Python Performance Benchmarks

A collection of benchmarks exploring various aspects of Python performance. This project aims to provide empirical data about different Python constructs, helping developers make informed decisions about their code.

Overview

This repository contains benchmarks for various Python operations, data structures, and programming patterns. Each benchmark is designed to provide clear, reproducible results that can guide practical development decisions.

Database System

The project uses SQLite to store and track benchmark results. This enables:

• Systematic collection of benchmark data
• Historical performance tracking
• Cross-environment result comparison
• Data-driven performance analysis

Database Setup

# Initialize the database and create required tables
python database.py

Database Schema

The database consists of two main tables:

benchmarks

• id: Primary key
• name: Benchmark name
• category: Category (e.g., "data-structures", "string-operations")
• description: Detailed description of what is being tested
• script_path: Path to the benchmark script

results

• id: Primary key
• benchmark_id: Foreign key referencing benchmarks table
• timestamp: When the benchmark was run
• execution_time: Total execution time
• output: Detailed benchmark results

Running Benchmarks

# Run all benchmarks and store results in database
python run_benchmarks.py

# Query and display results
python query_results.py

Structure

python-benchmarking/
├── benchmarks/                     # Main benchmarks directory
│   ├── 1-data-structures/         # ✓ Implemented
│   │   ├── dict-vs-list-membership.py
│   │   ├── list-vs-tuple-access.py
│   │   └── set-vs-list-membership.py
│   ├── 2-string-operations/       # 🔄 In Progress
│   ├── 3-function-calls/          # 📅 Planned
│   ├── 4-loops/                   # 📅 Planned
│   ├── 5-imports/                 # ⏳ Backlog
│   ├── 6-exception-handling/      # ⏳ Backlog
│   ├── 7-math-operations/         # ⏳ Backlog
│   ├── 8-sorting/                 # ⏳ Backlog
│   ├── 9-io-operations/           # ⏳ Backlog
│   ├── 10-multithreading-async/   # ⏳ Backlog
│   ├── 11-caching-lookup/         # ⏳ Backlog
│   ├── 12-python-internals/       # ⏳ Backlog
│   └── results/                   # Benchmark results in markdown
│       ├── 1-data-structures/
│       └── 2-string-operations/
├── database.py                    # Database initialization and management
├── main.py                        # Main application entry point
├── query_results.py              # Tools for querying benchmark results
└── run_benchmarks.py             # Benchmark execution script

Results

Results are stored in the SQLite database and can be analyzed using the provided query tools. The system tracks:

• Execution times across different runs
• Performance variations between environments
• Historical trends

Result Configurations

Machine	CPU	RAM	Python Version	OS	Date	Notes
zbuyhrer	Apple M2	16 GB	Python 3.12	MacOS Sonoma Version 14.5 (23F79)	Feb 2025	Initial Benchmarking

Contributing

When adding new benchmarks:

1. Create a new benchmark script in the appropriate category directory
2. Ensure the script outputs results in the expected format
3. Run the benchmark using run_benchmarks.py
4. Verify results are properly stored in the database
5. Update documentation as needed

Future Improvements

• Web interface for viewing and comparing results
• Automated environment detection and logging
• Statistical analysis of benchmark results
• Performance visualization tools
• Cross-version Python testing