.25
+
+**New features**
+
+- Add styling for assertion messages. See `assertion-message-styling`
+  for more information.
+- Add `output_processors` and `filter_func` parameters to
+  [`datajudge.WithinRequiremen.tadd_uniques_equality_constraint`][datajudge.requirements.WithinRequirement.add_uniques_equality_constraint],
+  [`datajudge.WithinRequirement.add_uniques_superset_constraint`][datajudge.requirements.WithinRequirement.add_uniques_superset_constraint]
+  and
+  [`datajudge.WithinRequirement.add_uniques_subset_constraint`][datajudge.requirements.WithinRequirement.add_uniques_subset_constraint].
+- Add `output_processors`, `filter_func` and `compare_distinct`
+  parameters to
+  [`datajudge.BetweenRequirement.add_uniques_equality_constraint`][datajudge.requirements.BetweenRequirement.add_uniques_equality_constraint],
+  [`datajudge.BetweenRequirement.add_uniques_superset_constraint`][datajudge.requirements.BetweenRequirement.add_uniques_superset_constraint]
+  and
+  [`datajudge.BetweenRequirement.add_uniques_subset_constraint`][datajudge.requirements.BetweenRequirement.add_uniques_subset_constraint].
+- Add `output_processors` parameter to
+  [`datajudge.WithinRequirement.add_functional_dependency_constraint`][datajudge.requirements.WithinRequirement.add_functional_dependency_constraint].
+
+**Other changes**
+
+- Provide a `py.typed` file.
+- Remove usage of `pkg_resources`.
+
+## 1.8.0 - 2023.06.16
+
+**New features**
+
+- Implement
+  [`datajudge.WithinRequirement.add_functional_dependency_constraint`][datajudge.requirements.WithinRequirement.add_functional_dependency_constraint].
+
+**Other changes**
+
+- Improve error message when a `DataReference` is constructed
+  with a single column name instead of specifying a list of columns.
+
+## 1.7.0 - 2023.05.11
+
+**New features**
+
+- Implement
+  [`datajudge.WithinRequirement.add_categorical_bound_constraint`][datajudge.requirements.WithinRequirement.add_categorical_bound_constraint].
+- Extended [`datajudge.WithinRequirement.add_column_type_constraint`][datajudge.requirements.WithinRequirement.add_column_type_constraint] to
+  support column type specification using string format,
+  backend-specific SQLAlchemy types, and SQLAlchemy's generic types.
+- Implement [`datajudge.WithinRequirement.add_numeric_no_gap_constraint`][datajudge.requirements.WithinRequirement.add_numeric_no_gap_constraint],
+  [`datajudge.WithinRequirement.add_numeric_no_overlap_constraint`][datajudge.requirements.WithinRequirement.add_numeric_no_overlap_constraint],
+
+## 1.6.0 - 2023.04.12
+
+**Other changes**
+
+- Ensure compatibility with `sqlalchemy` \>= 2.0.
+
+## 1.5.0 - 2023.03.14
+
+**New features**
+
+- Implement
+  [`datajudge.BetweenRequirement.add_max_null_fraction_constraint`][datajudge.requirements.BetweenRequirement.add_max_null_fraction_constraint] and
+  [`datajudge.WithinRequirement.add_max_null_fraction_constraint`][datajudge.requirements.WithinRequirement.add_max_null_fraction_constraint].
+- Implement
+  [`datajudge.BetweenRequirement.add_numeric_percentile_constraint`][datajudge.requirements.BetweenRequirement.add_numeric_percentile_constraint] and
+  [`datajudge.WithinRequirement.add_numeric_percentile_constraint`][datajudge.requirements.WithinRequirement.add_numeric_percentile_constraint].
+
+## 1.4.0 - 2023.02.24
+
+**New features**
+
+- Add partial and experimental support for db2 as a backend.
+
+## 1.3.0 - 2023.01.17
+
+**New features**
+
+- Implement [`datajudge.BetweenRequirement.add_column_type_constraint`][datajudge.requirements.BetweenRequirement.add_column_type_constraint].
+  Previously, only the `WithinRequirement` method existed.
+- Implemented an option `infer_pk` to automatically retrieve and primary
+  key definition as part of
+  [`datajudge.WithinRequirement.add_uniqueness_constraint`][datajudge.requirements.WithinRequirement.add_uniqueness_constraint].
+- Added a `name` parameter to all `add_x_constraint` methods of
+  `WithinRequirement` and `BetweenRequirement`. This will give pytest
+  test a custom name.
+- Added preliminary support for Impala.
+
+**Other changes**
+
+- Improve assertion error for
+  [`datajudge.BetweenRequirement.add_row_matching_equality_constraint`][datajudge.requirements.BetweenRequirement.add_row_matching_equality_constraint].
+
+## 1.2.0 - 2022.10.21
+
+**New features**
+
+- Implemented specification of number of counterexamples in
+  [`datajudge.WithinRequirement.add_varchar_regex_constraint`][datajudge.requirements.WithinRequirement.add_varchar_regex_constraint].
+- Implemented in-database regex matching for some dialects via
+  `computation_in_db` parameter in
+  [`datajudge.WithinRequirement.add_varchar_regex_constraint`][datajudge.requirements.WithinRequirement.add_varchar_regex_constraint].
+- Added support for BigQuery backends.
+
+**Bug fix**
+
+- Snowflake-sqlalchemy version 1.4.0 introduced an unexpected change in
+  behaviour. This problem is resolved by pinning it to the previous
+  version, 1.3.4.
+
+## 1.1.1 - 2022.06.30
+
+**New: SQL implementation for KS-test**
+
+- The Kolgomorov Smirnov test is now implemented in pure SQL, shifting
+  the computation to the database engine, improving performance
+  tremendously.
+
+## 1.1.0 - 2022.06.01
+
+**New feature: Statistical Tests**
+
+- Implemented a new constraint
+  [`datajudge.constraints.stats.KolmogorovSmirnov2Sample`][datajudge.constraints.stats.KolmogorovSmirnov2Sample] for
+  [`datajudge.BetweenRequirement`][datajudge.requirements.BetweenRequirement] that performs a [Kolmogorov Smirnov
+  Test](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test)
+  between two data sources.
+
+## 1.0.1 - 2022.05.24
+
+**Bug fix:**
+
+- The method `is_deprecated` of [`datajudge.Condition`][datajudge.Condition] was called
+  despite not existing.
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
deleted file mode 100644
index 3cfa838b..00000000
--- a/CHANGELOG.rst
+++ /dev/null
@@ -1,133 +0,0 @@
-.. Versioning follows semantic versioning, see also
-   https://semver.org/spec/v2.0.0.html. The most important bits are:
-   * Update the major if you break the public API
-   * Update the minor if you add new functionality
-   * Update the patch if you fixed a bug
-
-Changelog
-=========
-
-1.9.0 - 2024.06.25
-------------------
-
-**New features**
-
-- Add styling for assertion messages. See :ref:`assertion-message-styling` for more information.
-
-- Add ``output_processors`` and ``filter_func`` parameters to
-  :meth:`datajudge.requirements.WithinRequirement.add_uniques_equality_constraint`,
-  :meth:`datajudge.requirements.WithinRequirement.add_uniques_superset_constraint`
-  and :meth:`datajudge.requirements.WithinRequirement.add_uniques_subset_constraint`.
-
-- Add ``output_processors``, ``filter_func`` and ``compare_distinct`` parameters to
-  :meth:`datajudge.requirements.BetweenRequirement.add_uniques_equality_constraint`,
-  :meth:`datajudge.requirements.BetweenRequirement.add_uniques_superset_constraint`
-  and :meth:`datajudge.requirements.BetweenRequirement.add_uniques_subset_constraint`.
-
-- Add ``output_processors`` parameter to
-  :meth:`datajudge.requirements.BetweenRequirement.add_functional_dependency_constraint`.
-
-**Other changes**
-
-- Provide a ``py.typed`` file.
-
-- Remove usage of ``pkg_resources``.
-
-
-1.8.0 - 2023.06.16
-------------------
-
-**New features**
-
-- Implement :meth:`datajudge.WithinRequirement.add_functional_dependency_constraint`.
-
-**Other changes**
-
-- Improve error message when a :class:`~datajudge.DataReference` is constructed with a single column name instead of specifying a list of columns.
-
-1.7.0 - 2023.05.11
-------------------
-
-**New features**
-
-- Implement :meth:`datajudge.WithinRequirement.add_categorical_bound_constraint`.
-- Extended :meth:`datajudge.WithinRequirement.add_column_type_constraint` to support column type specification using string format, backend-specific SQLAlchemy types, and SQLAlchemy's generic types.
-- Implement :meth:`datajudge.WithinRequirement.add_numeric_no_gap_constraint`, :meth:`datajudge.WithinRequirement.add_numeric_no_overlap_constraint`,
-
-1.6.0 - 2023.04.12
-------------------
-
-**Other changes**
-
-- Ensure compatibility with ``sqlalchemy`` >= 2.0.
-
-
-1.5.0 - 2023.03.14
-------------------
-
-**New features**
-
-- Implement :meth:`datajudge.BetweenRequirement.add_max_null_fraction_constraint` and
-  :meth:`datajudge.WithinRequirement.add_max_null_fraction_constraint`.
-- Implement :meth:`datajudge.BetweenRequirement.add_numeric_percentile_constraint` and
-  :meth:`datajudge.WithinRequirement.add_numeric_percentile_constraint`.
-
-
-1.4.0 - 2023.02.24
-------------------
-
-**New features**
-
-- Add partial and experimental support for db2 as a backend.
-
-
-1.3.0 - 2023.01.17
-------------------
-
-**New features**
-
-- Implement :meth:`~datajudge.BetweenRequirement.add_column_type_constraint`. Previously, only the ``WithinRequirement`` method existed.
-- Implemented an option ``infer_pk`` to automatically retrieve and primary key definition as part of :meth:`datajudge.WithinRequirement.add_uniqueness_constraint`.
-- Added a ``name`` parameter to all ``add_x_constraint`` methods of ``WithinRequirement`` and ``BetweenRequirement``. This will give pytest test a custom name.
-- Added preliminary support for Impala.
-
-**Other changes**
-
-- Improve assertion error for :meth:`~datajudge.WithinRequirement.add_row_matching_equality_constraint`.
-
-
-1.2.0 - 2022.10.21
-------------------
-
-**New features**
-
-- Implemented specification of number of counterexamples in :meth:`~datajudge.WithinRequirement.add_varchar_regex_constraint`.
-- Implemented in-database regex matching for some dialects via ``computation_in_db`` parameter in :meth:`~datajudge.WithinRequirement.add_varchar_regex_constraint`.
-- Added support for BigQuery backends.
-
-**Bug fix**
-
-- Snowflake-sqlalchemy version 1.4.0 introduced an unexpected change in behaviour. This problem is resolved by pinning it to the previous version, 1.3.4.
-
-
-1.1.1 - 2022.06.30
-------------------
-
-**New: SQL implementation for KS-test**
-
-- The Kolgomorov Smirnov test is now implemented in pure SQL, shifting the computation to the database engine, improving performance tremendously.
-
-1.1.0 - 2022.06.01
-------------------
-
-**New feature: Statistical Tests**
-
-- Implemented a new constraint :class:`~datajudge.constraints.stats.KolmogorovSmirnov2Sample` for :class:`~datajudge.BetweenRequirement` that performs a `Kolmogorov Smirnov Test <https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test>`_ between two data sources.
-
-1.0.1 - 2022.05.24
-------------------
-
-**Bug fix:**
-
-- The method :meth:`is_deprecated` of :class:`~datajudge.Condition` was called despite not existing.
-
diff --git a/README.md b/README.md
index f26a400e..e717a7b2 100644
--- a/README.md
+++ b/README.md
@@ -10,11 +10,19 @@ Express and test specifications against data from database.
 
 [Documentation](https://datajudge.readthedocs.io/en/latest/index.html)
 
+# Supported backends
+
+![PostgreSQL](https://img.shields.io/badge/PostgreSQL-4169E1?logo=postgresql&logoColor=white)
+![Microsoft SQL Server](https://img.shields.io/badge/SQL_Server-CC2927?logo=microsoftsqlserver&logoColor=white)
+![Snowflake](https://img.shields.io/badge/Snowflake-29B5E8?logo=snowflake&logoColor=white)
+![Google BigQuery](https://img.shields.io/badge/BigQuery-669DF6?logo=googlebigquery&logoColor=white)
+![DuckDB](https://img.shields.io/badge/DuckDB-FFF000?logo=duckdb&logoColor=black)
+
 # Usage
 
 `datajudge` can either be installed via pypi with `pip install datajudge` or via conda-forge with `conda install datajudge -c conda-forge`.
 
-Please refer to the [Getting Started](https://datajugde.readthedocs.io/en/latest/getting_started.html) section of our documentation for details.
+Please refer to the [Getting Started](https://datajudge.readthedocs.io/en/latest/getting-started/) section of our documentation for details.
 
 Expressing an expectations between different tables from a database may look as such:
 
@@ -31,5 +39,4 @@ companies_between_req = BetweenRequirement.from_tables(
 companies_between_req.add_row_superset_constraint(
     columns1=["name"], columns2=["name"], constant_max_missing_fraction=0
 )
- ```
-
+```
diff --git a/docker-compose.yaml b/docker-compose.yaml
deleted file mode 100644
index 1efa1490..00000000
--- a/docker-compose.yaml
+++ /dev/null
@@ -1,64 +0,0 @@
-# Taken from
-# https://github.com/ibis-project/ibis/blob/master/docker-compose.yml
-version: "3.4"
-services:
-  impala:
-    depends_on:
-      - impala-postgres
-      - kudu
-    environment:
-      PGPASSWORD: postgres
-    healthcheck:
-      interval: 30s
-      retries: 20
-      test:
-        - CMD-SHELL
-        - nc -z 127.0.0.1 21050 && nc -z 127.0.0.1 50070
-      timeout: 10s
-    hostname: localhost
-    image: ibisproject/impala:latest
-    ports:
-      - 21050:21050
-    networks:
-      - impala
-  impala-postgres:
-    user: postgres
-    hostname: postgres
-    environment:
-      POSTGRES_PASSWORD: postgres
-    healthcheck:
-      interval: 10s
-      retries: 3
-      test:
-        - CMD
-        - pg_isready
-      timeout: 5s
-    image: postgres:13.9-alpine
-    networks:
-      - impala
-  kudu:
-    cap_add:
-      - SYS_TIME
-    depends_on:
-      - kudu-tserver
-    environment:
-      KUDU_MASTER: "true"
-    image: ibisproject/kudu:latest
-    ports:
-      - 7051:7051
-      - 8051:8051
-    networks:
-      - impala
-  kudu-tserver:
-    cap_add:
-      - SYS_TIME
-    environment:
-      KUDU_MASTER: "false"
-    image: ibisproject/kudu:latest
-    ports:
-      - 7050:7050
-      - 8050:8050
-    networks:
-      - impala
-networks:
-  impala:
diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
new file mode 120000
index 00000000..04c99a55
--- /dev/null
+++ b/docs/CHANGELOG.md
@@ -0,0 +1 @@
+../CHANGELOG.md
\ No newline at end of file
diff --git a/docs/Makefile b/docs/Makefile
deleted file mode 100644
index d0c3cbf1..00000000
--- a/docs/Makefile
+++ /dev/null
@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/api-documentation.md b/docs/api-documentation.md
new file mode 100644
index 00000000..23122637
--- /dev/null
+++ b/docs/api-documentation.md
@@ -0,0 +1,3 @@
+# API Documentation
+
+::: datajudge
diff --git a/docs/development.md b/docs/development.md
new file mode 100644
index 00000000..7fd71a8e
--- /dev/null
+++ b/docs/development.md
@@ -0,0 +1,55 @@
+# Development
+
+`datajudge` development relies on [pixi](https://pixi.sh/latest/).
+In order to work on `datajudge`, you can create a development environment as follows:
+
+```bash
+git clone https://github.com/Quantco/datajudge
+cd datajudge
+pixi run postinstall
+```
+
+Unit tests can be run by executing
+
+```bash
+pixi run test
+```
+
+Integration tests are run against a specific backend at a time. As of now, we provide helper
+scripts to spin up either a Postgres or MSSQL backend.
+
+To run integration tests against Postgres, first start a docker container with a Postgres database:
+
+```bash
+./start_postgres.sh
+```
+
+Then, you can run tests against the database you just started with one of the Postgres-specific
+pixi environments, e.g.:
+
+```bash
+pixi run -e postgres-py312 test
+```
+
+Analogously, for MSSQL, run
+
+```bash
+./start_mssql.sh
+```
+
+and
+
+```bash
+pixi run -e mssql-py312 test
+```
+
+or
+
+```bash
+pixi run -e mssql-py312 test_freetds
+```
+
+depending on the driver you'd like to use.
+
+Please note that running tests against Snowflake and BigQuery requires authentication to be
+set up properly.
diff --git a/docs/examples/company-data.md b/docs/examples/company-data.md
new file mode 100644
index 00000000..8c275d11
--- /dev/null
+++ b/docs/examples/company-data.md
@@ -0,0 +1,128 @@
+# Company data
+
+To get started, we will create a sample database using sqlite that contains a list of companies.
+
+The table "companies_archive" contains three entries:
+
+**companies_archive**
+
+| id  | name    | num_employees |
+| --- | ------- | ------------- |
+| 1   | QuantCo | 90            |
+| 2   | Google  | 140,000       |
+| 3   | BMW     | 110,000       |
+
+While "companies" contains an additional entry:
+
+**companies**
+
+| id  | name    | num_employees |
+| --- | ------- | ------------- |
+| 1   | QuantCo | 100           |
+| 2   | Google  | 150,000       |
+| 3   | BMW     | 120,000       |
+| 4   | Apple   | 145,000       |
+
+```python
+import sqlalchemy as sa
+
+eng = sa.create_engine('sqlite:///example.db')
+
+with eng.connect() as con:
+    con.execute("CREATE TABLE companies (id INTEGER PRIMARY KEY, name TEXT, num_employees INTEGER)")
+    con.execute("INSERT INTO companies (name, num_employees) VALUES ('QuantCo', 100), ('Google', 150000), ('BMW', 120000), ('Apple', 145000)")
+    con.execute("CREATE TABLE companies_archive (id INTEGER PRIMARY KEY, name TEXT, num_employees INTEGER)")
+    con.execute("INSERT INTO companies_archive (name, num_employees) VALUES ('QuantCo', 90), ('Google', 140000), ('BMW', 110000)")
+```
+
+As an example, we will run 4 tests on this table:
+
+1. Does the table "companies" contain a column named "name"?
+2. Does the table "companies" contain at least 1 entry with the name "QuantCo"?
+3. Does the column "num_employees" of the "companies" table have all positive values?
+4. Does the column "name" of the table "companies" contain at least all the values of
+   the corresponding column in "companies_archive"?
+
+```python
+import pytest
+import sqlalchemy as sa
+
+from datajudge import (
+    Condition,
+    WithinRequirement,
+    BetweenRequirement,
+)
+from datajudge.pytest_integration import collect_data_tests
+
+
+# We create a Requirement, within a table. This object will contain
+# all the constraints we want to test on the specified table.
+# To test another table or test the same table against another table,
+# we would create another Requirement object.
+companies_req = WithinRequirement.from_table(
+    db_name="example", schema_name=None, table_name="companies"
+)
+
+# Constraint 1: Does the table "companies" contain a column named "name"?
+companies_req.add_column_existence_constraint(columns=["name"])
+
+# Constraint 2: Does the table "companies" contain at least 1 entry with the name "QuantCo"?
+condition = Condition(raw_string="name = 'QuantCo'")
+companies_req.add_n_rows_min_constraint(n_rows_min=1, condition=condition)
+
+# Constraint 3: Does the column "num_employees" of the "companies" table have all
+# positive values?
+companies_req.add_numeric_min_constraint(column="num_employees", min_value=1)
+
+# We create a new Requirement, this time between different tables.
+# Concretely, we intent to test constraints between the table "companies"
+# and the table "companies_archive".
+companies_between_req = BetweenRequirement.from_tables(
+    db_name1="example",
+    schema_name1=None,
+    table_name1="companies",
+    db_name2="example",
+    schema_name2=None,
+    table_name2="companies_archive",
+)
+
+# Constraint 4: Does the column "name" of the table "companies" contain at least all
+# the values of the corresponding column in "companies_archive"?
+companies_between_req.add_row_superset_constraint(
+    columns1=['name'], columns2=['name'], constant_max_missing_fraction=0
+)
+
+# collect_data_tests expects a pytest fixture with the name
+# "datajudge_engine" that is a SQLAlchemy engine
+
+@pytest.fixture()
+def datajudge_engine():
+    return sa.create_engine("sqlite:///example.db")
+
+# We gather our distinct Requirements in a list.
+requirements = [companies_req, companies_between_req]
+
+# "collect_data_tests" takes all requirements and turns their respective
+# Constraints into individual tests. pytest will be able to pick
+# up these tests.
+test_constraint = collect_data_tests(requirements)
+```
+
+Saving this file as `specification.py` and running `$ pytest specification.py`
+will verify that all constraints are satisfied. The output you see in the terminal
+should be similar to this:
+
+```
+=================================== test session starts ===================================
+...
+collected 4 items
+
+specification.py::test_constraint[ColumnExistence::companies] PASSED                [ 25%]
+specification.py::test_constraint[NRowsMin::companies] PASSED                       [ 50%]
+specification.py::test_constraint[NumericMin::companies] PASSED                     [ 75%]
+specification.py::test_constraint[RowSuperset::companies|companies_archive] PASSED  [100%]
+
+==================================== 4 passed in 0.31s ====================================
+```
+
+You can also use a formatted html report using the `--html=report.html` flag.
diff --git a/docs/examples/dates.md b/docs/examples/dates.md
new file mode 100644
index 00000000..68b2b4c5
--- /dev/null
+++ b/docs/examples/dates.md
@@ -0,0 +1,155 @@
+# Dates
+
+This example concerns itself with expressing `Constraint`s against data revolving
+around dates. While date `Constraint`s between tables exist, we will only illustrate
+`Constraint`s on a single table and reference values here. As a consequence, we will
+only use [`WithinRequirement`][datajudge.requirements.WithinRequirement], as opposed to [`BetweenRequirement`][datajudge.requirements.BetweenRequirement].
+
+Concretely, we will assume a table containing prices for a given product of id 1.
+Importantly, these prices are valid for a certain date range only. More precisely,
+we assume that the price for a product - identified via the `preduct_id` column -
+is indicated in the `price` column, the date from which it is valid - the date
+itself included - in `date_from` and the the until when it is valid - the date
+itself included - in the `date_to` column.
+
+Such a table might look as follows:
+
+**prices**
+
+| product_id | price | date_from | date_to  |
+| ---------- | ----- | --------- | -------- |
+| 1          | 13.99 | 22/01/01  | 22/01/10 |
+| 1          | 14.5  | 22/01/11  | 22/01/17 |
+| 1          | 13.37 | 22/01/16  | 22/01/31 |
+
+Given this table, we would like to ensure - for the sake of illustrational purposes -
+that 6 constraints are satisfied:
+
+1. All values from column `date_from` should be in January 2022.
+2. All values from column `date_to` should be in January 2022.
+3. The minimum value in column `date_from` should be the first of January 2022.
+4. The maximum value in column `date_to` should be the 31st of January 2022.
+5. There is no gap between `date_from` and `date_to`. In other words, every date
+   of January has to be assigned to at least one row for a given product.
+6. There is no overlap between `date_from` and `date_to`. In other words, every
+   date of January has to be assigned to at most one row for a given product.
+
+Assuming that such a table exists in database, we can write a specification against it.
+
+```python
+import pytest
+import sqlalchemy as sa
+
+from datajudge import WithinRequirement
+from datajudge.pytest_integration import collect_data_tests
+
+# We create a Requirement, within a table. This object will contain
+# all the constraints we want to test on the specified table.
+# To test another table or test the same table against another table,
+# we would create another Requirement object.
+prices_req = WithinRequirement.from_table(
+    db_name="example", schema_name=None, table_name="prices"
+)
+
+# Constraint 1:
+# All values from column date_from should be in January 2022.
+prices_req.add_date_between_constraint(
+    column="date_from",
+    lower_bound="'20220101'",
+    upper_bound="'20220131'",
+    # We don't tolerate any violations of the constraint:
+    min_fraction=1,
+)
+
+# Constraint 2:
+# All values from column date_to should be in January 2022.
+prices_req.add_date_between_constraint(
+    column="date_to",
+    lower_bound="'20220101'",
+    upper_bound="'20220131'",
+    # We don't tolerate any violations of the constraint:
+    min_fraction=1,
+)
+
+# Constraint 3:
+# The minimum value in column date_from should be the first of January 2022.
+
+# Ensure that the minimum is smaller or equal the reference value min_value.
+prices_req.add_date_min_constraint(column="date_from", min_value="'20220101'")
+# Ensure that the minimum is greater or equal the reference value min_value.
+prices_req.add_date_min_constraint(
+    column="date_from",
+    min_value="'20220101'",
+    use_upper_bound_reference=True,
+)
+
+# Constraint 4:
+# The maximum value in column date_to should be the 31st of January 2022.
+
+# Ensure that the maximum is greater or equal the reference value max_value.
+prices_req.add_date_max_constraint(column="date_to", max_value="'20220131'")
+# Ensure that the maximum is smaller or equal the reference value max_value.
+prices_req.add_date_max_constraint(
+    column="date_to",
+    max_value="'20220131'",
+    use_upper_bound_reference=True,
+)
+
+# Constraint 5:
+# There is no gap between date_from and date_to. In other words, every date
+# of January has to be assigned to at least one row for a given product.
+prices_req.add_date_no_gap_constraint(
+    start_column="date_from",
+    end_column="date_to",
+    # We don't want a gap of price date ranges for a given product.
+    # For different products, we allow arbitrary date gaps.
+    key_columns=["product_id"],
+    # As indicated in prose, date_from and date_to are included in ranges.
+    end_included=True,
+    # Again, we don't expect any violations of our constraint.
+    max_relative_violations=0,
+)
+
+# Constraint 6:
+# There is no overlap between date_from and date_to. In other words, every
+# of January has to be assigned to at most one row for a given product.
+princes_req.add_date_no_overlap_constraint(
+    start_column="date_from",
+    end_column="date_to",
+    # We want no overlap of price date ranges for a given product.
+    # For different products, we allow arbitrary date overlaps.
+    key_columns=["product_id"],
+    # As indicated in prose, date_from and date_to are included in ranges.
+    end_included=True,
+    # Again, we don't expect any violations of our constraint.
+    max_relative_violations=0,
+)
+
+@pytest.fixture()
+def datajudge_engine():
+# TODO: Insert actual connection string
+    return sa.create_engine("your_db://")
+
+# We gather our single Requirement in a list.
+requirements = [prices_req]
+
+# "collect_data_tests" takes all requirements and turns their respective
+# Constraints into individual tests. pytest will be able to pick
+# up these tests.
+test_constraint = collect_data_tests(requirements)
+```
+
+Please note that the `DateNoOverlap` and `DateNoGap` constraints also exist
+in a slightly different form: `DateNoOverlap2d` and `DateNoGap2d`.
+As the names suggest, these can operate in 'two date dimensions'.
+
+For example, let's assume a table with four date columns, representing two
+ranges in distinct dimensions, respectively:
+
+- `date_from`: Date from when a price is valid
+- `date_to`: Date until when a price is valid
+- `date_definition_from`: Date when a price definition was inserted
+- `date_definition_to`: Date until when a price definition was used
+
+Analogously to the unidimensional scenario illustrated here, one might care
+for certain constraints in two dimensions.
diff --git a/docs/examples/exploration.md b/docs/examples/exploration.md
new file mode 100644
index 00000000..c8cff67c
--- /dev/null
+++ b/docs/examples/exploration.md
@@ -0,0 +1,221 @@
+# Exploration
+
+While datajudge seeks to tackle the use case of expressing and evaluating tests against
+data, its fairly generic inner workings allow for using it in a rather explorative
+workflow as well.
+
+Let's first clarify terminology by exemplifying both scenarios. A person wishing to test
+data might ask the question
+
+> Has the number of rows not grown too much from version 1 of the table to version 2 of the table?
+
+whereas a person wishing to explore the data might ask the question
+
+> By how much has the number of rows grown from version 1 to version 2 of the table?
+
+Put differently, a test typically revolves around a binary outcome while an exploration
+usually doesn't.
+
+In the following we will attempt to illustrate possible usages of datajudge for
+exploration by looking at three simple examples.
+
+These examples rely on some insight about how most datajudge `Constraint` s work under
+the hood. Importantly, `Constraint` s typically come with
+
+- a `retrieve` method: this method fetches relevant data from database, given a
+  `DataReference`
+- a `get_factual_value` method: this is typically a wrapper around `retrieve` for the
+  first `DataReference` of the given `Requirement` / `Constraint`
+- a `get_target_value` method: this is either a wrapper around `retrieve` for the
+  second `DataReference` in the case of a `BetweenRequirement` or an echoing of the
+  `Constraint` s key reference value in the case of a `WithinRequirement`
+
+Moreover, as is the case when using datajudge for testing purposes, these approaches rely
+on a [sqlalchemy engine](ttps://docs.sqlalchemy.org/en/14/core/connections.html). The
+latter is the gateway to the database at hand.
+
+## Example 1: Comparing numbers of rows
+
+Assume we have two tables in the same database called `table1` and `table2`. Now we
+would like to compare their numbers of rows. Naturally, we would like to retrieve
+the respective numbers of rows before we can compare them. For this purpose we create
+a `BetweenTableRequirement` referring to both tables and add a `NRowsEquality`
+`Constraint` onto it.
+
+```python
+import sqlalchemy as sa
+from datajudge import BetweenRequirement
+
+engine = sa.create_engine(your_connection_string)
+req = BetweenRequirement.from_tables(
+    db_name,
+    schema_name,
+    "table1",
+    db_name,
+    schema_name,
+    "table2",
+)
+req.add_n_rows_equality_constraint()
+n_rows1 = req[0].get_factual_value(engine)
+n_rows2 = req[0].get_target_value(engine)
+```
+
+Note that here, we access the first (and only) `Constraint` that has been added to the
+`BetweenRequirement` by writing `req[0]`. `Requirements` are are sequences of
+`Constraint` s, after all.
+
+Once the numbers of rows are retrieved, we can compare them as we wish. For instance, we
+could compute the absolute and relative growth (or loss) of numbers of rows from
+`table1` to `table2`:
+
+```python
+absolute_change = abs(n_rows2 - n_rows1)
+relative_change = (absolute_change) / n_rows1 if n_rows1 != 0 else None
+```
+
+Importantly, many datajudge staples, such as `Condition` s can be used, too. We shall see
+this in our next example.
+
+## Example 2: Investigating unique values
+
+In this example we will suppose that there is a table called `table` consisting of
+several columns. Two of its columns are supposed to be called `col_int` and
+`col_varchar`. We are now interested in the unique values in these two columns combined.
+Put differently, we are wondering:
+
+> Which unique pairs of values in `col_int` and `col_varchar` have we encountered?
+
+To add to the mix, we will moreover only be interested in tuples in which `col_int` has a
+value of larger than 10.
+
+As before, we will start off by creating a `Requirement`. Since we are only dealing with
+a single table this time, we will create a `WithinRequirement`.
+
+```python
+import sqlalchemy as sa
+from datajudge import WithinRequirement, Condition
+
+engine = sa.create_engine(your_connection_string)
+
+req = requirements.WithinRequirement.from_table(
+    db_name,
+    schema_name,
+    "table",
+)
+
+condition = Condition(raw_string="col_int >= 10")
+
+req.add_uniques_equality_constraint(
+    columns=["col_int", "col_varchar"],
+    uniques=[], # This is really just a placeholder.
+    condition=condition,
+)
+uniques = req[0].get_factual_value(engine)
+```
+
+If one was to investigate this `uniques` variable further, one could, e.g. see the
+following:
+
+```python
+([(10, 'hi10'), (11, 'hi11'), (12, 'hi12'), (13, 'hi13'), (14, 'hi14'), (15, 'hi15'), (16, 'hi16'), (17, 'hi17'), (18, 'hi18'), (19, 'hi19')], [1, 100, 12, 1, 7, 8, 1, 1, 1337, 1])
+```
+
+This becomes easier to parse when inspecting the underlying `retrieve` method of the
+`UniquesEquality` `Constraint`: the first value of the tuple corresponds to the list
+of unique pairs in columns `col_int` and `col_varchar`. The second value of the tuple
+are the respective counts thereof.
+
+Moreoever, one could manually customize the underlying SQL query. In order to do so, one
+can use the fact that `retrieve` methods typically return an actual result or value
+as well as the sqlalchemy selections that led to said result or value. We can use these
+selections and compile them to a standard, textual SQL query:
+
+```python
+values, selections = req[0].retrieve(engine, constraint.ref)
+print(str(selections[0].compile(engine, compile_kwargs={"literal_binds": True}))
+```
+
+In the case from above, this would return the following query:
+
+```sql
+SELECT
+    anon_1.col_int,
+anon_1.col_varchar,
+count(*) AS count_1
+FROM
+    (SELECT
+    tempdb.dbo.table.col_int AS col_int,
+    tempdb.dbo.table.col_varchar AS col_varchar
+    FROM
+    tempdb.dbo.table WITH (NOLOCK)
+    WHERE col_int >= 10) AS anon_1
+GROUP BY anon_1.col_int, anon_1.col_varchar
+```
+
+## Example 3: Comparing column structure
+
+While we often care about value tuples of given columns, i.e. rows, it can also provide
+meaningful insights to compare the column structure of two tables. In particular, we
+might want to compare whether columns of one table are a subset or superset of another
+table. Moreover, for columns present in both tables, we'd like to learn about their
+respective types.
+
+In order to illustrate such an example, we will again assume that there are two tables
+called `table1` and `table2`, irrespective of prior examples.
+
+We can now create a `BetweenRequirement` for these two tables and use the
+`ColumnSubset` `Constraint`. As before, we will rely on the `get_factual_value`
+method to retrieve the values of interest for the first table passed to the
+`BetweenRequirement` and the `get_target_value` method for the second table passed
+to the `BetweenRequirement`.
+
+```python
+import sqlalchemy as sa
+from datajudge import BetweenRequirement
+
+engine = sa.create_engine(your_connection_string)
+
+req = BetweenRequirement.from_tables(
+    db_name,
+    schema_name,
+    "table1",
+    db_name,
+    schema_name,
+    "table2",
+)
+
+req.add_column_subset_constraint()
+
+columns1 = req[0].get_factual_value(engine)
+columns2 = req[0].get_target_value(engine)
+
+print(f"Columns present in both: {set(columns1) & set(columns2)}")
+print(f"Columns present in only table1: {set(columns1) - set(columns2)}")
+print(f"Columns present in only table2: {set(columns2) - set(columns1)}")
+```
+
+This could, for instance result in the following printout:
+
+```
+Columns present in both: {'col_varchar', 'col_int'}
+Columns present in only table1: set()
+Columns present in only table2: {'col_date'}
+```
+
+Now, we can investigate the types of the columns present in both tables:
+
+```python
+for column in set(columns1) & set(columns2):
+    req.add_column_type_constraint(column1=column, column2=column)
+type1 = req[0].get_factual_value(engine)
+type2 = req[0].get_target_value(engine)
+print(f"Column '{column}' has type '{type1}' in table1 and type '{type2}' in table2.")
+```
+
+Depending on the underlying database management system and data, the output of this
+could for instance be:
+
+```
+Column 'col_varchar' has type 'varchar' in table1 and type 'varchar' in table2.
+Column 'col_int' has type 'integer' in table1 and type 'integer' in table2.
+```
diff --git a/docs/examples/twitch.md b/docs/examples/twitch.md
new file mode 100644
index 00000000..545c8542
--- /dev/null
+++ b/docs/examples/twitch.md
@@ -0,0 +1,276 @@
+# Dumps of Twitch data
+
+This example is based on data capturing statistics and properties of popular Twitch channels.
+The setup is such that we have two data sets 'of the same kind' but from different points in time.
+
+In other words, a 'version' of the data set represents a temporal notion.
+For example, version 1 might stem from end of March and version 2 from end of April.
+Moreover, we will assume that the first, version 1, has been vetted and approved with the
+help of manual investigation and domain knowledge. The second data set, version 2, has just been
+made available. We would like to use it but can't be sure of its validity just yet. As a consequence
+we would like to assess the quality of the data in version 2.
+
+In order to have a database Postgres instance to begin with, it might be useful to use our
+[script](https://github.com/Quantco/datajudge/blob/main/start_postgres.sh), spinning up
+a dockerized Postgres database:
+
+```bash
+./start_postgres.sh
+```
+
+The original data set can be found on [kaggle](https://www.kaggle.com/datasets/aayushmishra1512/twitchdata).
+For the sake of this tutorial, we slightly process it and provide two versions of it.
+One can either recreate this by executing this
+[processing script](https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_process.py)
+oneself on the original data or download our processed files (
+[version 1](https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_version1.csv)
+and
+[version 2](https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_version2.csv))
+right away.
+
+Once both version of the data exist, they can be uploaded to the tabase. We provide an
+[uploading script](https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_upload.py)
+creating and populating one table per version of the data in a Postgres database. It resembles the
+following:
+
+```python
+address = os.environ.get("DB_ADDR", "localhost")
+connection_string = f"postgresql://datajudge:datajudge@{address}:5432/datajudge"
+engine = sa.create_engine(connection_string)
+df_v2.to_sql("twitch_v2", engine, schema="public", if_exists="replace")
+df_v1.to_sql("twitch_v1", engine, schema="public", if_exists="replace")
+```
+
+Once the tables are stored in a database, we can actually write a `datajudge`
+specification against them. But first, we'll have a look at what the data roughly
+looks like by investigating a random sample of four rows:
+
+**A sample of the data**
+
+| channel  | watch_time | stream_time | peak_viewers | average_viewers | followers | followers_gained | views_gained | partnered | mature | language   |
+| -------- | ---------- | ----------- | ------------ | --------------- | --------- | ---------------- | ------------ | --------- | ------ | ---------- |
+| xQcOW    | 6196161750 | 215250      | 222720       | 27716           | 3246298   | 1734810          | 93036735     | True      | False  | English    |
+| summit1g | 6091677300 | 211845      | 310998       | 25610           | 5310163   | 1374810          | 89705964     | True      | False  | English    |
+| Gaules   | 5644590915 | 515280      | 387315       | 10976           | 1767635   | 1023779          | 102611607    | True      | True   | Portuguese |
+| ESL_CSGO | 3970318140 | 517740      | 300575       | 7714            | 3944850   | 703986           | 106546942    | True      | False  | English    |
+
+Note that we expect both version 1 and version 2 to follow this structure. Due to them
+being assembled at different points in time, merely their rows shows differ.
+
+Now let's write an actual specification, expressing our expectations against the data.
+First, we need to make sure a connection to the database can be established at test execution
+time. How this is done exactly depends on how you set up your database. When using our
+default setup with running, this would look as follows:
+
+```python
+import os
+import pytest
+import sqlalchemy as sa
+
+
+@pytest.fixture(scope="module")
+def datajudge_engine():
+    address = os.environ.get("DB_ADDR", "localhost")
+    connection_string = f"postgresql://datajudge:datajudge@{address}:5432/datajudge"
+    return sa.create_engine(connection_string)
+```
+
+Once a way to connect to the database is defined, we want to declare our data sources and
+express expectations against them. In this example, we have two tables in the same database -
+one table per version of the Twitch data.
+
+Yet, let's start with a straightforward example only using version 2. We want to use our
+domain knowledge that constrains the values of the `language` column only to contain letters
+and have a length strictly larger than 0.
+
+```python
+from datajudge import WithinRequirement
+
+
+# Postgres' default database.
+db_name = "tempdb"
+# Postgres' default schema.
+schema_name = "public"
+
+within_requirement = WithinRequirement.from_table(
+    table_name="twitch_v2",
+    schema_name=schema_name,
+db_name=db_name,
+)
+within_requirement.add_varchar_regex_constraint(
+column="language",
+regex="^[a-zA-Z]+$",
+)
+```
+
+Done! Now onto comparisons between the table representing the approved version 1 of the
+data and the to be assessed version 2 of the data.
+
+```python
+from datajudge import BetweenRequirement, Condition
+
+between_requirement_version = BetweenRequirement.from_tables(
+    db_name1=db_name,
+    db_name2=db_name,
+    schema_name1=schema_name,
+    schema_name2=schema_name,
+    table_name1="twitch_v1",
+    table_name2="twitch_v2",
+)
+between_requirement_version.add_column_subset_constraint()
+between_requirement_version.add_column_superset_constraint()
+columns = ["channel", "partnered", "mature"]
+between_requirement_version.add_row_subset_constraint(
+columns 1=columns, columns2=columns, constant_max_missing_fraction=0
+)
+between_requirement_version.add_row_matching_equality_constraint(
+    matching_columns1=["channel"],
+    matching_columns2=["channel"],
+    comparison_columns1=["language"],
+    comparison_columns2=["language"],
+    max_missing_fraction=0,
+)
+
+between_requirement_version.add_ks_2sample_constraint(
+    column1="average_viewers",
+    column2="average_viewers",
+    significance_level=0.05,
+)
+between_requirement_version.add_uniques_equality_constraint(
+    columns1=["language"],
+    columns2=["language"],
+)
+```
+
+Now having compared the 'same kind of data' between version 1 and version 2,
+we may as well compare 'different kind of data' within version 2, as a means of
+a sanity check. This sanity check consists of checking whether the mean
+`average_viewer` value of mature channels should deviate at most 10% from
+the overall mean.
+
+```python
+between_requirement_columns = BetweenRequirement.from_tables(
+    db_name1=db_name,
+    db_name2=db_name,
+    schema_name1=schema_name,
+    schema_name2=schema_name,
+    table_name1="twitch_v2",
+    table_name2="twitch_v2",
+)
+
+between_requirement_columns.add_numeric_mean_constraint(
+    column1="average_viewers",
+    column2="average_viewers",
+    condition1=None,
+    condition2=Condition(raw_string="mature IS TRUE"),
+    max_absolute_deviation=0.1,
+)
+```
+
+Lastly, we need to collect all of our requirements in a list and make sure
+`pytest` can find them by calling `collect_data_tests`.
+
+```python
+from datajudge.pytest_integration import collect_data_tests
+requirements = [
+    within_requirement,
+    between_requirement_version,
+    between_requirement_columns,
+]
+test_func = collect_data_tests(requirements)
+```
+
+If we then test these expectations against the data by running
+
+```bash
+pytest specification.py`` -- where ``specification.py
+```
+
+contains all of the code outlined before (you can find it
+[here](https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_specification.py))
+-- we see that the new version of the data is
+not quite on par with what we'd expect:
+
+```bash
+$ pytest twitch_specification.py
+================================== test session starts ===================================
+platform darwin -- Python 3.10.5, pytest-7.1.2, pluggy-1.0.0
+rootdir: /Users/kevin/Code/datajudge/docs/source/examples
+plugins: html-3.1.1, cov-3.0.0, metadata-2.0.2
+collected 8 items
+
+twitch_specification.py F.....FF                                                   [100%]
+
+======================================== FAILURES ========================================
+____________________ test_func[VarCharRegex::tempdb.public.twitch_v2] ____________________
+
+constraint = <datajudge.constraints.varchar.VarCharRegex object at 0x10855da20>
+datajudge_engine = Engine(postgresql://datajudge:***@localhost:5432/datajudge)
+
+@pytest.mark.parametrize(
+    "constraint", all_constraints, ids=Constraint.get_description
+)
+def test_constraint(constraint, datajudge_engine):
+    test_result = constraint.test(datajudge_engine)
+>       assert test_result.outcome, test_result.failure_message
+E       AssertionError: tempdb.public.twitch_v2's column(s) 'language' breaks regex
+        '^[a-zA-Z]+$' in 0.045454545454545456 > 0.0 of the cases. In absolute terms, 1
+    of the 22 samples violated the regex. Some counterexamples consist of the
+    following: ['Sw3d1zh'].
+
+../../../src/datajudge/pytest_integration.py:25: AssertionError
+____________ test_func[UniquesEquality::public.twitch_v1 | public.twitch_v2] _____________
+
+constraint = <datajudge.constraints.uniques.UniquesEquality object at 0x10855d270>
+datajudge_engine = Engine(postgresql://datajudge:***@localhost:5432/datajudge)
+
+@pytest.mark.parametrize(
+    "constraint", all_constraints, ids=Constraint.get_description
+)
+def test_constraint(constraint, datajudge_engine):
+    test_result = constraint.test(datajudge_engine)
+>       assert test_result.outcome, test_result.failure_message
+E       AssertionError: tempdb.public.twitch_v1's column(s) 'language' doesn't have
+        the element(s) '{'Sw3d1zh'}' when compared with the reference values.
+
+../../../src/datajudge/pytest_integration.py:25: AssertionError
+______________ test_func[NumericMean::public.twitch_v2 | public.twitch_v2] _______________
+
+constraint = <datajudge.constraints.numeric.NumericMean object at 0x1084e1810>
+datajudge_engine = Engine(postgresql://datajudge:***@localhost:5432/datajudge)
+
+@pytest.mark.parametrize(
+    "constraint", all_constraints, ids=Constraint.get_description
+)
+def test_constraint(constraint, datajudge_engine):
+    test_result = constraint.test(datajudge_engine)
+>       assert test_result.outcome, test_result.failure_message
+E       AssertionError: tempdb.public.twitch_v2's column(s) 'average_viewers' has
+        mean 4734.9780000000000000, deviating more than 0.1 from
+    tempdb.public.twitch_v2's column(s) 'average_viewers''s
+    3599.9826086956521739. Condition on second table: WHERE mature IS TRUE
+
+../../../src/datajudge/pytest_integration.py:25: AssertionError
+================================ short test summary info =================================
+FAILED twitch_specification.py::test_func[VarCharRegex::tempdb.public.twitch_v2] - Asse...
+FAILED twitch_specification.py::test_func[UniquesEquality::public.twitch_v1 | public.twitch_v2]
+FAILED twitch_specification.py::test_func[NumericMean::public.twitch_v2 | public.twitch_v2]
+============================== 3 failed, 5 passed in 1.52s ===============================
+```
+
+Alternatively, you can also look at these test results in
+[this html report](./twitch_report.html)
+generated by
+[pytest-html](https://github.com/pytest-dev/pytest-html).
+
+Hence we see that we might not want to blindly trust version 2 of the data as is. Rather, we might need
+to investigate what is wrong with the data, what this has been caused by and how to fix it.
+
+Concretely, what exactly do we learn from the error messages?
+
+- The column `language` now has a row with value `'Sw3d1zh'`. This break two of our
+  constraints. The `VarCharRegex` constraint compared the columns' values to a regular
+  expression. The `UniquesEquality` constraint expected the unique values of the
+  `language` column to not have changed between version 1 and version 2.
+- The mean value of `average_viewers` of `mature` channels is substantially - more
+  than our 10% tolerance - lower than the global mean.
diff --git a/docs/source/examples/twitch_process.py b/docs/examples/twitch_process.py
similarity index 100%
rename from docs/source/examples/twitch_process.py
rename to docs/examples/twitch_process.py
diff --git a/docs/source/examples/twitch_report.html b/docs/examples/twitch_report.html
similarity index 99%
rename from docs/source/examples/twitch_report.html
rename to docs/examples/twitch_report.html
index b8168a62..5f47c11e 100644
--- a/docs/source/examples/twitch_report.html
+++ b/docs/examples/twitch_report.html
@@ -342,4 +342,4 @@ <h2>Results</h2>
           <td class="col-links"></td></tr>
         <tr>
           <td class="extra" colspan="4">
-            <div class="empty log">No log output captured.</div></td></tr></tbody></table></body></html>
\ No newline at end of file
+            <div class="empty log">No log output captured.</div></td></tr></tbody></table></body></html>
diff --git a/docs/source/examples/twitch_specification.py b/docs/examples/twitch_specification.py
similarity index 100%
rename from docs/source/examples/twitch_specification.py
rename to docs/examples/twitch_specification.py
diff --git a/docs/source/examples/twitch_upload.py b/docs/examples/twitch_upload.py
similarity index 100%
rename from docs/source/examples/twitch_upload.py
rename to docs/examples/twitch_upload.py
diff --git a/docs/source/examples/twitch_version1.csv b/docs/examples/twitch_version1.csv
similarity index 100%
rename from docs/source/examples/twitch_version1.csv
rename to docs/examples/twitch_version1.csv
diff --git a/docs/source/examples/twitch_version2.csv b/docs/examples/twitch_version2.csv
similarity index 100%
rename from docs/source/examples/twitch_version2.csv
rename to docs/examples/twitch_version2.csv
diff --git a/docs/source/examples/twitchdata.csv b/docs/examples/twitchdata.csv
similarity index 100%
rename from docs/source/examples/twitchdata.csv
rename to docs/examples/twitchdata.csv
diff --git a/docs/getting-started.md b/docs/getting-started.md
new file mode 100644
index 00000000..6d193af3
--- /dev/null
+++ b/docs/getting-started.md
@@ -0,0 +1,167 @@
+# Getting Started
+
+## Glossary
+
+- A `DataSource` represents a way to retrieve data from database. Typically, this corresponds to a table in the database. Yet, it could also be a more elaborate object. See the section on 'Alternative `DataSource` s' for more detail.
+
+- A `Constraint` captures a concrete expectation between either two `DataSource` s or a single `DataSource` and a reference value.
+
+- A `Requirement` captures all `Constraint` s between two given `DataSource` s or all `Constraint` s within a single `DataSource`. If a `Requirement` refers links to two `DataSource` s, it is a [`BetweenRequirement`][datajudge.requirements.BetweenRequirement]. If a `Requirement` merely refers to a single `DataSource`, it is a [`WithinRequirement`][datajudge.requirements.WithinRequirement].
+
+- Conceptually, a 'specification' captures all `Requirement` s against a database. In practice that means it is usually a separate python file which:
+    - gathers all relevant `Requirement` s
+    - turns these `Requirement` s' `Constraint` s into individual tests
+    - can be 'tested' by pytest
+
+## Creating a specification
+
+In order to get going, you might want to use the following snippet in a new python file.
+This file will represent a specification.
+
+```python
+import pytest
+import sqlalchemy as sa
+from datajudge.pytest_integration import collect_data_tests
+
+
+@pytest.fixture(scope="module")
+def datajudge_engine():
+    # TODO: Adapt connection string to database at hand.
+    return sa.create_engine("your_connection_string")
+
+
+# TODO: Insert Requirement objects to list.
+requirements = []
+
+test_constraints = collect_data_tests(requirements)
+```
+
+This file will eventually lead as an input to pytest. More on that in the section [Testing a specification](testing.md).
+
+In case you haven't worked with sqlalchemy engines before, you might need to install drivers to connect to your database. You might want to install snowflake-sqlalchemy when using Snowflake, pyscopg when using Postgres and platform-specific drivers ([Windows](https://docs.microsoft.com/en-us/sql/connect/odbc/windows/microsoft-odbc-driver-for-sql-server-on-windows?view=sql-server-ver15), [Linux](https://docs.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server?view=sql-server-ver15), [macOS](https://docs.microsoft.com/en-us/sql/connect/odbc/linux-mac/install-microsoft-odbc-driver-sql-server-macos?view=sql-server-ver15)) when using MSSQL.
+
+## Specifying Constraints
+
+In order to discover possible `Constraint` s, please investigate the `_add_*_constraint` methods
+for [`BetweenRequirement`][datajudge.requirements.BetweenRequirement] and [`WithinRequirement`][datajudge.requirements.WithinRequirement] respectively.
+
+These methods are meant to be mostly self-documenting through the usage of expressive parameters.
+
+Note that most `Constraint` s will allow for at least one [`Condition`][datajudge.condition.Condition]. A [`Condition`][datajudge.condition.Condition]
+can be thought of as a conditional event in probability theory or a filter/clause in a database
+query. Please consult the doc string of [`Condition`][datajudge.condition.Condition] for greater detail. For examples, please
+see `tests/unit/test_condition.py`.
+
+Many `Constraint` s have optional `columns` parameters. If no argument is given, all
+available columns will be used.
+
+## Defining limitations of change
+
+[`BetweenRequirement`][datajudge.requirements.BetweenRequirement] s allow for `Constraint` s expressing the limitation of a loss or gain. For example, the `NRowsMinGain` `Constraint`
+expresses by how much the number of rows must at least grow from the first `DataSource` to the second. In the example of `NRowsMinGain` ,
+this growth limitation is expressed relative to the number of rows of the first `DataSource`.
+
+Generally, such relative limitations can be defined in two ways:
+
+- manually, based on domain knowledge (e.g. 'at least 5% growth')
+
+- automatically, based on date ranges
+
+The former would translate to
+
+```
+#rows_table_2 > (1 + min_relative_gain) * #rows_table_1
+```
+
+while the latter would translate to
+
+```
+date_growth := (max_date_table_2 - min_date_table_2) / (max_date_table_1 - min_date_table_1)
+#rows_table_2 > (1 + date_growth) * #rows_table_1
+```
+
+In the latter case a date column must be passed during the instantiation of the [`BetweenRequirement`][datajudge.requirements.BetweenRequirement]. Moreover, the `date_range_*` must be passed
+in the respective `add_*_constraint` method. When using date ranges as an indicator of change, the `constant_max_*` argument can safely be ignored. Additionally,
+an additional buffer to the date growth can be added with help of the `date_range_gain_deviation` parameter:
+
+```
+date_growth := (max_date_table_2 - min_date_table_2) / (max_date_table_1 - min_date_table_1)
+#rows_table_2 > (1 + date_growth + date_range_gain_deviation) + * #rows_table_1
+```
+
+This example revolving around `NRowsMinGain` generalizes to many `Constraint` s concerned with growth, gain, loss or shrinkage limitations.
+
+## Testing a specification
+
+In order to test whether the `Constraint` s expressed in a specification hold true, you can simply run
+
+```bash
+pytest your_specification.py
+```
+
+This will produce results directly in your terminal. If you prefer to additionally generate a report,
+you can run
+
+```bash
+pytest your_specification.py --html=your_report.html
+```
+
+As the testing relies on [pytest](https://docs.pytest.org/en/latest), all of `pytest`'s features can be used. More on this in the article on [testing](testing.md).
+
+## Test information
+
+When calling a `Constraint`'s `test` method, a [`TestResult`][datajudge.constraints.base.TestResult] is returned. The latter comes with a
+`logging_message` field. This field comprises information about the test failure, the constraint at hand
+as well as the underlying database queries.
+
+Depending on the use case at hand, it might make sense to rely on this information for logging or data investigation
+purposes. Again, more on this in the article on [testing](testing.md).
+
+## Assertion Message Styling
+
+Constraints can use styling to increase the readability of their assertion messages.
+The styling can be set independently of the platform and converted to e.g. ANSI color codes for command line output or CSS color tags for HTML reports.
+The styling tags describe use cases and not concrete colors, so formatters can use arbitrary color palettes, and these are not fixed by the constraint.
+
+The following table lists all the supported codes, along with their descriptions and examples of how they can be used:
+
+### Supported styling codes
+
+| Code       | Description                                                     | Example                      |
+| ---------- | --------------------------------------------------------------- | ---------------------------- |
+| `numMatch` | Indicates the part of a number that matches the expected value. | `[numMatch]3.141[/numMatch]` |
+| `numDiff`  | Indicates the part of a number that differs.                    | `[numDiff]6[/numDiff]`       |
+
+## Alternative DataSources
+
+A `Requirement` is instantiated with either one or two fixed `DataSource`s.
+
+While the most typical example of a `DataSource` would be a table in a database, `datajudge` allows
+for other `DataSource`s as well. These are often derived from primitive tables of a database.
+
+| [`DataSource`][datajudge.data_source.DataSource]                     | explanation                                                 | `WithinRequirement` constructor                                                                 | `BetweenRequirement` constructor                                                                    |
+| -------------------------------------------------------------------- | ----------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- |
+| [`TableDataSource`][datajudge.data_source.TableDataSource]           | represents a table in a database                            | [`WithinRequirement.from_table`][datajudge.requirements.WithinRequirement.from_table]           | [`BetweenRequirement.from_tables`][datajudge.requirements.BetweenRequirement.from_tables]           |
+| [`ExpressionDataSource`][datajudge.data_source.ExpressionDataSource] | represents the result of a `sqlalchemy` expression          | [`WithinRequirement.from_expression`][datajudge.requirements.WithinRequirement.from_expression] | [`BetweenRequirement.from_expressions`][datajudge.requirements.BetweenRequirement.from_expressions] |
+| [`RawQueryDataSource`][datajudge.data_source.RawQueryDataSource]     | represents the result of a SQL query expressed via a string | [`WithinRequirement.from_raw_query`][datajudge.requirements.WithinRequirement.from_raw_query]   | [`BetweenRequirement.from_raw_queries`][datajudge.requirements.BetweenRequirement.from_raw_queries] |
+
+Typically, a user does not need to instantiate a corresponding `DataSource` themselves. Rather, this is taken care
+of by using the appropriate constructor for [`WithinRequirement`][datajudge.requirements.WithinRequirement] or [`BetweenRequirement`][datajudge.requirements.BetweenRequirement].
+
+Note that in principle, several tables can be combined to make up for a single `DataSource`. Yet, most of
+the time when trying to compare two tables, it is more convenient to create a [`BetweenRequirement`][datajudge.requirements.BetweenRequirement] and use
+the `from_tables` constructor.
