icefabric

An Apache Iceberg implementation of the Hydrofabric to disseminate continental hydrologic data

Note

To run any of the functions in this repo your AWS test account credentials need to be in your .env file and your .pyiceberg.yaml settings need to up to date with AWS_DEFAULT_REGION="us-east-1" set

The .env file is used for deploying to the test environment. A .prod.env file is used in-place of that if you're deploying to the production environment. By default, when not deploying locally, the test env/catalog is the default; prod needs to be specified when the API/dashboard is launched.

Getting Started

This repo is managed through UV and can be installed through:

uv sync --all-extras
source .venv/bin/activate

Note: Functionality is split into optional-dependencies in pyproject.toml. If you only require base functionality, install as uv sync. If you require some extras (e.g. icechunk, io), you can specify uv sync --extra icechunk --extra io as needed. For local develpoment, --all-extras is recommended for complete functionality.

Running/deploying the services

The following sections detail how to run the Icefabric services locally (standalone) as well as how to build/deploy them through Docker (either standalone or together behind an nginx reverse-proxy)

Running the API locally

To run the API locally, ensure your .env file (make sure to have your prod credentials in a .prod.env if deploying with the production env/catalog) in your project root has the right credentials, then run

python -m app.main

This should spin up the API services at localhost:8000/.

To specify the deploy environment/iceberg catalog used (test or production (OE)), add a deploy-env flag to the command. The flag should be formatted as --deploy-env <value>:

# Test
python -m app.main --catalog glue --deploy-env test
# Prod
python -m app.main --catalog glue --deploy-env prod

If you are running the API locally, you can run

python -m app.main --catalog sql

Building/deploying the API through Docker

To run the API locally with Docker, ensure your .env file (make sure to have your prod credentials in a .prod.env if deploying with the production env/catalog) in your project root has the right credentials, then run the compose.sh wrapper script to spin up the dashboard::

# Build
docker compose -f docker/compose.yaml build api --no-cache

# Run
./compose.sh api

To specify the deploy environment/iceberg catalog used (test or production (OE)), pass it in as an argument to the wrapper script:

# Test deploy (default)
./compose.sh api test
# Prod (OE) deploy
./compose.sh api prod

Running the dashboard locally

Besides the API, Icefabric also includes a frontend dashboard to interact with the Hydrofabric. The dashboard is implemented through streamlit. To run the dashboard locally, ensure your .env file (make sure to have your prod credentials in a .prod.env if deploying with the production env/catalog) in your project root has the right credentials (as with the API), then run:

uv run streamlit run app/streamlit/streamlit.py

The dashboard will spin up, and can be accessed in a browser at http://localhost:8501. Please note that the port number may change depending on availability. The command output will tell you the port number.

To specify the deploy environment/iceberg catalog used (test or production (OE)), add a deploy-env flag to the run command. The flag should be formatted as deploy-env=<value>:

# Test deploy (default)
uv run streamlit run app/streamlit/streamlit.py deploy-env=test
# Prod (OE) deploy
uv run streamlit run app/streamlit/streamlit.py deploy-env=prod

Building/deploying the dashboard through Docker

To run just the Dashboard locally with Docker, ensure your .env file (make sure to have your prod credentials in a .prod.env if deploying with the production env/catalog) in your project root has the right credentials, then run the compose.sh wrapper script to spin up the dashboard:

# Build
docker compose -f docker/compose.yaml build dashboard --no-cache

# Run
./compose.sh dashboard

To specify the deploy environment/iceberg catalog used (test or production (OE)), pass it in as an argument to the wrapper script:

# Test deploy (default)
./compose.sh dashboard test
# Prod (OE) deploy
./compose.sh dashboard prod

Full deployment with reverse proxy

To run the api and dashboard together, you can specify this to the docker compose wrapper script. The services will be routed behind an nginx reverse-proxy, with the underlying services only directly accessible from the localhost.

The api will be accesible @ http://localhost:80/api

The dashboard will be accesible @ http://localhost:80/dashboard

ensure your .env file (make sure to have your prod credentials in a .prod.env if deploying with the production env/catalog) in your project root has the right credentials, then run the compose.sh wrapper script to spin up everyting:

# Build
docker compose -f docker/compose.yaml build --no-cache

# Run
./compose.sh full

To specify the deploy environment/iceberg catalog used (test or production (OE)), pass it in as an argument to the wrapper script:

# Test deploy (default)
./compose.sh full test
# Prod (OE) deploy
./compose.sh full prod

Development

To ensure that icefabric follows the specified structure, be sure to install the local dev dependencies and run pre-commit install

Documentation

To build the user guide documentation for Icefabric locally, run the following commands:

uv pip install ".[docs]"
mkdocs serve -a localhost:8080

Docs will be spun up at localhost:8080/

Pytests

The tests folder is for all testing data so the global confest can pick it up. This allows all tests in the namespace packages to share the same scope without having to reference one another in tests

To run tests, run pytest -s from project root.

To run the subsetter tests, run pytest --run-slow as these tests take some time. Otherwise, they will be skipped

Smoke Tests

Smoke tests validate the deployed test API. These tests are skipped when the API_BASE_URL environment variable is not set, so they won't run during normal CI.

To run smoke tests against a deployed environment:

export API_BASE_URL="http://edfs.test.nextgenwaterprediction.com:8000/"
uv run pytest tests/smoke/ -v

The smoke tests currently verify:

• The API health endpoint is reachable
• Numeric fields (initial_value, min, max) in the parameter_metadata endpoint are never null