python performance benchmarks

Overview

performance benchmarks for python libraries and language features.

Available Benchmarks

Data Modeling

python data models comparison - performance comparison of dataclasses, pydantic, and sqlalchemy
dataclass vs pydantic evolution - performance changes across python versions 3.10-3.14

Methodology

all benchmarks follow consistent principles:

minimum 10,000 iterations for statistical significance
high-resolution timing via time.perf_counter()
testing across multiple python versions
real-world operations
reproducible test scripts

Running Benchmarks

benchmark scripts are available in /public/wiki/python/benchmarks/data-models/:

# example: data model benchmarks
cd public/wiki/python/benchmarks/data-models/
uv run --python 3.14 --with pydantic --with sqlalchemy python benchmark.py

Available Files

benchmark.py - main benchmark script
analyze.py - result analysis
visualize.py - main visualization
visualize_versions.py - version comparison chart
summary.json - aggregated results
README.md - detailed documentation

Contributing

when adding benchmarks:

clearly describe methodology
provide executable scripts
test across python versions
include visualizations
document dependencies