+
+## Column capitalization
+
+Different database management systems handle the capitalization of entities, such as column names, differently.
+For the time being:
+
+- Mssql: `datajudge` expects column name capitalization as is seen in database, either lowercase or uppercase.
+- Postgres: `datajudge` expects lowercase column names.
+- Snowflake: `datajudge` will lowercase independently of the capitalization provided.
+
+The Snowflake behavior is due to an upstream [bug](https://github.com/snowflakedb/snowflake-sqlalchemy/issues/157)
+in `snowflake-sqlalchemy`.
+
+This behavior is subject to change.
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 00000000..d27410a0
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,11 @@
+# datajudge
+
+`datajudge` allows for assessing whether data from database complies with reference information.
+
+While meant to be as agnostic to concrete database management systems as possible, `datajudge` currently explicitly supports:
+
+- Postgres
+- MSSQL
+- Snowflake
+- BigQuery
+- DuckDB
diff --git a/docs/installation.md b/docs/installation.md
new file mode 100644
index 00000000..94305cb5
--- /dev/null
+++ b/docs/installation.md
@@ -0,0 +1,17 @@
+# Installation
+
+To install, execute
+
+```bash
+$ pip install datajudge
+# or
+$ uv add datajudge
+```
+
+or to install it into a conda environment
+
+```bash
+$ pixi add datajudge
+# or
+$ conda install datajudge -c conda-forge
+```
diff --git a/docs/javascripts/external_links.js b/docs/javascripts/external_links.js
new file mode 100644
index 00000000..4812f097
--- /dev/null
+++ b/docs/javascripts/external_links.js
@@ -0,0 +1,6 @@
+// Modified from https://stackoverflow.com/a/4425214
+document.addEventListener("DOMContentLoaded", function () {
+    Array.from(document.links)
+        .filter(link => link.hostname != window.location.hostname)
+        .forEach(link => link.target = '_blank');
+});
diff --git a/docs/javascripts/katex.js b/docs/javascripts/katex.js
new file mode 100644
index 00000000..3ece4323
--- /dev/null
+++ b/docs/javascripts/katex.js
@@ -0,0 +1,11 @@
+// According to docs: https://squidfunk.github.io/mkdocs-material/reference/math/#katex
+document$.subscribe(({ body }) => {
+    renderMathInElement(body, {
+      delimiters: [
+        { left: "$$",  right: "$$",  display: true },
+        { left: "$",   right: "$",   display: false },
+        { left: "\\(", right: "\\)", display: false },
+        { left: "\\[", right: "\\]", display: true }
+      ],
+    })
+  })
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index 9534b018..00000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
-	exit /b 1
-)
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/docs/motivation.md b/docs/motivation.md
new file mode 100644
index 00000000..addee008
--- /dev/null
+++ b/docs/motivation.md
@@ -0,0 +1,26 @@
+# Motivation
+
+Ensuring data quality is of great importance for many use cases. `datajudge` seeks to make this convenient.
+
+`datajudge` allows for the expression of expectations held against data stored in databases. In particular, it allows for comparing different `DataSource`s. Yet, it also comes with functionalities to compare data from a single `DataSource` to fixed reference values derived from explicit domain knowledge.
+
+Not trying to reinvent the wheel, `datajudge` relies on `pytest` to execute the data expectations.
+
+## Comparisons between DataSources
+
+The data generating process can be obscure for a variety of reasons. In such scenarios one might ask the questions of
+
+- Has the data 'changed' over time?
+- Was the transformation of the data successful?
+
+In both cases one might want to compare different data -- either from different points in time or from different transformation steps -- to each other.
+
+## Why not Great Expectations?
+
+The major selling point is to be able to conveniently express expectations **between** different `DataSource`s. Great Expectations, in contrast, focuses on expectations against a single `DataSource`.
+
+Moreover, some users have pointed out the following advantages:
+
+- lots of 'query writing' is taken care of by having tailored `Constraint`s
+- easier and faster onboarding
+- assertion messages with counterexamples and other context information, speeding up the data debugging process
diff --git a/docs/source/report_failing_query1.png b/docs/report_failing_query1.png
similarity index 100%
rename from docs/source/report_failing_query1.png
rename to docs/report_failing_query1.png
diff --git a/docs/source/report_failing_query2.png b/docs/report_failing_query2.png
similarity index 100%
rename from docs/source/report_failing_query2.png
rename to docs/report_failing_query2.png
diff --git a/docs/source/conf.py b/docs/source/conf.py
deleted file mode 100644
index f41c8011..00000000
--- a/docs/source/conf.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-
-# -- Project information
-project = "datajudge"
-copyright = "(C) 2022 QuantCo Inc."
-author = "QuantCo Inc."
-
-release = "1.0"
-version = "1.0.0"
-
-extensions = [
-    "numpydoc",
-    "sphinxcontrib.apidoc",
-    "sphinx.ext.autodoc",
-]
-
-
-apidoc_module_dir = "../../src/datajudge"
-apidoc_output_dir = "api"
-apidoc_separate_modules = True
-apidoc_excluded_paths = [
-    "../../src/datajudge/db_access.py",
-    "../../src/datajudge/constraints",
-    # Requirements should be part of the exposed API documentation.
-    # Yet, they are already exposed via the top-level module.
-    "../../src/datajudge/requirements.py",
-]
-apidoc_extra_args = ["--implicit-namespaces"]
-
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
-html_theme = "sphinx_rtd_theme"
-html_static_path = ["_static"]
-
-autodoc_default_options = {
-    "members": True,
-    "member-order": "bysource",
-    "inherited-members": True,
-    "undoc-members": True,
-}
-autodoc_typehints = "description"
-
-# Copied from https://stackoverflow.com/questions/65198998/sphinx-warning-autosummary-stub-file-not-found-for-the-methods-of-the-class-c/
-# Also tested numpydoc_class_members_toctree = False but it does still create a TOC
-numpydoc_show_class_members = False
diff --git a/docs/source/development.rst b/docs/source/development.rst
deleted file mode 100644
index 6c2395ca..00000000
--- a/docs/source/development.rst
+++ /dev/null
@@ -1,55 +0,0 @@
-Development
-===========
-
-``datajudge`` development relies on [pixi](https://pixi.sh/latest/).
-In order to work on ``datajudge``, you can create a development environment as follows:
-
-::
-
-    git clone https://github.com/Quantco/datajudge
-    cd datajudge
-    pixi run postinstall
-
-Unit tests can be run by executing
-
-::
-
-   pixi run test
-
-Integration tests are run against a specific backend at a time. As of now, we provide helper
-scripts to spin up either a Postgres or MSSQL backend.
-
-To run integration tests against Postgres, first start a docker container with a Postgres database:
-
-::
-
-   ./start_postgres.sh
-
-In your current environment, install the ``psycopg2`` package.
-After this, you may execute integration tests as follows:
-
-::
-
-   pixi run -e postgres-py38 test
-
-Analogously, for MSSQL, run
-
-::
-
-   ./start_mssql.sh
-
-and
-
-::
-
-   pixi run -e mssql-py310 test
-
-or
-
-
-::
-
-   pixi run -e mssql-py310 test_freetds
-
-
-depending on the driver you'd like to use.
diff --git a/docs/source/examples/example.rst b/docs/source/examples/example.rst
deleted file mode 100644
index c7663077..00000000
--- a/docs/source/examples/example.rst
+++ /dev/null
@@ -1,151 +0,0 @@
-Example: Company data
-=====================
-
-
-To get started, we will create a sample database using sqlite that contains a list of companies.
-
-The table "companies_archive" contains three entries:
-
-.. list-table:: companies_archive
-   :header-rows: 1
-
-   * - id
-     - name
-     - num_employees
-   * - 1
-     - QuantCo
-     - 90
-   * - 2
-     - Google
-     - 140,000
-   * - 3
-     - BMW
-     - 110,000
-
-While "companies" contains an additional entry:
-
-.. list-table:: companies
-   :header-rows: 1
-
-   * - id
-     - name
-     - num_employees
-   * - 1
-     - QuantCo
-     - 100
-   * - 2
-     - Google
-     - 150,000
-   * - 3
-     - BMW
-     - 120,000
-   * - 4
-     - Apple
-     - 145,000
-
-
-.. code-block:: python
-
-    import sqlalchemy as sa
-
-    eng = sa.create_engine('sqlite:///example.db')
-
-    with eng.connect() as con:
-        con.execute("CREATE TABLE companies (id INTEGER PRIMARY KEY, name TEXT, num_employees INTEGER)")
-        con.execute("INSERT INTO companies (name, num_employees) VALUES ('QuantCo', 100), ('Google', 150000), ('BMW', 120000), ('Apple', 145000)")
-        con.execute("CREATE TABLE companies_archive (id INTEGER PRIMARY KEY, name TEXT, num_employees INTEGER)")
-        con.execute("INSERT INTO companies_archive (name, num_employees) VALUES ('QuantCo', 90), ('Google', 140000), ('BMW', 110000)")
-
-
-As an example, we will run 4 tests on this table:
-
-1. Does the table "companies" contain a column named "name"?
-2. Does the table "companies" contain at least 1 entry with the name "QuantCo"?
-3. Does the column "num_employees" of the "companies" table have all positive values?
-4. Does the column "name" of the table "companies" contain at least all the values of
-   the corresponding column in "companies_archive"?
-
-.. code-block:: python
-
-    import pytest
-    import sqlalchemy as sa
-
-    from datajudge import (
-        Condition,
-        WithinRequirement,
-        BetweenRequirement,
-    )
-    from datajudge.pytest_integration import collect_data_tests
-
-
-    # We create a Requirement, within a table. This object will contain
-    # all the constraints we want to test on the specified table.
-    # To test another table or test the same table against another table,
-    # we would create another Requirement object.
-    companies_req = WithinRequirement.from_table(
-        db_name="example", schema_name=None, table_name="companies"
-    )
-
-    # Constraint 1: Does the table "companies" contain a column named "name"?
-    companies_req.add_column_existence_constraint(columns=["name"])
-
-    # Constraint 2: Does the table "companies" contain at least 1 entry with the name "QuantCo"?
-    condition = Condition(raw_string="name = 'QuantCo'")
-    companies_req.add_n_rows_min_constraint(n_rows_min=1, condition=condition)
-
-    # Constraint 3: Does the column "num_employees" of the "companies" table have all
-    # positive values?
-    companies_req.add_numeric_min_constraint(column="num_employees", min_value=1)
-
-    # We create a new Requirement, this time between different tables.
-    # Concretely, we intent to test constraints between the table "companies"
-    # and the table "companies_archive".
-    companies_between_req = BetweenRequirement.from_tables(
-        db_name1="example",
-        schema_name1=None,
-        table_name1="companies",
-        db_name2="example",
-        schema_name2=None,
-        table_name2="companies_archive",
-    )
-
-    # Constraint 4: Does the column "name" of the table "companies" contain at least all
-    # the values of the corresponding column in "companies_archive"?
-    companies_between_req.add_row_superset_constraint(
-        columns1=['name'], columns2=['name'], constant_max_missing_fraction=0
-    )
-
-    # collect_data_tests expects a pytest fixture with the name
-    # "datajudge_engine" that is a SQLAlchemy engine
-
-    @pytest.fixture()
-    def datajudge_engine():
-        return sa.create_engine("sqlite:///example.db")
-
-    # We gather our distinct Requirements in a list.
-    requirements = [companies_req, companies_between_req]
-
-    # "collect_data_tests" takes all requirements and turns their respective
-    # Constraints into individual tests. pytest will be able to pick
-    # up these tests.
-    test_constraint = collect_data_tests(requirements)
-
-
-Saving this file as ``specification.py`` and running ``$ pytest specification.py``
-will verify that all constraints are satisfied. The output you see in the terminal
-should be similar to this:
-
-.. code-block::
-
-    =================================== test session starts ===================================
-    ...
-    collected 4 items
-
-    specification.py::test_constraint[ColumnExistence::companies] PASSED                [ 25%]
-    specification.py::test_constraint[NRowsMin::companies] PASSED                       [ 50%]
-    specification.py::test_constraint[NumericMin::companies] PASSED                     [ 75%]
-    specification.py::test_constraint[RowSuperset::companies|companies_archive] PASSED  [100%]
-
-    ==================================== 4 passed in 0.31s ====================================
-
-You can also use a formatted html report using the ``--html=report.html`` flag.
diff --git a/docs/source/examples/example_dates.rst b/docs/source/examples/example_dates.rst
deleted file mode 100644
index 702e5791..00000000
--- a/docs/source/examples/example_dates.rst
+++ /dev/null
@@ -1,169 +0,0 @@
-Example: Dates
-==============
-
-This example concerns itself with expressing ``Constraint``\s against data revolving
-around dates. While date ``Constraint``\s between tables exist, we will only illustrate
-``Constraint``\s on a single table and reference values here. As a consequence, we will
-only use ``WithinRequirement``, as opposed to ``BetweenRequirement``.
-
-Concretely, we will assume a table containing prices for a given product of id 1.
-Importantly, these prices are valid for a certain date range only. More precisely,
-we assume that the price for a product - identified via the ``preduct_id`` column
-- is indicated in the ``price`` column, the date from which it is valid - the date
-itself included - in ``date_from`` and the the until when it is valid - the date
-itself included - in the ``date_to`` column.
-
-Such a table might look as follows:
-
-.. list-table:: prices
-   :header-rows: 1
-
-   * - product_id
-     - price
-     - date_from
-     - date_to
-   * - 1
-     - 13.99
-     - 22/01/01
-     - 22/01/10
-   * - 1
-     - 14.5
-     - 22/01/11
-     - 22/01/17
-   * - 1
-     - 13.37
-     - 22/01/16
-     - 22/01/31
-
-Given this table, we would like to ensure - for the sake of illustrational purposes -
-that 6 constraints are satisfied:
-
-1. All values from column ``date_from`` should be in January 2022.
-2. All values from column ``date_to`` should be in January 2022.
-3. The minimum value in column ``date_from`` should be the first of January 2022.
-4. The maximum value in column ``date_to`` should be the 31st of January 2022.
-5. There is no gap between ``date_from`` and ``date_to``. In other words, every date
-   of January has to be assigned to at least one row for a given product.
-6. There is no overlap between ``date_from`` and ``date_to``. In other words, every
-   date of January has to be assigned to at most one row for a given product.
-
-
-Assuming that such a table exists in database, we can write a specification against it.
-
-.. code-block:: python
-
-    import pytest
-    import sqlalchemy as sa
-
-    from datajudge import WithinRequirement
-    from datajudge.pytest_integration import collect_data_tests
-
-    # We create a Requirement, within a table. This object will contain
-    # all the constraints we want to test on the specified table.
-    # To test another table or test the same table against another table,
-    # we would create another Requirement object.
-    prices_req = WithinRequirement.from_table(
-        db_name="example", schema_name=None, table_name="prices"
-    )
-
-    # Constraint 1:
-    # All values from column date_from should be in January 2022.
-    prices_req.add_date_between_constraint(
-        column="date_from",
-	lower_bound="'20220101'",
-	upper_bound="'20220131'",
-	# We don't tolerate any violations of the constraint:
-	min_fraction=1,
-    )
-
-    # Constraint 2:
-    # All values from column date_to should be in January 2022.
-    prices_req.add_date_between_constraint(
-        column="date_to",
-	lower_bound="'20220101'",
-	upper_bound="'20220131'",
-	# We don't tolerate any violations of the constraint:
-	min_fraction=1,
-    )
-
-    # Constraint 3:
-    # The minimum value in column date_from should be the first of January 2022.
-
-    # Ensure that the minimum is smaller or equal the reference value min_value.
-    prices_req.add_date_min_constraint(column="date_from", min_value="'20220101'")
-    # Ensure that the minimum is greater or equal the reference value min_value.
-    prices_req.add_date_min_constraint(
-        column="date_from",
-	min_value="'20220101'",
-	use_upper_bound_reference=True,
-    )
-
-    # Constraint 4:
-    # The maximum value in column date_to should be the 31st of January 2022.
-
-    # Ensure that the maximum is greater or equal the reference value max_value.
-    prices_req.add_date_max_constraint(column="date_to", max_value="'20220131'")
-    # Ensure that the maximum is smaller or equal the reference value max_value.
-    prices_req.add_date_max_constraint(
-        column="date_to",
-	max_value="'20220131'",
-	use_upper_bound_reference=True,
-    )
-
-    # Constraint 5:
-    # There is no gap between date_from and date_to. In other words, every date
-    # of January has to be assigned to at least one row for a given product.
-    prices_req.add_date_no_gap_constraint(
-        start_column="date_from",
-	end_column="date_to",
-	# We don't want a gap of price date ranges for a given product.
-	# For different products, we allow arbitrary date gaps.
-	key_columns=["product_id"],
-	# As indicated in prose, date_from and date_to are included in ranges.
-	end_included=True,
-	# Again, we don't expect any violations of our constraint.
-	max_relative_violations=0,
-    )
-
-    # Constraint 6:
-    # There is no overlap between date_from and date_to. In other words, every
-    # of January has to be assigned to at most one row for a given product.
-    princes_req.add_date_no_overlap_constraint(
-        start_column="date_from",
-	end_column="date_to",
-	# We want no overlap of price date ranges for a given product.
-	# For different products, we allow arbitrary date overlaps.
-	key_columns=["product_id"],
-	# As indicated in prose, date_from and date_to are included in ranges.
-	end_included=True,
-	# Again, we don't expect any violations of our constraint.
-	max_relative_violations=0,
-    )
-
-    @pytest.fixture()
-    def datajudge_engine():
-	# TODO: Insert actual connection string
-        return sa.create_engine("your_db://")
-
-    # We gather our single Requirement in a list.
-    requirements = [prices_req]
-
-    # "collect_data_tests" takes all requirements and turns their respective
-    # Constraints into individual tests. pytest will be able to pick
-    # up these tests.
-    test_constraint = collect_data_tests(requirements)
-
-Please note that the ``DateNoOverlap`` and ``DateNoGap`` constraints also exist
-in a slightly different form: ``DateNoOverlap2d`` and ``DateNoGap2d``.
-As the names suggest, these can operate in 'two date dimensions'.
-
-For example, let's assume a table with four date columns, representing two
-ranges in distinct dimensions, respectively:
-
-* ``date_from``: Date from when a price is valid
-* ``date_to``: Date until when a price is valid
-* ``date_definition_from``: Date when a price definition was inserted
-* ``date_definition_to``: Date until when a price definition was used
-
-Analogously to the unidimensional scenario illustrated here, one might care
-for certain constraints in two dimensions.
diff --git a/docs/source/examples/example_exploration.rst b/docs/source/examples/example_exploration.rst
deleted file mode 100644
index 04ffaf99..00000000
--- a/docs/source/examples/example_exploration.rst
+++ /dev/null
@@ -1,245 +0,0 @@
-Example: Exploration
-====================
-
-
-While datajudge seeks to tackle the use case of expressing and evaluating tests against
-data, its fairly generic inner workings allow for using it in a rather explorative
-workflow as well.
-
-Let's first clarify terminology by exemplifying both scenarios. A person wishing to test
-data might ask the question
-
-    Has the number of rows not grown too much from version 1 of the table to version 2
-    of the table?
-
-whereas a person wishing to explore the data might ask the question
-
-    By how much has the number of rows grown from version 1 to version 2 of the table?
-
-Put differently, a test typically revolves around a binary outcome while an exploration
-usually doesn't.
-
-In the following we will attempt to illustrate possible usages of datajudge for
-exploration by looking at three simple examples.
-
-These examples rely on some insight about how most datajudge ``Constraint`` s work under
-the hood. Importantly, ``Constraint`` s typically come with
-
-* a ``retrieve`` method: this method fetches relevant data from database, given a
-  ``DataReference``
-* a ``get_factual_value`` method: this is typically a wrapper around ``retrieve`` for the
-  first ``DataReference`` of the given ``Requirement`` / ``Constraint``
-* a ``get_target_value`` method: this is either a wrapper around ``retrieve`` for the
-  second ``DataReference`` in the case of a ``BetweenRequirement`` or an echoing of the
-  ``Constraint`` s key reference value in the case of a ``WithinRequirement``
-
-Moreover, as is the case when using datajudge for testing purposes, these approaches rely
-on a `sqlalchemy engine <https://docs.sqlalchemy.org/en/14/core/connections.html>`_. The
-latter is the gateway to the database at hand.
-
-Example 1: Comparing numbers of rows
-------------------------------------
-
-Assume we have two tables in the same database called ``table1`` and ``table2``. Now we
-would like to compare their numbers of rows. Naturally, we would like to retrieve
-the respective numbers of rows before we can compare them. For this purpose we create
-a ``BetweenTableRequirement`` referring to both tables and add a ``NRowsEquality``
-``Constraint`` onto it.
-
-
-.. code-block:: python
-
-    import sqlalchemy as sa
-    from datajudge import BetweenRequirement
-
-    engine = sa.create_engine(your_connection_string)
-    req = BetweenRequirement.from_tables(
-        db_name,
-        schema_name,
-        "table1",
-        db_name,
-        schema_name,
-        "table2",
-    )
-    req.add_n_rows_equality_constraint()
-    n_rows1 = req[0].get_factual_value(engine)
-    n_rows2 = req[0].get_target_value(engine)
-
-
-Note that here, we access the first (and only) ``Constraint`` that has been added to the
-``BetweenRequirement`` by writing ``req[0]``. ``Requirements`` are are sequences of
-``Constraint`` s, after all.
-
-Once the numbers of rows are retrieved, we can compare them as we wish. For instance, we
-could compute the absolute and relative growth (or loss) of numbers of rows from
-``table1`` to ``table2``:
-
-.. code-block:: python
-
-    absolute_change = abs(n_rows2 - n_rows1)
-    relative_change = (absolute_change) / n_rows1 if n_rows1 != 0 else None
-
-
-Importantly, many datajudge staples, such as ``Condition`` s can be used, too. We shall see
-this in our next example.
-
-Example 2: Investigating unique values
---------------------------------------
-
-In this example we will suppose that there is a table called ``table`` consisting of
-several columns. Two of its columns are supposed to be called ``col_int`` and
-``col_varchar``. We are now interested in the unique values in these two columns combined.
-Put differently, we are wondering:
-
-    Which unique pairs of values in ``col_int`` and ``col_varchar`` have we encountered?
-
-To add to the mix, we will moreover only be interested in tuples in which ``col_int`` has a
-value of larger than 10.
-
-As before, we will start off by creating a ``Requirement``. Since we are only dealing with
-a single table this time, we will create a ``WithinRequirement``.
-
-
-.. code-block:: python
-
-    import sqlalchemy as sa
-    from datajudge import WithinRequirement, Condition
-
-    engine = sa.create_engine(your_connection_string)
-
-    req = requirements.WithinRequirement.from_table(
-        db_name,
-	schema_name,
-	"table",
-    )
-
-    condition = Condition(raw_string="col_int >= 10")
-
-    req.add_uniques_equality_constraint(
-        columns=["col_int", "col_varchar"],
-	uniques=[], # This is really just a placeholder.
-        condition=condition,
-    )
-    uniques = req[0].get_factual_value(engine)
-
-
-If one was to investigate this ``uniques`` variable further, one could, e.g. see the
-following:
-
-
-.. code-block:: python
-
-    ([(10, 'hi10'), (11, 'hi11'), (12, 'hi12'), (13, 'hi13'), (14, 'hi14'), (15, 'hi15'), (16, 'hi16'), (17, 'hi17'), (18, 'hi18'), (19, 'hi19')], [1, 100, 12, 1, 7, 8, 1, 1, 1337, 1])
-
-
-This becomes easier to parse when inspecting the underlying ``retrieve`` method of the
-``UniquesEquality`` ``Constraint``: the first value of the tuple corresponds to the list
-of unique pairs in columns ``col_int`` and ``col_varchar``. The second value of the tuple
-are the respective counts thereof.
-
-Moreoever, one could manually customize the underlying SQL query. In order to do so, one
-can use the fact that ``retrieve`` methods typically return an actual result or value
-as well as the sqlalchemy selections that led to said result or value. We can use these
-selections and compile them to a standard, textual SQL query:
-
-
-.. code-block:: python
-
-    values, selections = req[0].retrieve(engine, constraint.ref)
-    print(str(selections[0].compile(engine, compile_kwargs={"literal_binds": True}))
-
-
-In the case from above, this would return the following query:
-
-
-.. code-block:: sql
-
-    SELECT
-        anon_1.col_int,
-	anon_1.col_varchar,
-	count(*) AS count_1
-    FROM
-        (SELECT
-	    tempdb.dbo.table.col_int AS col_int,
-	    tempdb.dbo.table.col_varchar AS col_varchar
-        FROM
-	    tempdb.dbo.table WITH (NOLOCK)
-        WHERE col_int >= 10) AS anon_1
-    GROUP BY anon_1.col_int, anon_1.col_varchar
-
-
-Example 3: Comparing column structure
--------------------------------------
-
-While we often care about value tuples of given columns, i.e. rows, it can also provide
-meaningful insights to compare the column structure of two tables. In particular, we
-might want to compare whether columns of one table are a subset or superset of another
-table. Moreover, for columns present in both tables, we'd like to learn about their
-respective types.
-
-In order to illustrate such an example, we will again assume that there are two tables
-called ``table1`` and ``table2``, irrespective of prior examples.
-
-We can now create a ``BetweenRequirement`` for these two tables and use the
-``ColumnSubset`` ``Constraint``. As before, we will rely on the ``get_factual_value``
-method to retrieve the values of interest for the first table passed to the
-``BetweenRequirement`` and the ``get_target_value`` method for the second table passed
-to the ``BetweenRequirement``.
-
-.. code-block:: python
-
-    import sqlalchemy as sa
-    from datajudge import BetweenRequirement
-
-    engine = sa.create_engine(your_connection_string)
-
-    req = BetweenRequirement.from_tables(
-        db_name,
-        schema_name,
-        "table1",
-        db_name,
-        schema_name,
-        "table2",
-    )
-
-    req.add_column_subset_constraint()
-
-    columns1 = req[0].get_factual_value(engine)
-    columns2 = req[0].get_target_value(engine)
-
-    print(f"Columns present in both: {set(columns1) & set(columns2)}")
-    print(f"Columns present in only table1: {set(columns1) - set(columns2)}")
-    print(f"Columns present in only table2: {set(columns2) - set(columns1)}")
-
-
-This could, for instance result in the following printout:
-
-.. code-block::
-
-    Columns present in both: {'col_varchar', 'col_int'}
-    Columns present in only table1: set()
-    Columns present in only table2: {'col_date'}
-
-
-Now, we can investigate the types of the columns present in both tables:
-
-
-.. code-block:: python
-
-    for column in set(columns1) & set(columns2):
-        req.add_column_type_constraint(column1=column, column2=column)
-	type1 = req[0].get_factual_value(engine)
-	type2 = req[0].get_target_value(engine)
-	print(f"Column '{column}' has type '{type1}' in table1 and type '{type2}' in table2.")
-
-
-Depending on the underlying database management system and data, the output of this
-could for instance be:
-
-
-.. code-block::
-
-   Column 'col_varchar' has type 'varchar' in table1 and type 'varchar' in table2.
-   Column 'col_int' has type 'integer' in table1 and type 'integer' in table2.
-
-
diff --git a/docs/source/examples/example_twitch.rst b/docs/source/examples/example_twitch.rst
deleted file mode 100644
index 2666831c..00000000
--- a/docs/source/examples/example_twitch.rst
+++ /dev/null
@@ -1,332 +0,0 @@
-Example: Dumps of Twitch data
-=============================
-
-This example is based on data capturing statistics and properties of popular Twitch channels.
-The setup is such that we have two data sets 'of the same kind' but from different points in time.
-
-In other words, a 'version' of the data set represents a temporal notion.
-For example, version 1 might stem from end of March and version 2 from end of April.
-Moreover, we will assume that the first, version 1, has been vetted and approved with the
-help of manual investigation and domain knowledge. The second data set, version 2, has just been
-made available. We would like to use it but can't be sure of its validity just yet. As a consequence
-we would like to assess the quality of the data in version 2.
-
-In order to have a database Postgres instance to begin with, it might be useful to use our
-`script <https://github.com/Quantco/datajudge/blob/main/start_postgres.sh>`_, spinning up
-a dockerized Postgres database:
-
-.. code-block:: console
-
-  $ ./start_postgres.sh
-
-
-The original data set can be found on `kaggle <https://www.kaggle.com/datasets/aayushmishra1512/twitchdata>`_.
-For the sake of this tutorial, we slightly process it and provide two versions of it.
-One can either recreate this by executing this
-`processing script <https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_process.py>`_
-oneself on the original data or download our processed files (
-`version 1 <https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_version1.csv>`_
-and
-`version 2 <https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_version2.csv>`_)
-right away.
-
-Once both version of the data exist, they can be uploaded to the tabase. We provide an
-`uploading script <https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_upload.py>`_
-creating and populating one table per version of the data in a Postgres database. It resembles the
-following:
-
-.. code-block:: python
-
-    address = os.environ.get("DB_ADDR", "localhost")
-    connection_string = f"postgresql://datajudge:datajudge@{address}:5432/datajudge"
-    engine = sa.create_engine(connection_string)
-    df_v2.to_sql("twitch_v2", engine, schema="public", if_exists="replace")
-    df_v1.to_sql("twitch_v1", engine, schema="public", if_exists="replace")
-
-
-Once the tables are stored in a database, we can actually write a ``datajudge``
-specification against them. But first, we'll have a look at what the data roughly
-looks like by investigating a random sample of four rows:
-
-.. list-table:: A sample of the data
-   :header-rows: 1
-
-   * - channel
-     - watch_time
-     - stream_time
-     - peak_viewers
-     - average_viewers
-     - followers
-     - followers_gained
-     - views_gained
-     - partnered
-     - mature
-     - language
-   * - xQcOW
-     - 6196161750
-     - 215250
-     - 222720
-     - 27716
-     - 3246298
-     - 1734810
-     - 93036735
-     - True
-     - False
-     - English
-   * - summit1g
-     - 6091677300
-     - 211845
-     - 310998
-     - 25610
-     - 5310163
-     - 1374810
-     - 89705964
-     - True
-     - False
-     - English
-   * - Gaules
-     - 5644590915
-     - 515280
-     - 387315
-     - 10976
-     - 1767635
-     - 1023779
-     - 102611607
-     - True
-     - True
-     - Portuguese
-   * - ESL_CSGO
-     - 3970318140
-     - 517740
-     - 300575
-     - 7714
-     - 3944850
-     - 703986
-     - 106546942
-     - True
-     - False
-     - English
-
-Note that we expect both version 1 and version 2 to follow this structure. Due to them
-being assembled at different points in time, merely their rows shows differ.
-
-
-Now let's write an actual specification, expressing our expectations against the data.
-First, we need to make sure a connection to the database can be established at test execution
-time. How this is done exactly depends on how you set up your database. When using our
-default setup with running, this would look as follows:
-
-.. code-block:: python
-
-    import os
-    import pytest
-    import sqlalchemy as sa
-
-
-    @pytest.fixture(scope="module")
-    def datajudge_engine():
-        address = os.environ.get("DB_ADDR", "localhost")
-        connection_string = f"postgresql://datajudge:datajudge@{address}:5432/datajudge"
-        return sa.create_engine(connection_string)
-
-Once a way to connect to the database is defined, we want to declare our data sources and
-express expectations against them. In this example, we have two tables in the same database -
-one table per version of the Twitch data.
-
-
-Yet, let's start with a straightforward example only using version 2. We want to use our
-domain knowledge that constrains the values of the ``language`` column only to contain letters
-and have a length strictly larger than 0.
-
-
-.. code-block:: python
-
-    from datajudge import WithinRequirement
-
-
-    # Postgres' default database.
-    db_name = "tempdb"
-    # Postgres' default schema.
-    schema_name = "public"
-
-    within_requirement = WithinRequirement.from_table(
-        table_name="twitch_v2",
-        schema_name=schema_name,
-	db_name=db_name,
-    )
-    within_requirement.add_varchar_regex_constraint(
-	column="language",
-	regex="^[a-zA-Z]+$",
-    )
-
-
-Done! Now onto comparisons between the table representing the approved version 1 of the
-data and the to be assessed version 2 of the data.
-
-.. code-block:: python
-
-    from datajudge import BetweenRequirement, Condition
-
-    between_requirement_version = BetweenRequirement.from_tables(
-	db_name1=db_name,
-	db_name2=db_name,
-	schema_name1=schema_name,
-	schema_name2=schema_name,
-	table_name1="twitch_v1",
-	table_name2="twitch_v2",
-    )
-    between_requirement_version.add_column_subset_constraint()
-    between_requirement_version.add_column_superset_constraint()
-    columns = ["channel", "partnered", "mature"]
-    between_requirement_version.add_row_subset_constraint(
-	columns1=columns, columns2=columns, constant_max_missing_fraction=0
-    )
-    between_requirement_version.add_row_matching_equality_constraint(
-	matching_columns1=["channel"],
-	matching_columns2=["channel"],
-	comparison_columns1=["language"],
-	comparison_columns2=["language"],
-	max_missing_fraction=0,
-    )
-
-    between_requirement_version.add_ks_2sample_constraint(
-	column1="average_viewers",
-	column2="average_viewers",
-	significance_level=0.05,
-    )
-    between_requirement_version.add_uniques_equality_constraint(
-	columns1=["language"],
-	columns2=["language"],
-    )
-
-
-Now having compared the 'same kind of data' between version 1 and version 2,
-we may as well compare 'different kind of data' within version 2, as a means of
-a sanity check. This sanity check consists of checking whether the mean
-``average_viewer`` value of mature channels should deviate at most 10% from
-the overall mean.
-
-.. code-block:: python
-
-    between_requirement_columns = BetweenRequirement.from_tables(
-	db_name1=db_name,
-	db_name2=db_name,
-	schema_name1=schema_name,
-	schema_name2=schema_name,
-	table_name1="twitch_v2",
-	table_name2="twitch_v2",
-    )
-
-    between_requirement_columns.add_numeric_mean_constraint(
-	column1="average_viewers",
-	column2="average_viewers",
-	condition1=None,
-	condition2=Condition(raw_string="mature IS TRUE"),
-	max_absolute_deviation=0.1,
-    )
-
-
-Lastly, we need to collect all of our requirements in a list and make sure
-``pytest`` can find them by calling ``collect_data_tests``.
-
-
-.. code-block:: python
-
-    from datajudge.pytest_integration import collect_data_tests
-    requirements = [
-	within_requirement,
-	between_requirement_version,
-	between_requirement_columns,
-    ]
-    test_func = collect_data_tests(requirements)
-
-If we then test these expectations against the data by running
-``$ pytest specification.py`` -- where ``specification.py``
-contains all of the code outlined before (you can find it
-`here <https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_specification.py>`_ )
--- we see that the new version of the data is
-not quite on par with what we'd expect:
-
-.. code-block:: console
-
-    $ pytest twitch_specification.py
-    ================================== test session starts ===================================
-    platform darwin -- Python 3.10.5, pytest-7.1.2, pluggy-1.0.0
-    rootdir: /Users/kevin/Code/datajudge/docs/source/examples
-    plugins: html-3.1.1, cov-3.0.0, metadata-2.0.2
-    collected 8 items
-
-    twitch_specification.py F.....FF                                                   [100%]
-
-    ======================================== FAILURES ========================================
-    ____________________ test_func[VarCharRegex::tempdb.public.twitch_v2] ____________________
-
-    constraint = <datajudge.constraints.varchar.VarCharRegex object at 0x10855da20>
-    datajudge_engine = Engine(postgresql://datajudge:***@localhost:5432/datajudge)
-
-	@pytest.mark.parametrize(
-	    "constraint", all_constraints, ids=Constraint.get_description
-	)
-	def test_constraint(constraint, datajudge_engine):
-	    test_result = constraint.test(datajudge_engine)
-    >       assert test_result.outcome, test_result.failure_message
-    E       AssertionError: tempdb.public.twitch_v2's column(s) 'language' breaks regex
-            '^[a-zA-Z]+$' in 0.045454545454545456 > 0.0 of the cases. In absolute terms, 1
-	    of the 22 samples violated the regex. Some counterexamples consist of the
-	    following: ['Sw3d1zh'].
-
-    ../../../src/datajudge/pytest_integration.py:25: AssertionError
-    ____________ test_func[UniquesEquality::public.twitch_v1 | public.twitch_v2] _____________
-
-    constraint = <datajudge.constraints.uniques.UniquesEquality object at 0x10855d270>
-    datajudge_engine = Engine(postgresql://datajudge:***@localhost:5432/datajudge)
-
-	@pytest.mark.parametrize(
-	    "constraint", all_constraints, ids=Constraint.get_description
-	)
-	def test_constraint(constraint, datajudge_engine):
-	    test_result = constraint.test(datajudge_engine)
-    >       assert test_result.outcome, test_result.failure_message
-    E       AssertionError: tempdb.public.twitch_v1's column(s) 'language' doesn't have
-            the element(s) '{'Sw3d1zh'}' when compared with the reference values.
-
-    ../../../src/datajudge/pytest_integration.py:25: AssertionError
-    ______________ test_func[NumericMean::public.twitch_v2 | public.twitch_v2] _______________
-
-    constraint = <datajudge.constraints.numeric.NumericMean object at 0x1084e1810>
-    datajudge_engine = Engine(postgresql://datajudge:***@localhost:5432/datajudge)
-
-	@pytest.mark.parametrize(
-	    "constraint", all_constraints, ids=Constraint.get_description
-	)
-	def test_constraint(constraint, datajudge_engine):
-	    test_result = constraint.test(datajudge_engine)
-    >       assert test_result.outcome, test_result.failure_message
-    E       AssertionError: tempdb.public.twitch_v2's column(s) 'average_viewers' has
-            mean 4734.9780000000000000, deviating more than 0.1 from
-	    tempdb.public.twitch_v2's column(s) 'average_viewers''s
-	    3599.9826086956521739. Condition on second table: WHERE mature IS TRUE
-
-    ../../../src/datajudge/pytest_integration.py:25: AssertionError
-    ================================ short test summary info =================================
-    FAILED twitch_specification.py::test_func[VarCharRegex::tempdb.public.twitch_v2] - Asse...
-    FAILED twitch_specification.py::test_func[UniquesEquality::public.twitch_v1 | public.twitch_v2]
-    FAILED twitch_specification.py::test_func[NumericMean::public.twitch_v2 | public.twitch_v2]
-    ============================== 3 failed, 5 passed in 1.52s ===============================
-
-Alternatively, you can also look at these test results in
-`this html report <https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_report.html>`_
-generated by
-`pytest-html <https://github.com/pytest-dev/pytest-html>`_.
-
-Hence we see that we might not want to blindly trust version 2 of the data as is. Rather, we might need
-to investigate what is wrong with the data, what this has been caused by and how to fix it.
-
-Concretely, what exactly do we learn from the error messages?
-
-* The column ``language`` now has a row with value ``'Sw3d1zh'``. This break two of our
-  constraints. The ``VarCharRegex`` constraint compared the columns' values to a regular
-  expression. The ``UniquesEquality`` constraint expected the unique values of the
-  ``language`` column to not have changed between version 1 and version 2.
-* The mean value of ``average_viewers`` of ``mature`` channels is substantially - more
-  than our 10% tolerance - lower than the global mean.
diff --git a/docs/source/examples/examples.rst b/docs/source/examples/examples.rst
deleted file mode 100644
index 236cbef5..00000000
--- a/docs/source/examples/examples.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-.. toctree::
-   :maxdepth: 2
-
-   Example: Companies <example.rst>
-   Example: Twitch <example_twitch.rst>
-   Example: Dates <example_dates.rst>
-   Example: Exploration <example_exploration.rst>
diff --git a/docs/source/getting_started.rst b/docs/source/getting_started.rst
deleted file mode 100644
index 9c397b11..00000000
--- a/docs/source/getting_started.rst
+++ /dev/null
@@ -1,208 +0,0 @@
-Getting Started
-===============
-
-
-Glossary
---------
-
-- A ``DataSource`` represents a way to retrieve data from database. Typically, this corresponds to a table in the database. Yet, it could also be a more elaborate object. See the section on 'Alternative ``DataSource`` s' for more detail.
-
-- A ``Constraint`` captures a concrete expectation between either two ``DataSource`` s or a single ``DataSource`` and a reference value.
-
-- A ``Requirement`` captures all ``Constraint`` s between two given ``DataSource`` s or all ``Constraint`` s within a single ``DataSource``. If a ``Requirement`` refers links to two ``DataSource`` s, it is a ``BetweenRequirement``. If a ``Requirement`` merely refers to a single ``DataSource``, it is a ``WithinRequirement``.
-
-- Conceptually, a 'specification' captures all ``Requirement`` s against a database. In practice that means it is usually a separate python file which:
-
-  - gathers all relevant ``Requirement`` s
-  - turns these ``Requirement`` s' ``Constraint`` s into individual tests
-  - can be 'tested' by pytest
-
-
-Creating a specification
-------------------------
-
-In order to get going, you might want to use the following snippet in a new python file.
-This file will represent a specification.
-
-.. code-block:: python
-
-    import pytest
-    import sqlalchemy as sa
-    from datajudge.pytest_integration import collect_data_tests
-
-
-    @pytest.fixture(scope="module")
-    def datajudge_engine():
-        # TODO: Adapt connection string to database at hand.
-        return sa.create_engine("your_connection_string")
-
-
-    # TODO: Insert Requirement objects to list.
-    requirements = []
-
-    test_constraints = collect_data_tests(requirements)
-
-This file will eventually lead as an input to pytest. More on that in the section 'Testing a specification'.
-
-In case you haven't worked with sqlalchemy engines before, you might need to install drivers to connect to your database. You might want to install snowflake-sqlalchemy when using Snowflake, pyscopg when using Postgres and platform-specific drivers (`Windows <https://docs.microsoft.com/en-us/sql/connect/odbc/windows/microsoft-odbc-driver-for-sql-server-on-windows?view=sql-server-ver15>`_, `Linux <https://docs.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server?view=sql-server-ver15>`_, `macOS <https://docs.microsoft.com/en-us/sql/connect/odbc/linux-mac/install-microsoft-odbc-driver-sql-server-macos?view=sql-server-ver15>`_) when using MSSQL.
-
-
-Specifying Constraints
-----------------------
-
-In order to discover possible ``Constraint`` s, please investigate the ``_add_*_constraint`` methods
-for :class:`~datajudge.requirements.BetweenRequirement` and :class:`~datajudge.requirements.WithinRequirement` respectively.
-
-These methods are meant to be mostly self-documenting through the usage of expressive parameters.
-
-Note that most ``Constraint`` s will allow for at least one ``Condition``. A ``Condition``
-can be thought of as a conditional event in probability theory or a filter/clause in a database
-query. Please consult the doc string of ``Condition`` for greater detail. For examples, please
-see ``tests/unit/test_condition.py``.
-
-Many ``Constraint`` s have optional ``columns`` parameters. If no argument is given, all
-available columns will be used.
-
-
-Defining limitations of change
-------------------------------
-
-``BetweenRequirement`` s allow for ``Constraint`` s expressing the limitation of a loss or gain. For example, the ``NRowsMinGain`` ``Constraint``
-expresses by how much the number of rows must at least grow from the first ``DataSource`` to the second. In the example of ``NRowsMinGain`` ,
-this growth limitation is expressed relative to the number of rows of the first ``DataSource``.
-
-Generally, such relative limitations can be defined in two ways:
-
-- manually, based on domain knowledge (e.g. 'at least 5% growth')
-  
-- automatically, based on date ranges
-
-The former would translate to
-
-::
-
-    #rows_table_2 > (1 + min_relative_gain) * #rows_table_1
-
-while the latter would translate to
-
-::
-
-   date_growth := (max_date_table_2 - min_date_table_2) / (max_date_table_1 - min_date_table_1)
-   #rows_table_2 > (1 + date_growth) * #rows_table_1
-
-
-In the latter case a date column must be passed during the instantiation of the ``BetweenRequirement``. Moreover, the ``date_range_*`` must be passed
-in the respective ``add_*_constraint`` method. When using date ranges as an indicator of change, the ``constant_max_*`` argument can safely be ignored. Additionally,
-an additional buffer to the date growth can be added with help of the ``date_range_gain_deviation`` parameter:
-
-::
-
-   date_growth := (max_date_table_2 - min_date_table_2) / (max_date_table_1 - min_date_table_1)
-   #rows_table_2 > (1 + date_growth + date_range_gain_deviation) + * #rows_table_1
-
-This example revolving around ``NRowsMinGain`` generalizes to many ``Constraint`` s concerned with growth, gain, loss or shrinkage limitations.
-
-
-Testing a specification
------------------------
-
-In order to test whether the ``Constraint`` s expressed in a specification hold true, you can simply run
-
-::
-
-    pytest your_specification.py
-
-This will produce results directly in your terminal. If you prefer to additionally generate a report,
-you can run
-
-::
-
-   pytest your_specification.py --html=your_report.html
-
-As the testing relies on `pytest <https://docs.pytest.org/en/latest/>`_, all of `pytest`'s features can be used. More on this in the article on :doc:`testing <testing>`.
-
-
-Test information
-----------------
-
-When calling a ``Constraint``'s ``test`` method, a ``TestResult`` is returned. The latter comes with a
-``logging_message`` field. This field comprises information about the test failure, the constraint at hand
-as well as the underlying database queries.
-
-Depending on the use case at hand, it might make sense to rely on this information for logging or data investigation
-purposes. Again, more on this in the article on :doc:`testing <testing>`.
-
-Assertion Message Styling
--------------------------
-Constraints can use styling to increase the readability of their assertion messages.
-The styling can be set independently of the platform and converted to e.g. ANSI color codes for command line output or CSS color tags for HTML reports.
-The styling tags describe use cases and not concrete colors, so formatters can use arbitrary color palettes, and these are not fixed by the constraint.
-
-The following table lists all the supported codes, along with their descriptions and examples of how they can be used:
-
-
-.. list-table:: Supported styling codes
-   :header-rows: 1
-
-   * - Code
-     - Description
-     - Example
-   * - `numMatch`
-     - Indicates the part of a number that matches the expected value.
-     - `[numMatch]3.141[/numMatch]`
-   * - `numDiff`
-     - Indicates the part of a number that differs.
-     - `[numDiff]6[/numDiff]`
-
-Alternative DataSources
----------------------------
-
-A ``Requirement`` is instantiated with either one or two fixed ``DataSource`` s.
-
-While the most typical example of a ``DataSource`` would be a table in a database, ``datajudge`` allows
-for other ``DataSource`` s as well. These are often derived from primitive tables of a database.
-
-.. list-table:: DataSources
-   :header-rows: 1
-
-   * - :class:`~datajudge.db_access.DataSource`
-     - explanation
-     - :class:`~datajudge.requirements.WithinRequirement` constructor
-     - :class:`~datajudge.requirements.BetweenRequirement` constructor
-   * - :class:`~datajudge.db_access.TableDataSource`
-     - represents a table in a database
-     - :meth:`~datajudge.requirements.WithinRequirement.from_table`
-     - :meth:`~datajudge.requirements.BetweenRequirement.from_tables`
-   * - :class:`~datajudge.db_access.ExpressionDataSource`
-     - represents the result of a ``sqlalchemy`` expression
-     - :meth:`~datajudge.requirements.WithinRequirement.from_expression`
-     - :meth:`~datajudge.requirements.BetweenRequirement.from_expressions`
-   * - :class:`~datajudge.db_access.RawQueryDataSource`
-     - represents the result of a sql query expressed via a string
-     - :meth:`~datajudge.requirements.WithinRequirement.from_raw_query`
-     - :meth:`~datajudge.requirements.BetweenRequirement.from_raw_queries`
-
-
-Typically, a user does not need to instantiate a corresponding ``DataSource`` themselves. Rather, this is taken care
-of by using the appropriate constructor for ``WithinRequirement`` or ``BetweenRequirement``.
-
-Note that in principle, several tables can be combined to make up for a single ``DataSource``. Yet, most of
-the time when trying to compare two tables, it is more convenient to create a ``BetweenRequirement`` and use
-the ``from_tables`` constructor.
-
-
-Column capitalization
----------------------
-
-Different database management systems handle the capitalization of entities, such as column names, differently.
-For the time being:
-
-- Mssql: ``datajudge`` expects column name capitalization as is seen in database, either lowercase or uppercase.
-- Postgres: ``datajudge`` expects lowercase column names.
-- Snowflake: ``datajudge`` will lowercase independently of the capitalization provided.
-
-The Snowflake behavior is due to an upstream `bug <https://github.com/snowflakedb/snowflake-sqlalchemy/issues/157>`_
-in snowflake-sqlalchemy.
-
-This behavior is subject to change.
-
diff --git a/docs/source/index.rst b/docs/source/index.rst
deleted file mode 100644
index d7917ce6..00000000
--- a/docs/source/index.rst
+++ /dev/null
@@ -1,23 +0,0 @@
-Welcome to datajudges's documentation!
-======================================
-
-``datajudge`` allows for assessing whether data from database complies with reference information.
-
-While meant to be as agnostic to concrete database management systems as possible, ``datajudge`` currently explicitly supports:
-
-- Postgres
-- MSSQL
-- Snowflake
-
-Contents
---------
-
-.. toctree::
-
-   installation
-   Getting Started <getting_started.rst>
-   testing
-   motivation
-   Examples <examples/examples.rst>
-   development
-   API Reference <api/datajudge.rst>
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
deleted file mode 100644
index 7311cf59..00000000
--- a/docs/source/installation.rst
+++ /dev/null
@@ -1,14 +0,0 @@
-Installation
-============
-
-To install, execute
-
-::
-
-    pip install datajudge
-
-or from a conda environment
-
-::
-
-    conda install datajudge -c conda-forge
diff --git a/docs/source/motivation.rst b/docs/source/motivation.rst
deleted file mode 100644
index b4368bdf..00000000
--- a/docs/source/motivation.rst
+++ /dev/null
@@ -1,31 +0,0 @@
-Motivation
-==========
-
-Ensuring data quality is of great importance for many use cases. ``datajudge`` seeks to make this convenient.
-
-``datajudge`` allows for the expression of expectations held against data stored in databases. In particular, it allows for comparing different ``DataSource`` s. Yet, it also comes with functionalities to compare data from a single ``DataSource`` to fixed reference values derived from explicit domain knowledge.
-
-Not trying to reinvent the wheel, ``datajudge`` relies on ``pytest`` to execute the data expectations.
-
-
-Comparisons between DataSources
--------------------------------
-
-The data generating process can be obscure for a variety of reasons. In such scenarios one might ask the questions of
-
-- Has the data 'changed' over time?
-- Was the transformation of the data successful?
-
-In both cases one might want to compare different data -- either from different points in time or from different transformation steps -- to each other.
-
-
-Why not Great Expectations?
----------------------------
-
-The major selling point is to be able to conveniently express expectations **between** different ``DataSource`` s. Great Expectations, in contrast, focuses on expectations against a single ``DataSource``.
-
-Moreover, some users have pointed out the following advantages:
-
-- lots of 'query writing' is taken care of by having tailored ``Constraint`` s
-- easier and faster onboarding
-- assertion messages with counterexamples and other context information, speeding up the data debugging process
diff --git a/docs/source/testing.rst b/docs/source/testing.rst
deleted file mode 100644
index 6e3b2e39..00000000
--- a/docs/source/testing.rst
+++ /dev/null
@@ -1,324 +0,0 @@
-Testing
-=======
-
-While ``datajudge`` allows to express expectations via specifications, ``Requirement`` s
-and ``Constraint`` s, the execution of tests is delegated to pytest. As a
-consequence, one may use any functionalities that pytest has to offer. Here,
-we want to illustrate some of these advanced functionalities that might turn out useful.
-
-Yet, it should be noted that for most intents and purposes, using ``datajudge`` 's helper
-function :func:`~datajudge.pytest_integration.collect_data_tests` is a good starting
-point. It should work out of the box and hides some complexity. For exemplary applications
-see, the
-:doc:`companies example <examples/example>` or the
-:doc:`twitch example <examples/example_twitch>`.
-
-Throughout this article we will not rely on ``collect_data_tests``. Instead we will more
-explicitly create a mechanism turning a List of ``Requirement`` objects into something
-that can be tested by pytest manually. Importantly, we want every ``Constraint`` of every
-``Requirement`` to be tested independently of each other. For instance, we would not like
-one failing test to halt all others.
-
-Many of these approaches rely on adapting pytest's ``conftest.py``. If you are not familiar
-with this concept, you might want to read up on it
-`in the pytest docs <https://docs.pytest.org/en/6.2.x/writing_plugins.html#conftest-py-plugins>`_.
-
-Subselection
-------------
-
-Most often one might want to run all tests defined by a specification.
-
-Yet, for example after believing to have fixed a data problem, one might simply
-want to test whether a single test, which had previously been failing, succeeds
-at last.
-
-Another example for when one would like to test a subset of tests is if the data at
-hand is not available in its entirety. Rather, it could be that one would like
-to run a subset of the test suite against a subsample of the typical dataset.
-
-In this section, we present two approaches to do a subselection of tests.
-
-Ex-post: subselecting generated tests
-*************************************
-
-Instead of merely running  ``$ pytest specification.py`` one may add pytests's
-``-k`` flag and specify the ``Constraint`` (s) one cares about.
-
-Importantly, every ``Constraint`` object can be identified via a name. If one wants
-to figure out how this string is built, please refer to the implementation of
-:meth:`~datajudge.constraints.base.Constraint.get_description`.
-Otherwise, one could also just run all of the tests once and investigate
-the resulting test report to find the relevant names.
-
-When only caring about the ``UniquesEquality`` constraint in our
-:doc:`twitch example <examples/example_twitch>`.
-one might for instance use the following prefix the filter for it:
-
-.. code-block:: console
-
-  $ pytest twitch_specification.py -k "UniquesEquality::public.twitch_v1"
-
-Ex-ante: Defining categories of tests
-*************************************
-
-Another option to subselect a certain set of tests is by use of
-`pytest markers <https://docs.pytest.org/en/7.1.x/example/markers.html>`_.
-The following is one way of using markers in conjunction with ``datajudge``.
-
-In this particular illustration we'll allow for two markers:
-
-* ``basic``: indicating that only truly fundamental tests should be run
-* ``all``: indicating that any available test should be run
-
-For that matter we'll add a bit of pytest magic to the respective ``conftest.py``.
-
-.. code-block:: python
-  :caption: ``conftest.py``
-
-  def pytest_generate_tests(metafunc):
-      if "basic_constraint" in metafunc.fixturenames:
-          metafunc.parametrize(
-              "basic_constraint",
-              # Find these functions in specification.py.
-              metafunc.module.get_basic_constraints(),
-              ids=metafunc.module.idfn,
-	  )
-      if "constraint" in metafunc.fixturenames:
-          metafunc.parametrize(
-              "constraint",
-              # Find these functions in specification.py.
-              metafunc.module.get_all_constraints(),
-              ids=metafunc.module.idfn,
-          )
-
-
-Moreover, we'll have to register these markers in pytest's ``pytest.ini`` file.
-You can read more about these files
-`here <https://docs.pytest.org/en/6.2.x/customize.html>`_.
-
-
-.. code-block::
-   :caption: ``pytest.ini``
-
-   [pytest]
-   addopts = --strict-markers
-   markers = basic: basic specification
-	     all: entire specification
-
-Once that is taken care of, one can adapt one's specification as follows:
-
-.. code-block:: python
-  :caption: ``specification.py``
-
-  def get_basic_requirements() -> List[Requirement]:
-      # Create relevant Requirement objects and respective Constraints.
-      # ...
-
-      return requirements
-
-  def get_advanced_requirements() -> List[Requirement]:
-      # Create relevant Requirement objects and respective Constraints.
-      # ...
-
-      return requirements
-
-  def get_basic_constraints() -> List[Constraint]:
-      return [constraint for requirement in get_basic_requirements() for constraint in requirement]
-
-  def get_all_constraints() -> List[Constraint]:
-      all_requirements = get_basic_requirements() + get_advanced_requirements()
-      return [constraint for requirement in all_requirements for constraint in requirement]
-
-  # Function used in conftest.py.
-  # Given a constraint, returns an identifier used to refer to it as a test.
-  def idfn(constraint):
-      return constraint.get_description()
-
-  @pytest.mark.basic
-  def test_basic_constraint(basic_constraint: Constraint, datajudge_engine):
-      test_result = basic_constraint.test(datajudge_engine)
-      assert test_result.outcome, test_result.failure_message
-
-  @pytest.mark.all
-  def test_all_constraint(constraint: Constraint, datajudge_engine):
-      test_result = constraint.test(datajudge_engine)
-      assert test_result.outcome, test_result.failure_message
-
-Once these changes are taken care of, one may run
-
-.. code-block:: console
-
-  $ pytest specification.py -m basic
-
-to only test the basic ``Requirement`` s or
-
-.. code-block:: console
-
-  $ pytest specification.py -m all
-
-to test all ``Requirement`` s.
-
-
-Using parameters in a specification
------------------------------------
-
-A given specification might rely on identifiers such as database names or
-table names. Moreover it might be that, e.g. when iterating from one version
-of the data to another, these names change.
-
-In other words, it could be that the logic should remain unchanged while pointers
-to data might change. Therefore, one might just as well consider
-those pointers or identifiers as parameters of the specification.
-
-For the sake of concreteness, we will assume here that we wish frame two
-identifiers as parameters:
-
-* ``new_db``: the name of the 'new database'
-* ``old_db``: the name of the 'old database'
-
-In light of that we will again adapt pytest's ``conftest.py``:
-
-.. code-block:: python
-  :caption: ``conftest.py``
-
-  def pytest_addoption(parser):
-      parser.addoption("--new_db", action="store", help="name of the new database")
-      parser.addoption("--old_db", action="store", help="name of the old database")
-
-
-  def pytest_generate_tests(metafunc):
-      params = {
-          "db_name_new": metafunc.config.option.new_db,
-          "db_name_old": metafunc.config.option.old_db,
-      }
-      metafunc.parametrize(
-          "constraint",
-          metafunc.module.get_constraints(params),
-          ids=metafunc.module.idfn,
-      )
-
-Now, we can make the creation of our ``Requirement`` s and ``Constraint`` s
-dependent on these parameters:
-
-.. code-block:: python
-  :caption: ``specification.py``
-
-  def get_requirements(params):
-      between_requirement = BetweenRequirement.from_tables(
-          db_name1=params["old_db"],
-	  db_name2=params["new_db"],
-	  # ...
-      )
-      # ...
-      return requirements
-
-  def get_constraints(params):
-      return [
-	  constraint for requirement in get_requirements(params) for constraint in requirement
-      ]
-
-  def idfn(constraint):
-      return constraint.get_description()
-
-  def test_constraint(constraint, datajudge_engine):
-      test_result = constraint.test(datajudge_engine)
-      assert test_result.outcome, test_result.failure_message
-
-Once the specification is defined to be dependent on such parameters, they can
-simply be passed via CLI:
-
-.. code-block:: console
-
-  $ pytest specification.py --new_db=db_v1 --old_db=db_v2
-
-Html reports
-------------
-
-By default, running ``pytest`` tests will output test results to one's respective shell.
-Alternatively, one might want to generate an html report summarizing and expanding on
-all test results. This can be advantageous for
-
-* Sharing test results with colleagues
-* Archiving and tracking test results over time
-* Make underlying sql queries conveniently accessible
-
-Concretely, such an html report can be generated by
-`pytest-html <https://github.com/pytest-dev/pytest-html>`_. Once installed, using it is as simple
-as appending ``--html=myreport.html`` to the pytest call.
-
-In our twitch example, this generates `this html report <https://github.com/Quantco/datajudge/tree/main/docs/source/examples/twitch_report.html>`_.
-
-
-Retrieving queries
-------------------
-
-Usually we not only care about knowing whether there is a problem with the data
-at hand and what it is. Rather, we would also like to fix it as fast and
-conveniently as possible.
-
-For that matter, ``datajudge`` makes the queries it uses to assert testing predicates
-available via the :class:`datajudge.constraints.base.TestResult`
-class. Hence, if a test is failing, the user can jumpstart the investigation of the
-problem by reusing and potentially adapting the underlying queries.
-
-Instead of simply running ``assert constraint.test(engine).outcome``, one may add
-the ``TestResult`` 's ``logging_message`` to e.g. a ``logger`` or add it to pytest
-``extra``:
-
-.. code-block:: python
-
-  from pytest_html import extras
-
-  def test_constraint(constraint: Constraint, engine, extra):
-    test_result = constraint.test(engine)
-    message = test_result.logging_message
-
-    if not test_result.outcome:
-      # Send to logger.
-      logger.info(message)
-      # Add to html report.
-      extra.append(
-        extras.extra(
-          content=message,
-          format_type="text",
-          name="failing_query",
-          mime_type="text/plain",
-          extension="sql",
-        )
-      )
-
-   assert test_result.outcome
-
-
-Such a ``logging_message`` - with ready to execute sql queries - can look as follows:
-
-.. code-block:: sql
-
-  /*
-  Failure message:
-  tempdb.public.twitch_v1's column(s) 'language' doesn't have the
-  element(s) '{'Sw3d1zh'}' when compared with the reference values.
-  */
-
-   --Factual queries:
-   SELECT anon_1.language, count(*) AS count_1
-  FROM (SELECT public.twitch_v1.language AS language
-  FROM public.twitch_v1) AS anon_1 GROUP BY anon_1.language
-
-  -- Target queries:
-   SELECT anon_1.language, count(*) AS count_1
-  FROM (SELECT public.twitch_v2.language AS language
-  FROM public.twitch_v2) AS anon_1 GROUP BY anon_1.language
-
-
-If using a mechanism - as previously outlined - to forward these messages to
-an html report, this can look as follows:
-
-
-.. image:: report_failing_query1.png
-  :width: 800
-
-
-.. image:: report_failing_query2.png
-  :width: 800
-
diff --git a/docs/styles/custom.css b/docs/styles/custom.css
new file mode 100644
index 00000000..9329ae9f
--- /dev/null
+++ b/docs/styles/custom.css
@@ -0,0 +1,16 @@
+/*
+Hides the In[ ] blocks in rendered jupyter notebooks.
+Out[ ] blocks are not displayed.
+*/
+.jp-InputPrompt, .jp-OutputPrompt {
+    display: none !important;
+}
+
+/*
+Note: For some reason, ".jp-OutputArea-area pre" didn't work as selector.
+But by introducing the custom class that is added through the plugin to jupyter
+input or output cells, we can target the pre tags inside the cells.
+*/
+.jupyter-custom-highlight .jp-OutputArea-area > pre {
+    white-space: pre;
+}
diff --git a/docs/testing.md b/docs/testing.md
new file mode 100644
index 00000000..c5a72fa1
--- /dev/null
+++ b/docs/testing.md
@@ -0,0 +1,298 @@
+# Testing
+
+While `datajudge` allows to express expectations via specifications, `Requirement`s
+and `Constraint`s, the execution of tests is delegated to pytest. As a
+consequence, one may use any functionalities that pytest has to offer. Here,
+we want to illustrate some of these advanced functionalities that might turn out useful.
+
+Yet, it should be noted that for most intents and purposes, using `datajudge` 's helper
+function [`collect_data_tests`][datajudge.pytest_integration.collect_data_tests] is a good starting
+point. It should work out of the box and hides some complexity. For exemplary applications
+see, the
+[companies example](examples/company-data.md) or the [twitch example](examples/twitch.md).
+
+Throughout this article we will not rely on `collect_data_tests`. Instead we will more
+explicitly create a mechanism turning a List of `Requirement` objects into something
+that can be tested by pytest manually. Importantly, we want every `Constraint` of every
+`Requirement` to be tested independently of each other. For instance, we would not like
+one failing test to halt all others.
+
+Many of these approaches rely on adapting pytest's `conftest.py`. If you are not familiar
+with this concept, you might want to read up on it
+[in the pytest docs](https://docs.pytest.org/en/latest/writing_plugins.html#conftest-py-plugins).
+
+## Subselection
+
+Most often one might want to run all tests defined by a specification.
+
+Yet, for example after believing to have fixed a data problem, one might simply
+want to test whether a single test, which had previously been failing, succeeds
+at last.
+
+Another example for when one would like to test a subset of tests is if the data at
+hand is not available in its entirety. Rather, it could be that one would like
+to run a subset of the test suite against a subsample of the typical dataset.
+
+In this section, we present two approaches to do a subselection of tests.
+
+### Ex-post: subselecting generated tests
+
+Instead of merely running `$ pytest specification.py` one may add pytests's
+`-k` flag and specify the `Constraint` (s) one cares about.
+
+Importantly, every `Constraint` object can be identified via a name. If one wants
+to figure out how this string is built, please refer to the implementation of
+:meth:`~datajudge.constraints.base.Constraint.get_description`.
+Otherwise, one could also just run all of the tests once and investigate
+the resulting test report to find the relevant names.
+
+When only caring about the `UniquesEquality` constraint in our
+:doc:`twitch example <examples/example_twitch>`.
+one might for instance use the following prefix the filter for it:
+
+```bash
+pytest twitch_specification.py -k "UniquesEquality::public.twitch_v1"
+```
+
+### Ex-ante: Defining categories of tests
+
+Another option to subselect a certain set of tests is by use of
+[pytest markers](https://docs.pytest.org/en/latest/example/markers.html).
+The following is one way of using markers in conjunction with `datajudge`.
+
+In this particular illustration we'll allow for two markers:
+
+- `basic`: indicating that only truly fundamental tests should be run
+- `all`: indicating that any available test should be run
+
+For that matter we'll add a bit of pytest magic to the respective `conftest.py`.
+
+```python title="conftest.py"
+def pytest_generate_tests(metafunc):
+    if "basic_constraint" in metafunc.fixturenames:
+        metafunc.parametrize(
+            "basic_constraint",
+            # Find these functions in specification.py.
+            metafunc.module.get_basic_constraints(),
+            ids=metafunc.module.idfn,
+        )
+    if "constraint" in metafunc.fixturenames:
+        metafunc.parametrize(
+            "constraint",
+            # Find these functions in specification.py.
+            metafunc.module.get_all_constraints(),
+            ids=metafunc.module.idfn,
+        )
+```
+
+Moreover, we'll have to register these markers in pytest's `[tool.pytest.ini_options]` in `pyproject.toml`.
+You can read more about these files [here](https://docs.pytest.org/en/latest/customize.html).
+
+```toml title="pyproject.toml"
+[tool.pytest.ini_options]
+addopts = "--strict-markers"
+markers = [
+  "basic: basic specification",
+  "all: entire specification",
+]
+```
+
+Once that is taken care of, one can adapt one's specification as follows:
+
+```python title="specification.py"
+def get_basic_requirements() -> List[Requirement]:
+    # Create relevant Requirement objects and respective Constraints.
+    # ...
+    return requirements
+
+def get_advanced_requirements() -> List[Requirement]:
+    # Create relevant Requirement objects and respective Constraints.
+    # ...
+    return requirements
+
+def get_basic_constraints() -> List[Constraint]:
+    return [constraint for requirement in get_basic_requirements() for constraint in requirement]
+
+def get_all_constraints() -> List[Constraint]:
+    all_requirements = get_basic_requirements() + get_advanced_requirements()
+    return [constraint for requirement in all_requirements for constraint in requirement]
+
+# Function used in conftest.py.
+# Given a constraint, returns an identifier used to refer to it as a test.
+def idfn(constraint):
+    return constraint.get_description()
+
+@pytest.mark.basic
+def test_basic_constraint(basic_constraint: Constraint, datajudge_engine):
+    test_result = basic_constraint.test(datajudge_engine)
+    assert test_result.outcome, test_result.failure_message
+
+@pytest.mark.all
+def test_all_constraint(constraint: Constraint, datajudge_engine):
+    test_result = constraint.test(datajudge_engine)
+    assert test_result.outcome, test_result.failure_message
+```
+
+Once these changes are taken care of, one may run
+
+```bash
+pytest specification.py -m basic
+```
+
+to only test the basic `Requirement` s or
+
+```bash
+pytest specification.py -m all
+```
+
+to test all `Requirement`s.
+
+## Using parameters in a specification
+
+A given specification might rely on identifiers such as database names or
+table names. Moreover it might be that, e.g. when iterating from one version
+of the data to another, these names change.
+
+In other words, it could be that the logic should remain unchanged while pointers
+to data might change. Therefore, one might just as well consider
+those pointers or identifiers as parameters of the specification.
+
+For the sake of concreteness, we will assume here that we wish frame two
+identifiers as parameters:
+
+- `new_db`: the name of the 'new database'
+- `old_db`: the name of the 'old database'
+
+In light of that we will again adapt pytest's `conftest.py`:
+
+```python title="conftest.py"
+def pytest_addoption(parser):
+    parser.addoption("--new_db", action="store", help="name of the new database")
+    parser.addoption("--old_db", action="store", help="name of the old database")
+
+
+def pytest_generate_tests(metafunc):
+    params = {
+        "db_name_new": metafunc.config.option.new_db,
+        "db_name_old": metafunc.config.option.old_db,
+    }
+    metafunc.parametrize(
+        "constraint",
+        metafunc.module.get_constraints(params),
+        ids=metafunc.module.idfn,
+    )
+```
+
+Now, we can make the creation of our `Requirement`s and `Constraint`s
+dependent on these parameters:
+
+```python title="application.py"
+def get_requirements(params):
+    between_requirement = BetweenRequirement.from_tables(
+        db_name1=params["old_db"],
+        db_name2=params["new_db"],
+        # ...
+    )
+    # ...
+    return requirements
+
+def get_constraints(params):
+    return [
+        constraint for requirement in get_requirements(params) for constraint in requirement
+    ]
+
+def idfn(constraint):
+    return constraint.get_description()
+
+def test_constraint(constraint, datajudge_engine):
+    test_result = constraint.test(datajudge_engine)
+    assert test_result.outcome, test_result.failure_message
+```
+
+Once the specification is defined to be dependent on such parameters, they can
+simply be passed via CLI:
+
+```bash
+pytest specification.py --new_db=db_v1 --old_db=db_v2
+```
+
+## Html reports
+
+By default, running `pytest` tests will output test results to one's respective shell.
+Alternatively, one might want to generate an html report summarizing and expanding on
+all test results. This can be advantageous for
+
+- Sharing test results with colleagues
+- Archiving and tracking test results over time
+- Make underlying sql queries conveniently accessible
+
+Concretely, such an html report can be generated by
+[pytest-html](https://github.com/pytest-dev/pytest-html). Once installed, using it is as simple
+as appending `--html=myreport.html` to the pytest call.
+
+In our twitch example, this generates [this html report](https://github.com/Quantco/datajudge/tree/main/docs/examples/twitch_report.html).
+
+## Retrieving queries
+
+Usually we not only care about knowing whether there is a problem with the data
+at hand and what it is. Rather, we would also like to fix it as fast and
+conveniently as possible.
+
+For that matter, `datajudge` makes the queries it uses to assert testing predicates
+available via the :class:`datajudge.constraints.base.TestResult`
+class. Hence, if a test is failing, the user can jumpstart the investigation of the
+problem by reusing and potentially adapting the underlying queries.
+
+Instead of simply running `assert constraint.test(engine).outcome`, one may add
+the `TestResult`'s `logging_message` to e.g. a `logger` or add it to pytest
+`extra`:
+
+```python
+from pytest_html import extras
+
+def test_constraint(constraint: Constraint, engine, extra):
+    test_result = constraint.test(engine)
+    message = test_result.logging_message
+
+    if not test_result.outcome:
+      # Send to logger.
+      logger.info(message)
+      # Add to html report.
+      extra.append(
+        extras.extra(
+          content=message,
+          format_type="text",
+          name="failing_query",
+          mime_type="text/plain",
+          extension="sql",
+        )
+      )
+
+    assert test_result.outcome
+```
+
+Such a `logging_message` - with ready to execute sql queries - can look as follows:
+
+```sql
+/*
+Failure message:
+tempdb.public.twitch_v1's column(s) 'language' doesn't have the
+element(s) '{'Sw3d1zh'}' when compared with the reference values.
+*/
+
+  --Factual queries:
+  SELECT anon_1.language, count(*) AS count_1
+FROM (SELECT public.twitch_v1.language AS language
+FROM public.twitch_v1) AS anon_1 GROUP BY anon_1.language
+
+-- Target queries:
+  SELECT anon_1.language, count(*) AS count_1
+FROM (SELECT public.twitch_v2.language AS language
+FROM public.twitch_v2) AS anon_1 GROUP BY anon_1.language
+```
+
+If using a mechanism - as previously outlined - to forward these messages to
+an html report, this can look as follows:
+
+![report failing query 1](./report_failing_query1.png)
+![report failing query 2](./report_failing_query2.png)
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 00000000..75c4d40f
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,91 @@
+site_name: datajudge
+site_description: Assessing whether data from database complies with reference information.
+site_url: https://quantco.github.io/datajudge
+theme:
+  name: material
+  palette:
+    # Palette toggle for automatic mode
+    - media: "(prefers-color-scheme)"
+      toggle:
+        icon: material/brightness-auto
+        name: Switch to light mode
+    # Palette toggle for light mode
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+      primary: indigo
+    # Palette toggle for dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      toggle:
+        icon: material/brightness-4
+        name: Switch to system preference
+      primary: indigo
+  features:
+    - content.action.edit
+    - search.suggest
+    - search.highlight
+    - content.code.annotate
+    - content.code.copy
+    - navigation.tabs
+  icon:
+    repo: fontawesome/brands/github-alt
+    edit: material/pencil
+repo_name: quantco/datajudge
+repo_url: https://github.com/quantco/datajudge
+edit_uri: edit/main/docs/
+plugins:
+  - search
+  - mkdocstrings:
+      handlers:
+        python:
+          paths:
+            - src
+            - "!src/datajudge/db_access.py"
+          options:
+            docstring_section_style: spacy
+            docstring_style: numpy
+            merge_init_into_class: true
+            unwrap_annotated: true
+            separate_signature: true
+            show_if_no_docstring: true
+            show_signature_annotations: true
+            show_submodules: true
+            show_symbol_type_heading: true
+            filters:
+              - "!^_" # exclude private and dunder elements
+              - "!db_access"
+
+nav:
+  - installation.md
+  - getting-started.md
+  - testing.md
+  - motivation.md
+  - Examples:
+      - examples/company-data.md
+      - examples/twitch.md
+      - examples/dates.md
+      - examples/exploration.md
+  - development.md
+  - api-documentation.md
+  - Changelog: CHANGELOG.md
+
+markdown_extensions:
+  - admonition
+  - pymdownx.highlight
+  - pymdownx.superfences
+  - pymdownx.inlinehilite
+  - pymdownx.arithmatex:
+      generic: true
+
+extra_javascript:
+  - javascripts/katex.js
+  - javascripts/external_links.js
+  - https://unpkg.com/katex@0/dist/katex.min.js
+  - https://unpkg.com/katex@0/dist/contrib/auto-render.min.js
+
+extra_css:
+  - https://unpkg.com/katex@0/dist/katex.min.css
+  - styles/custom.css
diff --git a/pixi.lock b/pixi.lock
index 260cc454..baec4f91 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -1,6969 +1,10424 @@
-version: 5
+version: 6
 environments:
-  bigquery-py38:
+  bigquery-py310:
     channels:
     - url: https://conda.anaconda.org/conda-forge/
+    options:
+      pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.9.5-py38h01eb140_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.3.1-pyhd8ed1ab_0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/async-timeout-4.0.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-23.2.0-pyh71513ae_0.conda
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   - libedit >=3.1.20191231,<4.0a0
   - libgcc-ng >=12
   - libstdcxx-ng >=12
-  - openssl >=3.1.2,<4.0a0
+  - openssl >=3.3.1,<4.0a0
   license: MIT
   license_family: MIT
-  size: 1371181
-  timestamp: 1692097755782
-- kind: conda
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-  - openssl >=3.1.2,<4.0a0
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+  - libedit >=3.1.20191231,<4.0a0
+  - openssl >=3.3.1,<4.0a0
   license: MIT
   license_family: MIT
-  size: 1195575
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-- kind: conda
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   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   license: MIT
   license_family: MIT
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+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - zstd >=1.5.7,<1.6.0a0
   constrains:
-  - binutils_impl_linux-64 2.40
+  - binutils_impl_linux-64 2.45
   license: GPL-3.0-only
   license_family: GPL
-  size: 707602
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-- kind: conda
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-  url: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240116.2-cxx17_h59595ed_0.conda
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-  md5: 682bdbe046a68f749769b492f3625c5c
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-  - libstdcxx-ng >=12
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
   constrains:
-  - libabseil-static =20240116.2=cxx17*
-  - abseil-cpp =20240116.2
+  - libabseil-static =20250512.1=cxx17*
+  - abseil-cpp =20250512.1
   license: Apache-2.0
   license_family: Apache
-  size: 1266634
-  timestamp: 1714403128134
-- kind: conda
-  name: libabseil
-  version: '20240116.2'
-  build: cxx17_h63175ca_0
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-  url: https://conda.anaconda.org/conda-forge/win-64/libabseil-20240116.2-cxx17_h63175ca_0.conda
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-  md5: 31b8c712b478ba94896707c159c60499
+  size: 1310612
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+  sha256: 7f0ee9ae7fa2cf7ac92b0acf8047c8bac965389e48be61bf1d463e057af2ea6a
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-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
+  - __osx >=11.0
+  - libcxx >=18
   constrains:
-  - abseil-cpp =20240116.2
-  - libabseil-static =20240116.2=cxx17*
+  - libabseil-static =20250512.1=cxx17*
+  - abseil-cpp =20250512.1
   license: Apache-2.0
   license_family: Apache
-  size: 1781843
-  timestamp: 1714404063887
-- kind: conda
-  name: libabseil
-  version: '20240116.2'
-  build: cxx17_hebf3989_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libabseil-20240116.2-cxx17_hebf3989_0.conda
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-  md5: edc3edb68fd9cbb014ac675dc73006c2
+  size: 1174081
+  timestamp: 1750194620012
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+  sha256: 78790771f44e146396d9ae92efbe1022168295afd8d174f653a1fa16f0f0fa32
+  md5: d6a4cd236fc1c69a1cfc9698fb5e391f
   depends:
-  - libcxx >=16
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.42.34438
   constrains:
-  - abseil-cpp =20240116.2
-  - libabseil-static =20240116.2=cxx17*
+  - libabseil-static =20250512.1=cxx17*
+  - abseil-cpp =20250512.1
   license: Apache-2.0
   license_family: Apache
-  size: 1143678
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-- kind: conda
-  name: libblas
-  version: 3.9.0
-  build: 20_linux64_openblas
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-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-20_linux64_openblas.conda
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-  depends:
-  - libopenblas >=0.3.25,<0.3.26.0a0
-  - libopenblas >=0.3.25,<1.0a0
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-  - liblapacke 3.9.0 20_linux64_openblas
-  - libcblas 3.9.0 20_linux64_openblas
-  - blas * openblas
-  - liblapack 3.9.0 20_linux64_openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 14433
-  timestamp: 1700568383457
-- kind: conda
-  name: libblas
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-  build: 20_osxarm64_openblas
-  build_number: 20
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-20_osxarm64_openblas.conda
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-  depends:
-  - libopenblas >=0.3.25,<0.3.26.0a0
-  - libopenblas >=0.3.25,<1.0a0
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-  - liblapack 3.9.0 20_osxarm64_openblas
-  - liblapacke 3.9.0 20_osxarm64_openblas
-  - libcblas 3.9.0 20_osxarm64_openblas
-  - blas * openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 14722
-  timestamp: 1700568881837
-- kind: conda
-  name: libblas
-  version: 3.9.0
-  build: 22_linux64_openblas
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-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-22_linux64_openblas.conda
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-  depends:
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-  - libopenblas >=0.3.27,<1.0a0
-  constrains:
-  - libcblas 3.9.0 22_linux64_openblas
-  - blas * openblas
-  - liblapacke 3.9.0 22_linux64_openblas
-  - liblapack 3.9.0 22_linux64_openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 14537
-  timestamp: 1712542250081
-- kind: conda
-  name: libblas
-  version: 3.9.0
-  build: 22_osxarm64_openblas
-  build_number: 22
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-22_osxarm64_openblas.conda
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-  depends:
-  - libopenblas >=0.3.27,<0.3.28.0a0
-  - libopenblas >=0.3.27,<1.0a0
-  constrains:
-  - blas * openblas
-  - liblapack 3.9.0 22_osxarm64_openblas
-  - liblapacke 3.9.0 22_osxarm64_openblas
-  - libcblas 3.9.0 22_osxarm64_openblas
-  license: BSD-3-Clause
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+  - bzip2 >=1.0.8,<2.0a0
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+  - libxml2 >=2.13.8,<2.14.0a0
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+  - lz4-c >=1.10.0,<1.11.0a0
+  - lzo >=2.10,<3.0a0
+  - openssl >=3.5.0,<4.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  license: BSD-2-Clause
   license_family: BSD
-  size: 14824
-  timestamp: 1712542396471
-- kind: conda
-  name: libblas
-  version: 3.9.0
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-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-22_win64_mkl.conda
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-  md5: 65c56ecdeceffd6c32d3d54db7e02c6e
-  depends:
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-  constrains:
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-  - blas * mkl
-  - libcblas 3.9.0 22_win64_mkl
-  - liblapack 3.9.0 22_win64_mkl
-  license: BSD-3-Clause
+  size: 883383
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+  - bzip2 >=1.0.8,<2.0a0
+  - libgcc >=14
+  - liblzma >=5.8.1,<6.0a0
+  - libxml2
+  - libxml2-16 >=2.14.6
+  - libzlib >=1.3.1,<2.0a0
+  - lz4-c >=1.10.0,<1.11.0a0
+  - lzo >=2.10,<3.0a0
+  - openssl >=3.5.4,<4.0a0
+  - zstd >=1.5.7,<1.6.0a0
+  license: BSD-2-Clause
   license_family: BSD
-  size: 5182602
-  timestamp: 1712542984136
-- kind: conda
-  name: libcblas
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-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda
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-  depends:
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+  depends:
+  - libopenblas >=0.3.30,<0.3.31.0a0
+  - libopenblas >=0.3.30,<1.0a0
   constrains:
-  - liblapacke 3.9.0 20_linux64_openblas
-  - blas * openblas
-  - liblapack 3.9.0 20_linux64_openblas
+  - mkl <2026
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+  - libcblas   3.11.0   5*_openblas
+  - blas 2.305   openblas
+  - liblapacke 3.11.0   5*_openblas
   license: BSD-3-Clause
   license_family: BSD
-  size: 14383
-  timestamp: 1700568410580
-- kind: conda
-  name: libcblas
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-20_osxarm64_openblas.conda
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-  - liblapack 3.9.0 20_osxarm64_openblas
-  - liblapacke 3.9.0 20_osxarm64_openblas
-  - blas * openblas
+  - libcblas   3.11.0   5*_openblas
+  - liblapack  3.11.0   5*_openblas
+  - liblapacke 3.11.0   5*_openblas
+  - blas 2.305   openblas
+  - mkl <2026
   license: BSD-3-Clause
   license_family: BSD
-  size: 14642
-  timestamp: 1700568912840
-- kind: conda
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-  url: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-22_linux64_openblas.conda
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+  depends:
+  - mkl >=2025.3.0,<2026.0a0
   constrains:
-  - liblapack 3.9.0 22_linux64_openblas
-  - blas * openblas
-  - liblapacke 3.9.0 22_linux64_openblas
+  - liblapack  3.11.0   5*_mkl
+  - libcblas   3.11.0   5*_mkl
+  - blas 2.305   mkl
+  - liblapacke 3.11.0   5*_mkl
   license: BSD-3-Clause
   license_family: BSD
-  size: 14438
-  timestamp: 1712542270166
-- kind: conda
-  name: libcblas
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-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-22_osxarm64_openblas.conda
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-  depends:
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+  md5: 6636a2b6f1a87572df2970d3ebc87cc0
+  depends:
+  - libblas 3.11.0 5_h4a7cf45_openblas
   constrains:
-  - blas * openblas
-  - liblapack 3.9.0 22_osxarm64_openblas
-  - liblapacke 3.9.0 22_osxarm64_openblas
+  - liblapacke 3.11.0   5*_openblas
+  - blas 2.305   openblas
+  - liblapack  3.11.0   5*_openblas
   license: BSD-3-Clause
   license_family: BSD
-  size: 14741
-  timestamp: 1712542420590
-- kind: conda
-  name: libcblas
-  version: 3.9.0
-  build: 22_win64_mkl
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   license: BSD-3-Clause
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-- kind: conda
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-- kind: conda
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-- kind: conda
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-- kind: conda
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+  size: 24194
+  timestamp: 1764460141901
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
+  sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7
+  md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829
+  depends:
+  - __glibc >=2.17,<3.0.a0
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 601375
+  timestamp: 1764777111296
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-hbf9d68e_6.conda
+  sha256: 9485ba49e8f47d2b597dd399e88f4802e100851b27c21d7525625b0b4025a5d9
+  md5: ab136e4c34e97f34fb621d2592a393d8
+  depends:
+  - __osx >=11.0
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 433413
+  timestamp: 1764777166076
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
+  sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2
+  md5: 053b84beec00b71ea8ff7a4f84b55207
+  depends:
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libzlib >=1.3.1,<2.0a0
+  license: BSD-3-Clause
+  license_family: BSD
+  size: 388453
+  timestamp: 1764777142545
diff --git a/pixi.toml b/pixi.toml
index 0684544b..def79fed 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -1,4 +1,4 @@
-[project]
+[workspace]
 name = "datajudge"
 channels = ["conda-forge"]
 platforms = ["linux-64", "osx-arm64", "win-64"]
@@ -7,33 +7,33 @@ platforms = ["linux-64", "osx-arm64", "win-64"]
 postinstall = "pip install --no-build-isolation --no-deps --disable-pip-version-check -e ."
 
 [dependencies]
-python = ">=3.8"
+python = ">=3.10"
 scipy = "*"
 sqlalchemy = ">=1.4,<3"
 colorama = "*"
 pytest = "*"
+flit-core = "*"
+flit = "*"
 
 [feature.docs.dependencies]
-make = "*"
-numpydoc = "*"
-sphinx = "*"
-sphinx_rtd_theme = "*"
-sphinxcontrib-apidoc = "*"
-sphinx-autodoc-typehints = "*"
+mkdocs-material = "*"
+mkdocstrings = "*"
+mkdocstrings-python = ">=2.0"
 [feature.docs.tasks]
-docs = "cd docs && make html "
-readthedocs = "rm -rf $READTHEDOCS_OUTPUT/html && cp -r docs/build/html $READTHEDOCS_OUTPUT/html"
+docs = "mkdocs serve"
+docs-build = "mkdocs build --strict"
+readthedocs = "rm -rf $READTHEDOCS_OUTPUT/html && cp -r site/ $READTHEDOCS_OUTPUT/html"
 
-[feature.py38.dependencies]
-python = "3.8.*"
-[feature.py39.dependencies]
-python = "3.9.*"
 [feature.py310.dependencies]
 python = "3.10.*"
 [feature.py311.dependencies]
 python = "3.11.*"
 [feature.py312.dependencies]
 python = "3.12.*"
+[feature.py313.dependencies]
+python = "3.13.*"
+[feature.py314.dependencies]
+python = "3.14.*"
 
 [feature.unit.tasks]
 test = "pytest tests/unit"
@@ -45,7 +45,7 @@ freetds = "*"
 [feature.mssql.tasks]
 test = "pytest tests/integration --backend=mssql"
 test_freetds = "pytest tests/integration --backend=mssql-freetds"
-coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=mssql"
+coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=mssql-freetds"
 
 [feature.postgres.dependencies]
 psycopg2 = "*"
@@ -54,8 +54,11 @@ test = "pytest tests/integration --backend=postgres"
 coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=postgres"
 
 [feature.db2.dependencies]
-ibm_db = "*"
+ibm_db = "!=3.2.5"
 ibm_db_sa = "*"
+# sqlalchemy 1.0 is (temporarily) not compatible with ibm_db_sa, see
+# https://github.com/ibmdb/python-ibmdbsa/issues/165
+sqlalchemy = ">=2.0"
 [feature.db2.tasks]
 test = "pytest tests/integration --backend=db2"
 coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=db2"
@@ -66,18 +69,20 @@ snowflake-sqlalchemy = "*"
 test = "pytest tests/integration --backend=snowflake"
 coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=snowflake"
 
+[feature.duckdb.dependencies]
+duckdb-engine = "*"
+# See https://github.com/Mause/duckdb_engine/issues/1379
+sqlalchemy = "<2.0.45"
+[feature.duckdb.tasks]
+test = "pytest tests/integration --backend=duckdb"
+coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=duckdb"
+
 [feature.bigquery.dependencies]
 sqlalchemy-bigquery = "*"
 [feature.bigquery.tasks]
 test = "pytest tests/integration --backend=bigquery -v -n=16"
 coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=bigquery -v -n=16"
 
-[feature.impala.dependencies]
-impyla = "*"
-[feature.impala.tasks]
-test = "pytest tests/integration --backend=impala"
-coverage = "pytest tests/integration --cov=datajudge --cov-report=xml --cov-append --backend=impala"
-
 [feature.sa1.dependencies]
 sqlalchemy = "1.*"
 
@@ -87,75 +92,73 @@ sqlalchemy = "2.*"
 [feature.test.dependencies]
 pytest-cov = "*"
 pytest-xdist = "*"
-
-[feature.mypy.dependencies]
+pytest-html = "*"
+ty = "*"
 mypy = "*"
-types-setuptools = "*"
 types-colorama = "*"
 pandas-stubs = "*"
-types-jinja2 = "*"
+
+[feature.test.target.unix.dependencies]
+pytest-memray = "*"
+memray = "*"
 
 [feature.lint.dependencies]
 pre-commit = "*"
-black = "*"
-flake8 = "*"
-isort = "*"
+docformatter = "*"
+ruff = "*"
+prettier = "*"
 taplo = "*"
+pre-commit-hooks = "*"
 typos = "*"
-pyupgrade = "*"
 
 [feature.lint.tasks]
 pre-commit-install = "pre-commit install"
 pre-commit-run = "pre-commit run -a"
 
-[host-dependencies]
-flit-core = "*"
-flit = "*"
-
 [environments]
 default = ["test"]
 docs = ["docs"]
 
-py38 = ["test", "py38", "unit"]
-py39 = ["test", "py39", "unit"]
 py310 = ["test", "py310", "unit"]
 py311 = ["test", "py311", "unit"]
 py312 = ["test", "py312", "unit"]
+py313 = ["test", "py313", "unit"]
+py314 = ["test", "py314", "unit"]
 sa1 = ["sa1", "test", "unit"]
 sa2 = ["sa2", "test", "unit"]
 
-mssql-py38 = ["mssql", "py38", "test"]
-mssql-py39 = ["mssql", "py39", "test"]
 mssql-py310 = ["mssql", "py310", "test"]
 mssql-py311 = ["mssql", "py311", "test"]
 mssql-py312 = ["mssql", "py312", "test"]
+mssql-py313 = ["mssql", "py313", "test"]
+mssql-py314 = ["mssql", "py314", "test"]
 mssql-sa1 = ["mssql", "sa1", "test"]
 mssql-sa2 = ["mssql", "sa2", "test"]
 
-postgres-py38 = ["postgres", "py38", "test"]
-postgres-py39 = ["postgres", "py39", "test"]
 postgres-py310 = ["postgres", "py310", "test"]
 postgres-py311 = ["postgres", "py311", "test"]
 postgres-py312 = ["postgres", "py312", "test"]
+postgres-py313 = ["postgres", "py313", "test"]
+postgres-py314 = ["postgres", "py314", "test"]
 postgres-sa1 = ["postgres", "sa1", "test"]
 postgres-sa2 = ["postgres", "sa2", "test"]
 
-db2-py38 = ["db2", "py38", "test"]
-db2-py39 = ["db2", "py39", "test"]
 db2-py310 = ["db2", "py310", "test"]
-db2-sa1 = ["db2", "sa1", "test"]
+# Can't be solved.
+# db2-py311 = ["db2", "py311", "test"]
+db2-py312 = ["db2", "py312", "test"]
+db2-py313 = ["db2", "py313", "test"]
+# Can't be solved.
+# db2-py314 = ["db2", "py314", "test"]
 
-snowflake-py38 = ["snowflake", "py38", "test"]
-snowflake-sa1 = ["snowflake", "sa1", "test"]
+snowflake-py310 = ["snowflake", "py310", "test"]
 
-bigquery-py38 = ["bigquery", "py38", "test"]
-bigquery-sa1 = ["bigquery", "sa1", "test"]
+duckdb-py310 = ["duckdb", "py310", "test"]
+duckdb-py314 = ["duckdb", "py314", "test"]
+duckdb-sa2 = ["duckdb", "sa2", "test"]
 
-# We specify sqlalchemy 1 due to this bug:
-# https://github.com/cloudera/impyla/issues/512
-impala-py38 = ["impala", "py38", "sa1", "test"]
-impala-sa1 = ["impala", "sa1", "test"]
+bigquery-py310 = ["bigquery", "py310", "test"]
+bigquery-sa1 = ["bigquery", "sa1", "test"]
+bigquery-sa2 = ["bigquery", "sa2", "test"]
 
 lint = { features = ["lint"], no-default-feature = true }
-
-mypy = ["mypy"]
diff --git a/pyproject.toml b/pyproject.toml
index 875f16cd..bf41a47f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,46 +10,77 @@ classifiers = [
   "License :: OSI Approved :: BSD License",
   "Operating System :: OS Independent",
   "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.8",
-  "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Programming Language :: Python :: 3.13",
+  "Programming Language :: Python :: 3.14",
   "Topic :: Database",
 ]
 readme = "README.md"
 dynamic = ["version", "description"]
+requires-python = ">=3.10"
 
-requires-python = ">=3.8.0"
-
-dependencies = ["sqlalchemy >=1.4", "setuptools", "scipy", "colorama"]
+dependencies = ["sqlalchemy >=1.4", "scipy", "colorama"]
 
 [project.urls]
 Source = "https://github.com/quantco/datajudge"
 
-[tool.black]
-exclude = '''
-/(
-    \.eggs
-  | \.git
-  | \.venv
-  | build
-  | dist
-)/
-'''
-
-[tool.isort]
-multi_line_output = 3
-include_trailing_comma = true
-ensure_newline_before_comments = true
-line_length = 88
-known_first_party = "datajudge"
-skip_glob = '\.eggs/*,\.git/*,\.venv/*,build/*,dist/*'
-default_section = 'THIRDPARTY'
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint]
+ignore = [
+  "N803", # https://docs.astral.sh/ruff/rules/invalid-argument-name
+  "N806", # https://docs.astral.sh/ruff/rules/non-lowercase-variable-in-function
+  "E501", # https://docs.astral.sh/ruff/faq/#is-the-ruff-linter-compatible-with-black
+  "D100",
+  "D101",
+  "D102",
+  "D103",
+  "D104",
+  "D105",
+  "D106",
+  "D107",
+]
+select = [
+  # pyflakes
+  "F",
+  # pycodestyle
+  "E",
+  "W",
+  # isort
+  "I",
+  # pep8-naming
+  "N",
+  # pyupgrade
+  "UP",
+  # pydocstyle
+  "D",
+]
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+[tool.ruff.lint.per-file-ignores]
+"**/{tests,test_utils}/*" = ["D"]
+
+[tool.ruff.lint.isort]
+known-first-party = ["datajudge"]
+
+[tool.ruff.format]
+quote-style = "double"
+indent-style = "space"
+
+[tool.ty.terminal]
+error-on-warning = true
 
 [tool.mypy]
+python_version = '3.10'
 no_implicit_optional = true
 allow_empty_bodies = true
 check_untyped_defs = true
+disable_error_code = ["method-assign"]
 
 [[tool.mypy.overrides]]
-module = ["scipy.*", "impala.*", "pytest_html"]
+module = ["scipy.*", "pytest_html"]
 ignore_missing_imports = true
diff --git a/run_integration_tests_postgres.sh b/run_integration_tests_postgres.sh
index 24d5a1bc..46bf5412 100755
--- a/run_integration_tests_postgres.sh
+++ b/run_integration_tests_postgres.sh
@@ -3,7 +3,7 @@
 docker stop $(docker ps -q --filter name=postgres_datajudge)
 
 ./start_postgres.sh &
-bash -c "while true; do printf '\nPress enter once postgres is ready: '; sleep 1; done" & 
+bash -c "while true; do printf '\nPress enter once postgres is ready: '; sleep 1; done" &
 
 read -p "Press enter to once postgres is ready: "
 kill %%
@@ -11,4 +11,3 @@ kill %%
 echo "STARTING PYTEST"
 pixi run -e postgres-py38 test $@
 docker stop $(docker ps -q --filter name=postgres_datajudge)
-
diff --git a/src/datajudge/__init__.py b/src/datajudge/__init__.py
index 35943a5a..91999da4 100644
--- a/src/datajudge/__init__.py
+++ b/src/datajudge/__init__.py
@@ -1,21 +1,29 @@
-"""datajudge allows to assess  whether data from database complies with reference
-information."""
+"""datajudge allows to assess  whether data from database complies with referenceinformation."""
 
-import importlib.metadata
+import warnings
 
+import sqlalchemy as sa
+
+if sa.__version__.startswith("1."):
+    warnings.warn(
+        "SQLAlchemy 1.x is deprecated and will no longer be supported in future "
+        "versions of datajudge. Please upgrade to SQLAlchemy 2.x.",
+        FutureWarning,
+        stacklevel=2,
+    )
+
+from .condition import Condition
 from .constraints.base import Constraint
-from .db_access import Condition
+from .data_source import DataSource
 from .requirements import BetweenRequirement, Requirement, WithinRequirement
 
 __all__ = [
     "BetweenRequirement",
     "Condition",
     "Constraint",
+    "DataSource",
     "Requirement",
     "WithinRequirement",
 ]
 
-try:
-    __version__ = importlib.metadata.distribution(__name__).version
-except Exception:
-    __version__ = "1.9.0"
+__version__ = "1.13.0"
diff --git a/src/datajudge/_engines.py b/src/datajudge/_engines.py
new file mode 100644
index 00000000..b1e527c0
--- /dev/null
+++ b/src/datajudge/_engines.py
@@ -0,0 +1,25 @@
+import sqlalchemy as sa
+
+
+def is_mssql(engine: sa.engine.Engine) -> bool:
+    return engine.name == "mssql"
+
+
+def is_postgresql(engine: sa.engine.Engine) -> bool:
+    return engine.name == "postgresql"
+
+
+def is_snowflake(engine: sa.engine.Engine) -> bool:
+    return engine.name == "snowflake"
+
+
+def is_bigquery(engine: sa.engine.Engine) -> bool:
+    return engine.name == "bigquery"
+
+
+def is_db2(engine: sa.engine.Engine) -> bool:
+    return engine.name == "ibm_db_sa"
+
+
+def is_duckdb(engine: sa.engine.Engine) -> bool:
+    return engine.name == "duckdb"
diff --git a/src/datajudge/condition.py b/src/datajudge/condition.py
new file mode 100644
index 00000000..47fe5c0a
--- /dev/null
+++ b/src/datajudge/condition.py
@@ -0,0 +1,78 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class Condition:
+    """Condition allows for further narrowing down of a DataSource in a Constraint.
+
+    A ``Condition`` can be thought of as a filter, the content of a sql 'where' clause
+    or a condition as known from probability theory.
+
+    While a ``DataSource`` is expressed more generally, one might be interested
+    in testing properties of a specific part of said ``DataSource`` in light
+    of a particular constraint. Hence using ``Condition`` allows for the reusage
+    of a ``DataSource``, in lieu of creating a new custom ``DataSource`` with
+    the ``Condition`` implicitly built in.
+
+    A ``Condition`` can either be 'atomic', i.e. not further reducible to sub-conditions
+    or 'composite', i.e. combining multiple subconditions. In the former case, it can
+    be instantiated with help of the ``raw_string`` parameter, e.g. ``"col1 > 0"``. In the
+    latter case, it can be instantiated with help of the ``conditions`` and
+    ``reduction_operator`` parameters. ``reduction_operator`` allows for two values: ``"and"`` (logical
+    conjunction) and ``"or"`` (logical disjunction). Note that composition of ``Condition``
+    supports arbitrary degrees of nesting.
+    """
+
+    raw_string: str | None = None
+    conditions: Sequence[Condition] | None = None
+    reduction_operator: str | None = None
+
+    def __post_init__(self):
+        if self._is_atomic() and self.conditions is not None:
+            raise ValueError(
+                "Condition can either be instantiated atomically, with "
+                "the raw_query parameter, or in a composite fashion, with "
+                "the conditions parameter. "
+                "Exactly one of them needs to be provided, yet both are."
+            )
+        if not self._is_atomic() and (
+            self.conditions is None or len(self.conditions) == 0
+        ):
+            raise ValueError(
+                "Condition can either be instantiated atomically, with "
+                "the raw_query parameter, or in a composite fashion, with "
+                "the conditions parameter. "
+                "Exactly one of them needs to be provided, yet none is."
+            )
+        if not self._is_atomic() and self.reduction_operator not in ["and", "or"]:
+            raise ValueError(
+                "reuction_operator has to be either 'and' or 'or' but "
+                f"obtained {self.reduction_operator}."
+            )
+
+    def _is_atomic(self) -> bool:
+        return self.raw_string is not None
+
+    def __str__(self) -> str:
+        if self._is_atomic():
+            if self.raw_string is None:
+                raise ValueError(
+                    "Condition can either be instantiated atomically, with "
+                    "the raw_query parameter, or in a composite fashion, with "
+                    "the conditions parameter. "
+                    "Exactly one of them needs to be provided, yet none is."
+                )
+            return self.raw_string
+        if not self.conditions:
+            raise ValueError("This should never happen thanks to __post__init.")
+        return f" {self.reduction_operator} ".join(
+            f"({condition})" for condition in self.conditions
+        )
+
+    def _snowflake_str(self) -> str:
+        # Temporary method - should be removed as soon as snowflake-sqlalchemy
+        # bug is fixed.
+        return str(self)
diff --git a/src/datajudge/constraints/base.py b/src/datajudge/constraints/base.py
index dff7b808..9e2810e3 100644
--- a/src/datajudge/constraints/base.py
+++ b/src/datajudge/constraints/base.py
@@ -1,22 +1,26 @@
 import abc
+from collections.abc import Callable, Collection, Sequence
 from dataclasses import dataclass, field
 from functools import lru_cache
-from typing import Any, Callable, Collection, List, Optional, Tuple, TypeVar, Union
+from typing import Any, TypeVar
 
 import sqlalchemy as sa
+from sqlalchemy.sql import selectable
 
 from ..db_access import DataReference
 from ..formatter import Formatter
 from ..utils import OutputProcessor, output_processor_limit
 
-DEFAULT_FORMATTER = Formatter()
+_T = TypeVar("_T")
 
-T = TypeVar("T")
-OptionalSelections = Optional[List[sa.sql.expression.Select]]
-ToleranceGetter = Callable[[sa.engine.Engine], float]
+_DEFAULT_FORMATTER = Formatter()
 
+_Select = selectable.Select | selectable.CompoundSelect
+_OptionalSelections = Sequence[_Select] | None
+_ToleranceGetter = Callable[[sa.engine.Engine], float]
 
-def uncommon_substrings(string1: str, string2: str) -> Tuple[str, str]:
+
+def _uncommon_substrings(string1: str, string2: str) -> tuple[str, str]:
     qualifiers1 = string1.split(".")
     qualifiers2 = string2.split(".")
     if qualifiers1[0] != qualifiers2[0]:
@@ -28,18 +32,20 @@ def uncommon_substrings(string1: str, string2: str) -> Tuple[str, str]:
 
 @dataclass(frozen=True)
 class TestResult:
+    """The result of the execution of a Constraint."""
+
     outcome: bool
-    _failure_message: Optional[str] = field(default=None, repr=False)
-    _constraint_description: Optional[str] = field(default=None, repr=False)
-    _factual_queries: Optional[str] = field(default=None, repr=False)
-    _target_queries: Optional[str] = field(default=None, repr=False)
+    _failure_message: str | None = field(default=None, repr=False)
+    _constraint_description: str | None = field(default=None, repr=False)
+    _factual_queries: str | None = field(default=None, repr=False)
+    _target_queries: str | None = field(default=None, repr=False)
 
-    def formatted_failure_message(self, formatter: Formatter) -> Optional[str]:
+    def formatted_failure_message(self, formatter: Formatter) -> str | None:
         return (
             formatter.fmt_str(self._failure_message) if self._failure_message else None
         )
 
-    def formatted_constraint_description(self, formatter: Formatter) -> Optional[str]:
+    def formatted_constraint_description(self, formatter: Formatter) -> str | None:
         return (
             formatter.fmt_str(self._constraint_description)
             if self._constraint_description
@@ -47,12 +53,12 @@ def formatted_constraint_description(self, formatter: Formatter) -> Optional[str
         )
 
     @property
-    def failure_message(self) -> Optional[str]:
-        return self.formatted_failure_message(DEFAULT_FORMATTER)
+    def failure_message(self) -> str | None:
+        return self.formatted_failure_message(_DEFAULT_FORMATTER)
 
     @property
-    def constraint_description(self) -> Optional[str]:
-        return self.formatted_constraint_description(DEFAULT_FORMATTER)
+    def constraint_description(self) -> str | None:
+        return self.formatted_constraint_description(_DEFAULT_FORMATTER)
 
     @property
     def logging_message(self):
@@ -99,49 +105,74 @@ class Constraint(abc.ABC):
     """Express a DataReference constraint against either another DataReference or a reference value.
 
     Constraints against other DataReferences are typically referred to as 'between' constraints.
-    Please use the the `ref2` argument to instantiate such a constraint.
+    Please use the the ``ref2`` argument to instantiate such a constraint.
     Constraints against a fixed reference value are typically referred to as 'within' constraints.
-    Please use the `ref_value` argument to instantiate such a constraint.
+    Please use the ``ref_value`` argument to instantiate such a constraint.
 
     A constraint typically relies on the comparison of factual and target values. The former
     represent the key quantity of interest as seen in the database, the latter the key quantity of
     interest as expected a priori. Such a comparison is meant to be carried out in the `test`
     method.
 
-    In order to obtain such values, the `retrieve` method defines a mapping from DataReference,
-    be it the DataReference of primary interest, `ref`, or a baseline DataReference, `ref2`, to
-    value. If `ref_value` is already provided, usually no further mapping needs to be taken care of.
+    In order to obtain such values, the ``retrieve`` method defines a mapping from DataReference,
+    be it the DataReference of primary interest, ``ref``, or a baseline DataReference, ``ref2``, to
+    value. If ``ref_value`` is already provided, usually no further mapping needs to be taken care of.
+
+    By default, retrieved arguments are cached indefinitely ``@lru_cache(maxsize=None)``.
+    This can be controlled by setting the `cache_size` argument to a different value.
+    ``0`` disables caching.
     """
 
     def __init__(
         self,
         ref: DataReference,
         *,
-        ref2: Optional[DataReference] = None,
-        ref_value: Optional[Any] = None,
-        name: Optional[str] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
+        ref2: DataReference | None = None,
+        ref_value: Any = None,
+        name: str | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        cache_size=None,
     ):
         self._check_if_valid_between_or_within(ref2, ref_value)
-        self.ref = ref
-        self.ref2 = ref2
-        self.ref_value = ref_value
+        self._ref = ref
+        self._ref2 = ref2
+        self._ref_value = ref_value
         self.name = name
-        self.factual_selections: OptionalSelections = None
-        self.target_selections: OptionalSelections = None
-        self.factual_queries: Optional[List[str]] = None
-        self.target_queries: Optional[List[str]] = None
+        self._factual_selections: _OptionalSelections = None
+        self._target_selections: _OptionalSelections = None
+        self._factual_queries: list[str] | None = None
+        self._target_queries: list[str] | None = None
 
         if (output_processors is not None) and (
             not isinstance(output_processors, list)
         ):
             output_processors = [output_processors]
-        self.output_processors = output_processors
+
+        self._output_processors: list[OutputProcessor] | None = output_processors
+
+        self._cache_size = cache_size
+        self._setup_caching()
+
+    def _setup_caching(self):
+        # We don't use cache or lru_cache decorators since those would lead
+        # to class-based, not instance-based caching.
+        #
+        # Using this approach has the added benefit of allowing the class to be garbage collected
+        # according to https://rednafi.com/python/lru_cache_on_methods/
+        # and https://docs.astral.sh/ruff/rules/cached-instance-method/
+        self._get_factual_value: Callable[[Constraint, sa.engine.Engine], Any] = (
+            lru_cache(self._cache_size)(self._get_factual_value)
+        )
+        self._get_target_value: Callable[[Constraint, sa.engine.Engine], Any] = (
+            lru_cache(self._cache_size)(self._get_target_value)
+        )
 
     def _check_if_valid_between_or_within(
-        self, ref2: Optional[DataReference], ref_value: Optional[Any]
+        self,
+        ref2: DataReference | None,
+        ref_value: Any,
     ):
         """Check whether exactly one of ref2 and ref_value arguments have been used."""
         class_name = self.__class__.__name__
@@ -156,98 +187,101 @@ def _check_if_valid_between_or_within(
                 f"{class_name}. Use exactly either of them."
             )
 
-    @lru_cache(maxsize=None)
-    def get_factual_value(self, engine: sa.engine.Engine) -> Any:
-        factual_value, factual_selections = self.retrieve(engine, self.ref)
-        self.factual_selections = factual_selections
+    def _get_factual_value(self, engine: sa.engine.Engine) -> Any:
+        factual_value, factual_selections = self._retrieve(engine, self._ref)
+        self._factual_selections = factual_selections
         return factual_value
 
-    @lru_cache(maxsize=None)
-    def get_target_value(self, engine: sa.engine.Engine) -> Any:
-        if self.ref2 is None:
-            return self.ref_value
-        target_value, target_selections = self.retrieve(engine, self.ref2)
-        self.target_selections = target_selections
+    def _get_target_value(self, engine: sa.engine.Engine) -> Any:
+        if self._ref2 is None:
+            return self._ref_value
+        target_value, target_selections = self._retrieve(engine, self._ref2)
+        self._target_selections = target_selections
         return target_value
 
     def get_description(self) -> str:
         if self.name is not None:
             return self.name
-        if self.ref2 is None:
-            data_source_string = str(self.ref.data_source)
+        if self._ref2 is None:
+            data_source_string = str(self._ref.data_source)
         else:
-            data_source1_string = str(self.ref.data_source)
-            data_source2_string = str(self.ref2.data_source)
+            data_source1_string = str(self._ref.data_source)
+            data_source2_string = str(self._ref2.data_source)
 
-            data_source1_substring, data_source2_substring = uncommon_substrings(
+            data_source1_substring, data_source2_substring = _uncommon_substrings(
                 data_source1_string, data_source2_string
             )
             data_source_string = f"{data_source1_substring} | {data_source2_substring}"
         return self.__class__.__name__ + "::" + data_source_string
 
     @property
-    def target_prefix(self) -> str:
-        return f"{self.ref2}'s " if (self.ref2 is not None) else ""
+    def _target_prefix(self) -> str:
+        return f"{self._ref2}'s " if (self._ref2 is not None) else ""
 
     @property
-    def condition_string(self) -> str:
-        if self.ref.condition is None and (
-            self.ref2 is None or self.ref2.condition is None
+    def _condition_string(self) -> str:
+        if self._ref.condition is None and (
+            self._ref2 is None or self._ref2.condition is None
         ):
             return ""
-        ref1_clause = self.ref.get_clause_string()
-        if self.ref2 is None:
+        ref1_clause = self._ref._get_clause_string()
+        if self._ref2 is None:
             # within constraint
             return f"Condition: {ref1_clause}"
-        ref2_clause = self.ref2.get_clause_string()
-        if self.ref.condition == self.ref2.condition:
+        ref2_clause = self._ref2._get_clause_string()
+        if self._ref.condition == self._ref2.condition:
             return f"Condition on both tables: {ref1_clause}; "
-        if self.ref.condition is None:
+        if self._ref.condition is None:
             return f"Condition on second table: {ref2_clause}; "
-        if self.ref2.condition is None:
+        if self._ref2.condition is None:
             return f"Condition on first table: {ref1_clause}; "
         return (
             f"Condition on first table: {ref1_clause}. "
             f"Condition on second table: {ref2_clause}. "
         )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[Any, OptionalSelections]:
+    ) -> tuple[Any, _OptionalSelections]:
         """Retrieve the value of interest for a DataReference from database."""
-        pass
+        raise NotImplementedError()
 
-    def compare(
+    def _compare(
         self, value_factual: Any, value_target: Any
-    ) -> Tuple[bool, Optional[str]]:
-        pass
+    ) -> tuple[bool, str | None]:
+        raise NotImplementedError()
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        value_factual = self.get_factual_value(engine)
-        value_target = self.get_target_value(engine)
-        is_success, assertion_message = self.compare(value_factual, value_target)
+        # ty can't figure out that this is a method and that self is passed
+        # as the first argument.
+        value_factual = self._get_factual_value(engine=engine)  # type: ignore[missing-argument]
+        # ty can't figure out that this is a method and that self is passed
+        # as the first argument.
+        value_target = self._get_target_value(engine=engine)  # type: ignore[missing-argument]
+        is_success, assertion_message = self._compare(value_factual, value_target)
+
         if is_success:
             return TestResult.success()
 
         factual_queries = None
-        if self.factual_selections:
+        if self._factual_selections:
             factual_queries = [
                 str(
                     factual_selection.compile(
                         engine, compile_kwargs={"literal_binds": True}
                     )
                 )
-                for factual_selection in self.factual_selections
+                for factual_selection in self._factual_selections
             ]
         target_queries = None
-        if self.target_selections:
+        if self._target_selections:
             target_queries = [
                 str(
                     target_selection.compile(
                         engine, compile_kwargs={"literal_binds": True}
                     )
                 )
-                for target_selection in self.target_selections
+                for target_selection in self._target_selections
             ]
         return TestResult.failure(
             assertion_message,
@@ -256,14 +290,14 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
             target_queries,
         )
 
-    def apply_output_formatting(self, values: Collection) -> Collection:
-        if self.output_processors is not None:
-            for output_processor in self.output_processors:
+    def _apply_output_formatting(self, values: Collection) -> Collection:
+        if self._output_processors is not None:
+            for output_processor in self._output_processors:
                 values, _ = output_processor(values)
         return values
 
 
-def format_sample(sample, ref: DataReference) -> str:
+def _format_sample(sample, ref: DataReference) -> str:
     """Build a string from a database row indicating its column values."""
     if ref.columns is None:
         return str(sample)
diff --git a/src/datajudge/constraints/column.py b/src/datajudge/constraints/column.py
index df90608b..d989d039 100644
--- a/src/datajudge/constraints/column.py
+++ b/src/datajudge/constraints/column.py
@@ -1,75 +1,73 @@
+from __future__ import annotations
+
 import abc
-from typing import List, Optional, Tuple, Union
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference, is_snowflake, lowercase_column_names
-from .base import Constraint, OptionalSelections
+from .base import Constraint, _OptionalSelections
 
 
 class Column(Constraint, abc.ABC):
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[List[str], OptionalSelections]:
+    ) -> tuple[list[str], _OptionalSelections]:
         # TODO: This does not 'belong' here. Rather, `retrieve` should be free of
         # side effects. This should be removed as soon as snowflake column capitalization
         # is fixed by snowflake-sqlalchemy.
-        if is_snowflake(engine) and self.ref_value is not None:
-            self.ref_value = lowercase_column_names(self.ref_value)  # type: ignore
+        if is_snowflake(engine) and self._ref_value is not None:
+            self._ref_value = lowercase_column_names(self._ref_value)
         return db_access.get_column_names(engine, ref)
 
 
 class ColumnExistence(Column):
     def __init__(
-        self, ref: DataReference, columns: List[str], name: Optional[str] = None
+        self,
+        ref: DataReference,
+        columns: list[str],
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref_value=columns, name=name)
+        super().__init__(ref, ref_value=columns, name=name, cache_size=cache_size)
 
-    def compare(
-        self, column_names_factual: List[str], column_names_target: List[str]
-    ) -> Tuple[bool, str]:
-        excluded_columns = list(
-            filter(lambda c: c not in column_names_factual, column_names_target)
-        )
+    def _compare(
+        self, value_factual: list[str], value_target: list[str]
+    ) -> tuple[bool, str]:
+        excluded_columns = list(filter(lambda c: c not in value_factual, value_target))
         assertion_message = (
-            f"{self.ref} doesn't have column(s) " f"{', '.join(excluded_columns)}."
+            f"{self._ref} doesn't have column(s) {', '.join(excluded_columns)}."
         )
         result = len(excluded_columns) == 0
         return result, assertion_message
 
 
 class ColumnSubset(Column):
-    def compare(
-        self, column_names_factual: List[str], column_names_target: List[str]
-    ) -> Tuple[bool, str]:
-        missing_columns = list(
-            filter(lambda c: c not in column_names_target, column_names_factual)
-        )
+    def _compare(
+        self, value_factual: list[str], value_target: list[str]
+    ) -> tuple[bool, str]:
+        missing_columns = list(filter(lambda c: c not in value_target, value_factual))
         assertion_message = (
-            f"{self.ref2} doesn't have column(s) " f"{', '.join(missing_columns)}. "
+            f"{self._ref2} doesn't have column(s) {', '.join(missing_columns)}. "
         )
         result = len(missing_columns) == 0
         return result, assertion_message
 
 
 class ColumnSuperset(Column):
-    def compare(
-        self, column_names_factual: List[str], column_names_target: List[str]
-    ) -> Tuple[bool, str]:
-        missing_columns = list(
-            filter(lambda c: c not in column_names_factual, column_names_target)
-        )
+    def _compare(
+        self, value_factual: list[str], value_target: list[str]
+    ) -> tuple[bool, str]:
+        missing_columns = list(filter(lambda c: c not in value_factual, value_target))
         assertion_message = (
-            f"{self.ref} doesn't have column(s) " f"{', '.join(missing_columns)}."
+            f"{self._ref} doesn't have column(s) {', '.join(missing_columns)}."
         )
         result = len(missing_columns) == 0
         return result, assertion_message
 
 
 class ColumnType(Constraint):
-    """
-    A class used to represent a ColumnType constraint.
+    """A class used to represent a ColumnType constraint.
 
     This class enables flexible specification of column types either in string format or using SQLAlchemy's type hierarchy.
     It checks whether a column's type matches the specified type, allowing for checks against backend-specific types,
@@ -83,32 +81,36 @@ def __init__(
         self,
         ref: DataReference,
         *,
-        ref2: Optional[DataReference] = None,
-        column_type: Optional[Union[str, sa.types.TypeEngine]] = None,
-        name: Optional[str] = None,
+        ref2: DataReference | None = None,
+        column_type: str | sa.types.TypeEngine | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, ref_value=column_type, name=name)
-        self.column_type = column_type
+        super().__init__(
+            ref,
+            ref2=ref2,
+            ref_value=column_type,
+            name=name,
+            cache_size=cache_size,
+        )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[sa.types.TypeEngine, OptionalSelections]:
+    ) -> tuple[sa.types.TypeEngine, _OptionalSelections]:
         result, selections = db_access.get_column_type(engine, ref)
         return result, selections
 
-    def compare(self, column_type_factual, column_type_target) -> Tuple[bool, str]:
-        assertion_message = (
-            f"{self.ref} is {column_type_factual} " f"instead of {column_type_target}."
-        )
+    def _compare(self, value_factual, value_target) -> tuple[bool, str]:
+        assertion_message = f"{self._ref} is {value_factual} instead of {value_target}."
 
-        if isinstance(column_type_target, sa.types.TypeEngine):
-            result = isinstance(column_type_factual, type(column_type_target))
+        if isinstance(value_target, sa.types.TypeEngine):
+            result = isinstance(value_factual, type(value_target))
         else:
-            column_type = str(column_type_factual).lower()
+            column_type = str(value_factual).lower()
             # Integer columns loaded from snowflake database may be referred to as decimal with
             # 0 scale. More here:
             # https://docs.snowflake.com/en/sql-reference/data-types-numeric.html#decimal-numeric
             if column_type == "decimal(38, 0)":
                 column_type = "integer"
-            result = column_type.startswith(column_type_target.lower())
+            result = column_type.startswith(value_target.lower())
         return result, assertion_message
diff --git a/src/datajudge/constraints/date.py b/src/datajudge/constraints/date.py
index d4015358..0e781e2d 100644
--- a/src/datajudge/constraints/date.py
+++ b/src/datajudge/constraints/date.py
@@ -1,19 +1,21 @@
+from __future__ import annotations
+
 import datetime as dt
-from typing import Any, Optional, Tuple, Union
+from typing import Any
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
-from .base import Constraint, OptionalSelections, TestResult
-from .interval import NoGapConstraint, NoOverlapConstraint
+from .base import Constraint, _OptionalSelections
+from .interval import NoGapConstraint, NoOverlapConstraint, _Selects
 
-INPUT_DATE_FORMAT = "'%Y-%m-%d'"
+_INPUT_DATE_FORMAT = "'%Y-%m-%d'"
 
-Date = Union[str, dt.date, dt.datetime]
+Date = str | dt.date | dt.datetime
 
 
-def get_format_from_column_type(column_type: str) -> str:
+def _get_format_from_column_type(column_type: str) -> str:
     if column_type.lower() == "date":
         return "%Y-%m-%d"
     if column_type.lower() in ["datetime", "datetime2", "smalldatetime"]:
@@ -21,7 +23,7 @@ def get_format_from_column_type(column_type: str) -> str:
     raise ValueError(f"Illegal date column type: {column_type}")
 
 
-def convert_to_date(db_result: Date, format: str) -> dt.date:
+def _convert_to_date(db_result: Date, format: str) -> dt.date:
     if isinstance(db_result, dt.datetime):
         return db_result.date()
     if isinstance(db_result, dt.date):
@@ -38,43 +40,52 @@ def __init__(
         ref: DataReference,
         use_lower_bound_reference: bool,
         column_type: str,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
         *,
-        ref2: Optional[DataReference] = None,
-        min_value: Optional[str] = None,
+        ref2: DataReference | None = None,
+        min_value: str | None = None,
     ):
-        self.format = get_format_from_column_type(column_type)
-        self.use_lower_bound_reference = use_lower_bound_reference
-        min_date: Optional[dt.date] = None
+        self._format = _get_format_from_column_type(column_type)
+        self._use_lower_bound_reference = use_lower_bound_reference
+        min_date: dt.date | None = None
         if min_value is not None:
-            min_date = dt.datetime.strptime(min_value, INPUT_DATE_FORMAT).date()
-        super().__init__(ref, ref2=ref2, ref_value=min_date, name=name)
+            min_date = dt.datetime.strptime(min_value, _INPUT_DATE_FORMAT).date()
+        super().__init__(
+            ref,
+            ref2=ref2,
+            ref_value=min_date,
+            name=name,
+            cache_size=cache_size,
+        )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[dt.date, OptionalSelections]:
+    ) -> tuple[dt.date, _OptionalSelections]:
         result, selections = db_access.get_min(engine, ref)
-        return convert_to_date(result, self.format), selections
-
-    def compare(self, min_factual: dt.date, min_target: dt.date) -> Tuple[bool, str]:
-        if min_target is None:
-            return TestResult(True, "")
-        if min_factual is None:
-            return TestResult(min_target == 0, "Empty set.")
-        if self.use_lower_bound_reference:
+        return _convert_to_date(result, self._format), selections
+
+    def _compare(
+        self, value_factual: dt.date, value_target: dt.date
+    ) -> tuple[bool, str | None]:
+        if value_target is None:
+            return True, None
+        if value_factual is None:
+            return value_target == 0, "Empty set."
+        if self._use_lower_bound_reference:
             assertion_text = (
-                f"{self.ref} has min {min_factual} < "
-                f"{self.target_prefix} {min_target}. "
-                f"{self.condition_string}"
+                f"{self._ref} has min {value_factual} < "
+                f"{self._target_prefix} {value_target}. "
+                f"{self._condition_string}"
             )
-            result = min_factual >= min_target
+            result = value_factual >= value_target
         else:
             assertion_text = (
-                f"{self.ref} has min {min_factual} > "
-                f"{self.target_prefix} {min_target}. "
-                f"{self.condition_string}"
+                f"{self._ref} has min {value_factual} > "
+                f"{self._target_prefix} {value_target}. "
+                f"{self._condition_string}"
             )
-            result = min_factual <= min_target
+            result = value_factual <= value_target
         return result, assertion_text
 
 
@@ -84,43 +95,52 @@ def __init__(
         ref: DataReference,
         use_upper_bound_reference: bool,
         column_type: str,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
         *,
-        ref2: Optional[DataReference] = None,
-        max_value: Optional[str] = None,
+        ref2: DataReference | None = None,
+        max_value: str | None = None,
     ):
-        self.format = get_format_from_column_type(column_type)
-        self.use_upper_bound_reference = use_upper_bound_reference
-        max_date: Optional[dt.date] = None
+        self._format = _get_format_from_column_type(column_type)
+        self._use_upper_bound_reference = use_upper_bound_reference
+        max_date: dt.date | None = None
         if max_value is not None:
-            max_date = dt.datetime.strptime(max_value, INPUT_DATE_FORMAT).date()
-        super().__init__(ref, ref2=ref2, ref_value=max_date, name=name)
+            max_date = dt.datetime.strptime(max_value, _INPUT_DATE_FORMAT).date()
+        super().__init__(
+            ref,
+            ref2=ref2,
+            ref_value=max_date,
+            name=name,
+            cache_size=cache_size,
+        )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[dt.date, OptionalSelections]:
+    ) -> tuple[dt.date, _OptionalSelections]:
         value, selections = db_access.get_max(engine, ref)
-        return convert_to_date(value, self.format), selections
+        return _convert_to_date(value, self._format), selections
 
-    def compare(self, max_factual: dt.date, max_target: dt.date) -> Tuple[bool, str]:
-        if max_factual is None:
+    def _compare(
+        self, value_factual: dt.date, value_target: dt.date
+    ) -> tuple[bool, str | None]:
+        if value_factual is None:
             return True, None
-        if max_target is None:
-            return max_factual == 0, "Empty reference set."
-        if self.use_upper_bound_reference:
+        if value_target is None:
+            return value_factual == 0, "Empty reference set."
+        if self._use_upper_bound_reference:
             assertion_text = (
-                f"{self.ref} has max {max_factual} > "
-                f"{self.target_prefix} {max_target}. "
-                f"{self.condition_string}"
+                f"{self._ref} has max {value_factual} > "
+                f"{self._target_prefix} {value_target}. "
+                f"{self._condition_string}"
             )
-            result = max_factual <= max_target
+            result = value_factual <= value_target
         else:
             assertion_text = (
-                f"{self.ref} has max {max_factual} < "
-                f"{self.target_prefix} {max_target}. "
-                f"{self.condition_string}"
+                f"{self._ref} has max {value_factual} < "
+                f"{self._target_prefix} {value_target}. "
+                f"{self._condition_string}"
             )
-            result = max_factual >= max_target
+            result = value_factual >= value_target
 
         return result, assertion_text
 
@@ -132,94 +152,99 @@ def __init__(
         min_fraction: float,
         lower_bound: str,
         upper_bound: str,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref_value=min_fraction, name=name)
-        self.lower_bound = lower_bound
-        self.upper_bound = upper_bound
+        super().__init__(ref, ref_value=min_fraction, name=name, cache_size=cache_size)
+        self._lower_bound = lower_bound
+        self._upper_bound = upper_bound
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[float, OptionalSelections]:
+    ) -> tuple[float | None, _OptionalSelections]:
         return db_access.get_fraction_between(
-            engine, ref, self.lower_bound, self.upper_bound
+            engine, ref, self._lower_bound, self._upper_bound
         )
 
-    def compare(
-        self, fraction_factual: float, fraction_target: float
-    ) -> Tuple[bool, str]:
+    def _compare(self, value_factual: float, value_target: float) -> tuple[bool, str]:
         assertion_text = (
-            f"{self.ref} has {fraction_factual} < "
-            f"{fraction_target} of values between {self.lower_bound} and "
-            f"{self.upper_bound}. {self.condition_string} "
+            f"{self._ref} has {value_factual} < "
+            f"{value_target} of values between {self._lower_bound} and "
+            f"{self._upper_bound}. {self._condition_string} "
         )
-        result = fraction_factual >= fraction_target
+        result = value_factual >= value_target
         return result, assertion_text
 
 
 class DateNoOverlap(NoOverlapConstraint):
     _DIMENSIONS = 1
 
-    def compare(self, factual: Tuple[int, int], target: Any) -> Tuple[bool, str]:
-        n_violation_keys, n_distinct_key_values = factual
+    def _compare(
+        self, value_factual: tuple[int, int], value_target: Any
+    ) -> tuple[bool, str | None]:
+        n_violation_keys, n_distinct_key_values = value_factual
         if n_distinct_key_values == 0:
-            return TestResult.success()
+            return True, None
         violation_fraction = n_violation_keys / n_distinct_key_values
         assertion_text = (
-            f"{self.ref} has a ratio of {violation_fraction} > "
-            f"{self.max_relative_n_violations} keys in columns {self.key_columns} "
-            f"with overlapping date ranges in {self.start_columns[0]} and {self.end_columns[0]}."
+            f"{self._ref} has a ratio of {violation_fraction} > "
+            f"{self._max_relative_n_violations} keys in columns {self._key_columns} "
+            f"with overlapping date ranges in {self._start_columns[0]} and {self._end_columns[0]}."
             f"E.g. for: {self.sample}."
         )
-        result = violation_fraction <= self.max_relative_n_violations
+        result = violation_fraction <= self._max_relative_n_violations
         return result, assertion_text
 
 
 class DateNoOverlap2d(NoOverlapConstraint):
     _DIMENSIONS = 2
 
-    def compare(self, factual: Tuple[int, int], target: Any) -> Tuple[bool, str]:
-        n_violation_keys, n_distinct_key_values = factual
+    def _compare(
+        self, value_factual: tuple[int, int], value_target: Any
+    ) -> tuple[bool, str | None]:
+        n_violation_keys, n_distinct_key_values = value_factual
         if n_distinct_key_values == 0:
-            return TestResult.success()
+            return True, None
         violation_fraction = n_violation_keys / n_distinct_key_values
         assertion_text = (
-            f"{self.ref} has a ratio of {violation_fraction} > "
-            f"{self.max_relative_n_violations} keys in columns {self.key_columns} "
-            f"with overlapping date ranges in {self.start_columns[0]} and {self.end_columns[0]}."
-            f"and {self.start_columns[1]} and {self.end_columns[1]}."
+            f"{self._ref} has a ratio of {violation_fraction} > "
+            f"{self._max_relative_n_violations} keys in columns {self._key_columns} "
+            f"with overlapping date ranges in {self._start_columns[0]} and {self._end_columns[0]}."
+            f"and {self._start_columns[1]} and {self._end_columns[1]}."
             f"E.g. for: {self.sample}."
         )
-        result = violation_fraction <= self.max_relative_n_violations
+        result = violation_fraction <= self._max_relative_n_violations
         return result, assertion_text
 
 
 class DateNoGap(NoGapConstraint):
     _DIMENSIONS = 1
 
-    def select(self, engine: sa.engine.Engine, ref: DataReference):
+    def _select(self, engine: sa.engine.Engine, ref: DataReference) -> _Selects:
         sample_selection, n_violations_selection = db_access.get_date_gaps(
             engine,
             ref,
-            self.key_columns,
-            self.start_columns[0],
-            self.end_columns[0],
-            self.legitimate_gap_size,
+            self._key_columns,
+            self._start_columns[0],
+            self._end_columns[0],
+            self._legitimate_gap_size,
         )
         # TODO: Once get_unique_count also only returns a selection without
         # executing it, one would want to list this selection here as well.
         return sample_selection, n_violations_selection
 
-    def compare(self, factual: Tuple[int, int], target: Any) -> Tuple[bool, str]:
-        n_violation_keys, n_distinct_key_values = factual
+    def _compare(
+        self, value_factual: tuple[int, int], value_target: Any
+    ) -> tuple[bool, str | None]:
+        n_violation_keys, n_distinct_key_values = value_factual
         if n_distinct_key_values == 0:
-            return TestResult.success()
+            return True, None
         violation_fraction = n_violation_keys / n_distinct_key_values
         assertion_text = (
-            f"{self.ref} has a ratio of {violation_fraction} > "
-            f"{self.max_relative_n_violations} keys in columns {self.key_columns} "
-            f"with a gap in the date range in {self.start_columns[0]} and {self.end_columns[0]}."
+            f"{self._ref} has a ratio of {violation_fraction} > "
+            f"{self._max_relative_n_violations} keys in columns {self._key_columns} "
+            f"with a gap in the date range in {self._start_columns[0]} and {self._end_columns[0]}."
             f"E.g. for: {self.sample}."
         )
-        result = violation_fraction <= self.max_relative_n_violations
+        result = violation_fraction <= self._max_relative_n_violations
         return result, assertion_text
diff --git a/src/datajudge/constraints/groupby.py b/src/datajudge/constraints/groupby.py
index fd474816..5a110173 100644
--- a/src/datajudge/constraints/groupby.py
+++ b/src/datajudge/constraints/groupby.py
@@ -1,10 +1,12 @@
-from typing import Any, Optional, Tuple
+from __future__ import annotations
+
+from typing import Any
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
-from .base import Constraint, OptionalSelections
+from .base import Constraint, _OptionalSelections
 
 
 class AggregateNumericRangeEquality(Constraint):
@@ -13,40 +15,44 @@ def __init__(
         ref: DataReference,
         aggregation_column: str,
         start_value: int = 0,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
         *,
         tolerance: float = 0,
-        ref2: Optional[DataReference] = None,
+        ref2: DataReference | None = None,
     ):
         super().__init__(ref, ref2=ref2, ref_value=object(), name=name)
-        self.aggregation_column = aggregation_column
-        self.tolerance = tolerance
-        self.start_value = start_value
+        self._aggregation_column = aggregation_column
+        self._tolerance = tolerance
+        self._start_value = start_value
         self._selection = None
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[Any, OptionalSelections]:
+    ) -> tuple[Any, _OptionalSelections]:
         result, selections = db_access.get_column_array_agg(
-            engine, ref, self.aggregation_column
+            engine, ref, self._aggregation_column
         )
         result = {fact[:-1]: fact[-1] for fact in result}
         return result, selections
 
-    def compare(self, factual: Any, target: Any) -> Tuple[bool, Optional[str]]:
+    def _compare(
+        self, value_factual: Any, value_target: Any
+    ) -> tuple[bool, str | None]:
         def missing_from_range(values, start=0):
             return set(range(start, max(values) + start)) - set(values)
 
         results = {
-            k: missing_from_range(v, self.start_value) for k, v in factual.items()
+            k: missing_from_range(v, self._start_value)
+            for k, v in value_factual.items()
         }
         failed_results = dict(filter(lambda x: len(x[1]) > 0, results.items()))
 
-        if len(failed_results) / len(factual) > self.tolerance:
+        if len(failed_results) / len(value_factual) > self._tolerance:
             assertion_text = (
-                f"{self.ref} has unfulfilled continuity requirement for "
+                f"{self._ref} has unfulfilled continuity requirement for "
                 f"(key, missing values): `{failed_results}`."
-                f"{self.condition_string}"
+                f"{self._condition_string}"
             )
             return False, assertion_text
         return True, None
diff --git a/src/datajudge/constraints/interval.py b/src/datajudge/constraints/interval.py
index a97f807c..f078b39d 100644
--- a/src/datajudge/constraints/interval.py
+++ b/src/datajudge/constraints/interval.py
@@ -1,11 +1,16 @@
+from __future__ import annotations
+
 import abc
-from typing import Any, List, Optional, Tuple
+from typing import Any, TypeAlias
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
-from .base import Constraint, OptionalSelections
+from .base import Constraint, _OptionalSelections, _Select
+
+# Both sa.Select and sa.CompoundSelect inherit from sa.GenerativeSelect.
+_Selects: TypeAlias = tuple[_Select, _Select]
 
 
 class IntervalConstraint(Constraint):
@@ -14,46 +19,46 @@ class IntervalConstraint(Constraint):
     def __init__(
         self,
         ref: DataReference,
-        key_columns: Optional[List[str]],
-        start_columns: List[str],
-        end_columns: List[str],
+        key_columns: list[str] | None,
+        start_columns: list[str],
+        end_columns: list[str],
         max_relative_n_violations: float,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         super().__init__(ref, ref_value=object(), name=name)
-        self.key_columns = key_columns
-        self.start_columns = start_columns
-        self.end_columns = end_columns
-        self.max_relative_n_violations = max_relative_n_violations
+        self._key_columns = key_columns
+        self._start_columns = start_columns
+        self._end_columns = end_columns
+        self._max_relative_n_violations = max_relative_n_violations
         self._validate_dimensions()
 
     @abc.abstractmethod
-    def select(self, engine: sa.engine.Engine, ref: DataReference):
-        pass
+    def _select(self, engine: sa.engine.Engine, ref: DataReference) -> _Selects: ...
 
     def _validate_dimensions(self):
-        if (length := len(self.start_columns)) != self._DIMENSIONS:
+        if (length := len(self._start_columns)) != self._DIMENSIONS:
             raise ValueError(
                 f"Expected {self._DIMENSIONS} start_column(s), got {length}."
             )
-        if (length := len(self.end_columns)) != self._DIMENSIONS:
+        if (length := len(self._end_columns)) != self._DIMENSIONS:
             raise ValueError(
                 f"Expected {self._DIMENSIONS} end_column(s), got {length}."
             )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[Tuple[int, int], OptionalSelections]:
+    ) -> tuple[tuple[int, int], _OptionalSelections]:
         keys_ref = DataReference(
-            data_source=self.ref.data_source,
-            columns=self.key_columns,
-            condition=self.ref.condition,
+            data_source=self._ref.data_source,
+            columns=self._key_columns,
+            condition=self._ref.condition,
         )
         n_distinct_key_values, n_keys_selections = db_access.get_unique_count(
             engine, keys_ref
         )
 
-        sample_selection, n_violations_selection = self.select(engine, ref)
+        sample_selection, n_violations_selection = self._select(engine, ref)
         with engine.connect() as connection:
             self.sample = connection.execute(sample_selection).first()
             n_violation_keys = int(
@@ -68,14 +73,15 @@ class NoOverlapConstraint(IntervalConstraint):
     def __init__(
         self,
         ref: DataReference,
-        key_columns: Optional[List[str]],
-        start_columns: List[str],
-        end_columns: List[str],
+        key_columns: list[str] | None,
+        start_columns: list[str],
+        end_columns: list[str],
         max_relative_n_violations: float,
         end_included: bool,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        self.end_included = end_included
+        self._end_included = end_included
         super().__init__(
             ref,
             key_columns,
@@ -83,38 +89,41 @@ def __init__(
             end_columns,
             max_relative_n_violations,
             name=name,
+            cache_size=cache_size,
         )
 
-    def select(self, engine: sa.engine.Engine, ref: DataReference):
+    def _select(self, engine: sa.engine.Engine, ref: DataReference) -> _Selects:
         sample_selection, n_violations_selection = db_access.get_interval_overlaps_nd(
             engine,
             ref,
-            self.key_columns,
-            start_columns=self.start_columns,
-            end_columns=self.end_columns,
-            end_included=self.end_included,
+            self._key_columns,
+            start_columns=self._start_columns,
+            end_columns=self._end_columns,
+            end_included=self._end_included,
         )
         # TODO: Once get_unique_count also only returns a selection without
         # executing it, one would want to list this selection here as well.
         return sample_selection, n_violations_selection
 
     @abc.abstractmethod
-    def compare(self, factual: Any, target: Any):
-        pass
+    def _compare(
+        self, value_factual: Any, value_target: Any
+    ) -> tuple[bool, str | None]: ...
 
 
 class NoGapConstraint(IntervalConstraint):
     def __init__(
         self,
         ref: DataReference,
-        key_columns: Optional[List[str]],
-        start_columns: List[str],
-        end_columns: List[str],
+        key_columns: list[str] | None,
+        start_columns: list[str],
+        end_columns: list[str],
         max_relative_n_violations: float,
         legitimate_gap_size: float,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        self.legitimate_gap_size = legitimate_gap_size
+        self._legitimate_gap_size = legitimate_gap_size
         super().__init__(
             ref,
             key_columns,
@@ -122,12 +131,13 @@ def __init__(
             end_columns,
             max_relative_n_violations,
             name=name,
+            cache_size=cache_size,
         )
 
     @abc.abstractmethod
-    def select(self, engine: sa.engine.Engine, ref: DataReference):
-        pass
+    def _select(self, engine: sa.engine.Engine, ref: DataReference) -> _Selects: ...
 
     @abc.abstractmethod
-    def compare(self, factual: Tuple[int, int], target: Any) -> Tuple[bool, str]:
-        pass
+    def _compare(
+        self, value_factual: tuple[int, int], value_target: Any
+    ) -> tuple[bool, str | None]: ...
diff --git a/src/datajudge/constraints/miscs.py b/src/datajudge/constraints/miscs.py
index e3e8caa2..f01dce64 100644
--- a/src/datajudge/constraints/miscs.py
+++ b/src/datajudge/constraints/miscs.py
@@ -1,46 +1,47 @@
+from __future__ import annotations
+
 import warnings
-from typing import List, Optional, Set, Tuple
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
-from .base import Constraint, OptionalSelections, TestResult, format_sample
+from .base import Constraint, TestResult, _format_sample, _OptionalSelections
 
 
 class PrimaryKeyDefinition(Constraint):
-    def __init__(self, ref, primary_keys: List[str], name: Optional[str] = None):
+    def __init__(
+        self,
+        ref,
+        primary_keys: list[str],
+        name: str | None = None,
+        cache_size=None,
+    ):
         super().__init__(ref, ref_value=set(primary_keys), name=name)
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[Set[str], OptionalSelections]:
-        if db_access.is_impala(engine):
-            raise NotImplementedError("Primary key retrieval does not work for Impala.")
-        values, selections = db_access.get_primary_keys(engine, self.ref)
+    ) -> tuple[set[str], _OptionalSelections]:
+        values, selections = db_access.get_primary_keys(engine, self._ref)
         return set(values), selections
 
     # Note: Exact equality!
-    def compare(
-        self, primary_keys_factual: Set[str], primary_keys_target: Set[str]
-    ) -> Tuple[bool, Optional[str]]:
+    def _compare(
+        self, value_factual: set[str], value_target: set[str]
+    ) -> tuple[bool, str | None]:
         assertion_message = ""
         result = True
         # If both are true, just report one.
-        if len(primary_keys_factual.difference(primary_keys_target)) > 0:
-            example_key = next(
-                iter(primary_keys_factual.difference(primary_keys_target))
-            )
+        if len(value_factual.difference(value_target)) > 0:
+            example_key = next(iter(value_factual.difference(value_target)))
             assertion_message = (
-                f"{self.ref} incorrectly includes " f"{example_key} as primary key."
+                f"{self._ref} incorrectly includes {example_key} as primary key."
             )
             result = False
-        if len(primary_keys_target.difference(primary_keys_factual)) > 0:
-            example_key = next(
-                iter(primary_keys_target.difference(primary_keys_factual))
-            )
+        if len(value_target.difference(value_factual)) > 0:
+            example_key = next(iter(value_target.difference(value_factual)))
             assertion_message = (
-                f"{self.ref} doesn't include " f"{example_key} as primary key."
+                f"{self._ref} doesn't include {example_key} as primary key."
             )
             result = False
         return result, assertion_message
@@ -55,7 +56,8 @@ def __init__(
         max_duplicate_fraction: float = 0,
         max_absolute_n_duplicates: int = 0,
         infer_pk_columns: bool = False,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         if max_duplicate_fraction != 0 and max_absolute_n_duplicates != 0:
             raise ValueError(
@@ -71,7 +73,7 @@ def __init__(
             ref_value = ("relative", 0)
 
         self.infer_pk_columns = infer_pk_columns
-        super().__init__(ref, ref_value=ref_value, name=name)
+        super().__init__(ref, ref_value=ref_value, name=name, cache_size=cache_size)
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
         if self.infer_pk_columns and db_access.is_bigquery(engine):
@@ -80,22 +82,24 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
         # only check for primary keys when actually defined
         # otherwise default back to searching the whole table
         if self.infer_pk_columns and (
-            pk_columns := db_access.get_primary_keys(engine, self.ref)[0]
+            pk_columns := db_access.get_primary_keys(engine, self._ref)[0]
         ):
-            self.ref.columns = pk_columns
+            self._ref.columns = pk_columns
             if not pk_columns:  # there were no primary keys found
                 warnings.warn(
-                    f"""No primary keys found in {self.ref}.
+                    f"""No primary keys found in {self._ref}.
                     Uniqueness will be tested for all columns."""
                 )
 
-        unique_count, unique_selections = db_access.get_unique_count(engine, self.ref)
-        row_count, row_selections = db_access.get_row_count(engine, self.ref)
+        unique_count, unique_selections = db_access.get_unique_count(engine, self._ref)
+        row_count, row_selections = db_access.get_row_count(engine, self._ref)
         self.factual_selections = row_selections
         self.target_selections = unique_selections
         if row_count == 0:
             return TestResult(True, "No occurrences.")
-        tolerance_kind, tolerance_value = self.ref_value  # type: ignore
+
+        tolerance_kind, tolerance_value = self._ref_value
+
         if tolerance_kind == "relative":
             result = unique_count >= row_count * (1 - tolerance_value)
         elif tolerance_kind == "absolute":
@@ -106,35 +110,35 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
             )
         if result:
             return TestResult.success()
-        sample, _ = db_access.get_duplicate_sample(engine, self.ref)
-        sample_string = format_sample(sample, self.ref)
+        sample, _ = db_access.get_duplicate_sample(engine, self._ref)
+        sample_string = _format_sample(sample, self._ref)
         assertion_text = (
-            f"{self.ref} has {row_count} rows > {unique_count} "
+            f"{self._ref} has {row_count} rows > {unique_count} "
             f"uniques. This surpasses the max_duplicate_fraction of "
-            f"{self.ref_value}. An example tuple breaking the "
+            f"{self._ref_value}. An example tuple breaking the "
             f"uniqueness condition is: {sample_string}."
         )
         return TestResult.failure(assertion_text)
 
 
 class FunctionalDependency(Constraint):
-    def __init__(self, ref: DataReference, key_columns: List[str], **kwargs):
+    def __init__(self, ref: DataReference, key_columns: list[str], **kwargs):
         super().__init__(ref, ref_value=object(), **kwargs)
         self.key_columns = key_columns
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
         violations, _ = db_access.get_functional_dependency_violations(
-            engine, self.ref, self.key_columns
+            engine, self._ref, self.key_columns
         )
         if not violations:
             return TestResult.success()
 
         assertion_text = (
-            f"{self.ref} has violations of functional dependence (in total {len(violations)} rows):\n"
+            f"{self._ref} has violations of functional dependence (in total {len(violations)} rows):\n"
             + "\n".join(
                 [
                     f"{violation}"
-                    for violation in self.apply_output_formatting(
+                    for violation in self._apply_output_formatting(
                         [tuple(elem) for elem in violations]
                     )
                 ]
@@ -148,12 +152,19 @@ def __init__(
         self,
         ref,
         *,
-        ref2: Optional[DataReference] = None,
-        max_null_fraction: Optional[float] = None,
+        ref2: DataReference | None = None,
+        max_null_fraction: float | None = None,
         max_relative_deviation: float = 0,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, ref_value=max_null_fraction, name=name)
+        super().__init__(
+            ref,
+            ref2=ref2,
+            ref_value=max_null_fraction,
+            name=name,
+            cache_size=cache_size,
+        )
         if max_null_fraction is not None and not (0 <= max_null_fraction <= 1):
             raise ValueError(
                 f"max_null_fraction was expected to lie within [0, 1] but is "
@@ -165,16 +176,16 @@ def __init__(
             )
         self.max_relative_deviation = max_relative_deviation
 
-    def retrieve(self, engine: sa.engine.Engine, ref: DataReference):
+    def _retrieve(self, engine: sa.engine.Engine, ref: DataReference):
         return db_access.get_missing_fraction(engine=engine, ref=ref)
 
-    def compare(
-        self, missing_fraction_factual: float, missing_fracion_target: float
-    ) -> Tuple[bool, Optional[str]]:
-        threshold = missing_fracion_target * (1 + self.max_relative_deviation)
-        result = missing_fraction_factual <= threshold
+    def _compare(
+        self, value_factual: float, value_target: float
+    ) -> tuple[bool, str | None]:
+        threshold = value_target * (1 + self.max_relative_deviation)
+        result = value_factual <= threshold
         assertion_text = (
-            f"{missing_fraction_factual} of {self.ref} values are NULL "
-            f"while only {self.target_prefix}{threshold} were allowed to be NULL."
+            f"{value_factual} of {self._ref} values are NULL "
+            f"while only {self._target_prefix}{threshold} were allowed to be NULL."
         )
         return result, assertion_text
diff --git a/src/datajudge/constraints/nrows.py b/src/datajudge/constraints/nrows.py
index fd29c367..23daa2f3 100644
--- a/src/datajudge/constraints/nrows.py
+++ b/src/datajudge/constraints/nrows.py
@@ -1,35 +1,43 @@
+from __future__ import annotations
+
 import abc
-from typing import Optional, Tuple
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
 from ..utils import format_difference
-from .base import Constraint, OptionalSelections, TestResult, ToleranceGetter
+from .base import Constraint, TestResult, _OptionalSelections, _ToleranceGetter
 
 
 class NRows(Constraint, abc.ABC):
     def __init__(
         self,
-        ref,
+        ref: DataReference,
         *,
-        ref2: Optional[DataReference] = None,
-        n_rows: Optional[int] = None,
-        name: Optional[str] = None,
+        ref2: DataReference | None = None,
+        n_rows: int | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, ref_value=n_rows, name=name)
+        super().__init__(
+            ref,
+            ref2=ref2,
+            ref_value=n_rows,
+            name=name,
+            cache_size=cache_size,
+        )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[int, OptionalSelections]:
+    ) -> tuple[int, _OptionalSelections]:
         return db_access.get_row_count(engine, ref)
 
 
 class NRowsMin(NRows):
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[int, OptionalSelections]:
+    ) -> tuple[int, _OptionalSelections]:
         # Explicitly set a row_limit since we only care about the binary outcome
         # "are there enough rows" and not the actual value. This speeds up queries
         # substantially and allows for dealing with tables with more rows than the
@@ -38,42 +46,44 @@ def retrieve(
         # former is unfortunately not available via sqlalchemy as of now.
         # Other db systems/dialects seem to deal with this problem by casting to
         # bigint, which we do by default.
-        return db_access.get_row_count(engine=engine, ref=ref, row_limit=self.ref_value)
+        return db_access.get_row_count(
+            engine=engine, ref=ref, row_limit=self._ref_value
+        )
 
-    def compare(self, n_rows_factual: int, n_rows_target: int) -> Tuple[bool, str]:
-        result = n_rows_factual >= n_rows_target
+    def _compare(self, value_factual: int, value_target: int) -> tuple[bool, str]:
+        result = value_factual >= value_target
         assertion_text = (
-            f"{self.ref} has {n_rows_factual} "
-            f"< {self.target_prefix} {n_rows_target} rows. "
-            f"{self.condition_string}"
+            f"{self._ref} has {value_factual} "
+            f"< {self._target_prefix} {value_target} rows. "
+            f"{self._condition_string}"
         )
         return result, assertion_text
 
 
 class NRowsMax(NRows):
-    def compare(self, n_rows_factual: int, n_rows_target: int) -> Tuple[bool, str]:
-        result = n_rows_factual <= n_rows_target
-        n_rows_factual_fmt, n_rows_target_fmt = format_difference(
-            n_rows_factual, n_rows_target
+    def _compare(self, value_factual: int, value_target: int) -> tuple[bool, str]:
+        result = value_factual <= value_target
+        value_factual_fmt, value_target_fmt = format_difference(
+            value_factual, value_target
         )
         assertion_text = (
-            f"{self.ref} has {n_rows_factual_fmt} "
-            f"> {self.target_prefix} {n_rows_target_fmt} rows. "
-            f"{self.condition_string}"
+            f"{self._ref} has {value_factual_fmt} "
+            f"> {self._target_prefix} {value_target_fmt} rows. "
+            f"{self._condition_string}"
         )
         return result, assertion_text
 
 
 class NRowsEquality(NRows):
-    def compare(self, n_rows_factual: int, n_rows_target: int) -> Tuple[bool, str]:
-        result = n_rows_factual == n_rows_target
-        n_rows_factual_fmt, n_rows_target_fmt = format_difference(
-            n_rows_factual, n_rows_target
+    def _compare(self, value_factual: int, value_target: int) -> tuple[bool, str]:
+        result = value_factual == value_target
+        value_factual_fmt, value_target_fmt = format_difference(
+            value_factual, value_target
         )
         assertion_text = (
-            f"{self.ref} has {n_rows_factual_fmt} row(s) "
-            f"instead of {self.target_prefix} {n_rows_target_fmt}. "
-            f"{self.condition_string}"
+            f"{self._ref} has {value_factual_fmt} row(s) "
+            f"instead of {self._target_prefix} {value_target_fmt}. "
+            f"{self._condition_string}"
         )
         return result, assertion_text
 
@@ -83,23 +93,24 @@ def __init__(
         self,
         ref: DataReference,
         ref2: DataReference,
-        max_relative_loss_getter: ToleranceGetter,
-        name: Optional[str] = None,
+        max_relative_loss_getter: _ToleranceGetter,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, name=name)
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
         self.max_relative_loss_getter = max_relative_loss_getter
 
-    def compare(self, n_rows_factual: int, n_rows_target: int) -> Tuple[bool, str]:
-        if n_rows_target == 0:
+    def _compare(self, value_factual: int, value_target: int) -> tuple[bool, str]:
+        if value_target == 0:
             return True, "Empty target table."
-        if n_rows_factual > n_rows_target:
+        if value_factual > value_target:
             return True, "Row gain."
-        relative_loss = (n_rows_target - n_rows_factual) / n_rows_target
+        relative_loss = (value_target - value_factual) / value_target
         assertion_text = (
-            f"The #rows from {self.ref} have decreased by "
-            f"{relative_loss:%} compared to table {self.ref2}. "
+            f"The #rows from {self._ref} have decreased by "
+            f"{relative_loss:%} compared to table {self._ref2}. "
             f"They were expected to decrease by at most {self.max_relative_loss:%}. "
-            f"{self.condition_string}"
+            f"{self._condition_string}"
         )
         result = relative_loss <= self.max_relative_loss
         return result, assertion_text
@@ -114,29 +125,30 @@ def __init__(
         self,
         ref: DataReference,
         ref2: DataReference,
-        max_relative_gain_getter: ToleranceGetter,
-        name: Optional[str] = None,
+        max_relative_gain_getter: _ToleranceGetter,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, name=name)
-        self.max_relative_gain_getter = max_relative_gain_getter
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
+        self._max_relative_gain_getter = max_relative_gain_getter
 
-    def compare(self, n_rows_factual: int, n_rows_target: int) -> Tuple[bool, str]:
-        if n_rows_target == 0:
+    def _compare(self, value_factual: int, value_target: int) -> tuple[bool, str]:
+        if value_target == 0:
             return True, "Empty target table."
-        if n_rows_factual < n_rows_target:
+        if value_factual < value_target:
             return True, "Row loss."
-        relative_gain = (n_rows_factual - n_rows_target) / n_rows_target
+        relative_gain = (value_factual - value_target) / value_target
         assertion_text = (
-            f"{self.ref} has {relative_gain:%} gain in #rows compared to "
-            f"{self.ref2}. It was only allowed "
-            f"to increase by {self.max_relative_gain:%}. "
-            f"{self.condition_string}"
+            f"{self._ref} has {relative_gain:%} gain in #rows compared to "
+            f"{self._ref2}. It was only allowed "
+            f"to increase by {self._max_relative_gain:%}. "
+            f"{self._condition_string}"
         )
-        result = relative_gain <= self.max_relative_gain
+        result = relative_gain <= self._max_relative_gain
         return result, assertion_text
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        self.max_relative_gain = self.max_relative_gain_getter(engine)
+        self._max_relative_gain = self._max_relative_gain_getter(engine)
         return super().test(engine)
 
 
@@ -145,27 +157,28 @@ def __init__(
         self,
         ref: DataReference,
         ref2: DataReference,
-        min_relative_gain_getter: ToleranceGetter,
-        name: Optional[str] = None,
+        min_relative_gain_getter: _ToleranceGetter,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, name=name)
-        self.min_relative_gain_getter = min_relative_gain_getter
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
+        self._min_relative_gain_getter = min_relative_gain_getter
 
-    def compare(self, n_rows_factual: int, n_rows_target: int) -> Tuple[bool, str]:
-        if n_rows_target == 0:
+    def _compare(self, value_factual: int, value_target: int) -> tuple[bool, str]:
+        if value_target == 0:
             return True, "Empty target table."
-        if n_rows_factual < n_rows_target:
+        if value_factual < value_target:
             return False, "Row loss."
-        relative_gain = (n_rows_factual - n_rows_target) / n_rows_target
+        relative_gain = (value_factual - value_target) / value_target
         assertion_text = (
-            f"{self.ref} has {relative_gain:%} gain in #rows compared to "
-            f"{self.ref2}. It was supposed "
-            f"to increase at least by {self.min_relative_gain:%}. "
-            f"{self.condition_string}"
+            f"{self._ref} has {relative_gain:%} gain in #rows compared to "
+            f"{self._ref2}. It was supposed "
+            f"to increase at least by {self._min_relative_gain:%}. "
+            f"{self._condition_string}"
         )
-        result = relative_gain >= self.min_relative_gain
+        result = relative_gain >= self._min_relative_gain
         return result, assertion_text
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        self.min_relative_gain = self.min_relative_gain_getter(engine)
+        self._min_relative_gain = self._min_relative_gain_getter(engine)
         return super().test(engine)
diff --git a/src/datajudge/constraints/numeric.py b/src/datajudge/constraints/numeric.py
index 07516c7a..fc4e64ea 100644
--- a/src/datajudge/constraints/numeric.py
+++ b/src/datajudge/constraints/numeric.py
@@ -1,43 +1,52 @@
-from typing import Any, Optional, Tuple
+from __future__ import annotations
+
+from typing import Any
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
-from .base import Constraint, OptionalSelections, TestResult
-from .interval import NoGapConstraint, NoOverlapConstraint
+from .base import Constraint, TestResult, _OptionalSelections
+from .interval import NoGapConstraint, NoOverlapConstraint, _Selects
 
 
 class NumericMin(Constraint):
     def __init__(
         self,
         ref: DataReference,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
         *,
-        ref2: Optional[DataReference] = None,
-        min_value: Optional[float] = None,
+        ref2: DataReference | None = None,
+        min_value: float | None = None,
     ):
-        super().__init__(ref, ref2=ref2, ref_value=min_value, name=name)
+        super().__init__(
+            ref,
+            ref2=ref2,
+            ref_value=min_value,
+            name=name,
+            cache_size=cache_size,
+        )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[float, OptionalSelections]:
+    ) -> tuple[float, _OptionalSelections]:
         return db_access.get_min(engine, ref)
 
-    def compare(
-        self, min_factual: float, min_target: float
-    ) -> Tuple[bool, Optional[str]]:
-        if min_target is None:
+    def _compare(
+        self, value_factual: float, value_target: float
+    ) -> tuple[bool, str | None]:
+        if value_target is None:
             return True, None
-        if min_factual is None:
-            return min_target == 0, "Empty set."
+        if value_factual is None:
+            return value_target == 0, "Empty set."
         assertion_text = (
-            f"{self.ref} has min "
-            f"{min_factual} instead of {self.target_prefix}"
-            f"{min_target} . "
-            f"{self.condition_string}"
+            f"{self._ref} has min "
+            f"{value_factual} instead of {self._target_prefix}"
+            f"{value_target} . "
+            f"{self._condition_string}"
         )
-        result = min_factual >= min_target
+        result = value_factual >= value_target
         return result, assertion_text
 
 
@@ -45,37 +54,39 @@ class NumericMax(Constraint):
     def __init__(
         self,
         ref: DataReference,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
         *,
-        ref2: Optional[DataReference] = None,
-        max_value: Optional[float] = None,
+        ref2: DataReference | None = None,
+        max_value: float | None = None,
     ):
         super().__init__(
             ref,
             ref2=ref2,
             ref_value=max_value,
             name=name,
+            cache_size=cache_size,
         )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[float, OptionalSelections]:
+    ) -> tuple[float, _OptionalSelections]:
         return db_access.get_max(engine, ref)
 
-    def compare(
-        self, max_factual: float, max_target: float
-    ) -> Tuple[bool, Optional[str]]:
-        if max_factual is None:
+    def _compare(
+        self, value_factual: float, value_target: float
+    ) -> tuple[bool, str | None]:
+        if value_factual is None:
             return True, None
-        if max_target is None:
-            return max_factual == 0, "Empty reference set."
+        if value_target is None:
+            return value_factual == 0, "Empty reference set."
         assertion_text = (
-            f"{self.ref} has max "
-            f"{max_factual} instead of {self.target_prefix}"
-            f"{max_target}. "
-            f"{self.condition_string}"
+            f"{self._ref} has max "
+            f"{value_factual} instead of {self._target_prefix}"
+            f"{value_target}. "
+            f"{self._condition_string}"
         )
-        result = max_factual <= max_target
+        result = value_factual <= value_target
         return result, assertion_text
 
 
@@ -86,34 +97,35 @@ def __init__(
         min_fraction: float,
         lower_bound: float,
         upper_bound: float,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref_value=min_fraction, name=name)
-        self.lower_bound = lower_bound
-        self.upper_bound = upper_bound
+        super().__init__(ref, ref_value=min_fraction, name=name, cache_size=cache_size)
+        self._lower_bound = lower_bound
+        self._upper_bound = upper_bound
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[float, OptionalSelections]:
+    ) -> tuple[float | None, _OptionalSelections]:
         return db_access.get_fraction_between(
             engine,
             ref,
-            self.lower_bound,
-            self.upper_bound,
+            self._lower_bound,
+            self._upper_bound,
         )
 
-    def compare(
-        self, fraction_factual: float, fraction_target: float
-    ) -> Tuple[bool, Optional[str]]:
-        if fraction_factual is None:
+    def _compare(
+        self, value_factual: float, value_target: float
+    ) -> tuple[bool, str | None]:
+        if value_factual is None:
             return True, "Empty selection."
         assertion_text = (
-            f"{self.ref} "
-            f"has {fraction_factual} < {fraction_target} of rows "
-            f"between {self.lower_bound} and {self.upper_bound}. "
-            f"{self.condition_string}"
+            f"{self._ref} "
+            f"has {value_factual} < {value_target} of rows "
+            f"between {self._lower_bound} and {self._upper_bound}. "
+            f"{self._condition_string}"
         )
-        result = fraction_factual >= fraction_target
+        result = value_factual >= value_target
         return result, assertion_text
 
 
@@ -122,28 +134,35 @@ def __init__(
         self,
         ref: DataReference,
         max_absolute_deviation: float,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
         *,
-        ref2: Optional[DataReference] = None,
-        mean_value: Optional[float] = None,
+        ref2: DataReference | None = None,
+        mean_value: float | None = None,
     ):
         super().__init__(
             ref,
             ref2=ref2,
             ref_value=mean_value,
             name=name,
+            cache_size=cache_size,
         )
-        self.max_absolute_deviation = max_absolute_deviation
+        self._max_absolute_deviation = max_absolute_deviation
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[float, OptionalSelections]:
+    ) -> tuple[float, _OptionalSelections]:
         result, selections = db_access.get_mean(engine, ref)
         return result, selections
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        mean_factual = self.get_factual_value(engine)
-        mean_target = self.get_target_value(engine)
+        # ty can't figure out that this is a method and that self is passed
+        # as the first argument.
+        mean_factual = self._get_factual_value(engine=engine)  # type: ignore[missing-argument]
+        # ty can't figure out that this is a method and that self is passed
+        # as the first argument.
+        mean_target = self._get_target_value(engine=engine)  # type: ignore[missing-argument]
+
         if mean_factual is None or mean_target is None:
             return TestResult(
                 mean_factual is None and mean_target is None,
@@ -151,13 +170,13 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
             )
         deviation = abs(mean_factual - mean_target)
         assertion_text = (
-            f"{self.ref} "
+            f"{self._ref} "
             f"has mean {mean_factual}, deviating more than "
-            f"{self.max_absolute_deviation} from "
-            f"{self.target_prefix} {mean_target}. "
-            f"{self.condition_string}"
+            f"{self._max_absolute_deviation} from "
+            f"{self._target_prefix} {mean_target}. "
+            f"{self._condition_string}"
         )
-        result = deviation <= self.max_absolute_deviation
+        result = deviation <= self._max_absolute_deviation
         return TestResult(result, assertion_text)
 
 
@@ -166,18 +185,20 @@ def __init__(
         self,
         ref: DataReference,
         percentage: float,
-        max_absolute_deviation: Optional[float] = None,
-        max_relative_deviation: Optional[float] = None,
-        name: Optional[str] = None,
+        max_absolute_deviation: float | None = None,
+        max_relative_deviation: float | None = None,
+        name: str | None = None,
+        cache_size=None,
         *,
-        ref2: Optional[DataReference] = None,
-        expected_percentile: Optional[float] = None,
+        ref2: DataReference | None = None,
+        expected_percentile: float | None = None,
     ):
         super().__init__(
             ref,
             ref2=ref2,
             ref_value=expected_percentile,
             name=name,
+            cache_size=cache_size,
         )
         if not (0 <= percentage <= 100):
             raise ValueError(
@@ -197,43 +218,43 @@ def __init__(
             raise ValueError(
                 f"max_relative_deviation must be at least 0 but is {max_relative_deviation}."
             )
-        self.max_absolute_deviation = max_absolute_deviation
-        self.max_relative_deviation = max_relative_deviation
+        self._max_absolute_deviation = max_absolute_deviation
+        self._max_relative_deviation = max_relative_deviation
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[float, OptionalSelections]:
+    ) -> tuple[float, _OptionalSelections]:
         result, selections = db_access.get_percentile(engine, ref, self.percentage)
         return result, selections
 
-    def compare(
-        self, percentile_factual: float, percentile_target: float
-    ) -> Tuple[bool, Optional[str]]:
-        abs_diff = abs(percentile_factual - percentile_target)
+    def _compare(
+        self, value_factual: float, value_target: float
+    ) -> tuple[bool, str | None]:
+        abs_diff = abs(value_factual - value_target)
         if (
-            self.max_absolute_deviation is not None
-            and abs_diff > self.max_absolute_deviation
+            self._max_absolute_deviation is not None
+            and abs_diff > self._max_absolute_deviation
         ):
             assertion_message = (
-                f"The {self.percentage}-th percentile of {self.ref} was "
-                f"expected to be {self.target_prefix}{percentile_target} but was "
-                f"{percentile_factual}, resulting in an absolute difference of "
+                f"The {self.percentage}-th percentile of {self._ref} was "
+                f"expected to be {self._target_prefix}{value_target} but was "
+                f"{value_factual}, resulting in an absolute difference of "
                 f"{abs_diff}. The maximally allowed absolute deviation would've been "
-                f"{self.max_absolute_deviation}."
+                f"{self._max_absolute_deviation}."
             )
             return False, assertion_message
-        if self.max_relative_deviation is not None:
-            if percentile_target == 0:
+        if self._max_relative_deviation is not None:
+            if value_target == 0:
                 raise ValueError("Cannot compute relative deviation wrt 0.")
             if (
-                rel_diff := abs_diff / abs(percentile_target)
-            ) > self.max_relative_deviation:
+                rel_diff := abs_diff / abs(value_target)
+            ) > self._max_relative_deviation:
                 assertion_message = (
-                    f"The {self.percentage}-th percentile of {self.ref}  was "
-                    f"expected to be {self.target_prefix}{percentile_target} but "
-                    f"was {percentile_factual}, resulting in a relative difference of "
+                    f"The {self.percentage}-th percentile of {self._ref}  was "
+                    f"expected to be {self._target_prefix}{value_target} but "
+                    f"was {value_factual}, resulting in a relative difference of "
                     f"{rel_diff}. The maximally allowed relative deviation would've been "
-                    f"{self.max_relative_deviation}."
+                    f"{self._max_relative_deviation}."
                 )
                 return False, assertion_message
         return True, None
@@ -242,47 +263,51 @@ def compare(
 class NumericNoGap(NoGapConstraint):
     _DIMENSIONS = 1
 
-    def select(self, engine: sa.engine.Engine, ref: DataReference):
+    def _select(self, engine: sa.engine.Engine, ref: DataReference) -> _Selects:
         sample_selection, n_violations_selection = db_access.get_numeric_gaps(
             engine,
             ref,
-            self.key_columns,
-            self.start_columns[0],
-            self.end_columns[0],
-            self.legitimate_gap_size,
+            self._key_columns,
+            self._start_columns[0],
+            self._end_columns[0],
+            self._legitimate_gap_size,
         )
         # TODO: Once get_unique_count also only returns a selection without
         # executing it, one would want to list this selection here as well.
         return sample_selection, n_violations_selection
 
-    def compare(self, factual: Tuple[int, int], target: Any) -> Tuple[bool, str]:
-        n_violation_keys, n_distinct_key_values = factual
+    def _compare(
+        self, value_factual: tuple[int, int], value_target: Any
+    ) -> tuple[bool, str]:
+        n_violation_keys, n_distinct_key_values = value_factual
         if n_distinct_key_values == 0:
-            return TestResult.success()
+            return True, "No key values found."
         violation_fraction = n_violation_keys / n_distinct_key_values
         assertion_text = (
-            f"{self.ref} has a ratio of {violation_fraction} > "
-            f"{self.max_relative_n_violations} keys in columns {self.key_columns} "
-            f"with a gap in the range in {self.start_columns[0]} and {self.end_columns[0]}."
+            f"{self._ref} has a ratio of {violation_fraction} > "
+            f"{self._max_relative_n_violations} keys in columns {self._key_columns} "
+            f"with a gap in the range in {self._start_columns[0]} and {self._end_columns[0]}."
             f"E.g. for: {self.sample}."
         )
-        result = violation_fraction <= self.max_relative_n_violations
+        result = violation_fraction <= self._max_relative_n_violations
         return result, assertion_text
 
 
 class NumericNoOverlap(NoOverlapConstraint):
     _DIMENSIONS = 1
 
-    def compare(self, factual: Tuple[int, int], target: Any) -> Tuple[bool, str]:
-        n_violation_keys, n_distinct_key_values = factual
+    def _compare(
+        self, value_factual: tuple[int, int], value_target: Any
+    ) -> tuple[bool, str]:
+        n_violation_keys, n_distinct_key_values = value_factual
         if n_distinct_key_values == 0:
-            return TestResult.success()
+            return True, "No key values found."
         violation_fraction = n_violation_keys / n_distinct_key_values
         assertion_text = (
-            f"{self.ref} has a ratio of {violation_fraction} > "
-            f"{self.max_relative_n_violations} keys in columns {self.key_columns} "
-            f"with overlapping ranges in {self.start_columns[0]} and {self.end_columns[0]}."
+            f"{self._ref} has a ratio of {violation_fraction} > "
+            f"{self._max_relative_n_violations} keys in columns {self._key_columns} "
+            f"with overlapping ranges in {self._start_columns[0]} and {self._end_columns[0]}."
             f"E.g. for: {self.sample}."
         )
-        result = violation_fraction <= self.max_relative_n_violations
+        result = violation_fraction <= self._max_relative_n_violations
         return result, assertion_text
diff --git a/src/datajudge/constraints/row.py b/src/datajudge/constraints/row.py
index 90985906..eb86b35f 100644
--- a/src/datajudge/constraints/row.py
+++ b/src/datajudge/constraints/row.py
@@ -1,12 +1,13 @@
+from __future__ import annotations
+
 import abc
-from functools import lru_cache
-from typing import List, Optional, Tuple
+from functools import cache
 
 import sqlalchemy as sa
 
 from .. import db_access
-from ..db_access import DataReference, MatchAndCompare
-from .base import Constraint, TestResult, ToleranceGetter, format_sample
+from ..db_access import DataReference, _MatchAndCompare
+from .base import Constraint, TestResult, _format_sample, _ToleranceGetter
 
 
 class Row(Constraint, abc.ABC):
@@ -14,143 +15,160 @@ def __init__(
         self,
         ref: DataReference,
         ref2: DataReference,
-        max_missing_fraction_getter: ToleranceGetter,
-        name: Optional[str] = None,
+        max_missing_fraction_getter: _ToleranceGetter,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, name=name)
-        self.max_missing_fraction_getter = max_missing_fraction_getter
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
+        self._max_missing_fraction_getter = max_missing_fraction_getter
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        if db_access.is_impala(engine):
-            raise NotImplementedError("Currently not implemented for impala.")
-        self.max_missing_fraction = self.max_missing_fraction_getter(engine)
-        self.ref1_minus_ref2_sample, _ = db_access.get_row_difference_sample(
-            engine, self.ref, self.ref2
+        if self._ref is None or self._ref2 is None:
+            raise ValueError()
+        self._max_missing_fraction = self._max_missing_fraction_getter(engine)
+        self._ref1_minus_ref2_sample, _ = db_access.get_row_difference_sample(
+            engine, self._ref, self._ref2
         )
-        self.ref2_minus_ref1_sample, _ = db_access.get_row_difference_sample(
-            engine, self.ref2, self.ref
+        self._ref2_minus_ref1_sample, _ = db_access.get_row_difference_sample(
+            engine, self._ref2, self._ref
         )
         return super().test(engine)
 
 
 class RowEquality(Row):
-    @lru_cache(maxsize=None)
-    def get_factual_value(self, engine: sa.engine.Engine) -> Tuple[int, int]:
+    def _get_factual_value(self, engine: sa.engine.Engine) -> tuple[int, int]:
+        if self._ref is None or self._ref2 is None:
+            raise ValueError()
         n_rows_missing_left, selections_left = db_access.get_row_difference_count(
-            engine, self.ref, self.ref2
+            engine, self._ref, self._ref2
         )
         n_rows_missing_right, selections_right = db_access.get_row_difference_count(
-            engine, self.ref2, self.ref
+            engine, self._ref2, self._ref
         )
-        self.factual_selections = [*selections_left, *selections_right]
+        self._factual_selections = [*selections_left, *selections_right]
         return n_rows_missing_left, n_rows_missing_right
 
-    @lru_cache(maxsize=None)
-    def get_target_value(self, engine: sa.engine.Engine) -> int:
+    def _get_target_value(self, engine: sa.engine.Engine) -> int:
+        if self._ref is None or self._ref2 is None:
+            raise ValueError()
         n_rows_total, selections = db_access.get_unique_count_union(
-            engine, self.ref, self.ref2
+            engine, self._ref, self._ref2
         )
         self.target_selections = selections
         return n_rows_total
 
     # fraction: (|T1 - T2| + |T2 - T1|) / |T1 U T2|
-    def compare(
-        self, n_rows_missing_tuple: Tuple[int, int], n_rows_total: int
-    ) -> Tuple[bool, Optional[str]]:
+    def _compare(
+        # We are abusing the _compare method's interface here. Rather than receiving
+        # factual and target values, we get missing and overall values.
+        self,
+        n_rows_missing_tuple: tuple[int, int],
+        n_rows_total: int,
+    ) -> tuple[bool, str | None]:  # type: ignore[invalid-method-override]
         n_rows_missing_left, n_rows_missing_right = n_rows_missing_tuple
         missing_fraction = (n_rows_missing_left + n_rows_missing_right) / n_rows_total
-        result = missing_fraction <= self.max_missing_fraction
+        result = missing_fraction <= self._max_missing_fraction
         if result:
             return result, None
-        if self.ref2 is None:
+        if self._ref2 is None:
             raise ValueError("RowEquality constraint requires ref2.")
         if n_rows_missing_left > 0:
-            sample_string = format_sample(self.ref1_minus_ref2_sample, self.ref2)
+            sample_string = _format_sample(self._ref1_minus_ref2_sample, self._ref2)
         else:
-            sample_string = format_sample(self.ref2_minus_ref1_sample, self.ref)
+            sample_string = _format_sample(self._ref2_minus_ref1_sample, self._ref)
         assertion_message = (
             f"{missing_fraction} > "
-            f"{self.max_missing_fraction} of rows differ "
-            f"between {self.ref} and "
-            f"{self.ref2}. E.g. for "
+            f"{self._max_missing_fraction} of rows differ "
+            f"between {self._ref} and "
+            f"{self._ref2}. E.g. for "
             f"{sample_string}."
         )
         return result, assertion_message
 
 
 class RowSubset(Row):
-    @lru_cache(maxsize=None)
-    def get_factual_value(self, engine: sa.engine.Engine) -> int:
+    @cache
+    def _get_factual_value(self, engine: sa.engine.Engine) -> int:
+        if self._ref is None or self._ref2 is None:
+            raise ValueError()
         n_rows_missing, selections = db_access.get_row_difference_count(
             engine,
-            self.ref,
-            self.ref2,
+            self._ref,
+            self._ref2,
         )
         self.factual_selections = selections
         return n_rows_missing
 
-    @lru_cache(maxsize=None)
-    def get_target_value(self, engine: sa.engine.Engine) -> int:
-        n_rows_total, selections = db_access.get_unique_count(engine, self.ref)
+    @cache
+    def _get_target_value(self, engine: sa.engine.Engine) -> int:
+        n_rows_total, selections = db_access.get_unique_count(engine, self._ref)
         self.target_selections = selections
         return n_rows_total
 
-    @lru_cache(maxsize=None)
-    def compare(
-        self, n_rows_missing: int, n_rows_total: int
-    ) -> Tuple[bool, Optional[str]]:
+    @cache
+    def _compare(
+        self,
+        n_rows_missing: int,
+        n_rows_total: int,
+    ) -> tuple[bool, str | None]:
         if n_rows_total == 0:
             return True, None
         missing_fraction = n_rows_missing / n_rows_total
-        result = missing_fraction <= self.max_missing_fraction
+        result = missing_fraction <= self._max_missing_fraction
         if result:
             return result, None
-        sample_string = format_sample(self.ref1_minus_ref2_sample, self.ref)
+        sample_string = _format_sample(self._ref1_minus_ref2_sample, self._ref)
         assertion_message = (
             f"{missing_fraction} > "
-            f"{self.max_missing_fraction} of rows of "
-            f"{self.ref} are "
-            f"not in {self.ref2}. E.g. for "
+            f"{self._max_missing_fraction} of rows of "
+            f"{self._ref} are "
+            f"not in {self._ref2}. E.g. for "
             f"{sample_string}. "
-            f"{self.condition_string} "
+            f"{self._condition_string} "
         )
         return result, assertion_message
 
 
 class RowSuperset(Row):
-    @lru_cache(maxsize=None)
-    def get_factual_value(self, engine: sa.engine.Engine) -> int:
+    def _get_factual_value(self, engine: sa.engine.Engine) -> int:
+        if self._ref is None or self._ref2 is None:
+            raise ValueError()
         n_rows_missing, selections = db_access.get_row_difference_count(
-            engine, self.ref2, self.ref
+            engine, self._ref2, self._ref
         )
-        self.factual_selections = selections
+        self._factual_selections = selections
         return n_rows_missing
 
-    @lru_cache(maxsize=None)
-    def get_target_value(self, engine: sa.engine.Engine) -> int:
-        n_rows_total, selections = db_access.get_unique_count(engine, self.ref2)
-        self.target_selections = selections
+    def _get_target_value(self, engine: sa.engine.Engine) -> int:
+        if self._ref is None or self._ref2 is None:
+            raise ValueError()
+        n_rows_total, selections = db_access.get_unique_count(engine, self._ref2)
+        self._target_selections = selections
         return n_rows_total
 
-    def compare(
-        self, n_rows_missing: int, n_rows_total: int
-    ) -> Tuple[bool, Optional[str]]:
+    def _compare(
+        # We are abusing the _compare method's interface here. Rather than receiving
+        # factual and target values, we get missing and overall values.
+        self,
+        n_rows_missing: int,
+        n_rows_total: int,
+    ) -> tuple[bool, str | None]:  # type: ignore[invalid-method-override]
         if n_rows_total == 0:
             return True, None
         missing_fraction = n_rows_missing / n_rows_total
-        result = missing_fraction <= self.max_missing_fraction
+        result = missing_fraction <= self._max_missing_fraction
         if result:
             return result, None
-        if self.ref2 is None:
+        if self._ref2 is None:
             raise ValueError("RowSuperset constraint requires ref2.")
-        sample_string = format_sample(self.ref2_minus_ref1_sample, self.ref2)
+        sample_string = _format_sample(self._ref2_minus_ref1_sample, self._ref2)
         assertion_message = (
             f"{missing_fraction} > "
-            f"{self.max_missing_fraction} of rows of "
-            f"{self.ref2} are "
-            f"not in {self.ref}. E.g. for "
+            f"{self._max_missing_fraction} of rows of "
+            f"{self._ref2} are "
+            f"not in {self._ref}. E.g. for "
             f"{sample_string}. "
-            f"{self.condition_string} "
+            f"{self._condition_string} "
         )
         return result, assertion_message
 
@@ -160,20 +178,22 @@ def __init__(
         self,
         ref: DataReference,
         ref2: DataReference,
-        matching_columns1: List[str],
-        matching_columns2: List[str],
-        comparison_columns1: List[str],
-        comparison_columns2: List[str],
-        max_missing_fraction_getter: ToleranceGetter,
-        name: Optional[str] = None,
+        matching_columns1: list[str],
+        matching_columns2: list[str],
+        comparison_columns1: list[str],
+        comparison_columns2: list[str],
+        max_missing_fraction_getter: _ToleranceGetter,
+        name: str | None = None,
+        cache_size=None,
     ):
         super().__init__(
             ref,
             ref2=ref2,
             max_missing_fraction_getter=max_missing_fraction_getter,
             name=name,
+            cache_size=cache_size,
         )
-        self.match_and_compare = MatchAndCompare(
+        self._match_and_compare = _MatchAndCompare(
             matching_columns1,
             matching_columns2,
             comparison_columns1,
@@ -181,20 +201,22 @@ def __init__(
         )
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
+        if self._ref is None or self._ref2 is None:
+            raise ValueError()
         missing_fraction, n_rows_match, selections = db_access.get_row_mismatch(
-            engine, self.ref, self.ref2, self.match_and_compare
+            engine, self._ref, self._ref2, self._match_and_compare
         )
         self.factual_selections = selections
-        max_missing_fraction = self.max_missing_fraction_getter(engine)
+        max_missing_fraction = self._max_missing_fraction_getter(engine)
         result = missing_fraction <= max_missing_fraction
         if result:
             return TestResult.success()
         assertion_message = (
             f"{missing_fraction} > "
             f"{max_missing_fraction} of the rows differ "
-            f"on a match of {n_rows_match} rows between {self.ref} and "
-            f"{self.ref2}. "
-            f"{self.condition_string}"
-            f"{self.match_and_compare} "
+            f"on a match of {n_rows_match} rows between {self._ref} and "
+            f"{self._ref2}. "
+            f"{self._condition_string}"
+            f"{self._match_and_compare} "
         )
         return TestResult.failure(assertion_message)
diff --git a/src/datajudge/constraints/stats.py b/src/datajudge/constraints/stats.py
index 18bb234a..b7066fc4 100644
--- a/src/datajudge/constraints/stats.py
+++ b/src/datajudge/constraints/stats.py
@@ -1,6 +1,7 @@
+from __future__ import annotations
+
 import math
 import warnings
-from typing import List, Optional, Tuple
 
 import sqlalchemy as sa
 
@@ -15,22 +16,21 @@ def __init__(
         ref: DataReference,
         ref2: DataReference,
         significance_level: float = 0.05,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        self.significance_level = significance_level
-        super().__init__(ref, ref2=ref2, name=name)
+        self._significance_level = significance_level
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
 
     @staticmethod
-    def approximate_p_value(
-        d: float, n_samples: int, m_samples: int
-    ) -> Optional[float]:
-        """
-        Calculates the approximate p-value according to
+    def _approximate_p_value(d: float, n_samples: int, m_samples: int) -> float | None:
+        """Calculate the approximate p-value.
+
+        The computation is according to
         'A procedure to find exact critical values of Kolmogorov-Smirnov Test', Silvia Fachinetti, 2009
 
         Note: For environments with `scipy` installed, this method will return a quasi-exact p-value.
         """
-
         # approximation does not work for small sample sizes
         samples = min(n_samples, m_samples)
         if samples < 35:
@@ -54,12 +54,14 @@ def approximate_p_value(
         return 1.0 if approx_p > 1.0 else 0.0 if approx_p < 0.0 else approx_p
 
     @staticmethod
-    def check_acceptance(
+    def _check_acceptance(
         d_statistic: float, n_samples: int, m_samples: int, accepted_level: float
     ) -> bool:
         """
-        For a given test statistic, d, and the respective sample sizes `n` and `m`, this function
-        checks whether the null hypothesis can be rejected for an accepted significance level.
+        Check whether the null hypothesis can be rejected for an accepted significance level.
+
+        `d_statistic is the test statistic of interest, and `n_samples` and `m_samples`
+        correspond to the respective sample sizes.
 
         For more information, check out the `Wikipedia entry <https://w.wiki/5May>`_.
         """
@@ -73,11 +75,11 @@ def c(alpha: float):
         return d_statistic <= threshold
 
     @staticmethod
-    def calculate_statistic(
-        engine,
+    def _calculate_statistic(
+        engine: sa.engine.Engine,
         ref1: DataReference,
         ref2: DataReference,
-    ) -> Tuple[float, Optional[float], int, int, List]:
+    ) -> tuple[float, float | None, int, int, list[sa.Select]]:
         # retrieve test statistic d, as well as sample sizes m and n
         d_statistic, ks_selections = db_access.get_ks_2sample(
             engine,
@@ -89,7 +91,7 @@ def calculate_statistic(
         m_samples, m_selections = db_access.get_row_count(engine, ref2)
 
         # calculate approximate p-value
-        p_value = KolmogorovSmirnov2Sample.approximate_p_value(
+        p_value = KolmogorovSmirnov2Sample._approximate_p_value(
             d_statistic, n_samples, m_samples
         )
 
@@ -97,7 +99,7 @@ def calculate_statistic(
         return d_statistic, p_value, n_samples, m_samples, selections
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        if self.ref2 is None:
+        if self._ref2 is None:
             raise ValueError("KolmogorovSmirnov2Sample requires ref2.")
         (
             d_statistic,
@@ -105,18 +107,18 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
             n_samples,
             m_samples,
             selections,
-        ) = self.calculate_statistic(
+        ) = self._calculate_statistic(
             engine,
-            self.ref,
-            self.ref2,
+            self._ref,
+            self._ref2,
         )
-        result = self.check_acceptance(
-            d_statistic, n_samples, m_samples, self.significance_level
+        result = self._check_acceptance(
+            d_statistic, n_samples, m_samples, self._significance_level
         )
 
         assertion_text = (
             f"Null hypothesis (H0) for the 2-sample Kolmogorov-Smirnov test was rejected, i.e., "
-            f"the two samples ({self.ref} and {self.target_prefix}) "
+            f"the two samples ({self._ref} and {self._target_prefix}) "
             f"do not originate from the same distribution. "
             f"The test results are d={d_statistic}"
         )
@@ -138,3 +140,82 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
             )
 
         return TestResult.success()
+
+
+class AndersonDarling2Sample(Constraint):
+    _COEFFICIENT_MAP: dict[float, dict[str, float]] = {
+        0.25: {"b0": 0.675, "b1": -0.245, "b2": -0.105},
+        0.1: {"b0": 1.281, "b1": 0.25, "b2": -0.305},
+        0.05: {"b0": 1.645, "b1": 0.678, "b2": -0.362},
+        0.025: {"b0": 1.96, "b1": 1.149, "b2": -0.391},
+        0.01: {"b0": 2.326, "b1": 1.822, "b2": -0.396},
+        0.005: {"b0": 2.573, "b1": 2.364, "b2": -0.345},
+        0.001: {"b0": 3.085, "b1": 3.615, "b2": -0.154},
+    }
+
+    def __init__(
+        self,
+        ref: DataReference,
+        ref2: DataReference,
+        significance_level: float = 0.05,
+        name: str | None = None,
+        cache_size=None,
+    ):
+        self._significance_level = significance_level
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
+
+    @staticmethod
+    def approximate_critical_value(
+        size_sample1: int, size_sample2: int, significance_level: float
+    ) -> float:
+        """Approximate critical value for specific significance_level given sample sizes."""
+        if significance_level not in AndersonDarling2Sample._COEFFICIENT_MAP:
+            raise KeyError(
+                f"Significance-level {significance_level} not supported."
+                f" Please choose one of {list(AndersonDarling2Sample._COEFFICIENT_MAP.keys())}."
+            )
+
+        coefficients = AndersonDarling2Sample._COEFFICIENT_MAP[significance_level]
+        b0 = coefficients["b0"]
+        b1 = coefficients["b1"]
+        b2 = coefficients["b2"]
+        adjusted_sample_size1 = size_sample1 - 1
+        critical_value = (
+            b0 + b1 / math.sqrt(adjusted_sample_size1) + b2 / adjusted_sample_size1
+        )
+        return critical_value
+
+    @staticmethod
+    def compute_test_statistic(
+        sum1: float, sum2: float, sample_size1: int, sample_size2: int
+    ) -> float:
+        sample_size = sample_size1 + sample_size2
+        return ((sum1 * sample_size1) + (sum2 * sample_size2)) / sample_size
+
+    def test(self, engine: sa.engine.Engine) -> TestResult:
+        assert self._ref2 is not None
+        sample_size1, sample_size1_selections = db_access.get_row_count(
+            engine, self._ref
+        )
+        sample_size2, sample_size2_selections = db_access.get_row_count(
+            engine, self._ref2
+        )
+        sample_size = sample_size1 + sample_size2
+        sum1, sum2, sum_selections = db_access.get_anderson_darling_sums(
+            engine, self._ref, self._ref2, sample_size
+        )
+        test_statistic = self.compute_test_statistic(
+            sum1, sum2, sample_size1, sample_size2
+        )
+        critical_value = self.approximate_critical_value(
+            sample_size1, sample_size2, self._significance_level
+        )
+        result = test_statistic <= critical_value
+        assertion_text = "TODO"
+        if not result:
+            return TestResult.failure(
+                assertion_text,
+                self.get_description(),
+                sum_selections + sample_size1_selections + sample_size2_selections,
+            )
+        return TestResult.success()
diff --git a/src/datajudge/constraints/uniques.py b/src/datajudge/constraints/uniques.py
index 25db3568..34d350d0 100644
--- a/src/datajudge/constraints/uniques.py
+++ b/src/datajudge/constraints/uniques.py
@@ -1,33 +1,39 @@
+from __future__ import annotations
+
 import abc
 import warnings
 from collections import Counter
+from collections.abc import Callable, Collection
 from itertools import zip_longest
 from math import ceil, floor
-from typing import Callable, Collection, Dict, List, Optional, Set, Tuple, Union
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
 from ..utils import OutputProcessor, filternull_element, output_processor_limit
-from .base import Constraint, OptionalSelections, T, TestResult, ToleranceGetter
+from .base import _T, Constraint, TestResult, _OptionalSelections, _ToleranceGetter
 
 
-def _is_superset(values1: Collection[T], values2: Collection[T]) -> Tuple[bool, Set[T]]:
+def _is_superset(
+    values1: Collection[_T], values2: Collection[_T]
+) -> tuple[bool, set[_T]]:
     """Check whether values1 is a superset of values2."""
     remainder = set(values2) - set(values1)
     return len(remainder) == 0, remainder
 
 
-def _is_subset(values1: Collection[T], values2: Collection[T]) -> Tuple[bool, Set[T]]:
+def _is_subset(
+    values1: Collection[_T], values2: Collection[_T]
+) -> tuple[bool, set[_T]]:
     """Check whether values1 is a subset of values2."""
     remainder = set(values1) - set(values2)
     return len(remainder) == 0, remainder
 
 
 def _subset_violation_counts(
-    values1: Collection[T], counts: List[int], values2: Collection[T]
-) -> Tuple[bool, Dict[Union[T, int], int]]:
+    values1: Collection[_T], counts: list[int], values2: Collection[_T]
+) -> tuple[bool, dict[_T | int, int]]:
     """Count frequencies of elements from values1 not in values2."""
     remainder = {
         value: count
@@ -45,20 +51,19 @@ class Uniques(Constraint, abc.ABC):
     through parameter `uniques` or obtained from another `DataSource`.
 
     Null values in the columns ``columns`` are ignored. To assert the non-existence of them use
-    the :meth:`~datajudge.requirements.WithinRequirement.add_null_absence_constraint`` helper method
+    the [add_null_absence_constraint][datajudge.requirements.WithinRequirement.add_null_absence_constraint] helper method
     for ``WithinRequirement``.
     By default, the null filtering does not trigger if multiple columns are fetched at once.
     It can be configured in more detail by supplying a custom ``filter_func`` function.
-    Some exemplary implementations are available as :func:`~datajudge.utils.filternull_element`,
-    :func:`~datajudge.utils.filternull_never`, :func:`~datajudge.utils.filternull_element_or_tuple_all`,
-    :func:`~datajudge.utils.filternull_element_or_tuple_any`.
-    Passing ``None`` as the argument is equivalent to :func:`~datajudge.utils.filternull_element` but triggers a warning.
-    The current default of :func:`~datajudge.utils.filternull_element`
+    Some exemplary implementations are available as [filternull_element][datajudge.utils.filternull_element],
+    [filternull_never][datajudge.utils.filternull_never], [filternull_element_or_tuple_all][datajudge.utils.filternull_element_or_tuple_all],
+    [filternull_element_or_tuple_any][datajudge.utils.filternull_element_or_tuple_any].
+    Passing ``None`` as the argument is equivalent to [filternull_element][datajudge.utils.filternull_element] but triggers a warning.
+    The current default of [filternull_element][datajudge.utils.filternull_element]
     Cause (possibly often unintended) changes in behavior when the users adds a second column
     (filtering no longer can trigger at all).
-    The default will be changed to :func:`~datajudge.utils.filternull_element_or_tuple_all` in future versions.
-    To silence the warning, set ``filter_func`` explicitly..
-
+    The default will be changed to [filternull_element_or_tuple_all][datajudge.utils.filternull_element_or_tuple_all] in future versions.
+    To silence the warning, set ``filter_func`` explicitly.
 
     There are two ways to do some post processing of the data obtained from the
     database by providing a function to be executed. In general, no postprocessing
@@ -92,12 +97,12 @@ class Uniques(Constraint, abc.ABC):
     thus, they may differ from run to run despite the exact same situation being present,
     and can have an arbitrary length.
     To enforce a reproducible, limited output via (e.g.) sorting and slicing,
-    set `output_processors` to a callable or a list of callables. By default, only the first 100 elements are displayed (:func:`~datajudge.utils.output_processor_limit`).
+    set `output_processors` to a callable or a list of callables. By default, only the first 100 elements are displayed ([output_processor_limit][datajudge.utils.output_processor_limit]).
 
     Each callable takes in two collections, and returns modified (e.g. sorted) versions of them.
     In most cases, the second argument is simply None,
     but for `UniquesSubset` it is the counts of each of the elements.
-    The suggested functions are :func:`~datajudge.utils.output_processor_sort` and :func:`~datajudge.utils.output_processor_limit`
+    The suggested functions are [output_processor_sort][datajudge.utils.output_processor_sort] and [output_processor_limit][datajudge.utils.output_processor_limit]
     - see their respective docstrings for details.
 
     One use is of this constraint is to test for consistency in columns with expected
@@ -107,26 +112,28 @@ class Uniques(Constraint, abc.ABC):
     def __init__(
         self,
         ref: DataReference,
-        name: Optional[str] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
+        name: str | None = None,
+        cache_size=None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
         *,
-        ref2: Optional[DataReference] = None,
-        uniques: Optional[Collection] = None,
-        filter_func: Optional[Callable[[List[T]], List[T]]] = None,
-        map_func: Optional[Callable[[T], T]] = None,
-        reduce_func: Optional[Callable[[Collection], Collection]] = None,
+        ref2: DataReference | None = None,
+        uniques: Collection | None = None,
+        filter_func: Callable[[list[_T]], list[_T]] | None = None,
+        map_func: Callable[[_T], _T] | None = None,
+        reduce_func: Callable[[Collection], Collection] | None = None,
         max_relative_violations=0,
         compare_distinct=False,
     ):
-        ref_value: Optional[Tuple[Collection, List]]
+        ref_value: tuple[Collection, list] | None
         ref_value = (uniques, []) if uniques else None
         super().__init__(
             ref,
             ref2=ref2,
             ref_value=ref_value,
             name=name,
+            cache_size=cache_size,
             output_processors=output_processors,
         )
 
@@ -134,23 +141,23 @@ def __init__(
             warnings.warn("Using deprecated default null filter function.")
             filter_func = filternull_element
 
-        self.filter_func = filter_func
-        self.local_func = map_func
-        self.global_func = reduce_func
-        self.max_relative_violations = max_relative_violations
-        self.compare_distinct = compare_distinct
+        self._filter_func = filter_func
+        self._local_func = map_func
+        self._global_func = reduce_func
+        self._max_relative_violations = max_relative_violations
+        self._compare_distinct = compare_distinct
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[Tuple[List[T], List[int]], OptionalSelections]:
+    ) -> tuple[tuple[list, list[int]], _OptionalSelections]:
         uniques, selection = db_access.get_uniques(engine, ref)
         values = list(uniques.keys())
-        values = self.filter_func(values)
+        values = self._filter_func(values)
         counts = [uniques[value] for value in values]
-        if self.local_func:
-            values = list(map(self.local_func, values))
-        if self.global_func:
-            values = list(self.global_func(values))
+        if self._local_func:
+            values = list(map(self._local_func, values))
+        if self._global_func:
+            values = list(self._global_func(values))
             if not isinstance(values, Collection):
                 raise ValueError(
                     "The return value from `reduce_func` is not a Collection."
@@ -159,7 +166,7 @@ def retrieve(
 
 
 class UniquesEquality(Uniques):
-    def __init__(self, args, name: Optional[str] = None, **kwargs):
+    def __init__(self, args, name: str | None = None, cache_size=None, **kwargs):
         if kwargs.get("max_relative_violations"):
             raise RuntimeError(
                 "max_relative_violations is not supported for UniquesEquality."
@@ -168,55 +175,55 @@ def __init__(self, args, name: Optional[str] = None, **kwargs):
             raise RuntimeError("compare_distinct is not supported for UniquesEquality.")
         super().__init__(args, name=name, **kwargs)
 
-    def compare(
+    def _compare(
         self,
-        factual: Tuple[List[T], List[int]],
-        target: Tuple[Collection[T], List[int]],
-    ) -> Tuple[bool, Optional[str]]:
-        factual_values_list, _ = factual
+        value_factual: tuple[list[_T], list[int]],
+        value_target: tuple[Collection[_T], list[int]],
+    ) -> tuple[bool, str | None]:
+        factual_values_list, _ = value_factual
         factual_values = set(factual_values_list)
-        target_values_list, _ = target
+        target_values_list, _ = value_target
         target_values = set(target_values_list)
         is_subset, excess_values = _is_subset(factual_values, target_values)
         is_superset, lacking_values = _is_superset(factual_values, target_values)
         if not is_subset and not is_superset:
             assertion_text = (
-                f"{self.ref} doesn't have the element(s) "
-                f"'{self.apply_output_formatting(lacking_values)}' and has the excess element(s) "
-                f"'{self.apply_output_formatting(excess_values)}' when compared with the reference values. "
-                f"{self.condition_string}"
+                f"{self._ref} doesn't have the element(s) "
+                f"'{self._apply_output_formatting(lacking_values)}' and has the excess element(s) "
+                f"'{self._apply_output_formatting(excess_values)}' when compared with the reference values. "
+                f"{self._condition_string}"
             )
             return False, assertion_text
         if not is_subset:
             assertion_text = (
-                f"{self.ref} has the excess element(s) "
-                f"'{self.apply_output_formatting(excess_values)}' when compared with the reference values. "
-                f"{self.condition_string}"
+                f"{self._ref} has the excess element(s) "
+                f"'{self._apply_output_formatting(excess_values)}' when compared with the reference values. "
+                f"{self._condition_string}"
             )
             return False, assertion_text
         if not is_superset:
             assertion_text = (
-                f"{self.ref} doesn't have the element(s) "
-                f"'{self.apply_output_formatting(lacking_values)}' when compared with the reference values. "
-                f"{self.condition_string}"
+                f"{self._ref} doesn't have the element(s) "
+                f"'{self._apply_output_formatting(lacking_values)}' when compared with the reference values. "
+                f"{self._condition_string}"
             )
             return False, assertion_text
         return True, None
 
 
 class UniquesSubset(Uniques):
-    def compare(
+    def _compare(
         self,
-        factual: Tuple[List[T], List[int]],
-        target: Tuple[Collection[T], List[int]],
-    ) -> Tuple[bool, Optional[str]]:
-        factual_values, factual_counts = factual
-        target_values, _ = target
+        value_factual: tuple[list[_T], list[int]],
+        value_target: tuple[Collection[_T], list[int]],
+    ) -> tuple[bool, str | None]:
+        factual_values, factual_counts = value_factual
+        target_values, _ = value_target
 
         is_subset, remainder = _subset_violation_counts(
             factual_values, factual_counts, target_values
         )
-        if not self.compare_distinct:
+        if not self._compare_distinct:
             n_rows = sum(factual_counts)
             n_violations = sum(remainder.values())
         else:
@@ -226,42 +233,43 @@ def compare(
         if (
             n_rows > 0
             and (relative_violations := (n_violations / n_rows))
-            > self.max_relative_violations
+            > self._max_relative_violations
         ):
-            output_elemes, output_counts = list(remainder.keys()), list(
-                remainder.values()
+            output_elemes, output_counts = (
+                list(remainder.keys()),
+                list(remainder.values()),
             )
-            if self.output_processors is not None:
-                for output_processor in self.output_processors:
+            if self._output_processors is not None:
+                for output_processor in self._output_processors:
                     output_elemes, output_counts = output_processor(
                         output_elemes, output_counts
                     )
 
             assertion_text = (
-                f"{self.ref} has a fraction of {relative_violations} > "
-                f"{self.max_relative_violations} {'DISTINCT ' if self.compare_distinct else ''}values ({n_violations} / {n_rows}) not being an element of "
-                f"'{self.apply_output_formatting(set(target_values))}'. It has excess elements "
+                f"{self._ref} has a fraction of {relative_violations} > "
+                f"{self._max_relative_violations} {'DISTINCT ' if self._compare_distinct else ''}values ({n_violations} / {n_rows}) not being an element of "
+                f"'{self._apply_output_formatting(set(target_values))}'. It has excess elements "
                 f"'{output_elemes}' "
                 f"with counts {output_counts}."
-                f"{self.condition_string}"
+                f"{self._condition_string}"
             )
             return False, assertion_text
         return True, None
 
 
 class UniquesSuperset(Uniques):
-    def __init__(self, args, name: Optional[str] = None, **kwargs):
+    def __init__(self, args, name: str | None = None, cache_size=None, **kwargs):
         if kwargs.get("compare_distinct"):
             raise RuntimeError("compare_distinct is not supported for UniquesSuperset.")
         super().__init__(args, name=name, **kwargs)
 
-    def compare(
+    def _compare(
         self,
-        factual: Tuple[List[T], List[int]],
-        target: Tuple[Collection[T], List[int]],
-    ) -> Tuple[bool, Optional[str]]:
-        factual_values, _ = factual
-        target_values, _ = target
+        value_factual: tuple[list[_T], list[int]],
+        value_target: tuple[Collection[_T], list[int]],
+    ) -> tuple[bool, str | None]:
+        factual_values, _ = value_factual
+        target_values, _ = value_target
         is_superset, remainder = _is_superset(factual_values, target_values)
         if (
             len(factual_values) > 0
@@ -270,14 +278,14 @@ def compare(
                     (n_violations := (len(remainder))) / (n_rows := len(target_values))
                 )
             )
-            > self.max_relative_violations
+            > self._max_relative_violations
         ):
             assertion_text = (
-                f"{self.ref} has a fraction of "
-                f"{relative_violations} > {self.max_relative_violations} ({n_violations} / {n_rows}) "
-                f"lacking unique values of '{self.apply_output_formatting(set(target_values))}'. It "
-                f"doesn't have the unique value(s) '{self.apply_output_formatting(list(remainder))}'."
-                f"{self.condition_string}"
+                f"{self._ref} has a fraction of "
+                f"{relative_violations} > {self._max_relative_violations} ({n_violations} / {n_rows}) "
+                f"lacking unique values of '{self._apply_output_formatting(set(target_values))}'. It "
+                f"doesn't have the unique value(s) '{self._apply_output_formatting(list(remainder))}'."
+                f"{self._condition_string}"
             )
             return False, assertion_text
         return True, None
@@ -288,28 +296,35 @@ def __init__(
         self,
         ref: DataReference,
         *,
-        ref2: Optional[DataReference] = None,
-        n_uniques: Optional[int] = None,
-        name: Optional[str] = None,
+        ref2: DataReference | None = None,
+        n_uniques: int | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, ref_value=n_uniques, name=name)
+        super().__init__(
+            ref,
+            ref2=ref2,
+            ref_value=n_uniques,
+            name=name,
+            cache_size=cache_size,
+        )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[int, OptionalSelections]:
+    ) -> tuple[int, _OptionalSelections]:
         return db_access.get_unique_count(engine, ref)
 
 
 class NUniquesEquality(NUniques):
-    def compare(
-        self, n_uniques_factual: int, n_uniques_target: int
-    ) -> Tuple[bool, Optional[str]]:
-        result = n_uniques_factual == n_uniques_target
+    def _compare(
+        self, value_factual: int, value_target: int
+    ) -> tuple[bool, str | None]:
+        result = value_factual == value_target
         assertion_text = (
-            f"{self.ref} has {n_uniques_factual} "
-            f"unique(s) instead of {self.target_prefix}"
-            f"{n_uniques_target}. "
-            f"{self.condition_string}"
+            f"{self._ref} has {value_factual} "
+            f"unique(s) instead of {self._target_prefix}"
+            f"{value_target}. "
+            f"{self._condition_string}"
         )
         return result, assertion_text
 
@@ -319,28 +334,28 @@ def __init__(
         self,
         ref: DataReference,
         ref2: DataReference,
-        max_relative_loss_getter: ToleranceGetter,
-        name: Optional[str] = None,
+        max_relative_loss_getter: _ToleranceGetter,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, name=name)
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
         self.max_relative_loss_getter = max_relative_loss_getter
 
-    def compare(
-        self, n_uniques_factual: int, n_uniques_target: int
-    ) -> Tuple[bool, Optional[str]]:
-        if n_uniques_target == 0 or n_uniques_factual > n_uniques_target:
+    def _compare(
+        self, value_factual: int, value_target: int
+    ) -> tuple[bool, str | None]:
+        if value_target == 0 or value_factual > value_target:
             return True, None
-        relative_loss = (n_uniques_target - n_uniques_factual) / n_uniques_target
+        relative_loss = (value_target - value_factual) / value_target
         assertion_text = (
-            f"{self.ref} has lost {relative_loss} "
-            f"of #uniques of table {self.ref2}. It "
+            f"{self._ref} has lost {relative_loss} "
+            f"of #uniques of table {self._ref2}. It "
             f"was only allowed to decrease "
             f"{self.max_relative_loss}. "
-            f"{self.condition_string}"
+            f"{self._condition_string}"
         )
         result = relative_loss <= self.max_relative_loss
         return result, assertion_text
-        return TestResult(result, assertion_text)
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
         self.max_relative_loss = self.max_relative_loss_getter(engine)
@@ -352,38 +367,39 @@ def __init__(
         self,
         ref: DataReference,
         ref2: DataReference,
-        max_relative_gain_getter: ToleranceGetter,
-        name: Optional[str] = None,
+        max_relative_gain_getter: _ToleranceGetter,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref2=ref2, name=name)
-        self.max_relative_gain_getter = max_relative_gain_getter
+        super().__init__(ref, ref2=ref2, name=name, cache_size=cache_size)
+        self._max_relative_gain_getter = max_relative_gain_getter
 
-    def compare(
-        self, n_uniques_factual: int, n_uniques_target: int
-    ) -> Tuple[bool, Optional[str]]:
-        if n_uniques_target == 0:
+    def _compare(
+        self, value_factual: int, value_target: int
+    ) -> tuple[bool, str | None]:
+        if value_target == 0:
             return False, "Target table empty."
-        if n_uniques_factual < n_uniques_target:
+        if value_factual < value_target:
             return True, None
-        relative_gain = (n_uniques_factual - n_uniques_target) / n_uniques_target
+        relative_gain = (value_factual - value_target) / value_target
         assertion_text = (
-            f"{self.ref} has {relative_gain} of "
-            f"#uniques of {self.ref2}. It was only "
-            f"allowed to increase {self.max_relative_gain} . "
-            f"{self.condition_string}"
+            f"{self._ref} has {relative_gain} of "
+            f"#uniques of {self._ref2}. It was only "
+            f"allowed to increase {self._max_relative_gain} . "
+            f"{self._condition_string}"
         )
-        result = relative_gain <= self.max_relative_gain
+        result = relative_gain <= self._max_relative_gain
         return result, assertion_text
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        self.max_relative_gain = self.max_relative_gain_getter(engine)
+        self._max_relative_gain = self._max_relative_gain_getter(engine)
         return super().test(engine)
 
 
 class CategoricalBoundConstraint(Constraint):
-    """
-    `CategoricalBoundConstraint` is a constraint class that checks if the share of specific values
-    in a column falls within predefined bounds. It compares the actual distribution of values in a
+    """Constraint that checks if the share of specific values in a column falls within predefined bounds.
+
+    It compares the actual distribution of values in a
     `DataSource` column with a target distribution, supplied as a dictionary.
 
     Example use cases include testing for consistency in columns with expected categorical values
@@ -393,12 +409,12 @@ class CategoricalBoundConstraint(Constraint):
     ----------
     ref : DataReference
         A reference to the column in the data source.
-    distribution : Dict[T, Tuple[float, float]]
+    distribution : dict[_T, tuple[float, float]]
         A dictionary with unique values as keys and tuples of minimum and maximum allowed shares as values.
-    default_bounds : Tuple[float, float], optional, default=(0, 0)
+    default_bounds : tuple[float, float], optional, default=(0, 0)
         A tuple specifying the minimum and maximum bounds for values not explicitly outlined in the target
         distribution dictionary.
-    name : Optional[str], default=None
+    name : str | None, default=None
         An optional name for the constraint.
     max_relative_violations : float, optional, default=0
         A tolerance threshold (0 to 1) for the proportion of elements in the data that can violate the
@@ -408,61 +424,72 @@ class CategoricalBoundConstraint(Constraint):
     def __init__(
         self,
         ref: DataReference,
-        distribution: Dict[T, Tuple[float, float]],
-        default_bounds: Tuple[float, float] = (0, 0),
-        name: Optional[str] = None,
+        distribution: dict[_T, tuple[float, float]],
+        default_bounds: tuple[float, float] = (0, 0),
+        name: str | None = None,
+        cache_size=None,
         max_relative_violations: float = 0,
         **kwargs,
     ):
-        self.default_bounds = default_bounds
-        self.max_relative_violations = max_relative_violations
-        super().__init__(ref, ref_value=distribution, name=name, **kwargs)
+        self._default_bounds = default_bounds
+        self._max_relative_violations = max_relative_violations
+        super().__init__(
+            ref,
+            ref_value=distribution,
+            name=name,
+            cache_size=cache_size,
+            **kwargs,
+        )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[Counter, OptionalSelections]:
+    ) -> tuple[Counter, _OptionalSelections]:
         return db_access.get_uniques(engine, ref)
 
-    def compare(
+    def _compare(
         self,
-        factual: Counter,
-        target: Dict[T, Tuple[float, float]],
-    ) -> Tuple[bool, Optional[str]]:
-        # TODO: use .total() of Counter as soon as we can assume Python 3.10
-        total = sum(factual.values())
-        all_variants = factual.keys() | target.keys()
+        value_factual: Counter,
+        value_target: dict[_T, tuple[float, float]],
+    ) -> tuple[bool, str | None]:
+        total = value_factual.total()
+        all_variants = value_factual.keys() | value_target.keys()
         min_counts = Counter(
-            {k: target.get(k, self.default_bounds)[0] * total for k in all_variants}
+            {
+                k: value_target.get(k, self._default_bounds)[0] * total
+                for k in all_variants
+            }
         )
         max_counts = Counter(
-            {k: target.get(k, self.default_bounds)[1] * total for k in all_variants}
+            {
+                k: value_target.get(k, self._default_bounds)[1] * total
+                for k in all_variants
+            }
         )
 
-        violations = (factual - max_counts) + (min_counts - factual)
+        violations = (value_factual - max_counts) + (min_counts - value_factual)
 
         if (
-            # TODO: use .total() of Counter as soon as we can assume Python 3.10
-            relative_violations := (sum(violations.values()) / total)
-        ) > self.max_relative_violations:
+            relative_violations := violations.total() / total
+        ) > self._max_relative_violations:
             assertion_text = (
-                f"{self.ref} has {relative_violations * 100}% > "
-                f"{self.max_relative_violations * 100}% of element(s) violating the bound constraints:\n"
+                f"{self._ref} has {relative_violations * 100}% > "
+                f"{self._max_relative_violations * 100}% of element(s) violating the bound constraints:\n"
             )
 
             for variant in violations:
-                actual_share = factual[variant] / total
-                target_share = target.get(variant, self.default_bounds)
+                actual_share = value_factual[variant] / total
+                target_share = value_target.get(variant, self._default_bounds)
                 min_required = min_counts[variant]
                 max_required = max_counts[variant]
 
                 assertion_text += (
                     f"'{variant}' with a share of {actual_share * 100}% "
-                    f"({factual[variant]} out of {total}) "
+                    f"({value_factual[variant]} out of {total}) "
                     f"while a share between {target_share[0] * 100}% ({ceil(min_required)}) "
                     f"and {target_share[1] * 100}% ({floor(max_required)}) "
                     f"is required\n"
                 )
 
-            assertion_text += f"{self.condition_string}"
+            assertion_text += f"{self._condition_string}"
             return False, assertion_text
         return True, None
diff --git a/src/datajudge/constraints/varchar.py b/src/datajudge/constraints/varchar.py
index 2d5b5d4f..7938cad1 100644
--- a/src/datajudge/constraints/varchar.py
+++ b/src/datajudge/constraints/varchar.py
@@ -1,12 +1,14 @@
+from __future__ import annotations
+
 import itertools
 import re
-from typing import Optional, Tuple
+from typing import Any
 
 import sqlalchemy as sa
 
 from .. import db_access
 from ..db_access import DataReference
-from .base import Constraint, OptionalSelections, TestResult
+from .base import Constraint, TestResult, _OptionalSelections
 
 
 class VarCharRegexDb(Constraint):
@@ -17,25 +19,36 @@ def __init__(
         relative_tolerance: float = 0.0,
         aggregated: bool = True,
         n_counterexamples: int = 5,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref_value=relative_tolerance, name=name)
-        self.regex = regex
-        self.aggregated = aggregated
-        self.n_counterexamples = n_counterexamples
+        super().__init__(
+            ref,
+            ref_value=relative_tolerance,
+            name=name,
+            cache_size=cache_size,
+        )
+        self._regex = regex
+        self._aggregated = aggregated
+        self._n_counterexamples = n_counterexamples
 
-    def retrieve(self, engine: sa.engine.Engine, ref: DataReference):
+    def _retrieve(
+        self, engine: sa.engine.Engine, ref: DataReference
+    ) -> tuple[Any, _OptionalSelections]:
         (
-            n_violations,
-            counterexamples,
-        ), violations_selections = db_access.get_regex_violations(
+            (
+                n_violations,
+                counterexamples,
+            ),
+            violations_selections,
+        ) = db_access.get_regex_violations(
             engine=engine,
             ref=ref,
-            aggregated=self.aggregated,
-            regex=self.regex,
-            n_counterexamples=self.n_counterexamples,
+            aggregated=self._aggregated,
+            regex=self._regex,
+            n_counterexamples=self._n_counterexamples,
         )
-        if self.aggregated:
+        if self._aggregated:
             n_rows, n_rows_selections = db_access.get_unique_count(
                 engine=engine, ref=ref
             )
@@ -47,14 +60,14 @@ def retrieve(self, engine: sa.engine.Engine, ref: DataReference):
             counterexamples,
         ), violations_selections + n_rows_selections
 
-    def compare(self, violations_factual, violations_target):
+    def _compare(self, value_factual, value_target) -> tuple[bool, str]:
         (
             factual_n_violations,
             factual_n_rows,
             factual_counterexamples,
-        ) = violations_factual
+        ) = value_factual
         factual_relative_violations = factual_n_violations / factual_n_rows
-        result = factual_relative_violations <= violations_target
+        result = factual_relative_violations <= value_target
         counterexample_string = (
             (
                 "Some counterexamples consist of the following: "
@@ -64,11 +77,11 @@ def compare(self, violations_factual, violations_target):
             else ""
         )
         assertion_text = (
-            f"{self.ref} "
-            f"breaks regex '{self.regex}' in {factual_relative_violations} > "
-            f"{violations_target} of the cases. "
+            f"{self._ref} "
+            f"breaks regex '{self._regex}' in {factual_relative_violations} > "
+            f"{value_target} of the cases. "
             f"In absolute terms, {factual_n_violations} of the {factual_n_rows} samples "
-            f"violated the regex. {counterexample_string}{self.condition_string}"
+            f"violated the regex. {counterexample_string}{self._condition_string}"
         )
         return result, assertion_text
 
@@ -82,18 +95,19 @@ def __init__(
         relative_tolerance: float = 0.0,
         aggregated: bool = True,
         n_counterexamples: int = 5,
-        name: Optional[str] = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        super().__init__(ref, ref_value=regex, name=name)
-        self.allow_none = allow_none
-        self.relative_tolerance = relative_tolerance
-        self.aggregated = aggregated
-        self.n_counterexamples = n_counterexamples
+        super().__init__(ref, ref_value=regex, name=name, cache_size=cache_size)
+        self._allow_none = allow_none
+        self._relative_tolerance = relative_tolerance
+        self._aggregated = aggregated
+        self._n_counterexamples = n_counterexamples
 
     def test(self, engine: sa.engine.Engine) -> TestResult:
-        uniques_counter, selections = db_access.get_uniques(engine, self.ref)
+        uniques_counter, selections = db_access.get_uniques(engine, self._ref)
         self.factual_selections = selections
-        if not self.allow_none and uniques_counter.get(None):
+        if not self._allow_none and uniques_counter.get(None):
             return TestResult.failure(
                 "The column contains a None value when it's not allowed. "
                 "To ignore None values, please use `allow_none=True` option."
@@ -102,15 +116,13 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
             uniques_counter.pop(None)
 
         uniques_factual = list(uniques_counter.keys())
-        if not self.ref_value:
+        if not self._ref_value:
             return TestResult.failure("No regex pattern given")
 
-        pattern = re.compile(self.ref_value)
-        uniques_mismatching = {
-            x for x in uniques_factual if not pattern.match(x)  # type: ignore
-        }
+        pattern = re.compile(self._ref_value)
+        uniques_mismatching = {x for x in uniques_factual if not pattern.match(x)}
 
-        if self.aggregated:
+        if self._aggregated:
             n_violations = len(uniques_mismatching)
             n_total = len(uniques_factual)
         else:
@@ -119,26 +131,26 @@ def test(self, engine: sa.engine.Engine) -> TestResult:
 
         n_relative_violations = n_violations / n_total
 
-        if self.n_counterexamples == -1:
+        if self._n_counterexamples == -1:
             counterexamples = list(uniques_mismatching)
         else:
             counterexamples = list(
-                itertools.islice(uniques_mismatching, self.n_counterexamples)
+                itertools.islice(uniques_mismatching, self._n_counterexamples)
             )
 
         counterexample_string = (
-            ("Some counterexamples consist of the following: " f"{counterexamples}. ")
+            (f"Some counterexamples consist of the following: {counterexamples}. ")
             if counterexamples and len(counterexamples) > 0
             else ""
         )
 
-        if n_relative_violations > self.relative_tolerance:
+        if n_relative_violations > self._relative_tolerance:
             assertion_text = (
-                f"{self.ref} "
-                f"breaks regex '{self.ref_value}' in {n_relative_violations} > "
-                f"{self.relative_tolerance} of the cases. "
+                f"{self._ref} "
+                f"breaks regex '{self._ref_value}' in {n_relative_violations} > "
+                f"{self._relative_tolerance} of the cases. "
                 f"In absolute terms, {n_violations} of the {n_total} samples violated the regex. "
-                f"{counterexample_string}{self.condition_string}"
+                f"{counterexample_string}{self._condition_string}"
             )
             return TestResult.failure(assertion_text)
         return TestResult.success()
@@ -149,36 +161,38 @@ def __init__(
         self,
         ref,
         *,
-        ref2: Optional[DataReference] = None,
-        min_length: Optional[int] = None,
-        name: Optional[str] = None,
+        ref2: DataReference | None = None,
+        min_length: int | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         super().__init__(
             ref,
             ref2=ref2,
             ref_value=min_length,
             name=name,
+            cache_size=cache_size,
         )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[int, OptionalSelections]:
+    ) -> tuple[int, _OptionalSelections]:
         return db_access.get_min_length(engine, ref)
 
-    def compare(
-        self, length_factual: int, length_target: int
-    ) -> Tuple[bool, Optional[str]]:
-        if length_target is None:
+    def _compare(
+        self, value_factual: int, value_target: int
+    ) -> tuple[bool, str | None]:
+        if value_target is None:
             return True, None
-        if length_factual is None:
-            return length_target == 0, "Empty set."
+        if value_factual is None:
+            return value_target == 0, "Empty set."
         assertion_text = (
-            f"{self.ref} "
-            f"has min length {length_factual} instead of "
-            f"{self.target_prefix} {length_target}. "
-            f"{self.condition_string}"
+            f"{self._ref} "
+            f"has min length {value_factual} instead of "
+            f"{self._target_prefix} {value_target}. "
+            f"{self._condition_string}"
         )
-        result = length_factual >= length_target
+        result = value_factual >= value_target
         return result, assertion_text
 
 
@@ -187,34 +201,36 @@ def __init__(
         self,
         ref: DataReference,
         *,
-        ref2: Optional[DataReference] = None,
-        max_length: Optional[int] = None,
-        name: Optional[str] = None,
+        ref2: DataReference | None = None,
+        max_length: int | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         super().__init__(
             ref,
             ref2=ref2,
             ref_value=max_length,
             name=name,
+            cache_size=cache_size,
         )
 
-    def retrieve(
+    def _retrieve(
         self, engine: sa.engine.Engine, ref: DataReference
-    ) -> Tuple[int, OptionalSelections]:
+    ) -> tuple[int, _OptionalSelections]:
         return db_access.get_max_length(engine, ref)
 
-    def compare(
-        self, length_factual: int, length_target: int
-    ) -> Tuple[bool, Optional[str]]:
-        if length_factual is None:
+    def _compare(
+        self, value_factual: int, value_target: int
+    ) -> tuple[bool, str | None]:
+        if value_factual is None:
             return True, None
-        if length_target is None:
-            return length_factual == 0, "Reference value is None."
+        if value_target is None:
+            return value_factual == 0, "Reference value is None."
         assertion_text = (
-            f"{self.ref} "
-            f"has max length {length_factual} instead of "
-            f"{self.target_prefix} {length_target}. "
-            f"{self.condition_string}"
+            f"{self._ref} "
+            f"has max length {value_factual} instead of "
+            f"{self._target_prefix} {value_target}. "
+            f"{self._condition_string}"
         )
-        result = length_factual <= length_target
+        result = value_factual <= value_target
         return result, assertion_text
diff --git a/src/datajudge/data_source.py b/src/datajudge/data_source.py
new file mode 100644
index 00000000..dea2f2f3
--- /dev/null
+++ b/src/datajudge/data_source.py
@@ -0,0 +1,105 @@
+import functools
+from abc import ABC, abstractmethod
+from typing import final
+
+import sqlalchemy as sa
+from sqlalchemy.sql import selectable
+
+from ._engines import is_mssql
+
+
+class DataSource(ABC):
+    @abstractmethod
+    def __str__(self) -> str: ...
+
+    @abstractmethod
+    def _get_clause(self, engine: sa.engine.Engine) -> selectable.FromClause: ...
+
+
+@functools.lru_cache(maxsize=1)
+def _get_metadata() -> sa.MetaData:
+    return sa.MetaData()
+
+
+@final
+class TableDataSource(DataSource):
+    """A ``DataSource`` based on a table."""
+
+    def __init__(
+        self,
+        db_name: str,
+        table_name: str,
+        schema_name: str | None = None,
+    ):
+        self._db_name = db_name
+        self._table_name = table_name
+        self._schema_name = schema_name
+
+    def __str__(self) -> str:
+        if self._schema_name:
+            return f"{self._db_name}.{self._schema_name}.{self._table_name}"
+        return self._table_name
+
+    def _get_clause(self, engine: sa.engine.Engine) -> sa.Table:
+        schema = self._schema_name
+        if is_mssql(engine) and self._schema_name:
+            schema = self._db_name + "." + self._schema_name
+
+        return sa.Table(
+            self._table_name,
+            _get_metadata(),
+            autoload_with=engine,
+            schema=schema,
+        )
+
+
+@final
+class ExpressionDataSource(DataSource):
+    """A ``DataSource`` based on a sqlalchemy expression."""
+
+    def __init__(
+        self,
+        expression: selectable.FromClause | selectable.Select,
+        name: str,
+    ):
+        self._expression = expression
+        self.name = name
+
+    def __str__(self) -> str:
+        return self.name
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(expression={self._expression!r}, name={self.name})"
+
+    def _get_clause(self, engine: sa.engine.Engine) -> selectable.FromClause:
+        return self._expression.alias()
+
+
+@final
+class RawQueryDataSource(DataSource):
+    """A ``DataSource`` based on a SQL query as a string."""
+
+    def __init__(self, query_string: str, name: str, columns: list[str] | None = None):
+        self._query_string = query_string
+        self.name = name
+        self._columns = columns
+        wrapped_query = f"({query_string}) as t"
+        if columns is not None and len(columns) > 0:
+            subquery = (
+                sa.text(query_string)
+                .columns(*[sa.column(column_name) for column_name in columns])
+                .subquery()
+            )
+            self.clause = subquery
+        else:
+            wrapped_query = f"({query_string}) as t"
+            self.clause = sa.select("*").select_from(sa.text(wrapped_query)).alias()
+
+    def __str__(self) -> str:
+        return self.name
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(query_string={self._query_string}, name={self.name}, columns={self._columns})"
+
+    def _get_clause(self, engine: sa.engine.Engine) -> selectable.FromClause:
+        return self.clause
diff --git a/src/datajudge/db_access.py b/src/datajudge/db_access.py
index 2f94ff5a..cad60d51 100644
--- a/src/datajudge/db_access.py
+++ b/src/datajudge/db_access.py
@@ -1,51 +1,35 @@
 from __future__ import annotations
 
-import functools
 import json
 import operator
-from abc import ABC, abstractmethod
 from collections import Counter
+from collections.abc import Callable, Iterator, Sequence
 from dataclasses import dataclass
-from typing import Any, Callable, Sequence, final, overload
+from typing import Any, overload
 
 import sqlalchemy as sa
 from sqlalchemy.sql import selectable
-from sqlalchemy.sql.expression import FromClause
 
-
-def is_mssql(engine: sa.engine.Engine) -> bool:
-    return engine.name == "mssql"
-
-
-def is_postgresql(engine: sa.engine.Engine) -> bool:
-    return engine.name == "postgresql"
-
-
-def is_snowflake(engine: sa.engine.Engine) -> bool:
-    return engine.name == "snowflake"
-
-
-def is_bigquery(engine: sa.engine.Engine) -> bool:
-    return engine.name == "bigquery"
-
-
-def is_impala(engine: sa.engine.Engine) -> bool:
-    return engine.name == "impala"
-
-
-def is_db2(engine: sa.engine.Engine) -> bool:
-    return engine.name == "ibm_db_sa"
-
-
-def get_table_columns(table, column_names):
+from ._engines import (
+    is_bigquery,
+    is_db2,
+    is_duckdb,
+    is_mssql,
+    is_postgresql,
+    is_snowflake,
+)
+from .condition import Condition
+from .data_source import DataSource, TableDataSource
+
+
+def get_table_columns(
+    table: sa.Table | sa.Subquery, column_names: Sequence[str]
+) -> list[sa.ColumnElement]:
     return [table.c[column_name] for column_name in column_names]
 
 
-def apply_patches(engine: sa.engine.Engine):
-    """
-    Apply patches to e.g. specific dialect not implemented by sqlalchemy
-    """
-
+def apply_patches(engine: sa.engine.Engine) -> None:
+    """Apply patches to e.g. specific dialect not implemented by sqlalchemy."""
     if is_bigquery(engine):
         # Patch for the EXCEPT operator (see BigQuery set operators
         # https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax#set_operators)
@@ -93,87 +77,20 @@ def lowercase_column_names(column_names: str | list[str]) -> str | list[str]:
     return [column_name.lower() for column_name in column_names]
 
 
-@dataclass(frozen=True)
-class Condition:
-    """Condition allows for further narrowing down of a DataSource in a Constraint.
-
-    A ``Condition`` can be thought of as a filter, the content of a sql 'where' clause
-    or a condition as known from probability theory.
-
-    While a ``DataSource`` is expressed more generally, one might be interested
-    in testing properties of a specific part of said ``DataSource`` in light
-    of a particular constraint. Hence using ``Condition`` allows for the reusage
-    of a ``DataSource``, in lieu of creating a new custom ``DataSource`` with
-    the ``Condition`` implicitly built in.
-
-    A ``Condition`` can either be 'atomic', i.e. not further reducible to sub-conditions
-    or 'composite', i.e. combining multiple subconditions. In the former case, it can
-    be instantiated with help of the ``raw_string`` parameter, e.g. ``"col1 > 0"``. In the
-    latter case, it can be instantiated with help of the ``conditions`` and
-    ``reduction_operator`` parameters. ``reduction_operator`` allows for two values: ``"and"`` (logical
-    conjunction) and ``"or"`` (logical disjunction). Note that composition of ``Condition``
-    supports arbitrary degrees of nesting.
-    """
-
-    raw_string: str | None = None
-    conditions: Sequence[Condition] | None = None
-    reduction_operator: str | None = None
-
-    def __post_init__(self):
-        if self._is_atomic() and self.conditions is not None:
-            raise ValueError(
-                "Condition can either be instantiated atomically, with "
-                "the raw_query parameter, or in a composite fashion, with "
-                "the conditions parameter. "
-                "Exactly one of them needs to be provided, yet both are."
-            )
-        if not self._is_atomic() and (
-            self.conditions is None or len(self.conditions) == 0
-        ):
-            raise ValueError(
-                "Condition can either be instantiated atomically, with "
-                "the raw_query parameter, or in a composite fashion, with "
-                "the conditions parameter. "
-                "Exactly one of them needs to be provided, yet none is."
-            )
-        if not self._is_atomic() and self.reduction_operator not in ["and", "or"]:
-            raise ValueError(
-                "reuction_operator has to be either 'and' or 'or' but "
-                f"obtained {self.reduction_operator}."
-            )
-
-    def _is_atomic(self):
-        return self.raw_string is not None
-
-    def __str__(self):
-        if self._is_atomic():
-            return self.raw_string
-        if not self.conditions:
-            raise ValueError("This should never happen thanks to __post__init.")
-        return f" {self.reduction_operator} ".join(
-            f"({condition})" for condition in self.conditions
-        )
-
-    def snowflake_str(self):
-        # Temporary method - should be removed as soon as snowflake-sqlalchemy
-        # bug is fixed.
-        return str(self)
-
-
 @dataclass
-class MatchAndCompare:
+class _MatchAndCompare:
     matching_columns1: Sequence[str]
     matching_columns2: Sequence[str]
     comparison_columns1: Sequence[str]
     comparison_columns2: Sequence[str]
 
-    def _get_matching_columns(self):
+    def _get_matching_columns(self) -> Iterator[tuple[str, str]]:
         return zip(self.matching_columns1, self.matching_columns2)
 
-    def _get_comparison_columns(self):
+    def _get_comparison_columns(self) -> Iterator[tuple[str, str]]:
         return zip(self.comparison_columns1, self.comparison_columns2)
 
-    def __str__(self):
+    def __str__(self) -> str:
         return (
             f"Matched on {self.matching_columns1} and "
             f"{self.matching_columns2}. Compared on "
@@ -181,7 +98,7 @@ def __str__(self):
             f"{self.comparison_columns2}."
         )
 
-    def get_matching_string(self, table_variable1, table_variable2):
+    def get_matching_string(self, table_variable1: str, table_variable2: str) -> str:
         return " AND ".join(
             [
                 f"{table_variable1}.{column1} = {table_variable2}.{column2}"
@@ -189,7 +106,7 @@ def get_matching_string(self, table_variable1, table_variable2):
             ]
         )
 
-    def get_comparison_string(self, table_variable1, table_variable2):
+    def get_comparison_string(self, table_variable1: str, table_variable2: str) -> str:
         return " AND ".join(
             [
                 (
@@ -203,95 +120,6 @@ def get_comparison_string(self, table_variable1, table_variable2):
         )
 
 
-class DataSource(ABC):
-    @abstractmethod
-    def __str__(self) -> str:
-        pass
-
-    @abstractmethod
-    def get_clause(self, engine: sa.engine.Engine) -> FromClause:
-        pass
-
-
-@functools.lru_cache(maxsize=1)
-def get_metadata():
-    return sa.MetaData()
-
-
-@final
-class TableDataSource(DataSource):
-    def __init__(
-        self,
-        db_name: str,
-        table_name: str,
-        schema_name: str | None = None,
-    ):
-        self.db_name = db_name
-        self.table_name = table_name
-        self.schema_name = schema_name
-
-    def __str__(self) -> str:
-        if self.schema_name:
-            return f"{self.db_name}.{self.schema_name}.{self.table_name}"
-        return self.table_name
-
-    def get_clause(self, engine: sa.engine.Engine) -> FromClause:
-        schema = self.schema_name
-        if is_mssql(engine):
-            schema = self.db_name + "." + self.schema_name  # type: ignore
-
-        return sa.Table(
-            self.table_name,
-            get_metadata(),
-            autoload_with=engine,
-            schema=schema,
-        )
-
-
-@final
-class ExpressionDataSource(DataSource):
-    def __init__(self, expression: FromClause | sa.Select, name: str):
-        self.expression = expression
-        self.name = name
-
-    def __str__(self) -> str:
-        return self.name
-
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}(expression={self.expression!r}, name={self.name})"
-
-    def get_clause(self, engine: sa.engine.Engine) -> FromClause:
-        return self.expression.alias()
-
-
-@final
-class RawQueryDataSource(DataSource):
-    def __init__(self, query_string: str, name: str, columns: list[str] | None = None):
-        self.query_string = query_string
-        self.name = name
-        self.columns = columns
-        wrapped_query = f"({query_string}) as t"
-        if columns is not None and len(columns) > 0:
-            subquery = (
-                sa.text(query_string)
-                .columns(*[sa.column(column_name) for column_name in columns])
-                .subquery()
-            )
-            self.clause = subquery
-        else:
-            wrapped_query = f"({query_string}) as t"
-            self.clause = sa.select("*").select_from(sa.text(wrapped_query)).alias()
-
-    def __str__(self) -> str:
-        return self.name
-
-    def __repr__(self) -> str:
-        return f"{self.__class__.__name__}(query_string={self.query_string}, name={self.name}, columns={self.columns})"
-
-    def get_clause(self, engine: sa.engine.Engine) -> FromClause:
-        return self.clause
-
-
 class DataReference:
     def __init__(
         self,
@@ -309,8 +137,8 @@ def __init__(
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}(data_source={self.data_source!r}, columns={self.columns!r}, condition={self.condition!r})"
 
-    def get_selection(self, engine: sa.engine.Engine):
-        clause = self.data_source.get_clause(engine)
+    def get_selection(self, engine: sa.engine.Engine) -> sa.Select:
+        clause = self.data_source._get_clause(engine)
         if self.columns:
             column_names = self.get_columns(engine)
             if column_names is None:
@@ -323,7 +151,7 @@ def get_selection(self, engine: sa.engine.Engine):
         if self.condition is not None:
             text = str(self.condition)
             if is_snowflake(engine):
-                text = self.condition.snowflake_str()
+                text = self.condition._snowflake_str()
             selection = selection.where(sa.text(text))
         if is_mssql(engine) and isinstance(self.data_source, TableDataSource):
             # Allow dirty reads when using MSSQL.
@@ -334,7 +162,7 @@ def get_selection(self, engine: sa.engine.Engine):
             selection = selection.with_hint(clause, "WITH (NOLOCK)")
         return selection
 
-    def get_column(self, engine):
+    def get_column(self, engine: sa.engine.Engine) -> str:
         """Fetch the only relevant column of a DataReference."""
         if self.columns is None:
             raise ValueError(
@@ -351,7 +179,7 @@ def get_column(self, engine):
             )
         return columns[0]
 
-    def get_columns(self, engine) -> list[str] | None:
+    def get_columns(self, engine: sa.engine.Engine) -> list[str] | None:
         """Fetch all relevant columns of a DataReference."""
         if self.columns is None:
             return None
@@ -359,29 +187,31 @@ def get_columns(self, engine) -> list[str] | None:
             return lowercase_column_names(self.columns)
         return self.columns
 
-    def get_columns_or_pk_columns(self, engine):
+    def get_columns_or_pk_columns(self, engine: sa.engine.Engine) -> list[str] | None:
         return (
             self.columns
             if self.columns is not None
-            else get_primary_keys(engine, self.data_source)
+            else get_primary_keys(engine, self)[0]
         )
 
-    def get_column_selection_string(self):
+    def get_column_selection_string(self) -> str:
         if self.columns is None:
             return " * "
         return ", ".join(map(lambda x: f"'{x}'", self.columns))
 
-    def get_clause_string(self, *, return_where=True):
+    def _get_clause_string(self, *, return_where: bool = True) -> str:
         where_string = "WHERE " if return_where else ""
         return "" if self.condition is None else where_string + str(self.condition)
 
-    def __str__(self):
+    def __str__(self) -> str:
         if self.columns is None:
             return str(self.data_source)
         return f"{self.data_source}'s column(s) {self.get_column_selection_string()}"
 
 
-def merge_conditions(condition1, condition2):
+def merge_conditions(
+    condition1: Condition | None, condition2: Condition | None
+) -> Condition | None:
     if condition1 and condition2 is None:
         return None
     if condition1 is None:
@@ -391,7 +221,9 @@ def merge_conditions(condition1, condition2):
     return Condition(conditions=[condition1, condition2], reduction_operator="and")
 
 
-def get_date_span(engine, ref, date_column_name):
+def get_date_span(
+    engine: sa.engine.Engine, ref: DataReference, date_column_name: str
+) -> tuple[float, list[sa.Select]]:
     if is_snowflake(engine):
         date_column_name = lowercase_column_names(date_column_name)
     subquery = ref.get_selection(engine).alias()
@@ -418,22 +250,23 @@ def get_date_span(engine, ref, date_column_name):
                 )
             ]
         )
-    elif is_bigquery(engine):
+    elif is_duckdb(engine):
         selection = sa.select(
             *[
-                sa.func.date_diff(
-                    sa.func.max(column),
+                sa.func.datediff(
+                    sa.literal("day"),
                     sa.func.min(column),
-                    sa.literal_column("DAY"),
+                    sa.func.max(column),
                 )
             ]
         )
-    elif is_impala(engine):
+    elif is_bigquery(engine):
         selection = sa.select(
             *[
-                sa.func.datediff(
-                    sa.func.to_date(sa.func.max(column)),
-                    sa.func.to_date(sa.func.min(column)),
+                sa.func.date_diff(
+                    sa.func.max(column),
+                    sa.func.min(column),
+                    sa.literal_column("DAY"),
                 )
             ]
         )
@@ -451,7 +284,10 @@ def get_date_span(engine, ref, date_column_name):
             "Date spans not yet implemented for this sql dialect."
         )
 
-    date_span = engine.connect().execute(selection).scalar()
+    with engine.connect() as connection:
+        date_span = connection.execute(selection).scalar()
+    if date_span is None:
+        raise ValueError("Date span could not be fetched.")
     if date_span < 0:
         raise ValueError(
             f"Date span has negative value: {date_span}. It must be positive."
@@ -463,7 +299,13 @@ def get_date_span(engine, ref, date_column_name):
     return float(date_span), [selection]
 
 
-def get_date_growth_rate(engine, ref, ref2, date_column, date_column2):
+def _get_date_growth_rate(
+    engine: sa.engine.Engine,
+    ref: DataReference,
+    ref2: DataReference,
+    date_column: str,
+    date_column2: str,
+) -> tuple[float, list[sa.Select]]:
     date_span, selections = get_date_span(engine, ref, date_column)
     date_span2, selections2 = get_date_span(engine, ref2, date_column2)
     if date_span2 == 0:
@@ -478,7 +320,30 @@ def get_interval_overlaps_nd(
     start_columns: list[str],
     end_columns: list[str],
     end_included: bool,
-):
+) -> tuple[selectable.CompoundSelect, selectable.Select]:
+    r"""Create selectables for interval overlaps in n dimensions.
+
+    We define the presence of 'overlap' as presence of a non-empty intersection
+    between two intervals.
+
+    Given that we care about a single dimension and have two intervals :math:`t1` and :math:`t2`,
+    we define an overlap follows:
+
+     .. math::
+        \\begin{align} \\text{overlap}(t_1, t_2) \\Leftrightarrow
+            &(min(t_1) \\leq min(t_2) \\land max(t_1) \\geq min(t_2)) \\\\
+            &\\lor \\\\
+            &(min(t_2) \\leq min(t_1) \\land max(t_2) \\geq min(t_1))
+        \\end{align}
+
+    We can drop the second clause of the above disjunction if we define :math:`t_1` to be the 'leftmost'
+    interval. We do so when building our query.
+
+    Note that the above equations are representative of ``end_included=True`` and the second clause
+    of the conjunction would use a strict inequality if ``end_included=False``.
+
+    We define an overlap in several dimensions as the conjunction of overlaps in every single dimension.
+    """
     if is_snowflake(engine):
         if key_columns:
             key_columns = lowercase_column_names(key_columns)
@@ -502,10 +367,21 @@ def get_interval_overlaps_nd(
     table_key_columns = get_table_columns(table1, key_columns) if key_columns else []
 
     end_operator = operator.ge if end_included else operator.gt
-    violation_condition = sa.and_(
+
+    # We have a violation in two scenarios:
+    # 1. At least two entries are exactly equal in key and interval columns
+    # 2. Two entries are not exactly equal in key and interval_columns and fuilfill violation_condition
+
+    # Scenario 1
+    duplicate_selection = duplicates(table1)
+    duplicate_subquery = duplicate_selection.subquery()
+
+    # scenario 2
+    naive_violation_condition = sa.and_(
         *[
             sa.and_(
-                table1.c[start_columns[dimension]] < table2.c[start_columns[dimension]],
+                table1.c[start_columns[dimension]]
+                <= table2.c[start_columns[dimension]],
                 end_operator(
                     table1.c[end_columns[dimension]], table2.c[start_columns[dimension]]
                 ),
@@ -514,8 +390,24 @@ def get_interval_overlaps_nd(
         ]
     )
 
-    join_condition = sa.and_(*key_conditions, violation_condition)
-    violation_selection = sa.select(
+    interval_inequality_condition = sa.or_(
+        *[
+            sa.or_(
+                table1.c[start_columns[dimension]]
+                != table2.c[start_columns[dimension]],
+                table2.c[end_columns[dimension]] != table2.c[end_columns[dimension]],
+            )
+            for dimension in range(dimensionality)
+        ]
+    )
+
+    distinct_violation_condition = sa.and_(
+        naive_violation_condition,
+        interval_inequality_condition,
+    )
+
+    distinct_join_condition = sa.and_(*key_conditions, distinct_violation_condition)
+    distinct_violation_selection = sa.select(
         *table_key_columns,
         *[
             table.c[start_column]
@@ -527,7 +419,8 @@ def get_interval_overlaps_nd(
             for table in [table1, table2]
             for end_column in end_columns
         ],
-    ).select_from(table1.join(table2, join_condition))
+    ).select_from(table1.join(table2, distinct_join_condition))
+    distinct_violation_subquery = distinct_violation_selection.subquery()
 
     # Note, Kevin, 21/12/09
     # The following approach would likely be preferable to the approach used
@@ -544,6 +437,27 @@ def get_interval_overlaps_nd(
     #     violation_subquery
     # )
 
+    # Merge scenarios 1 and 2.
+    # We need to 'impute' the missing columns for the duplicate selection in order for the union between
+    # both selections to work.
+    duplicate_selection = sa.select(
+        *(
+            # Already existing columns
+            [
+                duplicate_subquery.c[column]
+                for column in distinct_violation_subquery.columns.keys()
+                if column in duplicate_subquery.columns.keys()
+            ]
+            # Fill all missing columns with NULLs.
+            + [
+                sa.null().label(column)
+                for column in distinct_violation_subquery.columns.keys()
+                if column not in duplicate_subquery.columns.keys()
+            ]
+        )
+    )
+    violation_selection = duplicate_selection.union(distinct_violation_selection)
+
     violation_subquery = violation_selection.subquery()
 
     keys = (
@@ -559,13 +473,13 @@ def get_interval_overlaps_nd(
 
 
 def _not_in_interval_condition(
-    main_table: sa.Table,
-    helper_table: sa.Table,
+    main_table: sa.Table | sa.Subquery,
+    helper_table: sa.Table | sa.Subquery,
     date_column: str,
     key_columns: list[str],
     start_column: str,
     end_column: str,
-):
+) -> sa.ColumnElement:
     return sa.not_(
         sa.exists(
             sa.select(helper_table).where(
@@ -592,7 +506,7 @@ def _get_interval_gaps(
     make_gap_condition: Callable[
         [sa.Engine, sa.Subquery, sa.Subquery, str, str, float], sa.ColumnElement[bool]
     ],
-):
+) -> tuple[sa.Select, sa.Select]:
     if is_snowflake(engine):
         if key_columns:
             key_columns = lowercase_column_names(key_columns)
@@ -699,11 +613,12 @@ def _date_gap_condition(
             )
             > legitimate_gap_size
         )
-    elif is_impala(engine):
+    elif is_duckdb(engine):
         gap_condition = (
             sa.func.datediff(
-                sa.func.to_date(start_table.c[start_column]),
-                sa.func.to_date(end_table.c[end_column]),
+                sa.literal("day"),
+                end_table.c[end_column],
+                start_table.c[start_column],
             )
             > legitimate_gap_size
         )
@@ -748,7 +663,7 @@ def get_date_gaps(
     start_column: str,
     end_column: str,
     legitimate_gap_size: float,
-):
+) -> tuple[sa.Select, sa.Select]:
     return _get_interval_gaps(
         engine,
         ref,
@@ -781,7 +696,7 @@ def get_numeric_gaps(
     start_column: str,
     end_column: str,
     legitimate_gap_size: float = 0,
-):
+) -> tuple[sa.Select, sa.Select]:
     return _get_interval_gaps(
         engine,
         ref,
@@ -797,7 +712,7 @@ def get_functional_dependency_violations(
     engine: sa.engine.Engine,
     ref: DataReference,
     key_columns: list[str],
-):
+) -> tuple[Any, list[sa.Select]]:
     selection = ref.get_selection(engine)
     uniques = selection.distinct().cte()
 
@@ -817,24 +732,28 @@ def get_functional_dependency_violations(
         uniques.join(violations_stmt, join_condition)
     )
 
-    result = engine.connect().execute(violation_tuples).fetchall()
+    with engine.connect() as connection:
+        result = connection.execute(violation_tuples).fetchall()
     return result, [violation_tuples]
 
 
 def get_row_count(
-    engine, ref, row_limit: int | None = None
+    engine: sa.engine.Engine, ref: DataReference, row_limit: int | None = None
 ) -> tuple[int, list[sa.Select]]:
     """Return the number of rows for a `DataReference`.
 
     If `row_limit` is given, the number of rows is capped at the limit.
     """
-    subquery = ref.get_selection(engine)
+    selection = ref.get_selection(engine)
     if row_limit:
-        subquery = subquery.limit(row_limit)
-    subquery = subquery.alias()
-    selection = sa.select(sa.cast(sa.func.count(), sa.BigInteger)).select_from(subquery)
-    result = int(str(engine.connect().execute(selection).scalar()))
-    return result, [selection]
+        selection = selection.limit(row_limit)
+    subquery = selection.alias()
+    final_selection = sa.select(sa.cast(sa.func.count(), sa.BigInteger)).select_from(
+        subquery
+    )
+    with engine.connect() as connection:
+        result = int(str(connection.execute(final_selection).scalar()))
+    return result, [final_selection]
 
 
 def get_column(
@@ -842,9 +761,10 @@ def get_column(
     ref: DataReference,
     *,
     aggregate_operator: Callable | None = None,
-):
+) -> tuple[Any, list[sa.Select]]:
     """
-    Queries the database for the values of the relevant column (as returned by `get_column(...)`).
+    Query the database for the values of the relevant column (as returned by `get_column(...)`).
+
     If an aggregation operation is passed, the results are aggregated accordingly
     and a single scalar value is returned.
     """
@@ -855,80 +775,112 @@ def get_column(
 
     if not aggregate_operator:
         selection = sa.select(column)
-        result = engine.connect().execute(selection).scalars().all()
+        with engine.connect() as connection:
+            result = connection.execute(selection).scalars().all()
 
     else:
         selection = sa.select(aggregate_operator(column))
-        result = engine.connect().execute(selection).scalar()
+        with engine.connect() as connection:
+            result = connection.execute(selection).scalar()
 
     return result, [selection]
 
 
-def get_min(engine, ref):
+def get_min(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[Any, list[sa.Select]]:
     column_operator = sa.func.min
     return get_column(engine, ref, aggregate_operator=column_operator)
 
 
-def get_max(engine, ref):
+def get_max(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[Any, list[sa.Select]]:
     column_operator = sa.func.max
     return get_column(engine, ref, aggregate_operator=column_operator)
 
 
-def get_mean(engine, ref):
+def get_mean(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[Any, list[sa.Select]]:
     def column_operator(column):
-        if is_impala(engine):
-            return sa.func.avg(column)
         return sa.func.avg(sa.cast(column, sa.DECIMAL))
 
     return get_column(engine, ref, aggregate_operator=column_operator)
 
 
-def get_percentile(engine, ref, percentage):
+def get_percentile(
+    engine: sa.engine.Engine, ref: DataReference, percentage: float
+) -> tuple[float, list[sa.Select]]:
     row_count = "dj_row_count"
     row_num = "dj_row_num"
     column_name = ref.get_column(engine)
-    column = ref.get_selection(engine).subquery().c[column_name]
-    subquery = (
-        sa.select(
-            column,
-            sa.func.row_number().over(order_by=column).label(row_num),
-            sa.func.count().over(partition_by=None).label(row_count),
-        )
-        .where(column.is_not(None))
-        .subquery()
+    base_selection = ref.get_selection(engine)
+    column = base_selection.subquery().c[column_name]
+
+    counting_selection = sa.select(
+        column,
+        sa.func.row_number().over(order_by=column).label(row_num),
+        sa.func.count().over(partition_by=None).label(row_count),
+    ).where(column.is_not(None))
+    counting_subquery = counting_selection.subquery()
+
+    inferior_selection = sa.select(*counting_subquery.columns).where(
+        counting_subquery.c[row_num] * 100.0 / counting_subquery.c[row_count]
+        < percentage
     )
-
-    constrained_selection = (
-        sa.select(*subquery.columns)
-        .where(subquery.c[row_num] * 100.0 / subquery.c[row_count] <= percentage)
-        .subquery()
+    inferior_subquery = inferior_selection.subquery()
+
+    argmin_selection = sa.select(
+        sa.case(
+            # Case 1: We we pick the next value.
+            (
+                sa.func.count(inferior_subquery.c[row_num]) > 0,
+                sa.func.max(inferior_subquery.c[row_num]) + 1,
+            ),
+            # Case 2: We pick the first value since the inferior subquery
+            # is empty.
+            (sa.func.count(inferior_subquery.c[row_num]) == 0, 1),
+            # Case 3: We received a reference without numerical values.
+            else_=None,
+        )
     )
 
-    max_selection = sa.select(
-        sa.func.max(constrained_selection.c[row_num])
-    ).scalar_subquery()
-    selection = sa.select(constrained_selection.c[column_name]).where(
-        constrained_selection.c[row_num] == max_selection
+    percentile_selection = sa.select(counting_subquery.c[column_name]).where(
+        counting_subquery.c[row_num] == argmin_selection.scalar_subquery()
     )
-    result = engine.connect().execute(selection).scalar()
-    return result, [selection]
+    with engine.connect() as connection:
+        intermediate_result = connection.execute(percentile_selection).scalar()
+    if intermediate_result is None:
+        raise ValueError("Percentile selection could not be fetched.")
+    result = float(intermediate_result)
+    return result, [percentile_selection]
 
 
-def get_min_length(engine, ref):
+def get_min_length(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[int, list[sa.Select]]:
     def column_operator(column):
         return sa.func.min(sa.func.length(column))
 
     return get_column(engine, ref, aggregate_operator=column_operator)
 
 
-def get_max_length(engine, ref):
+def get_max_length(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[int, list[sa.Select]]:
     def column_operator(column):
         return sa.func.max(sa.func.length(column))
 
     return get_column(engine, ref, aggregate_operator=column_operator)
 
 
-def get_fraction_between(engine, ref, lower_bound, upper_bound):
+def get_fraction_between(
+    engine: sa.engine.Engine,
+    ref: DataReference,
+    lower_bound: str | float,
+    upper_bound: str | float,
+) -> tuple[float | None, list[sa.Select]]:
     column = ref.get_column(engine)
     new_condition = Condition(
         conditions=[
@@ -944,7 +896,11 @@ def get_fraction_between(engine, ref, lower_bound, upper_bound):
     n_all, selections_all = get_row_count(engine, ref)
     n_filtered, selections_filtered = get_row_count(engine, new_ref)
     selections = [*selections_all, *selections_filtered]
-    return (n_filtered / n_all) if n_all > 0 else None, selections
+    if n_all is None or n_all == 0:
+        return (None, selections)
+    if n_filtered is None:
+        return (0.0, selections)
+    return n_filtered / n_all, selections
 
 
 def get_uniques(
@@ -952,10 +908,10 @@ def get_uniques(
 ) -> tuple[Counter, list[sa.Select]]:
     if not ref.get_columns(engine):
         return Counter({}), []
-    selection = ref.get_selection(engine).alias()
+    subquery = ref.get_selection(engine).alias()
     if (column_names := ref.get_columns(engine)) is None:
         raise ValueError("Need columns for get_uniques.")
-    columns = [selection.c[column_name] for column_name in column_names]
+    columns = [subquery.c[column_name] for column_name in column_names]
     selection = sa.select(*columns, sa.func.count()).group_by(*columns)
 
     def _scalar_accessor(row):
@@ -969,33 +925,48 @@ def _tuple_accessor(row):
     if len(columns) == 1:
         unique_from_row = _scalar_accessor
 
-    result = Counter(
-        {
-            unique_from_row(row): row[-1]
-            for row in engine.connect().execute(selection).fetchall()
-        }
-    )
+    with engine.connect() as connection:
+        result = Counter(
+            {
+                unique_from_row(row): row[-1]
+                for row in connection.execute(selection).fetchall()
+            }
+        )
     return result, [selection]
 
 
-def get_unique_count(engine, ref) -> tuple[int, list[sa.Select]]:
+def get_unique_count(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[int, list[sa.Select]]:
     selection = ref.get_selection(engine)
     subquery = selection.distinct().alias()
     selection = sa.select(sa.func.count()).select_from(subquery)
-    result = int(engine.connect().execute(selection).scalar())
+    with engine.connect() as connection:
+        intermediate_result = connection.execute(selection).scalar()
+    if intermediate_result is None:
+        raise ValueError("Unique count could not be fetched.")
+    result = int(intermediate_result)
     return result, [selection]
 
 
-def get_unique_count_union(engine, ref, ref2):
+def get_unique_count_union(
+    engine: sa.engine.Engine, ref: DataReference, ref2: DataReference
+) -> tuple[int, list[sa.Select]]:
     selection1 = ref.get_selection(engine)
     selection2 = ref2.get_selection(engine)
     subquery = sa.sql.union(selection1, selection2).alias().select().distinct().alias()
     selection = sa.select(sa.func.count()).select_from(subquery)
-    result = engine.connect().execute(selection).scalar()
+    with engine.connect() as connection:
+        intermediate_result = connection.execute(selection).scalar()
+    if intermediate_result is None:
+        raise ValueError("Unique count could not be fetched.")
+    result = int(intermediate_result)
     return result, [selection]
 
 
-def get_missing_fraction(engine, ref):
+def get_missing_fraction(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[float, list[sa.Select]]:
     selection = ref.get_selection(engine).subquery()
     n_rows_total_selection = sa.select(sa.func.count()).select_from(selection)
     n_rows_missing_selection = (
@@ -1007,48 +978,75 @@ def get_missing_fraction(engine, ref):
         n_rows_total = connection.execute(n_rows_total_selection).scalar()
         n_rows_missing = connection.execute(n_rows_missing_selection).scalar()
 
+    if n_rows_total is None or n_rows_missing is None:
+        return (0, [n_rows_total_selection, n_rows_missing_selection])
     return (
         n_rows_missing / n_rows_total,
         [n_rows_total_selection, n_rows_missing_selection],
     )
 
 
-def get_column_names(engine, ref):
-    table = ref.data_source.get_clause(engine)
+def get_column_names(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[list[str], None]:
+    table = ref.data_source._get_clause(engine)
     return [column.name for column in table.columns], None
 
 
-def get_column_type(engine, ref):
+def get_column_type(engine: sa.engine.Engine, ref: DataReference) -> tuple[Any, None]:
     table = ref.get_selection(engine).alias()
     column_type = next(iter(table.columns)).type
     return column_type, None
 
 
-def get_primary_keys(engine, ref):
-    table = ref.data_source.get_clause(engine)
-    return [column.name for column in table.primary_key.columns], None
+def get_primary_keys(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[list[str], None]:
+    data_source = ref.data_source
+
+    if isinstance(data_source, TableDataSource):
+        table = data_source._get_clause(engine)
+        return [column.name for column in table.primary_key.columns], None
 
+    raise NotImplementedError(
+        f"Cannot determine primary keys of a data source of type {type(data_source)}."
+    )
 
-def get_row_difference_sample(engine, ref, ref2):
+
+def get_row_difference_sample(
+    engine: sa.engine.Engine, ref: DataReference, ref2: DataReference
+) -> tuple[Any, list[sa.Select]]:
     selection1 = ref.get_selection(engine)
     selection2 = ref2.get_selection(engine)
     selection = sa.sql.except_(selection1, selection2).alias().select()
-    result = engine.connect().execute(selection).first()
+    with engine.connect() as connection:
+        result = connection.execute(selection).first()
     return result, [selection]
 
 
-def get_row_difference_count(engine, ref, ref2):
+def get_row_difference_count(
+    engine: sa.engine.Engine, ref: DataReference, ref2: DataReference
+) -> tuple[int, list[sa.Select]]:
     selection1 = ref.get_selection(engine)
     selection2 = ref2.get_selection(engine)
     subquery = (
         sa.sql.except_(selection1, selection2).alias().select().distinct().alias()
     )
     selection = sa.select(sa.func.count()).select_from(subquery)
-    result = engine.connect().execute(selection).scalar()
+    with engine.connect() as connection:
+        result_intermediate = connection.execute(selection).scalar()
+    if result_intermediate is None:
+        raise ValueError("Could not get the row difference count.")
+    result = int(result_intermediate)
     return result, [selection]
 
 
-def get_row_mismatch(engine, ref, ref2, match_and_compare):
+def get_row_mismatch(
+    engine: sa.engine.Engine,
+    ref: DataReference,
+    ref2: DataReference,
+    match_and_compare: _MatchAndCompare,
+) -> tuple[float, int, list[sa.Select]]:
     subselection1 = ref.get_selection(engine).alias()
     subselection2 = ref2.get_selection(engine).alias()
 
@@ -1086,17 +1084,21 @@ def get_row_mismatch(engine, ref, ref2, match_and_compare):
     selection_n_rows = sa.select(sa.func.count()).select_from(
         subselection1.join(subselection2, match)
     )
-    result_mismatch = engine.connect().execute(selection_difference).scalar()
-    result_n_rows = engine.connect().execute(selection_n_rows).scalar()
+    with engine.connect() as connection:
+        result_mismatch_intermediate = connection.execute(selection_difference).scalar()
+        result_n_rows_intermediate = connection.execute(selection_n_rows).scalar()
+    if result_mismatch_intermediate is None or result_n_rows_intermediate is None:
+        raise ValueError("Could not fetch number of mismatches.")
+    result_mismatch = float(result_mismatch_intermediate)
+    result_n_rows = int(result_n_rows_intermediate)
     return result_mismatch, result_n_rows, [selection_difference, selection_n_rows]
 
 
-def get_duplicate_sample(engine, ref):
-    initial_selection = ref.get_selection(engine).alias()
+def duplicates(subquery: sa.Subquery) -> sa.Select:
     aggregate_subquery = (
-        sa.select(initial_selection, sa.func.count().label("n_copies"))
-        .select_from(initial_selection)
-        .group_by(*initial_selection.columns)
+        sa.select(subquery, sa.func.count().label("n_copies"))
+        .select_from(subquery)
+        .group_by(*subquery.columns)
         .alias()
     )
     duplicate_selection = (
@@ -1110,14 +1112,23 @@ def get_duplicate_sample(engine, ref):
         .select_from(aggregate_subquery)
         .where(aggregate_subquery.c.n_copies > 1)
     )
-    result = engine.connect().execute(duplicate_selection).first()
+    return duplicate_selection
+
+
+def get_duplicate_sample(
+    engine: sa.engine.Engine, ref: DataReference
+) -> tuple[Any, list[sa.Select]]:
+    initial_selection = ref.get_selection(engine).alias()
+    duplicate_selection = duplicates(initial_selection)
+    with engine.connect() as connection:
+        result = connection.execute(duplicate_selection).first()
     return result, [duplicate_selection]
 
 
 def column_array_agg_query(
     engine: sa.engine.Engine, ref: DataReference, aggregation_column: str
-):
-    clause = ref.data_source.get_clause(engine)
+) -> list[sa.Select]:
+    clause = ref.data_source._get_clause(engine)
     if not (column_names := ref.get_columns(engine)):
         raise ValueError("There must be a column to group by")
     group_columns = [clause.c[column] for column in column_names]
@@ -1128,7 +1139,7 @@ def column_array_agg_query(
     return [selection]
 
 
-def snowflake_parse_variant_column(value: str):
+def snowflake_parse_variant_column(value: str) -> dict:
     # Snowflake returns non-primitive columns such as arrays as JSON string,
     # but we want them in their deserialized form.
     return json.loads(value)
@@ -1136,11 +1147,12 @@ def snowflake_parse_variant_column(value: str):
 
 def get_column_array_agg(
     engine: sa.engine.Engine, ref: DataReference, aggregation_column: str
-):
+) -> tuple[Any, list[sa.Select]]:
     selections = column_array_agg_query(engine, ref, aggregation_column)
-    result: Sequence[sa.engine.row.Row[Any]] | list[tuple[Any, ...]] = (
-        engine.connect().execute(selections[0]).fetchall()
-    )
+    with engine.connect() as connection:
+        result: Sequence[sa.engine.row.Row[Any]] | list[tuple[Any, ...]] = (
+            connection.execute(selections[0]).fetchall()
+        )
     if is_snowflake(engine):
         result = [
             (*t[:-1], list(map(int, snowflake_parse_variant_column(t[-1]))))
@@ -1149,7 +1161,9 @@ def get_column_array_agg(
     return result, selections
 
 
-def _cdf_selection(engine, ref: DataReference, cdf_label: str, value_label: str):
+def _cdf_selection(
+    engine: sa.engine.Engine, ref: DataReference, cdf_label: str, value_label: str
+) -> sa.Subquery:
     """Create an empirical cumulative distribution function values.
 
     Concretely, create a selection with values from ``value_label`` as well as
@@ -1178,8 +1192,12 @@ def _cdf_selection(engine, ref: DataReference, cdf_label: str, value_label: str)
 
 
 def _cross_cdf_selection(
-    engine, ref1: DataReference, ref2: DataReference, cdf_label: str, value_label: str
-):
+    engine: sa.engine.Engine,
+    ref1: DataReference,
+    ref2: DataReference,
+    cdf_label: str,
+    value_label: str,
+) -> tuple[sa.Select, str, str]:
     """Create a cross cumulative distribution function selection given two samples.
 
     Concretely, both ``DataReference``s are expected to have specified a single relevant column.
@@ -1262,11 +1280,74 @@ def _forward_filled_cdf_column(table, cdf_label, value_label, group_label):
     return filled_cross_cdf, cdf_label1, cdf_label2
 
 
+def get_anderson_darling_sums(
+    engine: sa.engine.Engine,
+    ref1: DataReference,
+    ref2: DataReference,
+    sample_size: int,
+) -> tuple[float, float, list[sa.Select]]:
+    r"""Return per-sample inner sum term of k-sample Anderson Darling test statistic.
+
+    For the first sample, this sum term equals
+
+    $$\sum_{j=1}^{N-1} \frac{(N F_1(j) - j n_1)^2}{j(N-j)}$$
+
+    while for the second sample, this sum term equals:
+
+    $$\sum_{j=1}^{N-1} \\frac{(N F_2(j) - j n_2)^2}{j(N-j)}$$
+
+    where:
+
+    * $`F_i`$ is the respective empirical cumulative distribution function
+    * $`n_i`$ is the respective sample size
+    """
+    cdf_label = "cdf"
+    value_label = "val"
+    filled_cross_cdf_selection, cdf_label1, cdf_label2 = _cross_cdf_selection(
+        engine, ref1, ref2, cdf_label, value_label
+    )
+    filled_cross_cdf = filled_cross_cdf_selection.subquery()
+
+    # Add a row number to the cross_cdf_selection.
+    row_number = "row_number"
+    selection = sa.select(
+        sa.func.row_number()
+        .over(
+            order_by=filled_cross_cdf.c[value_label],
+        )
+        .label(row_number),
+        filled_cross_cdf.c[value_label],
+        filled_cross_cdf.c[cdf_label1],
+        filled_cross_cdf.c[cdf_label2],
+    ).subquery()
+
+    def sum_selection(cdf_label):
+        return sa.select(
+            sa.func.sum(
+                (sample_size * selection.c[cdf_label] - selection.c[row_number])
+                * (sample_size * selection.c[cdf_label] - selection.c[row_number])
+                / (selection.c[row_number] * (sample_size - selection.c[row_number]))
+            )
+        )
+
+    sum_selection1 = sum_selection(cdf_label1)
+    sum_selection2 = sum_selection(cdf_label2)
+
+    with engine.connect() as connection:
+        sum1 = connection.execute(sum_selection1).scalar()
+        sum2 = connection.execute(sum_selection2).scalar()
+
+    if sum1 is None or sum2 is None:
+        raise ValueError("Failed to Computer Anderson Darling sums")
+
+    return sum1, sum2, [sum_selection1, sum_selection2]
+
+
 def get_ks_2sample(
     engine: sa.engine.Engine,
     ref1: DataReference,
     ref2: DataReference,
-):
+) -> tuple[float, list[sa.Select]]:
     """
     Run the query for the two-sample Kolmogorov-Smirnov test and return the test statistic d.
 
@@ -1291,23 +1372,28 @@ def get_ks_2sample(
     with engine.connect() as connection:
         d_statistic = connection.execute(final_selection).scalar()
 
+    if d_statistic is None:
+        raise ValueError("Could not compute d statistic.")
+
     return d_statistic, [final_selection]
 
 
-def get_regex_violations(engine, ref, aggregated, regex, n_counterexamples):
-    subquery = ref.get_selection(engine)
+def get_regex_violations(
+    engine: sa.engine.Engine,
+    ref: DataReference,
+    aggregated: bool,
+    regex: str,
+    n_counterexamples: int,
+) -> tuple[tuple[int, Any], list[sa.Select]]:
+    original_selection = ref.get_selection(engine)
     column = ref.get_column(engine)
     if aggregated:
-        subquery = subquery.distinct()
-    subquery = subquery.subquery()
-    if is_impala(engine):
-        violation_selection = sa.select(subquery.c[column]).where(
-            sa.not_(sa.func.regexp_like(subquery.c[column], regex))
-        )
-    else:
-        violation_selection = sa.select(subquery.c[column]).where(
-            sa.not_(subquery.c[column].regexp_match(regex))
-        )
+        original_selection = original_selection.distinct()
+    subquery = original_selection.subquery()
+
+    violation_selection = sa.select(subquery.c[column]).where(
+        sa.not_(subquery.c[column].regexp_match(regex))
+    )
     n_violations_selection = sa.select(sa.func.count()).select_from(
         violation_selection.subquery()
     )
@@ -1328,6 +1414,8 @@ def get_regex_violations(engine, ref, aggregated, regex, n_counterexamples):
 
     with engine.connect() as connection:
         n_violations_result = connection.execute(n_violations_selection).scalar()
+        if n_violations_result is None:
+            n_violations_result = 0
         if counterexamples_selection is None:
             counterexamples = []
         else:
diff --git a/src/datajudge/formatter.py b/src/datajudge/formatter.py
index cfd21d41..a9d5d75a 100644
--- a/src/datajudge/formatter.py
+++ b/src/datajudge/formatter.py
@@ -4,21 +4,21 @@
 from colorama import Back, just_fix_windows_console
 
 # example: match = [numMatch]...[/numMatch]
-STYLING_CODES = r"\[(numMatch|numDiff)\](.*?)\[/\1\]"
+_STYLING_CODES = r"\[(numMatch|numDiff)\](.*?)\[/\1\]"
 
 
 class Formatter(abc.ABC):
     def __init__(self):
-        self.known_bb_pattern = re.compile(STYLING_CODES)
+        self._known_bb_pattern = re.compile(_STYLING_CODES)
 
     # Just ignore styling in the default formatter
-    def apply_formatting(self, _: str, inner: str) -> str:
+    def _apply_formatting(self, style: str, inner: str) -> str:
         return inner
 
     def fmt_str(self, string: str) -> str:
         # Replace codes with platform specific styling
-        string = self.known_bb_pattern.sub(
-            lambda m: self.apply_formatting(m.group(1), m.group(2)), string
+        string = self._known_bb_pattern.sub(
+            lambda m: self._apply_formatting(m.group(1), m.group(2)), string
         )
 
         return string
@@ -29,8 +29,8 @@ def __init__(self):
         super().__init__()
         just_fix_windows_console()
 
-    def apply_formatting(self, code: str, inner: str) -> str:
-        if code == "numDiff":
+    def _apply_formatting(self, style: str, inner: str) -> str:
+        if style == "numDiff":
             return f"{Back.CYAN}{inner}{Back.RESET}"
         else:
             return inner
diff --git a/src/datajudge/pytest_integration.py b/src/datajudge/pytest_integration.py
index dabb0526..b4b52945 100644
--- a/src/datajudge/pytest_integration.py
+++ b/src/datajudge/pytest_integration.py
@@ -1,4 +1,4 @@
-from typing import Iterable
+from collections.abc import Iterable
 
 import pytest
 from packaging.version import Version
diff --git a/src/datajudge/requirements.py b/src/datajudge/requirements.py
index 18adb735..a8f62c68 100644
--- a/src/datajudge/requirements.py
+++ b/src/datajudge/requirements.py
@@ -1,19 +1,15 @@
+from __future__ import annotations
+
 from abc import ABC
-from collections.abc import MutableSequence
+from collections.abc import Callable, Collection, MutableSequence, Sequence
 from typing import (
-    Callable,
-    Collection,
-    Dict,
-    List,
-    Optional,
-    Sequence,
-    Tuple,
     TypeVar,
-    Union,
 )
 
 import sqlalchemy as sa
+from sqlalchemy.sql import selectable
 
+from .condition import Condition
 from .constraints import column as column_constraints
 from .constraints import date as date_constraints
 from .constraints import groupby as groupby_constraints
@@ -25,38 +21,46 @@
 from .constraints import uniques as uniques_constraints
 from .constraints import varchar as varchar_constraints
 from .constraints.base import Constraint, TestResult
-from .db_access import (
-    Condition,
-    DataReference,
+from .data_source import (
     DataSource,
     ExpressionDataSource,
     RawQueryDataSource,
     TableDataSource,
-    get_date_growth_rate,
+)
+from .db_access import (
+    DataReference,
+    _get_date_growth_rate,
 )
 from .utils import OutputProcessor, output_processor_limit
 
-T = TypeVar("T")
+_T = TypeVar("_T")
+
+
+def _check_significance_level(significance_level: float):
+    if significance_level <= 0.0 or significance_level > 1.0:
+        raise ValueError(
+            "The requested significance level has to be in ``(0.0, 1.0]``. Default is 0.05."
+        )
 
 
 class TableQualifier:
     def __init__(self, db_name: str, schema_name: str, table_name: str):
-        self.db_name = db_name
-        self.schema_name = schema_name
-        self.table_name = table_name
+        self._db_name = db_name
+        self._schema_name = schema_name
+        self._table_name = table_name
 
     def get_within_requirement(self):
         return WithinRequirement.from_table(
-            db_name=self.db_name,
-            schema_name=self.schema_name,
-            table_name=self.table_name,
+            db_name=self._db_name,
+            schema_name=self._schema_name,
+            table_name=self._table_name,
         )
 
     def get_between_requirement(self, table_qualifier):
         return BetweenRequirement.from_tables(
-            db_name1=self.db_name,
-            schema_name1=self.schema_name,
-            table_name1=self.table_name,
+            db_name1=self._db_name,
+            schema_name1=self._schema_name,
+            table_name1=self._table_name,
             db_name2=table_qualifier.db_name,
             schema_name2=table_qualifier.schema_name,
             table_name2=table_qualifier.table_name,
@@ -65,35 +69,36 @@ def get_between_requirement(self, table_qualifier):
 
 class Requirement(ABC, MutableSequence):
     def __init__(self):
-        self._constraints: List[Constraint] = []
-        self.data_source: DataSource
+        self._constraints: list[Constraint] = []
+        self._data_source: DataSource
 
     def insert(self, index: int, value: Constraint) -> None:
         self._constraints.insert(index, value)
 
-    def __getitem__(self, i):
-        return self._constraints[i]
+    def __getitem__(self, index):
+        return self._constraints[index]
 
-    def __setitem__(self, i, o) -> None:
-        self._constraints[i] = o
+    def __setitem__(self, index, value) -> None:
+        self._constraints[index] = value
 
-    def __delitem__(self, i) -> None:
-        del self._constraints[i]
+    def __delitem__(self, index) -> None:
+        del self._constraints[index]
 
     def __len__(self) -> int:
         return len(self._constraints)
 
-    def test(self, engine) -> List[TestResult]:
+    def test(self, engine) -> list[TestResult]:
         return [constraint.test(engine) for constraint in self]
 
 
 class WithinRequirement(Requirement):
     def __init__(self, data_source: DataSource):
-        self.data_source = data_source
+        self._data_source = data_source
         super().__init__()
 
     @classmethod
     def from_table(cls, db_name: str, schema_name: str, table_name: str):
+        """Create a `WithinRequirement` based on a table."""
         return cls(
             data_source=TableDataSource(
                 db_name=db_name, schema_name=schema_name, table_name=table_name
@@ -101,7 +106,7 @@ def from_table(cls, db_name: str, schema_name: str, table_name: str):
         )
 
     @classmethod
-    def from_raw_query(cls, query: str, name: str, columns: Optional[List[str]] = None):
+    def from_raw_query(cls, query: str, name: str, columns: list[str] | None = None):
         """Create a ``WithinRequirement`` based on a raw query string.
 
         The ``query`` parameter can be passed any query string returning rows, e.g.
@@ -116,7 +121,7 @@ def from_raw_query(cls, query: str, name: str, columns: Optional[List[str]] = No
         return cls(data_source=RawQueryDataSource(query, name, columns=columns))
 
     @classmethod
-    def from_expression(cls, expression: sa.sql.expression.FromClause, name: str):
+    def from_expression(cls, expression: selectable.FromClause, name: str):
         """Create a ``WithinRequirement`` based on a sqlalchemy expression.
 
         Any sqlalchemy object implementing the ``alias`` method can be passed as an
@@ -128,46 +133,54 @@ def from_expression(cls, expression: sa.sql.expression.FromClause, name: str):
         return cls(data_source=ExpressionDataSource(expression, name))
 
     def add_column_existence_constraint(
-        self, columns: List[str], name: Optional[str] = None
-    ):
+        self, columns: list[str], name: str | None = None, cache_size=None
+    ) -> None:
         # Note that columns are not meant to be part of the reference.
-        ref = DataReference(self.data_source)
-        self._constraints.append(column_constraints.ColumnExistence(ref, columns))
+        ref = DataReference(self._data_source)
+        self._constraints.append(
+            column_constraints.ColumnExistence(ref, columns, cache_size=cache_size)
+        )
 
     def add_primary_key_definition_constraint(
-        self, primary_keys: List[str], name: Optional[str] = None
-    ):
+        self,
+        primary_keys: list[str],
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Check that the primary key constraints in the database are exactly equal to the given column names.
 
         Note that this doesn't actually check that the primary key values are unique across the table.
         """
-        ref = DataReference(self.data_source)
+        ref = DataReference(self._data_source)
         self._constraints.append(
-            miscs_constraints.PrimaryKeyDefinition(ref, primary_keys, name=name)
+            miscs_constraints.PrimaryKeyDefinition(
+                ref, primary_keys, name=name, cache_size=cache_size
+            )
         )
 
     def add_uniqueness_constraint(
         self,
-        columns: Optional[List[str]] = None,
+        columns: list[str] | None = None,
         max_duplicate_fraction: float = 0,
-        condition: Optional[Condition] = None,
+        condition: Condition | None = None,
         max_absolute_n_duplicates: int = 0,
         infer_pk_columns: bool = False,
-        name: Optional[str] = None,
-    ):
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Columns should uniquely identify row.
 
-        Given a set of columns, satisfy conditions of a primary key, i.e.
-        uniqueness of tuples from said columns. This constraint has a tolerance
-        for inconsistencies, expressed via max_duplicate_fraction. The latter
+        Given a list of columns ``columns``, validate the condition of a primary key, i.e.
+        uniqueness of tuples in said columns. This constraint has a tolerance
+        for inconsistencies, expressed via ``max_duplicate_fraction``. The latter
         suggests that the number of uniques from said columns is larger or equal
-        to (1 - max_duplicate_fraction) the number of rows.
+        to ``1 - max_duplicate_fraction`` times the number of rows.
 
-        If infer_pk_columns is True, columns will be retrieved from the primary keys.
-        When columns=None and infer_pk_columns=False, the fallback is validating that all
-        rows in a table are unique.
+        If ``infer_pk_columns`` is ``True``, ``columns`` will be retrieved from the primary keys.
+        If ``columns`` is ``None`` and ``infer_pk_column`` is ``False``, the fallback is
+        validating that all rows in a table are unique.
         """
-        ref = DataReference(self.data_source, columns, condition)
+        ref = DataReference(self._data_source, columns, condition)
         self._constraints.append(
             miscs_constraints.Uniqueness(
                 ref,
@@ -175,21 +188,23 @@ def add_uniqueness_constraint(
                 max_absolute_n_duplicates=max_absolute_n_duplicates,
                 infer_pk_columns=infer_pk_columns,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_column_type_constraint(
         self,
         column: str,
-        column_type: Union[str, sa.types.TypeEngine],
-        name: Optional[str] = None,
-    ):
+        column_type: str | sa.types.TypeEngine,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """
         Check if a column type matches the expected column_type.
 
-        The column_type can be provided as a string (backend-specific type name), a backend-specific SQLAlchemy type, or a SQLAlchemy's generic type.
+        The ``column_type`` can be provided as a string (backend-specific type name), a backend-specific SQLAlchemy type, or a SQLAlchemy's generic type.
 
-        If SQLAlchemy's generic types are used, the check is performed using `isinstance`, which means that the actual type can also be a subclass of the target type.
+        If SQLAlchemy's generic types are used, the check is performed using ``isinstance``, which means that the actual type can also be a subclass of the target type.
         For more information on SQLAlchemy's generic types, see https://docs.sqlalchemy.org/en/20/core/type_basics.html
 
         Parameters
@@ -197,118 +212,140 @@ def add_column_type_constraint(
         column : str
             The name of the column to which the constraint will be applied.
 
-        column_type : Union[str, sa.types.TypeEngine]
+        column_type : str | sa.types.TypeEngine
             The expected type of the column. This can be a string, a backend-specific SQLAlchemy type, or a generic SQLAlchemy type.
 
-        name : Optional[str]
+        name : str | None
             An optional name for the constraint. If not provided, a name will be generated automatically.
         """
-        ref = DataReference(self.data_source, [column])
+        ref = DataReference(self._data_source, [column])
         self._constraints.append(
-            column_constraints.ColumnType(ref, column_type=column_type, name=name)
+            column_constraints.ColumnType(
+                ref,
+                column_type=column_type,
+                name=name,
+                cache_size=cache_size,
+            )
         )
 
     def add_null_absence_constraint(
         self,
         column: str,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
-            miscs_constraints.MaxNullFraction(ref, max_null_fraction=0, name=name)
+            miscs_constraints.MaxNullFraction(
+                ref, max_null_fraction=0, name=name, cache_size=cache_size
+            )
         )
 
     def add_max_null_fraction_constraint(
         self,
         column: str,
         max_null_fraction: float,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         """Assert that ``column`` has less than a certain fraction of ``NULL`` values.
 
         ``max_null_fraction`` is expected to lie within [0, 1].
         """
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             miscs_constraints.MaxNullFraction(
-                ref, max_null_fraction=max_null_fraction, name=name
+                ref,
+                max_null_fraction=max_null_fraction,
+                name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_n_rows_equality_constraint(
         self,
         n_rows: int,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        ref = DataReference(self.data_source, None, condition)
+        ref = DataReference(self._data_source, None, condition)
         self._constraints.append(
-            nrows_constraints.NRowsEquality(ref, n_rows=n_rows, name=name)
+            nrows_constraints.NRowsEquality(
+                ref, n_rows=n_rows, name=name, cache_size=cache_size
+            )
         )
 
     def add_n_rows_min_constraint(
         self,
         n_rows_min: int,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        ref = DataReference(self.data_source, None, condition)
+        ref = DataReference(self._data_source, None, condition)
         self._constraints.append(
-            nrows_constraints.NRowsMin(ref, n_rows=n_rows_min, name=name)
+            nrows_constraints.NRowsMin(
+                ref, n_rows=n_rows_min, name=name, cache_size=cache_size
+            )
         )
 
     def add_n_rows_max_constraint(
         self,
         n_rows_max: int,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        ref = DataReference(self.data_source, None, condition)
+        ref = DataReference(self._data_source, None, condition)
         self._constraints.append(
-            nrows_constraints.NRowsMax(ref, n_rows=n_rows_max, name=name)
+            nrows_constraints.NRowsMax(
+                ref, n_rows=n_rows_max, name=name, cache_size=cache_size
+            )
         )
 
     def add_uniques_equality_constraint(
         self,
-        columns: List[str],
-        uniques: Collection[T],
-        filter_func: Optional[Callable[[List[T]], List[T]]] = None,
-        map_func: Optional[Callable[[T], T]] = None,
-        reduce_func: Optional[Callable[[Collection], Collection]] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        columns: list[str],
+        uniques: Collection[_T],
+        filter_func: Callable[[list[_T]], list[_T]] | None = None,
+        map_func: Callable[[_T], _T] | None = None,
+        reduce_func: Callable[[Collection], Collection] | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         """Check if the data's unique values are equal to a given set of values.
 
-        The ``UniquesEquality`` constraint asserts if the values contained in a column
-        of a ``DataSource`` are strictly the ones of a reference set of expected values,
-        specified via the ``uniques`` parameter.
+        The `UniquesEquality` constraint asserts if the values contained in a column
+        of a `DataSource` are strictly the ones of a reference set of expected values,
+        specified via the `uniques` parameter.
+
+        Null values in the columns `columns` are ignored. To assert the non-existence of them use
+        the [`add_null_absence_constraint`][datajudge.requirements.WithinRequirement.add_null_absence_constraint] helper method
+        for `WithinRequirement`.
 
-        Null values in the columns ``columns`` are ignored. To assert the non-existence of them use
-        the :meth:`~datajudge.requirements.WithinRequirement.add_null_absence_constraint`` helper method
-        for ``WithinRequirement``.
         By default, the null filtering does not trigger if multiple columns are fetched at once.
-        It can be configured in more detail by supplying a custom ``filter_func`` function.
-        Some exemplary implementations are available as :func:`~datajudge.utils.filternull_element`,
-        :func:`~datajudge.utils.filternull_never`, :func:`~datajudge.utils.filternull_element_or_tuple_all`,
-        :func:`~datajudge.utils.filternull_element_or_tuple_any`.
-        Passing ``None`` as the argument is equivalent to :func:`~datajudge.utils.filternull_element` but triggers a warning.
-        The current default of :func:`~datajudge.utils.filternull_element`
+        It can be configured in more detail by supplying a custom `filter_func` function.
+        Some exemplary implementations are available as [`filternull_element`][datajudge.utils.filternull_element],
+        [`filternull_never`][datajudge.utils.filternull_never], [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all],
+        [`filternull_element_or_tuple_any`][datajudge.utils.filternull_element_or_tuple_any].
+        Passing `None` as the argument is equivalent to [`filternull_element`][datajudge.utils.filternull_element] but triggers a warning.
+        The current default of [`filternull_element`][datajudge.utils.filternull_element]
         Cause (possibly often unintended) changes in behavior when the users adds a second column
         (filtering no longer can trigger at all).
-        The default will be changed to :func:`~datajudge.utils.filternull_element_or_tuple_all` in future versions.
-        To silence the warning, set ``filter_func`` explicitly..
+        The default will be changed to [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all] in future versions.
+        To silence the warning, set `filter_func` explicitly.
 
-        See the ``Uniques`` class for further parameter details on ``map_func`` and
-        ``reduce_func``, and ``output_processors``.
+        See the `Uniques` class for further parameter details on `map_func` and
+        `reduce_func`, and `output_processors`.
         """
-
-        ref = DataReference(self.data_source, columns, condition)
+        ref = DataReference(self._data_source, columns, condition)
         self._constraints.append(
             uniques_constraints.UniquesEquality(
                 ref,
@@ -318,22 +355,24 @@ def add_uniques_equality_constraint(
                 reduce_func=reduce_func,
                 output_processors=output_processors,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_uniques_superset_constraint(
         self,
-        columns: List[str],
-        uniques: Collection[T],
+        columns: list[str],
+        uniques: Collection[_T],
         max_relative_violations: float = 0,
-        filter_func: Optional[Callable[[List[T]], List[T]]] = None,
-        map_func: Optional[Callable[[T], T]] = None,
-        reduce_func: Optional[Callable[[Collection], Collection]] = None,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
+        filter_func: Callable[[list[_T]], list[_T]] | None = None,
+        map_func: Callable[[_T], _T] | None = None,
+        reduce_func: Callable[[Collection], Collection] | None = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        cache_size=None,
     ):
         """Check if unique values of columns are contained in the reference data.
 
@@ -341,19 +380,20 @@ def add_uniques_superset_constraint(
         specified via ``uniques``, is contained in given columns of a ``DataSource``.
 
         Null values in the columns ``columns`` are ignored. To assert the non-existence of them use
-        the :meth:`~datajudge.requirements.WithinRequirement.add_null_absence_constraint`` helper method
+        the [`add_null_absence_constraint`][datajudge.requirements.WithinRequirement.add_null_absence_constraint] helper method
         for ``WithinRequirement``.
+
         By default, the null filtering does not trigger if multiple columns are fetched at once.
         It can be configured in more detail by supplying a custom ``filter_func`` function.
-        Some exemplary implementations are available as :func:`~datajudge.utils.filternull_element`,
-        :func:`~datajudge.utils.filternull_never`, :func:`~datajudge.utils.filternull_element_or_tuple_all`,
-        :func:`~datajudge.utils.filternull_element_or_tuple_any`.
-        Passing ``None`` as the argument is equivalent to :func:`~datajudge.utils.filternull_element` but triggers a warning.
-        The current default of :func:`~datajudge.utils.filternull_element`
-        Cause (possibly often unintended) changes in behavior when the users adds a second column
+        Some exemplary implementations are available as [`filternull_element`][datajudge.utils.filternull_element],
+        [`filternull_never`][datajudge.utils.filternull_never], [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all],
+        [`filternull_element_or_tuple_any`][datajudge.utils.filternull_element_or_tuple_any].
+        Passing ``None`` as the argument is equivalent to [`filternull_element`][datajudge.utils.filternull_element] but triggers a warning.
+        The current default of [`filternull_element`][datajudge.utils.filternull_element]
+        will cause (possibly often unintended) changes in behavior when the user adds a second column
         (filtering no longer can trigger at all).
-        The default will be changed to :func:`~datajudge.utils.filternull_element_or_tuple_all` in future versions.
-        To silence the warning, set ``filter_func`` explicitly..
+        The default will be changed to [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all] in future versions.
+        To silence the warning, set ``filter_func`` explicitly.
 
         ``max_relative_violations`` indicates what fraction of unique values of the given
         ``DataSource`` are not represented in the reference set of unique values. Please
@@ -365,8 +405,7 @@ def add_uniques_superset_constraint(
         See ``Uniques`` for further details on ``map_func``, ``reduce_func``,
         and ``output_processors``.
         """
-
-        ref = DataReference(self.data_source, columns, condition)
+        ref = DataReference(self._data_source, columns, condition)
         self._constraints.append(
             uniques_constraints.UniquesSuperset(
                 ref,
@@ -377,23 +416,25 @@ def add_uniques_superset_constraint(
                 reduce_func=reduce_func,
                 output_processors=output_processors,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_uniques_subset_constraint(
         self,
-        columns: List[str],
-        uniques: Collection[T],
+        columns: list[str],
+        uniques: Collection[_T],
         max_relative_violations: float = 0,
-        filter_func: Optional[Callable[[List[T]], List[T]]] = None,
+        filter_func: Callable[[list[_T]], list[_T]] | None = None,
         compare_distinct: bool = False,
-        map_func: Optional[Callable[[T], T]] = None,
-        reduce_func: Optional[Callable[[Collection], Collection]] = None,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
+        map_func: Callable[[_T], _T] | None = None,
+        reduce_func: Callable[[Collection], Collection] | None = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        cache_size=None,
     ):
         """Check if the data's unique values are contained in a given set of values.
 
@@ -402,19 +443,19 @@ def add_uniques_subset_constraint(
         ``uniques``.
 
         Null values in the columns ``columns`` are ignored. To assert the non-existence of them use
-        the :meth:`~datajudge.requirements.WithinRequirement.add_null_absence_constraint`` helper method
+        the [`add_null_absence_constraint`][datajudge.requirements.WithinRequirement.add_null_absence_constraint] helper method
         for ``WithinRequirement``.
         By default, the null filtering does not trigger if multiple columns are fetched at once.
         It can be configured in more detail by supplying a custom ``filter_func`` function.
-        Some exemplary implementations are available as :func:`~datajudge.utils.filternull_element`,
-        :func:`~datajudge.utils.filternull_never`, :func:`~datajudge.utils.filternull_element_or_tuple_all`,
-        :func:`~datajudge.utils.filternull_element_or_tuple_any`.
-        Passing ``None`` as the argument is equivalent to :func:`~datajudge.utils.filternull_element` but triggers a warning.
-        The current default of :func:`~datajudge.utils.filternull_element`
+        Some exemplary implementations are available as [`filternull_element`][datajudge.utils.filternull_element],
+        [`filternull_never`][datajudge.utils.filternull_never], [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all],
+        [`filternull_element_or_tuple_any`][datajudge.utils.filternull_element_or_tuple_any].
+        Passing ``None`` as the argument is equivalent to [`filternull_element`][datajudge.utils.filternull_element] but triggers a warning.
+        The current default of [`filternull_element`][datajudge.utils.filternull_element]
         Cause (possibly often unintended) changes in behavior when the users adds a second column
         (filtering no longer can trigger at all).
-        The default will be changed to :func:`~datajudge.utils.filternull_element_or_tuple_all` in future versions.
-        To silence the warning, set ``filter_func`` explicitly..
+        The default will be changed to [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all] in future versions.
+        To silence the warning, set ``filter_func`` explicitly.
 
 
         ``max_relative_violations`` indicates what fraction of rows of the given table
@@ -429,8 +470,7 @@ def add_uniques_subset_constraint(
         See ``Uniques`` for further details on ``map_func``, ``reduce_func``,
         and ``output_processors``.
         """
-
-        ref = DataReference(self.data_source, columns, condition)
+        ref = DataReference(self._data_source, columns, condition)
         self._constraints.append(
             uniques_constraints.UniquesSubset(
                 ref,
@@ -442,60 +482,65 @@ def add_uniques_subset_constraint(
                 reduce_func=reduce_func,
                 output_processors=output_processors,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_n_uniques_equality_constraint(
         self,
-        columns: Optional[List[str]],
+        columns: list[str] | None,
         n_uniques: int,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        ref = DataReference(self.data_source, columns, condition)
+        ref = DataReference(self._data_source, columns, condition)
         self._constraints.append(
-            uniques_constraints.NUniquesEquality(ref, n_uniques=n_uniques, name=name)
+            uniques_constraints.NUniquesEquality(
+                ref, n_uniques=n_uniques, name=name, cache_size=cache_size
+            )
         )
 
     def add_categorical_bound_constraint(
         self,
-        columns: List[str],
-        distribution: Dict[T, Tuple[float, float]],
-        default_bounds: Tuple[float, float] = (0, 0),
+        columns: list[str],
+        distribution: dict[_T, tuple[float, float]],
+        default_bounds: tuple[float, float] = (0, 0),
         max_relative_violations: float = 0,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """
-        Check if the distribution of unique values in columns falls within the
-        specified minimum and maximum bounds.
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        """Check if the distribution of unique values in columns falls within the specified minimum and maximum bounds.
 
-        The `CategoricalBoundConstraint` is added to ensure the distribution of unique values
-        in the specified columns of a `DataSource` falls within the given minimum and maximum
-        bounds defined in the `distribution` parameter.
+        The ``CategoricalBoundConstraint`` is added to ensure the distribution of unique values
+        in the specified columns of a ``DataSource`` falls within the given minimum and maximum
+        bounds defined in the ``distribution`` parameter.
 
         Parameters
         ----------
-        columns : List[str]
+        columns:
             A list of column names from the `DataSource` to apply the constraint on.
-        distribution : Dict[T, Tuple[float, float]]
+        distribution:
             A dictionary where keys represent unique values and the corresponding
             tuple values represent the minimum and maximum allowed proportions of the respective
             unique value in the columns.
-        default_bounds : Tuple[float, float], optional, default=(0, 0)
+        default_bounds:
             A tuple specifying the minimum and maximum allowed proportions for all
             elements not mentioned in the distribution. By default, it's set to (0, 0), which means
             all elements not present in `distribution` will cause a constraint failure.
-        max_relative_violations : float, optional, default=0
+        max_relative_violations:
             A tolerance threshold (0 to 1) for the proportion of elements in the data that can violate the
             bound constraints without triggering the constraint violation.
-        condition : Condition, optional
+        condition:
             An optional parameter to specify a `Condition` object to filter the data
             before applying the constraint.
-        name : str, optional
+        name:
             An optional parameter to provide a custom name for the constraint.
+        cache_size:
+            TODO
 
-        Example
+        Example:
         -------
         This method can be used to test for consistency in columns with expected categorical
         values or ensure that the distribution of values in a column adheres to a certain
@@ -513,8 +558,7 @@ def add_categorical_bound_constraint(
         )
         ```
         """
-
-        ref = DataReference(self.data_source, columns, condition)
+        ref = DataReference(self._data_source, columns, condition)
         self._constraints.append(
             uniques_constraints.CategoricalBoundConstraint(
                 ref,
@@ -522,29 +566,39 @@ def add_categorical_bound_constraint(
                 default_bounds=default_bounds,
                 max_relative_violations=max_relative_violations,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_numeric_min_constraint(
-        self, column: str, min_value: float, condition: Optional[Condition] = None
-    ):
-        """All values in column are greater or equal min_value."""
-        ref = DataReference(self.data_source, [column], condition)
+        self,
+        column: str,
+        min_value: float,
+        condition: Condition | None = None,
+        cache_size=None,
+    ) -> None:
+        """All values in ``column`` are greater or equal ``min_value``."""
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
-            numeric_constraints.NumericMin(ref, min_value=min_value)
+            numeric_constraints.NumericMin(
+                ref, min_value=min_value, cache_size=cache_size
+            )
         )
 
     def add_numeric_max_constraint(
         self,
         column: str,
         max_value: float,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """All values in column are less or equal max_value."""
-        ref = DataReference(self.data_source, [column], condition)
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        """All values in ``column`` are less or equal ``max_value``."""
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
-            numeric_constraints.NumericMax(ref, max_value=max_value, name=name)
+            numeric_constraints.NumericMax(
+                ref, max_value=max_value, name=name, cache_size=cache_size
+            )
         )
 
     def add_numeric_between_constraint(
@@ -553,9 +607,10 @@ def add_numeric_between_constraint(
         lower_bound: float,
         upper_bound: float,
         min_fraction: float,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Assert that the column's values lie between ``lower_bound`` and ``upper_bound``.
 
         Note that both bounds are inclusive.
@@ -564,7 +619,7 @@ def add_numeric_between_constraint(
         be considered in the denominator of ``min_fraction``. ``NULL`` values will never be
         considered to lie in the interval [``lower_bound``, ``upper_bound``].
         """
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             numeric_constraints.NumericBetween(
                 ref,
@@ -572,6 +627,7 @@ def add_numeric_between_constraint(
                 lower_bound,
                 upper_bound,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -580,17 +636,19 @@ def add_numeric_mean_constraint(
         column: str,
         mean_value: float,
         max_absolute_deviation: float,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """Assert the mean of the column deviates at most max_deviation from mean_value."""
-        ref = DataReference(self.data_source, [column], condition)
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        """Assert the mean of the column ``column`` deviates at most ``max_deviation`` from ``mean_value``."""
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             numeric_constraints.NumericMean(
                 ref,
                 max_absolute_deviation,
                 mean_value=mean_value,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -599,14 +657,15 @@ def add_numeric_percentile_constraint(
         column: str,
         percentage: float,
         expected_percentile: float,
-        max_absolute_deviation: Optional[float] = None,
-        max_relative_deviation: Optional[float] = None,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        max_absolute_deviation: float | None = None,
+        max_relative_deviation: float | None = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Assert that the ``percentage``-th percentile is approximately ``expected_percentile``.
 
-        The percentile is defined as the value present in ``column`` for which
+        The percentile is defined as the smallest value present in ``column`` for which
         ``percentage`` % of the values in ``column`` are less or equal. ``NULL`` values
         are ignored.
 
@@ -619,7 +678,7 @@ def add_numeric_percentile_constraint(
         At least one of ``max_absolute_deviation`` and ``max_relative_deviation`` must
         be provided.
         """
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             numeric_constraints.NumericPercentile(
                 ref,
@@ -628,6 +687,7 @@ def add_numeric_percentile_constraint(
                 max_absolute_deviation=max_absolute_deviation,
                 max_relative_deviation=max_relative_deviation,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -637,12 +697,13 @@ def add_date_min_constraint(
         min_value: str,
         use_lower_bound_reference: bool = True,
         column_type: str = "date",
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """Ensure all dates to be superior than min_value.
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        """Ensure all dates to be superior than ``min_value``.
 
-        Use string format: min_value="'20121230'".
+        Use string format: ``min_value="'20121230'"``.
 
         For more information on ``column_type`` values, see ``add_column_type_constraint``.
 
@@ -651,7 +712,7 @@ def add_date_min_constraint(
         If not ``use_upper_bound_reference``, the min of the first table has to
         be smaller or equal to ``min_value``.
         """
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             date_constraints.DateMin(
                 ref,
@@ -659,6 +720,7 @@ def add_date_min_constraint(
                 use_lower_bound_reference=use_lower_bound_reference,
                 column_type=column_type,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -668,21 +730,21 @@ def add_date_max_constraint(
         max_value: str,
         use_upper_bound_reference: bool = True,
         column_type: str = "date",
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """Ensure all dates to be superior than max_value.
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        """Ensure all dates to be superior than ``max_value``.
 
-        Use string format: max_value="'20121230'".
+        Use string format: ``max_value="'20121230'"``
 
-        For more information on ``column_type`` values, see ``add_column_type_constraint``.
+        For more information on ``column_type`` values, see [`add_column_type_constraint`][datajudge.requirements.WithinRequirement.add_column_type_constraint].
 
-        If ``use_upper_bound_reference``, the max of the first table has to be
-        smaller or equal to ``max_value``.
-        If not ``use_upper_bound_reference``, the max of the first table has to
-        be greater or equal to ``max_value``.
+        If ``use_upper_bound_reference`` is ``True``, the maximum date in ``column`` has to be smaller or
+        equal to ``max_value``. Otherwise the maximum date in ``column`` has to be greater or equal
+        to ``max_value``.
         """
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             date_constraints.DateMax(
                 ref,
@@ -690,6 +752,7 @@ def add_date_max_constraint(
                 use_upper_bound_reference=use_upper_bound_reference,
                 column_type=column_type,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -699,32 +762,40 @@ def add_date_between_constraint(
         lower_bound: str,
         upper_bound: str,
         min_fraction: float,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """Use string format: lower_bound="'20121230'"."""
-        ref = DataReference(self.data_source, [column], condition)
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        """Use string format: ``lower_bound="'20121230'"``."""
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
-            date_constraints.DateBetween(ref, min_fraction, lower_bound, upper_bound)
+            date_constraints.DateBetween(
+                ref,
+                min_fraction,
+                lower_bound,
+                upper_bound,
+                cache_size=cache_size,
+            )
         )
 
     def add_date_no_overlap_constraint(
         self,
         start_column: str,
         end_column: str,
-        key_columns: Optional[List[str]] = None,
+        key_columns: list[str] | None = None,
         end_included: bool = True,
         max_relative_n_violations: float = 0,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Constraint expressing that several date range rows may not overlap.
 
-        The ``DataSource`` under inspection must consist of at least one but up
+        The [`DataSource`][datajudge.DataSource] under inspection must consist of at least one but up
         to many ``key_columns``, identifying an entity, a ``start_column`` and an
         ``end_column``.
 
-        For a given row in this ``DataSource``, ``start_column`` and ``end_column`` indicate a
+        For a given row in this [`DataSource`][datajudge.DataSource], ``start_column`` and ``end_column`` indicate a
         date range. Neither of those columns should contain NULL values. Also, it
         should hold that for a given row, the value of ``end_column`` is strictly greater
         than the value of ``start_column``.
@@ -748,11 +819,10 @@ def add_date_no_overlap_constraint(
 
         For illustrative examples of this constraint, please refer to its test cases.
         """
-
         relevant_columns = [start_column, end_column] + (
             key_columns if key_columns else []
         )
-        ref = DataReference(self.data_source, relevant_columns, condition)
+        ref = DataReference(self._data_source, relevant_columns, condition)
         self._constraints.append(
             date_constraints.DateNoOverlap(
                 ref,
@@ -762,6 +832,7 @@ def add_date_no_overlap_constraint(
                 end_included=end_included,
                 max_relative_n_violations=max_relative_n_violations,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -771,31 +842,29 @@ def add_date_no_overlap_2d_constraint(
         end_column1: str,
         start_column2: str,
         end_column2: str,
-        key_columns: Optional[List[str]] = None,
+        key_columns: list[str] | None = None,
         end_included: bool = True,
         max_relative_n_violations: float = 0,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Express that several date range rows do not overlap in two date dimensions.
 
         The table under inspection must consist of at least one but up to many key columns,
-        identifying an entity. Per date dimension, a ``start_column`` and an
-        ``end_column`` should be provided.
-
-        For a given row in this table, ``start_column1`` and ``end_column1``
-        indicate a date range. Moreoever, for that same row, ``start_column2``
-        and ``end_column2`` indicate a date range.
-        These date ranges are expected to represent different date 'dimensions'.
-        Example: A row indicates a forecasted value used in production. ``start_column1``
-        and ``end_column1`` represent the timespan that was forecasted, e.g. the
-        weather from next Saturday to next Sunday. ``end_column1`` and ``end_column2``
+        identifying an entity. Per date dimension, a start column (``start_column1``, ``start_column2``)
+        and end (``end_column1``, ``end_column2``) column should be provided in order to define
+        date ranges.
+
+        Date ranges in different date dimensions are expected to represent different kinds
+        of dates. For example, let's say that a row in a table indicates an averag temperature
+        forecast. ``start_column1`` and ``end_column1`` could the date span that was forecasted,
+        e.g. the weather from next Saturday to next Sunday. ``end_column1`` and ``end_column2``
         might indicate the timespan when this forceast was used, e.g. from the
         previous Monday to Wednesday.
 
-        Neither of those columns should contain ``NULL`` values. Also it should
-        hold that for a given row, the value of ``end_column`` is strictly greater
-        than the value of ``start_column``.
+        Neither of those columns should contain ``NULL`` values. Also, the value of ``end_column_k``
+        should be strictly greater than the value of ``start_column_k``.
 
         Note that the values of ``start_column1`` and ``start_column2`` are expected to be
         included in each date range. By default, the values of ``end_column1`` and
@@ -823,7 +892,7 @@ def add_date_no_overlap_2d_constraint(
             else []
         )
         ref = DataReference(
-            self.data_source,
+            self._data_source,
             relevant_columns,
             condition,
         )
@@ -836,6 +905,7 @@ def add_date_no_overlap_2d_constraint(
                 end_included=end_included,
                 max_relative_n_violations=max_relative_n_violations,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -843,14 +913,14 @@ def add_date_no_gap_constraint(
         self,
         start_column: str,
         end_column: str,
-        key_columns: Optional[List[str]] = None,
+        key_columns: list[str] | None = None,
         end_included: bool = True,
         max_relative_n_violations: float = 0,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        """
-        Express that date range rows have no gap in-between them.
+        """Express that date range rows have no gap in-between them.
 
         The table under inspection must consist of at least one but up to many key columns,
         identifying an entity. Additionally, a ``start_column`` and an ``end_column``,
@@ -868,7 +938,7 @@ def add_date_no_gap_constraint(
         interest. A priori, a key is not a primary key, i.e., a key can have and often has
         several rows. Thereby, a key will often come with several date ranges.
 
-        If`` key_columns`` is ``None`` or ``[]``, all columns of the table will be
+        If ``key_columns`` is ``None`` or ``[]``, all columns of the table will be
         considered as composing the key.
 
         In order to express a tolerance for some violations of this gap property, use the
@@ -880,7 +950,7 @@ def add_date_no_gap_constraint(
         relevant_columns = (
             ([start_column, end_column] + key_columns) if key_columns else []
         )
-        ref = DataReference(self.data_source, relevant_columns, condition)
+        ref = DataReference(self._data_source, relevant_columns, condition)
         self._constraints.append(
             date_constraints.DateNoGap(
                 ref,
@@ -890,26 +960,31 @@ def add_date_no_gap_constraint(
                 max_relative_n_violations=max_relative_n_violations,
                 legitimate_gap_size=1 if end_included else 0,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_functional_dependency_constraint(
         self,
-        key_columns: List[str],
-        value_columns: List[str],
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
+        key_columns: list[str],
+        value_columns: list[str],
+        condition: Condition | None = None,
+        name: str | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        cache_size=None,
     ):
-        """
-        Expresses a functional dependency, a constraint where the `value_columns` are uniquely determined by the `key_columns`.
+        """Expresses a functional dependency, a constraint where the `value_columns` are uniquely determined by the `key_columns`.
+
         This means that for each unique combination of values in the `key_columns`, there is exactly one corresponding combination of values in the `value_columns`.
 
-        The ``add_unique_constraint`` constraint is a special case of this constraint, where the `key_columns` are a primary key,
-        and all other columns are included `value_columns`.
-        This constraint allows for a more general definition of functional dependencies, where the `key_columns` are not necessarily a primary key.
+        The ``add_unique_constraint`` constraint is a special case of this constraint, where the ``key_columns`` are a primary key,
+        and all other columns are included ``value_columns``.
+        This constraint allows for a more general definition of functional dependencies, where the ``key_columns`` are not necessarily a primary key.
+
+        An additional configuration option (for details see the analogous parameter in for ``Uniques``-constraints)
+        on how the output is sorted and how many counterexamples are shown is available as ``output_processors``.
 
         An additional configuration option (for details see the analogous parameter in for ``Uniques``-constraints)
         on how the output is sorted and how many counterexamples are shown is available as ``output_processors``.
@@ -917,13 +992,14 @@ def add_functional_dependency_constraint(
         For more information on functional dependencies, see https://en.wikipedia.org/wiki/Functional_dependency.
         """
         relevant_columns = key_columns + value_columns
-        ref = DataReference(self.data_source, relevant_columns, condition)
+        ref = DataReference(self._data_source, relevant_columns, condition)
         self._constraints.append(
             miscs_constraints.FunctionalDependency(
                 ref,
                 key_columns=key_columns,
                 output_processors=output_processors,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -931,14 +1007,15 @@ def add_numeric_no_gap_constraint(
         self,
         start_column: str,
         end_column: str,
-        key_columns: Optional[List[str]] = None,
+        key_columns: list[str] | None = None,
         legitimate_gap_size: float = 0,
         max_relative_n_violations: float = 0,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        """
-        Express that numeric interval rows have no gaps larger than some max value in-between them.
+        """Express that numeric interval rows have no gaps larger than some max value in-between them.
+
         The table under inspection must consist of at least one but up to many key columns,
         identifying an entity. Additionally, a ``start_column`` and an ``end_column``,
         indicating interval start and end values, should be provided.
@@ -953,7 +1030,7 @@ def add_numeric_no_gap_constraint(
         interest. A priori, a key is not a primary key, i.e., a key can have and often has
         several rows. Thereby, a key will often come with several intervals.
 
-        If`` key_columns`` is ``None`` or ``[]``, all columns of the table will be
+        If ``key_columns`` is ``None`` or ``[]``, all columns of the table will be
         considered as composing the key.
 
         In order to express a tolerance for some violations of this gap property, use the
@@ -965,7 +1042,7 @@ def add_numeric_no_gap_constraint(
         relevant_columns = (
             ([start_column, end_column] + key_columns) if key_columns else []
         )
-        ref = DataReference(self.data_source, relevant_columns, condition)
+        ref = DataReference(self._data_source, relevant_columns, condition)
         self._constraints.append(
             numeric_constraints.NumericNoGap(
                 ref,
@@ -975,6 +1052,7 @@ def add_numeric_no_gap_constraint(
                 legitimate_gap_size=legitimate_gap_size,
                 max_relative_n_violations=max_relative_n_violations,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -982,11 +1060,12 @@ def add_numeric_no_overlap_constraint(
         self,
         start_column: str,
         end_column: str,
-        key_columns: Optional[List[str]] = None,
+        key_columns: list[str] | None = None,
         end_included: bool = True,
         max_relative_n_violations: float = 0,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         """Constraint expressing that several numeric interval rows may not overlap.
 
@@ -1018,11 +1097,10 @@ def add_numeric_no_overlap_constraint(
 
         For illustrative examples of this constraint, please refer to its test cases.
         """
-
         relevant_columns = [start_column, end_column] + (
             key_columns if key_columns else []
         )
-        ref = DataReference(self.data_source, relevant_columns, condition)
+        ref = DataReference(self._data_source, relevant_columns, condition)
         self._constraints.append(
             numeric_constraints.NumericNoOverlap(
                 ref,
@@ -1032,6 +1110,7 @@ def add_numeric_no_overlap_constraint(
                 end_included=end_included,
                 max_relative_n_violations=max_relative_n_violations,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1039,15 +1118,15 @@ def add_varchar_regex_constraint(
         self,
         column: str,
         regex: str,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
         allow_none: bool = False,
         relative_tolerance: float = 0.0,
         aggregated: bool = True,
         n_counterexamples: int = 5,
+        cache_size=None,
     ):
-        """
-        Assesses whether the values in a column match a given regular expression pattern.
+        """Assesses whether the values in a column match a given regular expression pattern.
 
         The option ``allow_none`` can be used in cases where the column is defined as
         nullable and contains null values.
@@ -1065,7 +1144,7 @@ def add_varchar_regex_constraint(
         substantially more memory-saving, please consider using
         ``add_varchar_regex_constraint_db``.
         """
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             varchar_constraints.VarCharRegex(
                 ref,
@@ -1075,6 +1154,7 @@ def add_varchar_regex_constraint(
                 aggregated=aggregated,
                 n_counterexamples=n_counterexamples,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1082,14 +1162,14 @@ def add_varchar_regex_constraint_db(
         self,
         column: str,
         regex: str,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
         relative_tolerance: float = 0.0,
         aggregated: bool = True,
         n_counterexamples: int = 5,
+        cache_size=None,
     ):
-        """
-        Assesses whether the values in a column match a given regular expression pattern.
+        """Assesses whether the values in a column match a given regular expression pattern.
 
         How the tolerance factor is calculated can be controlled with the ``aggregated``
         flag. When ``True``, the tolerance is calculated using unique values. If not, the
@@ -1106,7 +1186,7 @@ def add_varchar_regex_constraint_db(
         This is typically slower and more expensive in terms of memory but available
         on all supported database mamangement systems.
         """
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
             varchar_constraints.VarCharRegexDb(
                 ref,
@@ -1115,6 +1195,7 @@ def add_varchar_regex_constraint_db(
                 aggregated=aggregated,
                 n_counterexamples=n_counterexamples,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1122,24 +1203,36 @@ def add_varchar_min_length_constraint(
         self,
         column: str,
         min_length: int,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
-            varchar_constraints.VarCharMinLength(ref, min_length=min_length, name=name)
+            varchar_constraints.VarCharMinLength(
+                ref,
+                min_length=min_length,
+                name=name,
+                cache_size=cache_size,
+            )
         )
 
     def add_varchar_max_length_constraint(
         self,
         column: str,
         max_length: int,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
-        ref = DataReference(self.data_source, [column], condition)
+        ref = DataReference(self._data_source, [column], condition)
         self._constraints.append(
-            varchar_constraints.VarCharMaxLength(ref, max_length=max_length, name=name)
+            varchar_constraints.VarCharMaxLength(
+                ref,
+                max_length=max_length,
+                name=name,
+                cache_size=cache_size,
+            )
         )
 
     def add_groupby_aggregation_constraint(
@@ -1148,8 +1241,9 @@ def add_groupby_aggregation_constraint(
         aggregation_column: str,
         start_value: int,
         tolerance: float = 0,
-        condition: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
     ):
         """Check whether array aggregate corresponds to an integer range.
 
@@ -1163,8 +1257,7 @@ def add_groupby_aggregation_constraint(
         In order to allow for slight deviations from this pattern, ``tolerance`` expresses
         the fraction of all grouped-by rows, which may be incomplete ranges.
         """
-
-        ref = DataReference(self.data_source, list(columns), condition)
+        ref = DataReference(self._data_source, list(columns), condition)
         self._constraints.append(
             groupby_constraints.AggregateNumericRangeEquality(
                 ref,
@@ -1172,6 +1265,7 @@ def add_groupby_aggregation_constraint(
                 tolerance=tolerance,
                 start_value=start_value,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1181,15 +1275,15 @@ def __init__(
         self,
         data_source: DataSource,
         data_source2: DataSource,
-        date_column: Optional[str] = None,
-        date_column2: Optional[str] = None,
+        date_column: str | None = None,
+        date_column2: str | None = None,
     ):
-        self.data_source = data_source
-        self.data_source2 = data_source2
-        self.ref = DataReference(self.data_source)
-        self.ref2 = DataReference(self.data_source2)
-        self.date_column = date_column
-        self.date_column2 = date_column2
+        self._data_source = data_source
+        self._data_source2 = data_source2
+        self._ref = DataReference(self._data_source)
+        self._ref2 = DataReference(self._data_source2)
+        self._date_column = date_column
+        self._date_column2 = date_column2
         super().__init__()
 
     @classmethod
@@ -1201,9 +1295,10 @@ def from_tables(
         db_name2: str,
         schema_name2: str,
         table_name2: str,
-        date_column: Optional[str] = None,
-        date_column2: Optional[str] = None,
+        date_column: str | None = None,
+        date_column2: str | None = None,
     ):
+        """Create a ``BetweenRequirement`` based on a table."""
         return cls(
             data_source=TableDataSource(
                 db_name=db_name1,
@@ -1226,10 +1321,10 @@ def from_raw_queries(
         query2: str,
         name1: str,
         name2: str,
-        columns1: Optional[List[str]] = None,
-        columns2: Optional[List[str]] = None,
-        date_column: Optional[str] = None,
-        date_column2: Optional[str] = None,
+        columns1: list[str] | None = None,
+        columns2: list[str] | None = None,
+        date_column: str | None = None,
+        date_column2: str | None = None,
     ):
         """Create a ``BetweenRequirement`` based on raw query strings.
 
@@ -1257,8 +1352,8 @@ def from_expressions(
         expression2,
         name1: str,
         name2: str,
-        date_column: Optional[str] = None,
-        date_column2: Optional[str] = None,
+        date_column: str | None = None,
+        date_column2: str | None = None,
     ):
         """Create a ``BetweenTableRequirement`` based on sqlalchemy expressions.
 
@@ -1277,178 +1372,233 @@ def from_expressions(
             date_column2=date_column2,
         )
 
-    def get_date_growth_rate(self, engine) -> float:
-        if self.date_column is None or self.date_column2 is None:
+    def _get_date_growth_rate(self, engine: sa.engine.Engine) -> float:
+        if self._date_column is None or self._date_column2 is None:
             raise ValueError("Date growth can't be computed without date column.")
-        date_growth_rate, _ = get_date_growth_rate(
-            engine, self.ref, self.ref2, self.date_column, self.date_column2
+        date_growth_rate, _ = _get_date_growth_rate(
+            engine, self._ref, self._ref2, self._date_column, self._date_column2
         )
         return date_growth_rate
 
-    def get_deviation_getter(
-        self, fix_value: Optional[float], deviation: Optional[float]
-    ):
+    def _get_deviation_getter(
+        self, fix_value: float | None, deviation: float | None
+    ) -> Callable[[sa.engine.Engine], float]:
         if fix_value is None and deviation is None:
-            return ValueError("No valid gain/loss/deviation given.")
+            raise ValueError("No valid gain/loss/deviation given.")
+
         if deviation is None:
+            # Mypy doesn't understand that this condition implies fix_value
+            # not being None.
+            if fix_value is None:
+                raise ValueError()
+
             return lambda engine: fix_value
+
         if fix_value is None:
-            return lambda engine: self.get_date_growth_rate(engine) + deviation
+            return lambda engine: self._get_date_growth_rate(engine) + deviation
         return lambda engine: max(
-            fix_value, self.get_date_growth_rate(engine) + deviation
+            fix_value, self._get_date_growth_rate(engine) + deviation
         )
 
     def add_n_rows_equality_constraint(
         self,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        ref = DataReference(self.data_source, condition=condition1)
-        ref2 = DataReference(self.data_source2, condition=condition2)
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        ref = DataReference(self._data_source, condition=condition1)
+        ref2 = DataReference(self._data_source2, condition=condition2)
         self._constraints.append(
-            nrows_constraints.NRowsEquality(ref, ref2=ref2, name=name)
+            nrows_constraints.NRowsEquality(
+                ref, ref2=ref2, name=name, cache_size=cache_size
+            )
         )
 
     def add_n_rows_max_gain_constraint(
         self,
-        constant_max_relative_gain: Optional[float] = None,
-        date_range_gain_deviation: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """#rows from first table <= #rows from second table * (1 + max_growth).
+        constant_max_relative_gain: float | None = None,
+        date_range_gain_deviation: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        r"""Assert that the number of rows hasn't grown by more than expected.
 
-        See readme for more information on max_growth.
+        In particular, assert that
+
+        $$n^{rows}_1 \leq n^{rows}_2 \cdot (1 + \text{cmrg})$$
+
+        See readme for more information on ``constant_max_relative_gain``.
         """
-        max_relative_gain_getter = self.get_deviation_getter(
+        max_relative_gain_getter = self._get_deviation_getter(
             constant_max_relative_gain, date_range_gain_deviation
         )
-        ref = DataReference(self.data_source, condition=condition1)
-        ref2 = DataReference(self.data_source2, condition=condition2)
+        ref = DataReference(self._data_source, condition=condition1)
+        ref2 = DataReference(self._data_source2, condition=condition2)
         self._constraints.append(
             nrows_constraints.NRowsMaxGain(
-                ref, ref2, max_relative_gain_getter, name=name
+                ref,
+                ref2,
+                max_relative_gain_getter,
+                name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_n_rows_min_gain_constraint(
         self,
-        constant_min_relative_gain: Optional[float] = None,
-        date_range_gain_deviation: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """#rows from first table  >= #rows from second table * (1 + min_growth).
+        constant_min_relative_gain: float | None = None,
+        date_range_gain_deviation: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        r"""Assert that the number of rows hasn't grown less than expected.
 
-        See readme for more information on min_growth.
+        In particular, assert that
+
+        $$n^{rows}_1 \geq n^{rows}_2 \cdot (1 + \text{cmrg})$$
+
+        See readme for more information on ``constant_min_relative_gain``.
         """
-        min_relative_gain_getter = self.get_deviation_getter(
+        min_relative_gain_getter = self._get_deviation_getter(
             constant_min_relative_gain, date_range_gain_deviation
         )
-        ref = DataReference(self.data_source, condition=condition1)
-        ref2 = DataReference(self.data_source2, condition=condition2)
+        ref = DataReference(self._data_source, condition=condition1)
+        ref2 = DataReference(self._data_source2, condition=condition2)
         self._constraints.append(
             nrows_constraints.NRowsMinGain(
-                ref, ref2, min_relative_gain_getter, name=name
+                ref,
+                ref2,
+                min_relative_gain_getter,
+                name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_n_rows_max_loss_constraint(
         self,
-        constant_max_relative_loss: Optional[float] = None,
-        date_range_loss_deviation: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """#rows from first table >= #rows from second table * (1 - max_loss).
+        constant_max_relative_loss: float | None = None,
+        date_range_loss_deviation: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        r"""Assert that the number of rows hasn't decreased too much.
 
-        See readme for more information on max_loss.
+        In particular, assert that
+
+        $$n^{rows}_1 \geq n^{rows}_2 \cdot (1 - \text{cmrl})$$
+
+        See readme for more information on ``constant_max_relative_loss``.
         """
-        max_relative_loss_getter = self.get_deviation_getter(
+        max_relative_loss_getter = self._get_deviation_getter(
             constant_max_relative_loss, date_range_loss_deviation
         )
-        ref = DataReference(self.data_source, condition=condition1)
-        ref2 = DataReference(self.data_source2, condition=condition2)
+        ref = DataReference(self._data_source, condition=condition1)
+        ref2 = DataReference(self._data_source2, condition=condition2)
         self._constraints.append(
             nrows_constraints.NRowsMaxLoss(
-                ref, ref2, max_relative_loss_getter, name=name
+                ref,
+                ref2,
+                max_relative_loss_getter,
+                name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_n_uniques_equality_constraint(
         self,
-        columns1: Optional[List[str]],
-        columns2: Optional[List[str]],
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        columns1: list[str] | None,
+        columns2: list[str] | None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
-            uniques_constraints.NUniquesEquality(ref, ref2=ref2, name=name)
+            uniques_constraints.NUniquesEquality(
+                ref, ref2=ref2, name=name, cache_size=cache_size
+            )
         )
 
     def add_n_uniques_max_gain_constraint(
         self,
-        columns1: Optional[List[str]],
-        columns2: Optional[List[str]],
-        constant_max_relative_gain: Optional[float] = None,
-        date_range_gain_deviation: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """#uniques or first table <= #uniques of second table* (1 + max_growth).
+        columns1: list[str] | None,
+        columns2: list[str] | None,
+        constant_max_relative_gain: float | None = None,
+        date_range_gain_deviation: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        r"""Assert that the number of uniques hasn't grown by too much.
+
+        In particular, assert that
 
-        #uniques in first table are defined based on columns1, #uniques in second
-        table are defined based on columns2.
+        $$n^{uniques}_1 \leq n^{uniques}_2 \cdot (1 - \text{cmrg})$$
 
-        See readme for more information on max_growth.
+        The number of uniques in first table are defined based on ``columns1``, the
+        number of uniques in second table are defined based on ``columns2``.
+
+        See readme for more information on ``constant_max_relative_gain``.
         """
-        max_relative_gain_getter = self.get_deviation_getter(
+        max_relative_gain_getter = self._get_deviation_getter(
             constant_max_relative_gain, date_range_gain_deviation
         )
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
             uniques_constraints.NUniquesMaxGain(
                 ref,
                 ref2,
                 max_relative_gain_getter,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_n_uniques_max_loss_constraint(
         self,
-        columns1: Optional[List[str]],
-        columns2: Optional[List[str]],
-        constant_max_relative_loss: Optional[float] = None,
-        date_range_loss_deviation: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        """#uniques in first table <= #uniques in second table * (1 - max_loss).
+        columns1: list[str] | None,
+        columns2: list[str] | None,
+        constant_max_relative_loss: float | None = None,
+        date_range_loss_deviation: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        r"""Assert that the number of uniques hasn't decreased too much.
+
+        In particular, assert that
 
-        #uniques in first table are defined based on columns1, #uniques in second
-        table are defined based on columns2.
+        $$n^{uniques}_1 \geq n^{uniques}_2 \cdot (1 - \text{cmrl})$$
 
-        See readme for more information on max_loss.
+        The number of uniques in first table are defined based on ``columns1``, the
+        number of uniques in second table are defined based on ``columns2``.
+
+        See readme for more information on ``constant_max_relative_loss``.
         """
-        max_relative_loss_getter = self.get_deviation_getter(
+        max_relative_loss_getter = self._get_deviation_getter(
             constant_max_relative_loss, date_range_loss_deviation
         )
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
             uniques_constraints.NUniquesMaxLoss(
-                ref, ref2, max_relative_loss_getter, name=name
+                ref,
+                ref2,
+                max_relative_loss_getter,
+                name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1457,23 +1607,25 @@ def add_max_null_fraction_constraint(
         column1: str,
         column2: str,
         max_relative_deviation: float,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Assert that the fraction of ``NULL`` values of one is at most that of the other.
 
-        Given that ``column2``\'s underlying data has a fraction ``q`` of ``NULL`` values, the
-        ``max_relative_deviation`` parameter allows ``column1``\'s underlying data to have a
+        Given that ``column2``'s underlying data has a fraction ``q`` of ``NULL`` values, the
+        ``max_relative_deviation`` parameter allows ``column1``'s underlying data to have a
         fraction ``(1 + max_relative_deviation) * q`` of ``NULL`` values.
-        """
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        """  # noqa: D301
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
             miscs_constraints.MaxNullFraction(
                 ref,
                 ref2=ref2,
                 max_relative_deviation=max_relative_deviation,
+                cache_size=cache_size,
             )
         )
 
@@ -1481,30 +1633,34 @@ def add_numeric_min_constraint(
         self,
         column1: str,
         column2: str,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
-            numeric_constraints.NumericMin(ref, ref2=ref2, name=name)
+            numeric_constraints.NumericMin(
+                ref, ref2=ref2, name=name, cache_size=cache_size
+            )
         )
 
     def add_uniques_equality_constraint(
         self,
-        columns1: List[str],
-        columns2: List[str],
-        filter_func: Optional[Callable[[List[T]], List[T]]] = None,
-        map_func: Optional[Callable[[T], T]] = None,
-        reduce_func: Optional[Callable[[Collection], Collection]] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        columns1: list[str],
+        columns2: list[str],
+        filter_func: Callable[[list[_T]], list[_T]] | None = None,
+        map_func: Callable[[_T], _T] | None = None,
+        reduce_func: Callable[[Collection], Collection] | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Check if the data's unique values in given columns are equal.
 
         The ``UniquesEquality`` constraint asserts if the values contained in a column
@@ -1512,26 +1668,25 @@ def add_uniques_equality_constraint(
         columns.
 
         Null values in the columns ``columns`` are ignored. To assert the non-existence of them use
-        the :meth:`~datajudge.requirements.WithinRequirement.add_null_absence_constraint`` helper method
+        the [`add_null_absence_constraint`][datajudge.requirements.WithinRequirement.add_null_absence_constraint] helper method
         for ``WithinRequirement``.
         By default, the null filtering does not trigger if multiple columns are fetched at once.
         It can be configured in more detail by supplying a custom ``filter_func`` function.
-        Some exemplary implementations are available as :func:`~datajudge.utils.filternull_element`,
-        :func:`~datajudge.utils.filternull_never`, :func:`~datajudge.utils.filternull_element_or_tuple_all`,
-        :func:`~datajudge.utils.filternull_element_or_tuple_any`.
-        Passing ``None`` as the argument is equivalent to :func:`~datajudge.utils.filternull_element` but triggers a warning.
-        The current default of :func:`~datajudge.utils.filternull_element`
+        Some exemplary implementations are available as [`filternull_element`][datajudge.utils.filternull_element],
+        [`filternull_never`][datajudge.utils.filternull_never], [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all],
+        [`filternull_element_or_tuple_any`][datajudge.utils.filternull_element_or_tuple_any].
+        Passing ``None`` as the argument is equivalent to [`filternull_element`][datajudge.utils.filternull_element] but triggers a warning.
+        The current default of [`filternull_element`][datajudge.utils.filternull_element]
         Cause (possibly often unintended) changes in behavior when the users adds a second column
         (filtering no longer can trigger at all).
-        The default will be changed to :func:`~datajudge.utils.filternull_element_or_tuple_all` in future versions.
+        The default will be changed to [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all] in future versions.
         To silence the warning, set ``filter_func`` explicitly..
 
-        See :class:`~datajudge.constraints.uniques.Uniques` for further parameter details on ``map_func``,
+        See [`Uniques`][datajudge.constraints.uniques.Uniques] for further parameter details on ``map_func``,
         ``reduce_func``, and ``output_processors``.
         """
-
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
             uniques_constraints.UniquesEquality(
                 ref,
@@ -1541,24 +1696,26 @@ def add_uniques_equality_constraint(
                 reduce_func=reduce_func,
                 output_processors=output_processors,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_uniques_superset_constraint(
         self,
-        columns1: List[str],
-        columns2: List[str],
+        columns1: list[str],
+        columns2: list[str],
         max_relative_violations: float = 0,
-        filter_func: Optional[Callable[[List[T]], List[T]]] = None,
-        map_func: Optional[Callable[[T], T]] = None,
-        reduce_func: Optional[Callable[[Collection], Collection]] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
-    ):
+        filter_func: Callable[[list[_T]], list[_T]] | None = None,
+        map_func: Callable[[_T], _T] | None = None,
+        reduce_func: Callable[[Collection], Collection] | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        cache_size=None,
+    ) -> None:
         """Check if unique values of columns are contained in the reference data.
 
         The ``UniquesSuperset`` constraint asserts that reference set of expected values,
@@ -1566,18 +1723,18 @@ def add_uniques_superset_constraint(
         is contained in given columns of a ``DataSource``.
 
         Null values in the columns ``columns`` are ignored. To assert the non-existence of them use
-        the :meth:`~datajudge.requirements.WithinRequirement.add_null_absence_constraint`` helper method
+        the [`add_null_absence_constraint`][datajudge.requirements.WithinRequirement.add_null_absence_constraint] helper method
         for ``WithinRequirement``.
         By default, the null filtering does not trigger if multiple columns are fetched at once.
         It can be configured in more detail by supplying a custom ``filter_func`` function.
-        Some exemplary implementations are available as :func:`~datajudge.utils.filternull_element`,
-        :func:`~datajudge.utils.filternull_never`, :func:`~datajudge.utils.filternull_element_or_tuple_all`,
-        :func:`~datajudge.utils.filternull_element_or_tuple_any`.
-        Passing ``None`` as the argument is equivalent to :func:`~datajudge.utils.filternull_element` but triggers a warning.
-        The current default of :func:`~datajudge.utils.filternull_element`
+        Some exemplary implementations are available as [`filternull_element`][datajudge.utils.filternull_element],
+        [`filternull_never`][datajudge.utils.filternull_never], [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all],
+        [`filternull_element_or_tuple_any`][datajudge.utils.filternull_element_or_tuple_any].
+        Passing ``None`` as the argument is equivalent to [`filternull_element`][datajudge.utils.filternull_element] but triggers a warning.
+        The current default of [`filternull_element`][datajudge.utils.filternull_element]
         Cause (possibly often unintended) changes in behavior when the users adds a second column
         (filtering no longer can trigger at all).
-        The default will be changed to :func:`~datajudge.utils.filternull_element_or_tuple_all` in future versions.
+        The default will be changed to [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all] in future versions.
         To silence the warning, set ``filter_func`` explicitly..
 
         ``max_relative_violations`` indicates what fraction of unique values of the given
@@ -1587,12 +1744,11 @@ def add_uniques_superset_constraint(
         One use of this constraint is to test for consistency in columns with expected
         categorical values.
 
-        See :class:`~datajudge.constraints.uniques.Uniques` for further details on ``map_func``, ``reduce_func``,
+        See [`Uniques`][datajudge.constraints.uniques.Uniques] for further details on ``map_func``, ``reduce_func``,
         and ``output_processors``.
         """
-
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
             uniques_constraints.UniquesSuperset(
                 ref,
@@ -1603,25 +1759,27 @@ def add_uniques_superset_constraint(
                 reduce_func=reduce_func,
                 output_processors=output_processors,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_uniques_subset_constraint(
         self,
-        columns1: List[str],
-        columns2: List[str],
+        columns1: list[str],
+        columns2: list[str],
         max_relative_violations: float = 0,
-        filter_func: Optional[Callable[[List[T]], List[T]]] = None,
+        filter_func: Callable[[list[_T]], list[_T]] | None = None,
         compare_distinct: bool = False,
-        map_func: Optional[Callable[[T], T]] = None,
-        reduce_func: Optional[Callable[[Collection], Collection]] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-        output_processors: Optional[
-            Union[OutputProcessor, List[OutputProcessor]]
-        ] = output_processor_limit,
-    ):
+        map_func: Callable[[_T], _T] | None = None,
+        reduce_func: Callable[[Collection], Collection] | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        output_processors: OutputProcessor
+        | list[OutputProcessor]
+        | None = output_processor_limit,
+        cache_size=None,
+    ) -> None:
         """Check if the given columns's unique values in are contained in reference data.
 
         The ``UniquesSubset`` constraint asserts if the values contained in given column of
@@ -1629,35 +1787,33 @@ def add_uniques_subset_constraint(
         ``DataSource``.
 
         Null values in the columns ``columns`` are ignored. To assert the non-existence of them use
-        the :meth:`~datajudge.requirements.WithinRequirement.add_null_absence_constraint`` helper method
+        the [`add_null_absence_constraint`][datajudge.requirements.WithinRequirement.add_null_absence_constraint] helper method
         for ``WithinRequirement``.
         By default, the null filtering does not trigger if multiple columns are fetched at once.
         It can be configured in more detail by supplying a custom ``filter_func`` function.
-        Some exemplary implementations are available as :func:`~datajudge.utils.filternull_element`,
-        :func:`~datajudge.utils.filternull_never`, :func:`~datajudge.utils.filternull_element_or_tuple_all`,
-        :func:`~datajudge.utils.filternull_element_or_tuple_any`.
-        Passing ``None`` as the argument is equivalent to :func:`~datajudge.utils.filternull_element` but triggers a warning.
-        The current default of :func:`~datajudge.utils.filternull_element`
+        Some exemplary implementations are available as [`filternull_element`][datajudge.utils.filternull_element],
+        [`filternull_never`][datajudge.utils.filternull_never], [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all],
+        [`filternull_element_or_tuple_any`][datajudge.utils.filternull_element_or_tuple_any].
+        Passing ``None`` as the argument is equivalent to [`filternull_element`][datajudge.utils.filternull_element] but triggers a warning.
+        The current default of [`filternull_element`][datajudge.utils.filternull_element]
         Cause (possibly often unintended) changes in behavior when the users adds a second column
         (filtering no longer can trigger at all).
-        The default will be changed to :func:`~datajudge.utils.filternull_element_or_tuple_all` in future versions.
-        To silence the warning, set ``filter_func`` explicitly..
-
+        The default will be changed to [`filternull_element_or_tuple_all`][datajudge.utils.filternull_element_or_tuple_all] in future versions.
+        To silence the warning, set ``filter_func`` explicitly.
         ``max_relative_violations`` indicates what fraction of rows of the given table
         may have values not included in the reference set of unique values. Please note
         that ``UniquesSubset`` and ``UniquesSuperset`` are not symmetrical in this regard.
 
         By default, the number of occurrences affects the computed fraction of violations.
         To disable this weighting, set ``compare_distinct=True``.
-        This argument does not have an effect on the test results for other :class:`~datajudge.constraints.uniques.Uniques` constraints,
+        This argument does not have an effect on the test results for other [`Uniques`][datajudge.constraints.uniques.Uniques] constraints,
         or if ``max_relative_violations`` is 0.
 
-        See :class:`~datajudge.constraints.uniques.Uniques` for further details on ``map_func``, ``reduce_func``,
+        See [`Uniques`][datajudge.constraints.uniques.Uniques] for further details on ``map_func``, ``reduce_func``,
         and ``output_processors``.
         """
-
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
             uniques_constraints.UniquesSubset(
                 ref,
@@ -1669,6 +1825,7 @@ def add_uniques_subset_constraint(
                 reduce_func=reduce_func,
                 output_processors=output_processors,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1676,14 +1833,17 @@ def add_numeric_max_constraint(
         self,
         column1: str,
         column2: str,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
-            numeric_constraints.NumericMax(ref, ref2=ref2, name=name)
+            numeric_constraints.NumericMax(
+                ref, ref2=ref2, name=name, cache_size=cache_size
+            )
         )
 
     def add_numeric_mean_constraint(
@@ -1691,18 +1851,20 @@ def add_numeric_mean_constraint(
         column1: str,
         column2: str,
         max_absolute_deviation: float,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
             numeric_constraints.NumericMean(
                 ref,
                 max_absolute_deviation,
                 ref2=ref2,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1711,15 +1873,16 @@ def add_numeric_percentile_constraint(
         column1: str,
         column2: str,
         percentage: float,
-        max_absolute_deviation: Optional[float] = None,
-        max_relative_deviation: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        max_absolute_deviation: float | None = None,
+        max_relative_deviation: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Assert that the ``percentage``-th percentile is approximately equal.
 
-        The percentile is defined as the value present in ``column1`` / ``column2``
+        The percentile is defined as the smallest value present in ``column1`` / ``column2``
         for which ``percentage`` % of the values in ``column1`` / ``column2`` are
         less or equal. ``NULL`` values are ignored.
 
@@ -1732,8 +1895,8 @@ def add_numeric_percentile_constraint(
         At least one of ``max_absolute_deviation`` and ``max_relative_deviation`` must
         be provided.
         """
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
             numeric_constraints.NumericPercentile(
                 ref,
@@ -1742,6 +1905,7 @@ def add_numeric_percentile_constraint(
                 max_relative_deviation=max_relative_deviation,
                 ref2=ref2,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1751,10 +1915,11 @@ def add_date_min_constraint(
         column2: str,
         use_lower_bound_reference: bool = True,
         column_type: str = "date",
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Ensure date min of first table is greater or equal date min of second table.
 
         The used columns of both tables need to be of the same type.
@@ -1766,8 +1931,8 @@ def add_date_min_constraint(
         If not ``use_upper_bound_reference``, the min of the first table has to
         be smaller or equal to the min of the second table.
         """
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
             date_constraints.DateMin(
                 ref,
@@ -1775,6 +1940,7 @@ def add_date_min_constraint(
                 use_lower_bound_reference=use_lower_bound_reference,
                 column_type=column_type,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1784,10 +1950,11 @@ def add_date_max_constraint(
         column2: str,
         use_upper_bound_reference: bool = True,
         column_type: str = "date",
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Compare date max of first table to date max of second table.
 
         The used columns of both tables need to be of the same type.
@@ -1799,8 +1966,8 @@ def add_date_max_constraint(
         If not ``use_upper_bound_reference``, the max of the first table has to
         be greater or equal to the max of the second table.
         """
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
             date_constraints.DateMax(
                 ref,
@@ -1808,6 +1975,7 @@ def add_date_max_constraint(
                 use_upper_bound_reference=use_upper_bound_reference,
                 column_type=column_type,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1815,85 +1983,113 @@ def add_varchar_min_length_constraint(
         self,
         column1: str,
         column2: str,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
-            varchar_constraints.VarCharMinLength(ref, ref2=ref2, name=name)
+            varchar_constraints.VarCharMinLength(
+                ref, ref2=ref2, name=name, cache_size=cache_size
+            )
         )
 
     def add_varchar_max_length_constraint(
         self,
         column1: str,
         column2: str,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
-        ref = DataReference(self.data_source, [column1], condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition2)
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        ref = DataReference(self._data_source, [column1], condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition2)
         self._constraints.append(
-            varchar_constraints.VarCharMaxLength(ref, ref2=ref2, name=name)
+            varchar_constraints.VarCharMaxLength(
+                ref, ref2=ref2, name=name, cache_size=cache_size
+            )
         )
 
-    def add_column_subset_constraint(self, name: Optional[str] = None):
+    def add_column_subset_constraint(
+        self, name: str | None = None, cache_size=None
+    ) -> None:
         """Columns of first table are subset of second table."""
         self._constraints.append(
-            column_constraints.ColumnSubset(self.ref, ref2=self.ref2, name=name)
+            column_constraints.ColumnSubset(
+                self._ref, ref2=self._ref2, name=name, cache_size=cache_size
+            )
         )
 
-    def add_column_superset_constraint(self, name: Optional[str] = None):
+    def add_column_superset_constraint(
+        self, name: str | None = None, cache_size=None
+    ) -> None:
         """Columns of first table are superset of columns of second table."""
         self._constraints.append(
-            column_constraints.ColumnSuperset(self.ref, ref2=self.ref2, name=name)
+            column_constraints.ColumnSuperset(
+                self._ref, ref2=self._ref2, name=name, cache_size=cache_size
+            )
         )
 
     def add_column_type_constraint(
-        self, column1: str, column2: str, name: Optional[str] = None
-    ):
-        "Check that the columns have the same type."
-        ref1 = DataReference(self.data_source, [column1])
-        ref2 = DataReference(self.data_source2, [column2])
+        self,
+        column1: str,
+        column2: str,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
+        """Check that the columns have the same type."""
+        ref1 = DataReference(self._data_source, [column1])
+        ref2 = DataReference(self._data_source2, [column2])
         self._constraints.append(
-            column_constraints.ColumnType(ref1, ref2=ref2, name=name)
+            column_constraints.ColumnType(
+                ref1, ref2=ref2, name=name, cache_size=cache_size
+            )
         )
 
     def add_row_equality_constraint(
         self,
-        columns1: Optional[List[str]],
-        columns2: Optional[List[str]],
+        columns1: list[str] | None,
+        columns2: list[str] | None,
         max_missing_fraction: float,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """At most ``max_missing_fraction`` of rows in T1 and T2 are absent in either.
 
-        In other words,
-        :math:`\\frac{|T1 - T2| + |T2 - T1|}{|T1 \\cup T2|} \\leq` ``max_missing_fraction``.
+        In other words
+
+        $$\\frac{|T1 - T2| + |T2 - T1|}{|T1 \\cup T2|} \\leq \\text{mmf}$$
+
         Rows from T1 are indexed in ``columns1``, rows from T2 are indexed in ``columns2``.
-        """
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        """  # noqa: D301
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
             row_constraints.RowEquality(
-                ref, ref2, lambda engine: max_missing_fraction, name=name
+                ref,
+                ref2,
+                lambda engine: max_missing_fraction,
+                name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_row_subset_constraint(
         self,
-        columns1: Optional[List[str]],
-        columns2: Optional[List[str]],
-        constant_max_missing_fraction: Optional[float],
-        date_range_loss_fraction: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        columns1: list[str] | None,
+        columns2: list[str] | None,
+        constant_max_missing_fraction: float | None,
+        date_range_loss_fraction: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """At most ``max_missing_fraction`` of rows in T1 are not in T2.
 
         In other words,
@@ -1904,55 +2100,66 @@ def add_row_subset_constraint(
         contrast to ``EXCEPT ALL``, this should lead to a set subtraction instead of
         a multiset subtraction. In other words, duplicates in T1 are treated as
         single occurrences.
-        """
-        max_missing_fraction_getter = self.get_deviation_getter(
+        """  # noqa: D301
+        max_missing_fraction_getter = self._get_deviation_getter(
             constant_max_missing_fraction, date_range_loss_fraction
         )
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
-            row_constraints.RowSubset(ref, ref2, max_missing_fraction_getter, name=name)
+            row_constraints.RowSubset(
+                ref,
+                ref2,
+                max_missing_fraction_getter,
+                name=name,
+                cache_size=cache_size,
+            )
         )
 
     def add_row_superset_constraint(
         self,
-        columns1: Optional[List[str]],
-        columns2: Optional[List[str]],
+        columns1: list[str] | None,
+        columns2: list[str] | None,
         constant_max_missing_fraction: float,
-        date_range_loss_fraction: Optional[float] = None,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        date_range_loss_fraction: float | None = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """At most ``max_missing_fraction`` of rows in T2 are not in T1.
 
-        In other words,
-        :math:`\\frac{|T2-T1|}{|T2|} \\leq` ``max_missing_fraction``.
+        In other words, $\\frac{|T2-T1|}{|T2|} \\leq$ ``max_missing_fraction``.
         Rows from T1 are indexed in ``columns1``, rows from T2 are indexed in
         ``columns2``.
-        """
-        max_missing_fraction_getter = self.get_deviation_getter(
+        """  # noqa: D301
+        max_missing_fraction_getter = self._get_deviation_getter(
             constant_max_missing_fraction, date_range_loss_fraction
         )
-        ref = DataReference(self.data_source, columns1, condition1)
-        ref2 = DataReference(self.data_source2, columns2, condition2)
+        ref = DataReference(self._data_source, columns1, condition1)
+        ref2 = DataReference(self._data_source2, columns2, condition2)
         self._constraints.append(
             row_constraints.RowSuperset(
-                ref, ref2, max_missing_fraction_getter, name=name
+                ref,
+                ref2,
+                max_missing_fraction_getter,
+                name=name,
+                cache_size=cache_size,
             )
         )
 
     def add_row_matching_equality_constraint(
         self,
-        matching_columns1: List[str],
-        matching_columns2: List[str],
-        comparison_columns1: List[str],
-        comparison_columns2: List[str],
+        matching_columns1: list[str],
+        matching_columns2: list[str],
+        comparison_columns1: list[str],
+        comparison_columns2: list[str],
         max_missing_fraction: float,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
-    ):
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
+        cache_size=None,
+    ) -> None:
         """Match tables in matching_columns, compare for equality in comparison_columns.
 
         This constraint is similar to the nature of the ``RowEquality``
@@ -1968,10 +2175,10 @@ def add_row_matching_equality_constraint(
         ``comparison_columns`` after performing an inner join on ``matching_columns``.
         """
         ref = DataReference(
-            self.data_source, matching_columns1 + comparison_columns1, condition1
+            self._data_source, matching_columns1 + comparison_columns1, condition1
         )
         ref2 = DataReference(
-            self.data_source2, matching_columns2 + comparison_columns2, condition2
+            self._data_source2, matching_columns2 + comparison_columns2, condition2
         )
         self._constraints.append(
             row_constraints.RowMatchingEquality(
@@ -1983,6 +2190,7 @@ def add_row_matching_equality_constraint(
                 comparison_columns2,
                 lambda engine: max_missing_fraction,
                 name=name,
+                cache_size=cache_size,
             )
         )
 
@@ -1990,32 +2198,54 @@ def add_ks_2sample_constraint(
         self,
         column1: str,
         column2: str,
-        condition1: Optional[Condition] = None,
-        condition2: Optional[Condition] = None,
-        name: Optional[str] = None,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        name: str | None = None,
         significance_level: float = 0.05,
-    ):
+        cache_size=None,
+    ) -> None:
         """
         Apply the so-called two-sample Kolmogorov-Smirnov test to the distributions of the two given columns.
-        The constraint is fulfilled, when the resulting p-value of the test is higher than the significance level
+
+        The constraint is fulfilled when the resulting p-value of the test is higher than the significance level
         (default is 0.05, i.e., 5%).
-        The signifance_level must be a value between 0.0 and 1.0.
+        The significance_level must be a value between 0.0 and 1.0.
         """
-
         if not column1 or not column2:
             raise ValueError(
                 "Column names have to be given for this test's functionality."
             )
 
-        if significance_level <= 0.0 or significance_level > 1.0:
-            raise ValueError(
-                "The requested significance level has to be in ``(0.0, 1.0]``. Default is 0.05."
-            )
+        _check_significance_level(significance_level)
 
-        ref = DataReference(self.data_source, [column1], condition=condition1)
-        ref2 = DataReference(self.data_source2, [column2], condition=condition2)
+        ref = DataReference(self._data_source, [column1], condition=condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition=condition2)
         self._constraints.append(
             stats_constraints.KolmogorovSmirnov2Sample(
-                ref, ref2, significance_level, name=name
+                ref,
+                ref2,
+                significance_level,
+                name=name,
+                cache_size=cache_size,
+            )
+        )
+
+    def add_anderson_darling_2sample_constraint(
+        self,
+        column1: str,
+        column2: str,
+        condition1: Condition | None = None,
+        condition2: Condition | None = None,
+        significance_level: float = 0.05,
+        cache_size=None,
+    ) -> None:
+        """Do."""
+        _check_significance_level(significance_level)
+
+        ref = DataReference(self._data_source, [column1], condition=condition1)
+        ref2 = DataReference(self._data_source2, [column2], condition=condition2)
+        self._constraints.append(
+            stats_constraints.AndersonDarling2Sample(
+                ref, ref2, significance_level=significance_level, cache_size=cache_size
             )
         )
diff --git a/src/datajudge/utils.py b/src/datajudge/utils.py
index cd4fd238..46d11db6 100644
--- a/src/datajudge/utils.py
+++ b/src/datajudge/utils.py
@@ -1,4 +1,7 @@
-from typing import Collection, List, Optional, Protocol, Tuple, Union
+from __future__ import annotations
+
+from collections.abc import Collection
+from typing import Protocol
 
 
 def _fmt_diff_part(s, d):
@@ -6,14 +9,17 @@ def _fmt_diff_part(s, d):
 
 
 def format_difference(
-    n1: Union[float, int], n2: Union[float, int], decimal_separator: bool = True
-) -> Tuple[str, str]:
+    n1: float | int, n2: float | int, decimal_separator: bool = True
+) -> tuple[str, str]:
     """
+    Format and highlight how two numbers differ.
+
     Given two numbers, n1 and n2, return a tuple of two strings,
     each representing one of the input numbers with the differing part highlighted.
     Highlighting is done using BBCode-like tags, which are replaced by the formatter.
 
-    Examples:
+    Examples
+    --------
         123, 123.0
         -> 123, 123[numDiff].0[/numDiff]
         122593859432, 122593859432347
@@ -24,7 +30,8 @@ def format_difference(
     - n2: The second number to compare.
     - decimal_separator: Whether to separate the decimal part of the numbers with commas.
 
-    Returns:
+    Returns
+    -------
     - A tuple of two strings, each representing one of the input numbers with the differing part highlighted.
     """
     if decimal_separator:
@@ -45,19 +52,20 @@ def format_difference(
 
 
 class OutputProcessor(Protocol):
-    def __call__(  # noqa: E704
+    def __call__(
         self,
         collection: Collection,
-        counts: Optional[Collection] = None,
+        counts: Collection | None = None,
     ) -> Collection: ...
 
 
 def output_processor_sort(
-    collection: Collection, counts: Optional[Collection] = None
-) -> Tuple[Collection, Optional[Collection]]:
+    collection: Collection, counts: Collection | None = None
+) -> tuple[Collection, Collection | None]:
     """
-    Sorts a collection of tuple elements in descending order of their counts,
-    and for ties, makes use of the ascending order of the elements themselves.
+    Sorts a collection of tuple elements in descending order of their counts.
+
+    If ties exist, the ascending order of the elements themselves is used.
 
     If the first element is not instanceof tuple,
     each element will be transparently packaged into a 1-tuple for processing;
@@ -88,14 +96,15 @@ def output_processor_sort(
 
 
 def output_processor_limit(
-    collection: Collection, counts: Optional[Collection] = None, limit: int = 100
-) -> Tuple[Collection, Optional[Collection]]:
+    collection: Collection, counts: Collection | None = None, limit: int = 100
+) -> tuple[Collection, Collection | None]:
     """
-    Limits the collection to the first ``limit`` elements.
-    If the list was shortened,
-    will add a ``limit+1``-th string element,
+    Limits the collection to the first `limit` elements.
+
+    If the list was shortened, will add a `limit+1`-th string element,
     informing the user of the truncation.
-    The default limit of ``100`` can be adjusted using ``functools.partial``
+
+    The default limit of ``100`` can be adjusted using `functools.partial`.
     """
     collection = list(collection)
 
@@ -113,15 +122,15 @@ def output_processor_limit(
     return ret_collection, ret_counts
 
 
-def filternull_element(values: List) -> List:
+def filternull_element(values: list) -> list:
     return [value for value in values if value is not None]
 
 
-def filternull_never(values: List) -> List:
+def filternull_never(values: list) -> list:
     return values
 
 
-def filternull_element_or_tuple_all(values: List) -> List:
+def filternull_element_or_tuple_all(values: list) -> list:
     return [
         value
         for value in values
@@ -130,7 +139,7 @@ def filternull_element_or_tuple_all(values: List) -> List:
     ]
 
 
-def filternull_element_or_tuple_any(values: List) -> List:
+def filternull_element_or_tuple_any(values: list) -> list:
     return [
         value
         for value in values
@@ -140,10 +149,11 @@ def filternull_element_or_tuple_any(values: List) -> List:
 
 
 def sort_tuple_none_aware(
-    collection: Collection[Tuple], ascending=True
-) -> Collection[Tuple]:
+    collection: Collection[tuple], ascending=True
+) -> Collection[tuple]:
     """
     Stable sort of a collection of tuples.
+
     Each tuple in the collection must have the same length,
     since they are treated as rows in a table,
     with ``elem[0]`` being the first column,
@@ -163,7 +173,7 @@ def sort_tuple_none_aware(
     if not all(isinstance(elem, tuple) and len(elem) == len(lst[0]) for elem in lst):
         raise ValueError("all elements must be tuples and have the same length")
 
-    dtypes_each_tupleelement: List[Optional[type]] = [None] * len(lst[0])
+    dtypes_each_tupleelement: list[type | None] = [None] * len(lst[0])
     for dtypeidx in range(len(dtypes_each_tupleelement)):
         for elem in lst:
             if elem[dtypeidx] is not None:
@@ -173,7 +183,7 @@ def sort_tuple_none_aware(
             # if all entries are None, just use a constant int() == 0
             dtypes_each_tupleelement[dtypeidx] = int
 
-    def replace_None_with_default(elem):
+    def replace_None_with_default(elem):  # noqa: N802
         return tuple(
             ((dtype() if dtype else None) if subelem is None else subelem)
             for dtype, subelem in zip(dtypes_each_tupleelement, elem)
diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py
index f7521a87..bba2b9db 100644
--- a/tests/integration/conftest.py
+++ b/tests/integration/conftest.py
@@ -3,12 +3,12 @@
 import os
 import random
 import urllib.parse
-from typing import Dict, List, Optional, Union
 
 import pytest
 import sqlalchemy as sa
 
-from datajudge.db_access import apply_patches, is_bigquery, is_db2, is_impala, is_mssql
+from datajudge._engines import is_bigquery, is_db2, is_duckdb, is_mssql
+from datajudge.db_access import apply_patches
 
 TEST_DB_NAME = "tempdb"
 SCHEMA = "dbo"  # 'dbo' is the standard schema in mssql
@@ -16,22 +16,11 @@
 
 def get_engine(backend) -> sa.engine.Engine:
     address = os.environ.get("DB_ADDR", "localhost")
-
-    if backend == "impala":
-        from impala.dbapi import connect
-
-        def conn_creator():
-            return connect(
-                host=address,
-                port=21050,
-                database="default",
-            )
-
-        return sa.create_engine("impala://", creator=conn_creator)
+    connect_args = {}
 
     if backend == "postgres":
         connection_string = f"postgresql://datajudge:datajudge@{address}:5432/datajudge"
-    if backend == "db2":
+    elif backend == "db2":
         connection_string = f"db2+ibm_db://db2inst1:password@{address}:50000/testdb"
     elif "mssql" in backend:
         connection_string = (
@@ -45,25 +34,57 @@ def conn_creator():
             )
             connection_string += f"?driver={msodbc_driver_name}"
     elif "snowflake" in backend:
+        # cryptography is a dependency of snowflake-connector,
+        # which is not present in the default environment
+        from cryptography.hazmat.primitives import (  # type: ignore[unresolved-import, import-not-found]
+            serialization,
+        )
+
+        if not (private_key_env := os.getenv("SNOWFLAKE_PRIVATE_KEY")):
+            raise ValueError("SNOWFLAKE_PRIVATE_KEY environment variable is not set")
+
+        try:
+            private_key = serialization.load_pem_private_key(
+                private_key_env.encode(),
+                password=None,
+            )
+        except ValueError:
+            raise ValueError(
+                "SNOWFLAKE_PRIVATE_KEY value is invalid or corrupted"
+            ) from None
+
+        pkb = private_key.private_bytes(
+            encoding=serialization.Encoding.DER,
+            format=serialization.PrivateFormat.PKCS8,
+            encryption_algorithm=serialization.NoEncryption(),
+        )
         user = os.environ.get("SNOWFLAKE_USER", "datajudge")
-        password = os.environ.get("SNOWFLAKE_PASSWORD")
         account = os.environ.get("SNOWFLAKE_ACCOUNT", "")
-        connection_string = f"snowflake://{user}:{password}@{account}/datajudge/DBO?warehouse=datajudge&role=accountadmin"
+        connection_string = f"snowflake://{user}@{account}/datajudge/DBO?warehouse=datajudge&role=accountadmin"
+        connect_args["private_key"] = pkb
     elif "bigquery" in backend:
-        # gcp_project = os.environ.get("GOOGLE_CLOUD_PROJECT", "scratch-361908")
         connection_string = "bigquery://"
+    elif backend == "duckdb":
+        connection_string = "duckdb:///:memory:"
+    else:
+        raise NotImplementedError(f"Backend {backend} not supported.")
 
+    # DuckDB uses SingletonThreadPool which doesn't support pool_size/max_overflow
+    pool_kwargs = {} if backend == "duckdb" else {"pool_size": 10, "max_overflow": 20}
     engine = sa.create_engine(
-        connection_string, echo=True, pool_size=10, max_overflow=20
+        connection_string,
+        echo=True,
+        **pool_kwargs,
+        connect_args=connect_args,
     )
     apply_patches(engine)
 
     return engine
 
 
-def _string_column(engine):
+def _string_column(engine, minlength_db2=40):
     if is_db2(engine):
-        return sa.String(40)
+        return sa.String(minlength_db2)
     return sa.String()
 
 
@@ -71,7 +92,7 @@ def _string_column(engine):
 def engine(backend):
     engine = get_engine(backend)
     with engine.begin() as conn:
-        if engine.name in ("postgresql", "bigquery", "impala"):
+        if engine.name in ("postgresql", "bigquery", "duckdb"):
             conn.execute(sa.text(f"CREATE SCHEMA IF NOT EXISTS {SCHEMA}"))
     return engine
 
@@ -160,6 +181,8 @@ def mix_table2(engine, metadata):
 
 @pytest.fixture(scope="module")
 def mix_table2_pk(engine, metadata):
+    if is_duckdb(engine):
+        pytest.skip("DuckDB doesn't support SERIAL type for primary keys")
     table_name = "mix_table2_pk"
     columns = [
         sa.Column("col_int", sa.Integer(), primary_key=True),
@@ -265,6 +288,19 @@ def date_table_overlap(engine, metadata):
             "date_end": datetime.datetime(2016, 1, 8),
         },
     ]
+    # Duplicate entries.
+    data += [
+        {
+            "id1": 6,
+            "date_start": datetime.datetime(2016, 1, 1),
+            "date_end": datetime.datetime(2016, 1, 10),
+        },
+        {
+            "id1": 6,
+            "date_start": datetime.datetime(2016, 1, 1),
+            "date_end": datetime.datetime(2016, 1, 10),
+        },
+    ]
     _handle_table(engine, metadata, table_name, columns, data)
     return TEST_DB_NAME, SCHEMA, table_name
 
@@ -382,6 +418,23 @@ def date_table_overlap_2d(engine, metadata):
             "date_end2": datetime.datetime(2017, 1, 7),
         },
     ]
+    # Duplication overlap.
+    data += [
+        {
+            "id1": 8,
+            "date_start1": datetime.datetime(2016, 1, 1),
+            "date_end1": datetime.datetime(2016, 1, 10),
+            "date_start2": datetime.datetime(2017, 1, 1),
+            "date_end2": datetime.datetime(2017, 1, 10),
+        },
+        {
+            "id1": 8,
+            "date_start1": datetime.datetime(2016, 1, 1),
+            "date_end1": datetime.datetime(2016, 1, 10),
+            "date_start2": datetime.datetime(2017, 1, 1),
+            "date_end2": datetime.datetime(2017, 1, 10),
+        },
+    ]
     _handle_table(engine, metadata, table_name, columns, data)
     return TEST_DB_NAME, SCHEMA, table_name
 
@@ -451,6 +504,19 @@ def integer_table_overlap(engine, metadata):
             "range_end": 8,
         },
     ]
+    # Duplicate entries.
+    data += [
+        {
+            "id1": 6,
+            "range_start": 1,
+            "range_end": 10,
+        },
+        {
+            "id1": 6,
+            "range_start": 1,
+            "range_end": 10,
+        },
+    ]
     _handle_table(engine, metadata, table_name, columns, data)
     return TEST_DB_NAME, SCHEMA, table_name
 
@@ -588,7 +654,7 @@ def float_table_gap(engine, metadata):
         sa.Column("range_start", sa.Float()),
         sa.Column("range_end", sa.Float()),
     ]
-    data: List[Dict[str, Union[int, float]]] = []
+    data: list[dict[str, int | float]] = []
     # Single entry should not be considered a gap.
     data += [
         {
@@ -719,10 +785,6 @@ def unique_table2(engine, metadata):
 
 @pytest.fixture(scope="module")
 def unique_table_extralong(engine, metadata):
-    if is_impala(engine):
-        pytest.skip(
-            "Skipping this larger output check for impala due to it being quite brittle"
-        )
     if is_bigquery(engine):
         pytest.skip(
             "Skipping this larger output check for bigquery since creating the table is very slow"
@@ -737,10 +799,28 @@ def unique_table_extralong(engine, metadata):
     return TEST_DB_NAME, SCHEMA, table_name
 
 
+@pytest.fixture(scope="module")
+def unique_table_largesize(engine, metadata):
+    if is_bigquery(engine):
+        pytest.skip(
+            "Skipping this larger output check for bigquery since creating the table is very slow"
+        )
+    table_name = "unique_table_largesize"
+    columns = [
+        sa.Column("col_int", sa.Integer()),
+        sa.Column("col_varchar", _string_column(engine, minlength_db2=2000)),
+    ]
+    data = [
+        {"col_int": i // 2, "col_varchar": "hi" * min(i, 900)} for i in range(12345)
+    ]
+    _handle_table(engine, metadata, table_name, columns, data)
+    return TEST_DB_NAME, SCHEMA, table_name
+
+
 @pytest.fixture(scope="module")
 def nested_table(engine, metadata):
     table_name = "nested_table"
-    columns: List[Union[sa.Column, str]] = [
+    columns: list[sa.Column | str] = [
         sa.Column("nested_varchar", _string_column(engine))
     ]
     data = [
@@ -825,10 +905,10 @@ def functional_dependency_table_multi_key(engine, metadata):
 @pytest.fixture(scope="module")
 def varchar_table1(engine, metadata):
     table_name = "varchar_table1"
-    columns: List[Union[sa.Column, str]] = [
+    columns: list[sa.Column | str] = [
         sa.Column("col_varchar", _string_column(engine)),
     ]
-    data: List[Dict[str, Optional[str]]] = [
+    data: list[dict[str, str | None]] = [
         {"col_varchar": "qq" * i} for i in range(1, 10)
     ]
     data.append({"col_varchar": None})
@@ -839,7 +919,7 @@ def varchar_table1(engine, metadata):
 @pytest.fixture(scope="module")
 def varchar_table2(engine, metadata):
     table_name = "varchar_table2"
-    columns: List[Union[sa.Column, str]] = [
+    columns: list[sa.Column | str] = [
         sa.Column("col_varchar", _string_column(engine)),
     ]
     data = [{"col_varchar": "qq" * i} for i in range(2, 11)]
@@ -850,7 +930,7 @@ def varchar_table2(engine, metadata):
 @pytest.fixture(scope="module")
 def varchar_table_real(engine, metadata):
     table_name = "varchar_table_real"
-    columns: List[Union[sa.Column, str]] = [
+    columns: list[sa.Column | str] = [
         sa.Column("col_varchar", _string_column(engine)),
     ]
     data = [
@@ -1017,7 +1097,7 @@ def random_normal_table(engine, metadata):
     Table with normally distributed values of varying means and sd 1.
     """
 
-    if is_bigquery(engine) or is_impala(engine):
+    if is_bigquery(engine):
         # It takes too long to insert the table into BigQuery,
         # test using this fixture must be disabled for BigQuery
         return None, None, None
@@ -1061,10 +1141,6 @@ def capitalization_table(engine, metadata):
     elif is_bigquery(engine):
         str_datatype = "STRING"
         primary_key = ""  # there is no primary key in BigQuery
-    elif is_impala(engine):
-        str_datatype = "STRING"
-        # Impala supports primary keys but uses a different grammar.
-        primary_key = ""
     elif is_db2(engine):
         str_datatype = "VARCHAR(20)"
         # Primary key needs to be non-nullable.
@@ -1124,8 +1200,8 @@ def pytest_addoption(parser):
                 "postgres",
                 "snowflake",
                 "bigquery",
-                "impala",
                 "db2",
+                "duckdb",
             )
         ),
         help="which database backend to use to run the integration tests",
diff --git a/tests/integration/pytest_testfile.py b/tests/integration/pytest_testfile.py
index 04b2e56c..f1b31e8d 100644
--- a/tests/integration/pytest_testfile.py
+++ b/tests/integration/pytest_testfile.py
@@ -32,7 +32,7 @@ def datajudge_engine():
 
 
 ref = DataReference(
-    data_source=example_req.data_source,
+    data_source=example_req._data_source,
     columns=["example"],
     condition=None,
 )
diff --git a/tests/integration/test_column_capitalization.py b/tests/integration/test_column_capitalization.py
index cef55ccf..d22a594c 100644
--- a/tests/integration/test_column_capitalization.py
+++ b/tests/integration/test_column_capitalization.py
@@ -1,9 +1,7 @@
 import pytest
 
 from datajudge import Condition, WithinRequirement
-from datajudge.db_access import is_bigquery, is_db2, is_impala, is_mssql, is_postgresql
-
-# These tests
+from datajudge.db_access import is_bigquery, is_db2, is_duckdb, is_mssql, is_postgresql
 
 
 @pytest.mark.parametrize("use_uppercase_column", [True, False])
@@ -13,15 +11,14 @@ def test_column_existence(
 ):
     if is_mssql(engine) and use_uppercase_column != use_uppercase_query:
         pytest.skip("Mssql interface expects exact capitalization.")
-    if is_bigquery(engine) and use_uppercase_column != use_uppercase_query:
+    elif is_bigquery(engine) and use_uppercase_column != use_uppercase_query:
         pytest.skip("BigQuery interface expects exact capitalization.")
-    if is_impala(engine) and use_uppercase_query:
-        pytest.skip(
-            "Impala interface transforms writes to lower-case, expects lower-case reads."
-        )
-    if is_postgresql(engine):
+    elif is_duckdb(engine) and use_uppercase_column != use_uppercase_query:
+        # See https://duckdb.org/docs/stable/sql/dialect/keywords_and_identifiers#case-sensitivity-of-identifiers
+        pytest.skip("DuckDB interface expects exact capitalization.")
+    elif is_postgresql(engine):
         pytest.skip("Postgres interface always expects lower-cased columns.")
-    if is_db2(engine) and use_uppercase_query:
+    elif is_db2(engine) and use_uppercase_query:
         pytest.skip(
             "Db2 interface transforms writes to lower-case, expects lower-case reads."
         )
diff --git a/tests/integration/test_data_source.py b/tests/integration/test_data_source.py
index d92d08f2..3e2d5f4e 100644
--- a/tests/integration/test_data_source.py
+++ b/tests/integration/test_data_source.py
@@ -2,12 +2,14 @@
 import sqlalchemy as sa
 
 from datajudge import BetweenRequirement, Condition
-from datajudge.db_access import (
-    DataReference,
+from datajudge._engines import is_bigquery
+from datajudge.data_source import (
     ExpressionDataSource,
     RawQueryDataSource,
+)
+from datajudge.db_access import (
+    DataReference,
     get_column_names,
-    is_bigquery,
 )
 
 
@@ -33,7 +35,7 @@ def test_custom_data_source_from_query(engine, int_table1, int_table2):
         f"SELECT * FROM {schema_name2}.{table_name2}"
     )
     data_source = RawQueryDataSource(query, "string query")
-    derived_clause = data_source.get_clause(engine)
+    derived_clause = data_source._get_clause(engine)
     derived_selection = sa.select(derived_clause)
     with engine.connect() as connection:
         rows = connection.execute(derived_selection).scalars().fetchall()
@@ -51,7 +53,7 @@ def test_custom_data_source_from_expression(engine, metadata, int_table1, int_ta
     ).subquery()
     selection = sa.select(subquery)
     data_source = ExpressionDataSource(selection, "expression")
-    derived_clause = data_source.get_clause(engine)
+    derived_clause = data_source._get_clause(engine)
     derived_selection = sa.select(derived_clause)
     with engine.connect() as connection:
         rows = connection.execute(derived_selection).scalars().fetchall()
diff --git a/tests/integration/test_integration.py b/tests/integration/test_integration.py
index 7a8b91c8..cce60045 100644
--- a/tests/integration/test_integration.py
+++ b/tests/integration/test_integration.py
@@ -1,15 +1,18 @@
+from __future__ import annotations
+
 import functools
 import textwrap
 
 import pytest
 import sqlalchemy as sa
+from sqlalchemy import event
 
 import datajudge.requirements as requirements
 from datajudge.db_access import (
     Condition,
     is_bigquery,
     is_db2,
-    is_impala,
+    is_duckdb,
     is_mssql,
     is_postgresql,
     is_snowflake,
@@ -37,6 +40,24 @@ def negation(boolean_value):
     return not boolean_value
 
 
+class QueryCollector:
+    def __init__(self):
+        self.queries = []
+
+    def __call__(self, conn, cursor, statement, parameters, context, executemany):
+        self.queries.append(statement)
+
+    def __enter__(self) -> QueryCollector:
+        event.listen(sa.engine.Engine, "before_cursor_execute", self)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        event.remove(sa.engine.Engine, "before_cursor_execute", self)
+
+    def __len__(self):
+        return len(self.queries)
+
+
 @pytest.mark.parametrize(
     "data",
     [
@@ -123,11 +144,11 @@ def test_n_rows_max_gain_between(engine, mix_table1, mix_table2, data):
         condition1,
         condition2,
     ) = data
-    req = requirements.BetweenRequirement.from_tables(  # type: ignore[misc]
+    req = requirements.BetweenRequirement.from_tables(
         *mix_table1,
         *mix_table2,
-        date_column="col_date",
-        date_column2="col_date",
+        date_column="col_date",  # type: ignore[parameter-already-assigned, misc]
+        date_column2="col_date",  # type: ignore[parameter-already-assigned, misc]
     )
     req.add_n_rows_max_gain_constraint(
         constant_max_relative_gain=constant_max_relative_gain,
@@ -350,9 +371,9 @@ def test_uniques_equality_within_with_outputcheck(engine, unique_table1, data):
     )
     test_result = req[0].test(engine)
     assert operation(test_result.outcome), test_result.failure_message
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
 
 
 @pytest.mark.parametrize(
@@ -458,9 +479,9 @@ def test_uniques_equality_between_with_outputcheck(
     )
     test_result = req[0].test(engine)
     assert operation(test_result.outcome), test_result.failure_message
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
 
 
 @pytest.mark.parametrize(
@@ -596,9 +617,9 @@ def test_uniques_superset_within_with_outputcheck(engine, unique_table1, data):
     )
     test_result = req[0].test(engine)
     assert operation(test_result.outcome), test_result.failure_message
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
 
 
 @pytest.mark.parametrize(
@@ -646,9 +667,9 @@ def test_uniques_superset_between_with_outputcheck(
     )
     test_result = req[0].test(engine)
     assert operation(test_result.outcome), test_result.failure_message
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
 
 
 @pytest.mark.parametrize(
@@ -970,9 +991,9 @@ def test_uniques_subset_within_complex_with_outputcheck(engine, unique_table1, d
     print(test_result)
     print(test_result.failure_message)
     assert operation(test_result.outcome), test_result.failure_message
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
 
 
 @pytest.mark.parametrize(
@@ -1035,9 +1056,117 @@ def test_uniques_subset_within_complex_with_outputcheck_extralong(
     print(test_result)
     print(test_result.failure_message)
     assert operation(test_result.outcome), test_result.failure_message
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            negation,
+            ["col_int", "col_varchar"],
+            [(0, "hi0"), (1, "hi0")],
+            0,
+            filternull_element_or_tuple_any,
+            False,
+            [output_processor_sort, functools.partial(output_processor_limit, limit=5)],
+            None,
+            None,
+            None,
+        ),
+    ],
+)
+@pytest.mark.limit_memory(limit="90 MB")
+def test_memory_no_caching(engine, unique_table_largesize, data):
+    (
+        operation,
+        columns,
+        uniques,
+        max_relative_violations,
+        filter_func,
+        compare_distinct,
+        output_processors,
+        function,
+        condition,
+        failure_message_suffix,
+    ) = data
+
+    req = requirements.WithinRequirement.from_table(*unique_table_largesize)
+    for i in range(20):
+        # typical use-case of datajudge: many (possibly memory-intensive) constraints
+        # are added, and subsequently tested
+        # if the user specifies a cache_size of 0,
+        # this should work even if keeping the intermediate results of all checks in memory is infeasible
+        req.add_uniques_subset_constraint(
+            columns,
+            uniques,
+            max_relative_violations,
+            filter_func=filter_func,
+            compare_distinct=compare_distinct,
+            output_processors=output_processors,
+            condition=condition,
+            map_func=function,
+            cache_size=0,
+        )
+
+    with QueryCollector() as query_collector:
+        for constraint in req:
+            test_result = constraint.test(engine)
+            assert operation(test_result.outcome)
+        assert len(query_collector) > 20, query_collector.queries
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            negation,
+            ["col_int", "col_varchar"],
+            [(0, "hi0"), (1, "hi0")],
+            0,
+            filternull_element_or_tuple_any,
+            False,
+            [functools.partial(output_processor_limit, limit=5)],
+            None,
+            None,
+            "column(s) 'col_int', 'col_varchar' has a fraction of 0.9997569866342649 > 0 DISTINCT values (8228 / 8230) not being an element of '[(0, 'hi0'), (1, 'hi0')]'. It has excess elements '[(2, 'hi1'), (3, 'hi2'), (5, 'hi3'), (6, 'hi4'), (8, 'hi5'), '<SHORTENED OUTPUT, displaying the first 5 / 8228 elements above>']' with counts [2, 2, 2, 2, 2, '<SHORTENED OUTPUT, displaying the first 5 / 8228 counts above>'].",
+        ),
+    ],
+)
+def test_caching(engine, unique_table_largesize, data):
+    (
+        operation,
+        columns,
+        uniques,
+        max_relative_violations,
+        filter_func,
+        compare_distinct,
+        output_processors,
+        function,
+        condition,
+        failure_message_suffix,
+    ) = data
+
+    req = requirements.WithinRequirement.from_table(*unique_table_largesize)
+    req.add_uniques_subset_constraint(
+        columns,
+        uniques,
+        max_relative_violations,
+        filter_func=filter_func,
+        compare_distinct=compare_distinct,
+        output_processors=output_processors,
+        condition=condition,
+        map_func=function,
+    )
+
+    with QueryCollector() as query_collector:
+        for constraint in req:
+            for i in range(20):
+                test_result = constraint.test(engine)
+                assert operation(test_result.outcome)
+        assert len(query_collector) < 20, query_collector.queries
 
 
 @pytest.mark.parametrize(
@@ -1114,9 +1243,9 @@ def test_uniques_subset_between_with_outputcheck(
     )
     test_result = req[0].test(engine)
     assert operation(test_result.outcome), test_result.failure_message
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
 
 
 @pytest.mark.parametrize(
@@ -1271,9 +1400,9 @@ def test_functional_dependency_within_multi_key_with_outputcheck(
 
     test_result = req[0].test(engine)
     assert operation(test_result.outcome)
-    assert test_result.failure_message.endswith(
-        failure_message_suffix
-    ), test_result.failure_message
+    assert test_result.failure_message.endswith(failure_message_suffix), (
+        test_result.failure_message
+    )
 
 
 def _flatten_and_filter(data):
@@ -1611,14 +1740,27 @@ def test_numeric_mean_between(engine, int_table1, int_table2, data):
 @pytest.mark.parametrize(
     "data",
     [
-        (identity, 20, 3, 0, 0, None),
-        (identity, 20, 2.8, 0.21, None, None),
-        (identity, 20, 2.8, None, 0.1, None),
-        (negation, 20, 2.8, 0, None, None),
-        (negation, 20, 2.8, None, 0, None),
-        (negation, 20, 2.8, 0, 0, None),
+        # The data at hand in int_table1 are [1, 2, ..., 19].
+        # According to the definition of percentile in our doc string,
+        # the 20th percentile should be the smallest value in our data
+        # for which 20% of the data is less or equal that value.
+        # For the value 3, we have that |{1,2,3}|/19 ~ .16 of the values
+        # are less or equal.
+        # For the value 4, we have that |{1,2,3,4}|/19 ~ .21 of the values
+        # are less or equal.
+        # Hence the expected 20th percentile should be 4.
+        (identity, 20, 4, 0, 0, None),
+        (identity, 20, 3.8, 0.21, None, None),
+        (identity, 20, 3.8, None, 0.1, None),
+        (negation, 20, 3.8, 0, None, None),
+        (negation, 20, 3.8, None, 0, None),
+        (negation, 20, 3.8, 0, 0, None),
         (negation, 20, 3.2, 0, 0, None),
-        (identity, 20, 2, 0, 0, Condition(raw_string="col_int <= 11")),
+        # The expected percentile changes when conditioning.
+        # |{1,2}|/11 ~ .18
+        # |{1,2,3}|/11 ~ .27
+        (identity, 20, 3, 0, 0, Condition(raw_string="col_int <= 11")),
+        (negation, 20, 2.8, 0, 0, Condition(raw_string="col_int <= 11")),
     ],
 )
 def test_numeric_percentile_within(engine, int_table1, data):
@@ -1646,7 +1788,17 @@ def test_numeric_percentile_within(engine, int_table1, data):
 @pytest.mark.parametrize(
     "data",
     [
-        # With the following condition, we expect the values [0, 0, 1, 1, None].
+        # With the following condition, we expect the following values
+        # to be present in unique_table1's column col_int:
+        # [0, 0, 1, 1, None]
+        (
+            identity,
+            24,
+            0,
+            0,
+            None,
+            Condition(raw_string="col_int <= 1 or col_int IS NULL"),
+        ),
         (
             identity,
             25,
@@ -1658,7 +1810,7 @@ def test_numeric_percentile_within(engine, int_table1, data):
         (
             identity,
             74,
-            0,
+            1,
             0,
             None,
             Condition(raw_string="col_int <= 1 or col_int IS NULL"),
@@ -1706,12 +1858,16 @@ def test_numeric_percentile_within_null(engine, unique_table1, data):
 @pytest.mark.parametrize(
     "data",
     [
+        # The 20th percentile of int_table1 is 4.
+        # The 20th percentile of int_table2 is 5.
+        # Hence, the absolute deviation is 1 and
+        # the relative deviation is 1/5 = .2.
         (identity, 20, 1, None, None, None),
-        (identity, 20, None, 0.25, None, None),
-        (identity, 20, 1, 0.25, None, None),
+        (identity, 20, None, 0.20, None, None),
+        (identity, 20, 1, 0.20, None, None),
         (negation, 20, 0, 0, None, None),
         (negation, 20, 0.9, None, None, None),
-        (negation, 20, None, 0.20, None, None),
+        (negation, 20, None, 0.19, None, None),
         (identity, 20, 0, 0, Condition(raw_string="col_int >=2"), None),
     ],
 )
@@ -1848,6 +2004,8 @@ def test_date_between_within(engine, date_table1, data):
         (identity, 1, Condition(raw_string="id1 = 4")),
         (negation, 0, Condition(raw_string="id1 = 5")),
         (identity, 1, Condition(raw_string="id1 = 5")),
+        (negation, 0, Condition(raw_string="id1 = 6")),
+        (identity, 1, Condition(raw_string="id1 = 6")),
     ],
 )
 @pytest.mark.parametrize("key_columns", [["id1"], [], None])
@@ -1878,6 +2036,8 @@ def test_date_no_overlap_within_varying_key_columns(
         (identity, 1, Condition(raw_string="id1 = 4")),
         (negation, 0, Condition(raw_string="id1 = 5")),
         (identity, 1, Condition(raw_string="id1 = 5")),
+        (negation, 0, Condition(raw_string="id1 = 6")),
+        (identity, 1, Condition(raw_string="id1 = 6")),
     ],
 )
 @pytest.mark.parametrize("key_columns", [["id1"], [], None])
@@ -1900,8 +2060,9 @@ def test_integer_no_overlap_within_varying_key_columns(
 @pytest.mark.parametrize(
     "data",
     [
-        (negation, 0.59, None),
-        (identity, 0.6, None),
+        # 2/6 ids succeed
+        (negation, 0.66, None),
+        (identity, 0.67, None),
         (identity, 0, Condition(raw_string="id1 IN (1, 2)")),
     ],
 )
@@ -1974,6 +2135,8 @@ def test_date_no_overlap_within_inclusion_exclusion(engine, date_table_overlap,
         (identity, 1, Condition(raw_string="id1 = 6")),
         (negation, 0, Condition(raw_string="id1 = 7")),
         (identity, 1, Condition(raw_string="id1 = 7")),
+        (negation, 0, Condition(raw_string="id1 = 8")),
+        (identity, 1, Condition(raw_string="id1 = 8")),
     ],
 )
 @pytest.mark.parametrize("key_columns", [["id1"], [], None])
@@ -1999,9 +2162,9 @@ def test_date_no_overlap_2d_within_varying_key_column(
     "data",
     [
         (identity, 0, Condition(raw_string="id1 IN (1, 2, 3, 4)")),
-        # 3/7 ids have violations.
-        (negation, 0.42, None),
-        (identity, 0.43, None),
+        # 4/8 ids have violations.
+        (negation, 0.49, None),
+        (identity, 0.50, None),
     ],
 )
 def test_date_no_overlap_2d_within_fixed_key_column(
@@ -2409,8 +2572,8 @@ def test_backend_dependent_condition(engine, mix_table1):
         is_postgresql(engine)
         or is_snowflake(engine)
         or is_bigquery(engine)
-        or is_impala(engine)
         or is_db2(engine)
+        or is_duckdb(engine)
     ):
         condition = Condition(raw_string="LENGTH(col_varchar) = 3")
     else:
@@ -2587,8 +2750,8 @@ def test_column_type_between(engine, get_fixture, data):
 def test_primary_key_definition_within(engine, pk_table, data):
     if is_bigquery(engine):
         pytest.skip("No primary key concept in BigQuery")
-    if is_impala(engine):
-        pytest.skip("Currently not implemented for impala.")
+    if is_duckdb(engine):
+        pytest.skip("DuckDB engine doesn't support reflection on primary keys")
 
     (operation, columns) = data
     req = requirements.WithinRequirement.from_table(*pk_table)
@@ -2623,7 +2786,7 @@ def test_uniqueness_within(engine, mix_table2, data):
     # is not correctly compiled when dealing with snowflake or bigquery.
     # Use the mod function instead
     if (
-        (is_snowflake(engine) or is_bigquery(engine) or is_impala(engine))
+        (is_snowflake(engine) or is_bigquery(engine))
         and condition is not None
         and condition.raw_string is not None
         and "% 2 = 0" in condition.raw_string
@@ -2646,10 +2809,10 @@ def test_uniqueness_within(engine, mix_table2, data):
     ],
 )
 def test_uniqueness_within_infer_pk(engine, data, mix_table2_pk):
-    if is_impala(engine):
-        pytest.skip("Primary key retrieval currently not implemented for impala.")
     if is_bigquery(engine):
         pytest.skip("No primary key concept in BigQuery")
+    if is_duckdb(engine):
+        pytest.skip("DuckDB engine doesn't support SERIAL type or PK reflection")
     # We purposefully select a non-unique column ["col_date"] to validate
     # that the reference columns are overwritten.
     operation, target_columns, selection_columns = data
@@ -2657,9 +2820,9 @@ def test_uniqueness_within_infer_pk(engine, data, mix_table2_pk):
     req.add_uniqueness_constraint(columns=selection_columns, infer_pk_columns=True)
     test_result = req[0].test(engine)
     # additional test: the PK columns are inferred during test time, i.e. we can check here if they were inferred correctly
-    assert (
-        req[0].ref.columns == target_columns
-    ), f"Incorrect columns were retrieved from table. {req[0].ref.columns} != {target_columns}"
+    assert req[0]._ref.columns == target_columns, (
+        f"Incorrect columns were retrieved from table. {req[0]._ref.columns} != {target_columns}"
+    )
     assert operation(test_result.outcome), test_result.failure_message
 
 
@@ -2728,8 +2891,6 @@ def test_max_null_fraction_between(engine, unique_table1, data):
 )
 def test_column_type_within(engine, mix_table1, data):
     (operation, col_name, type_name) = data
-    if is_impala(engine) and isinstance(type_name, str):
-        type_name = {"VARCHAR": "string", "INTEGER": "int"}[type_name]
     req = requirements.WithinRequirement.from_table(*mix_table1)
     req.add_column_type_constraint(col_name, type_name)
     test_result = req[0].test(engine)
@@ -2770,8 +2931,6 @@ def test_column_type_within(engine, mix_table1, data):
     ],
 )
 def test_row_equality_between(engine, mix_table1, mix_table2, data):
-    if is_impala(engine):
-        pytest.skip("Currently not implemented for Impala. EXCEPT throws syntax error.")
     (operation, columns, max_missing_fraction, condition1, condition2) = data
     req = requirements.BetweenRequirement.from_tables(*mix_table1, *mix_table2)
     req.add_row_equality_constraint(
@@ -2813,8 +2972,6 @@ def test_row_equality_between(engine, mix_table1, mix_table2, data):
     ],
 )
 def test_row_subset_between(engine, mix_table1, mix_table2, data):
-    if is_impala(engine):
-        pytest.skip("Currently not implemented for Impala. EXCEPT throws syntax error.")
     (
         operation,
         columns,
@@ -2867,8 +3024,6 @@ def test_row_subset_between(engine, mix_table1, mix_table2, data):
     ],
 )
 def test_row_superset_between(engine, mix_table2, mix_table1, data):
-    if is_impala(engine):
-        pytest.skip("Currently not implemented for Impala. EXCEPT throws syntax error.")
     (
         operation,
         columns,
@@ -2915,8 +3070,6 @@ def test_row_matching_equality(engine, row_match_table1, row_match_table2, data)
     # TODO: Not sure why this doesn't work
     if is_db2(engine):
         pytest.skip()
-    if is_impala(engine):
-        pytest.skip("Currently not implemented for Impala. EXCEPT throws syntax error.")
     (
         operation,
         matching_columns,
@@ -2947,8 +3100,6 @@ def test_groupby_aggregation_within(engine, groupby_aggregation_table_correct, k
     # TODO: This should actually work for db2
     if is_db2(engine):
         pytest.skip()
-    if is_impala(engine):
-        pytest.skip("array_agg does not exist for Impala.")
     req = requirements.WithinRequirement.from_table(*groupby_aggregation_table_correct)
     req.add_groupby_aggregation_constraint(key, "value", 1)
     test_result = req[0].test(engine)
@@ -2963,8 +3114,6 @@ def test_groupby_aggregation_within_with_failures(
     skip_if_mssql(engine)
     if is_db2(engine):
         pytest.skip()
-    if is_impala(engine):
-        pytest.skip("array_agg does not exist for Impala.")
     req = requirements.WithinRequirement.from_table(
         *groupby_aggregation_table_incorrect
     )
@@ -3087,7 +3236,7 @@ def test_ks_2sample_constraint_wrong_between(
     ],
 )
 def test_ks_2sample_random(engine, random_normal_table, configuration):
-    if is_bigquery(engine) or is_impala(engine) or is_db2(engine):
+    if is_bigquery(engine) or is_db2(engine):
         pytest.skip("It takes too long to insert the table.")
 
     (operation, col_1, col_2, min_p_value) = configuration
diff --git a/tests/integration/test_stats.py b/tests/integration/test_stats.py
index 812a6ebc..8a9109b0 100644
--- a/tests/integration/test_stats.py
+++ b/tests/integration/test_stats.py
@@ -1,15 +1,24 @@
 import pytest
 
-import datajudge
+from datajudge._engines import is_bigquery, is_db2
+from datajudge.constraints.stats import AndersonDarling2Sample, KolmogorovSmirnov2Sample
+from datajudge.data_source import TableDataSource
 from datajudge.db_access import (
     DataReference,
-    TableDataSource,
-    is_bigquery,
-    is_db2,
-    is_impala,
+    _cross_cdf_selection,
+    get_anderson_darling_sums,
+    get_row_count,
 )
 
 
+def identity(boolean_value):
+    return boolean_value
+
+
+def negation(boolean_value):
+    return not boolean_value
+
+
 def test_cross_cdf_selection(engine, cross_cdf_table1, cross_cdf_table2):
     # TODO: Fix this
     if is_db2(engine):
@@ -20,9 +29,7 @@ def test_cross_cdf_selection(engine, cross_cdf_table1, cross_cdf_table2):
     tds2 = TableDataSource(database2, table2, schema2)
     ref1 = DataReference(tds1, columns=["col_int"])
     ref2 = DataReference(tds2, columns=["col_int"])
-    selection, _, _ = datajudge.db_access._cross_cdf_selection(
-        engine, ref1, ref2, "cdf", "value"
-    )
+    selection, _, _ = _cross_cdf_selection(engine, ref1, ref2, "cdf", "value")
     with engine.connect() as connection:
         result = connection.execute(selection).fetchall()
     assert result is not None and len(result) > 0
@@ -47,7 +54,7 @@ def test_cross_cdf_selection(engine, cross_cdf_table1, cross_cdf_table2):
     ],
 )
 def test_ks_2sample_calculate_statistic(engine, random_normal_table, configuration):
-    if is_bigquery(engine) or is_impala(engine) or is_db2(engine):
+    if is_bigquery(engine) or is_db2(engine):
         pytest.skip("It takes too long to insert the table into BigQuery")
 
     col_1, col_2, expected_d, expected_p = configuration
@@ -62,15 +69,92 @@ def test_ks_2sample_calculate_statistic(engine, random_normal_table, configurati
         n_samples,
         m_samples,
         _,
-    ) = datajudge.constraints.stats.KolmogorovSmirnov2Sample.calculate_statistic(
-        engine, ref, ref2
-    )
+    ) = KolmogorovSmirnov2Sample._calculate_statistic(engine, ref, ref2)
 
-    assert (
-        abs(d_statistic - expected_d) <= 1e-10
-    ), f"The test statistic does not match: {expected_d} vs {d_statistic}"
+    assert abs(d_statistic - expected_d) <= 1e-10, (
+        f"The test statistic does not match: {expected_d} vs {d_statistic}"
+    )
 
     # 1e-05 should cover common p_values; if scipy is installed, a very accurate p_value is automatically calculated
-    assert (
-        abs(p_value - expected_p) <= 1e-05
-    ), f"The approx. p-value does not match: {expected_p} vs {p_value}"
+    assert abs(p_value - expected_p) <= 1e-05, (
+        f"The approx. p-value does not match: {expected_p} vs {p_value}"
+    )
+
+
+@pytest.mark.parametrize(
+    "configuration",
+    [
+        (identity, "value_0_1", "value_0_1", 0.05),
+        (negation, "value_0_1", "value_005_1", 0.05),
+        (negation, "value_0_1", "value_02_1", 0.05),
+        (negation, "value_0_1", "value_1_1", 0.05),
+    ],
+)
+def test_ad_2sample_hypothesis_testing(engine, random_normal_table, configuration):
+    """Test that the Anderson-Darling 2-sample test correctly rejects null hypothesis for different distributions."""
+    if is_bigquery(engine) or is_db2(engine):
+        pytest.skip("It takes too long to insert the table into BigQuery/DB2")
+
+    operation, col_1, col_2, significance_level = configuration
+    database, schema, table = random_normal_table
+    tds = TableDataSource(database, table, schema)
+    ref = DataReference(tds, columns=[col_1])
+    ref2 = DataReference(tds, columns=[col_2])
+
+    constraint = AndersonDarling2Sample(
+        ref, ref2, significance_level=significance_level
+    )
+    test_result = constraint.test(engine)
+
+    assert operation(test_result.outcome), test_result.failure_message
+
+
+def test_ad_2sample_similarity_effect_on_statistic(engine, random_normal_table):
+    """Test basic a property of the Anderson-Darling test statistic.
+
+    When comparing two similar distributions to two very different distributions,
+    the latter should lead to a higher test statistic than the former.
+    """
+    if is_bigquery(engine) or is_db2(engine):
+        pytest.skip("It takes too long to insert the table into BigQuery/DB2")
+
+    database, schema, table = random_normal_table
+    tds = TableDataSource(database, table, schema)
+
+    def _statistic(col1, col2):
+        ref_small1 = DataReference(tds, columns=["value_0_1"])
+        ref_small2 = DataReference(tds, columns=["value_005_1"])
+
+        sample_size1, _ = get_row_count(engine, ref_small1)
+        sample_size2, _ = get_row_count(engine, ref_small2)
+        sample_size = sample_size1 + sample_size2
+
+        sum1_small, sum2_small, _ = get_anderson_darling_sums(
+            engine, ref_small1, ref_small2, sample_size
+        )
+        return AndersonDarling2Sample.compute_test_statistic(
+            sum1_small, sum2_small, sample_size1, sample_size2
+        )
+
+    statistic_similar = _statistic("value_0_1", "value_005_1")
+    statistic_dissimilar = _statistic("value_0_1", "value_1_1")
+
+    # The statistic for more different distributions should be larger
+    assert statistic_dissimilar >= statistic_similar > 0, (
+        f"Statistic for large difference ({statistic_dissimilar}) should be "
+        f"larger than for small difference ({statistic_similar})"
+    )
+
+
+def test_ad_2sample_critical_value_monotonic_in_sl():
+    """Test the critical value approximation function."""
+    # Test that critical value increases with decreasing significance level
+    cv_025 = AndersonDarling2Sample.approximate_critical_value(100, 100, 0.25)
+    cv_01 = AndersonDarling2Sample.approximate_critical_value(100, 100, 0.1)
+    cv_005 = AndersonDarling2Sample.approximate_critical_value(100, 100, 0.05)
+    cv_001 = AndersonDarling2Sample.approximate_critical_value(100, 100, 0.01)
+
+    assert cv_025 < cv_01 < cv_005 < cv_001, (
+        f"Critical values should increase as significance level decreases: "
+        f"0.25->{cv_025}, 0.1->{cv_01}, 0.05->{cv_005}, 0.01->{cv_001}"
+    )
diff --git a/tests/unit/test_condition.py b/tests/unit/test_condition.py
index 29658928..9aa8b1cb 100644
--- a/tests/unit/test_condition.py
+++ b/tests/unit/test_condition.py
@@ -1,6 +1,6 @@
 import pytest
 
-from datajudge.db_access import Condition
+from datajudge.condition import Condition
 
 
 def test_equality():
diff --git a/tests/unit/test_db_access.py b/tests/unit/test_data_source.py
similarity index 90%
rename from tests/unit/test_db_access.py
rename to tests/unit/test_data_source.py
index 3cb7a1f7..508cad48 100644
--- a/tests/unit/test_db_access.py
+++ b/tests/unit/test_data_source.py
@@ -1,7 +1,7 @@
 import pytest
 import sqlalchemy as sa
 
-from datajudge.db_access import ExpressionDataSource, TableDataSource
+from datajudge.data_source import ExpressionDataSource, TableDataSource
 
 
 @pytest.mark.parametrize(
diff --git a/tests/unit/test_requirement.py b/tests/unit/test_requirement.py
new file mode 100644
index 00000000..c680b4bf
--- /dev/null
+++ b/tests/unit/test_requirement.py
@@ -0,0 +1,31 @@
+import inspect
+
+from datajudge import WithinRequirement
+from datajudge.requirements import BetweenRequirement
+
+
+def test_all_requirements_new_kwargs():
+    prices_req = WithinRequirement.from_table(
+        db_name="example", schema_name="schema", table_name="prices"
+    )
+
+    prices_req_between = BetweenRequirement.from_tables(
+        "example",
+        "schema1",
+        "table1",
+        "example",
+        "schema2",
+        "table2",
+    )
+
+    for req in [prices_req, prices_req_between]:
+        for add_func_name in dir(req):
+            if not add_func_name.startswith("add_"):
+                continue
+            add_func = getattr(req, add_func_name)
+            sig = inspect.signature(add_func)
+            assert "cache_size" in sig.parameters, (
+                add_func,
+                sig.parameters,
+                sig,
+            )
diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py
index 07105bd1..43fc1435 100644
--- a/tests/unit/test_utils.py
+++ b/tests/unit/test_utils.py
@@ -155,12 +155,14 @@ def test_output_processor_limit_defaults():
     input_counts = None
 
     input_main_copy = input_main.copy()
+    input_counts_copy = None
     assert output_processor_limit(input_main, input_counts) == (
         list(range(100))
         + ["<SHORTENED OUTPUT, displaying the first 100 / 12345 elements above>"],
         None,
     )
-    assert input_main == input_main_copy
+    assert input_main == input_main_copy  # verify inputs are not modified
+    assert input_counts == input_counts_copy
 
 
 def test_output_processor_limit_custom():
@@ -168,12 +170,14 @@ def test_output_processor_limit_custom():
     input_counts = None
 
     input_main_copy = input_main.copy()
+    input_counts_copy = None
     assert output_processor_limit(input_main, input_counts, limit=42) == (
         list(range(42))
         + ["<SHORTENED OUTPUT, displaying the first 42 / 12345 elements above>"],
         None,
     )
-    assert input_main == input_main_copy
+    assert input_main == input_main_copy  # verify inputs are not modified
+    assert input_counts == input_counts_copy
 
 
 def test_output_processor_limit_withcounts():
@@ -188,7 +192,7 @@ def test_output_processor_limit_withcounts():
         list(range(1, 42 + 1))
         + ["<SHORTENED OUTPUT, displaying the first 42 / 12345 counts above>"],
     )
-    assert input_main == input_main_copy
+    assert input_main == input_main_copy  # verify inputs are not modified
     assert input_counts == input_counts_copy