pychecksumcache 1.1.0

A lightweight Python library that uses MD5 checksums to track file changes and execute code only when files have been modified. PyChecksumCache maintains a persistent cache of file checksums, making it ideal for build systems, asset processors, incremental compilers, and any tools that need efficient change detection.

PyChecksumCache

A lightweight Python library that uses MD5 checksums to track file changes and execute code only when files have been modified. PyChecksumCache maintains a persistent cache of file checksums, making it ideal for build systems, asset processors, incremental compilers, and any tools that need efficient change detection.

How It Works

flowchart TD
    subgraph "Individual Processing"
        A[File to process] --> B{Calculate MD5}
        B --> C[Current checksum]
        D[(Checksum cache)] --> E{Compare with cached checksum}
        C --> E
        
        E -->|Different or not in cache| F[Execute code]
        E -->|Same| G[Skip processing]
        
        F --> H[Update cache with new checksum]
    end
    
    style A fill:#f9f9f9,stroke:#333,stroke-width:2px
    style D fill:#c9e6ff,stroke:#0066cc,stroke-width:2px
    style F fill:#d9f7be,stroke:#389e0d,stroke-width:2px
    style G fill:#ffccc7,stroke:#cf1322,stroke-width:2px

flowchart TD
    subgraph "Individual Processing"
        A1[Input Files] --> B1{Check\nChecksums}
        B1 -->|Changed| C1[Process\nEach Changed File]
        B1 -->|Unchanged| D1[Skip\nUnchanged Files]
        C1 --> E1[Multiple\nOutput Files]
    end
    
    subgraph "Aggregate Processing"
        A2[Input Files] --> B2{Any File\nChanged?}
        B2 -->|Yes| C2[Process All Files\nTogether]
        B2 -->|No| D2[Skip\nProcessing]
        C2 --> E2[Single\nOutput File]
    end
    
    style A1 fill:#f9f9f9,stroke:#333,stroke-width:2px
    style A2 fill:#f9f9f9,stroke:#333,stroke-width:2px
    
    style C1 fill:#d9f7be,stroke:#389e0d,stroke-width:2px
    style C2 fill:#d9f7be,stroke:#389e0d,stroke-width:2px
    
    style D1 fill:#ffccc7,stroke:#cf1322,stroke-width:2px
    style D2 fill:#ffccc7,stroke:#cf1322,stroke-width:2px
    
    style E1 fill:#b5f5ec,stroke:#13a8a8,stroke-width:2px
    style E2 fill:#ffd666,stroke:#d48806,stroke-width:2px

Features

• Track changes to files using MD5 checksums
• Persistent cache storage in JSON format

{
  "tests/file1.txt": "ff1e0283123d14cf8bd52ac449770017",
  "tests/file2.txt": "b445bf8b5da4cf880dd14e98c18c1bfa"
}

• Execute functions only when file content has changed
• Aggregate multiple files into a single output file
• Batch transform multiple files with automatic output management
• Full async/await support
• Works with Python 3.10+
• No external dependencies

Installation

pip install pychecksumcache

Or with uv:

uv pip install pychecksumcache

Quick Start

Basic Usage

import pytest
from pychecksumcache import PyChecksumCache
import os

input_files = ["tests/file1.txt", "tests/file2.txt"]
output_folder = "output"
output_extension = ".generated.txt"

@pytest.mark.asyncio
async def test_pychecksumcache_skipped():
    # Define a transformation function
    def transform_func(input_path, output_path):
        with open(input_path, 'r') as infile, open(output_path, 'w') as outfile:
            content = infile.read()
            # Apply some transformation
            transformed = content.upper()  # For example, convert to uppercase
            outfile.write(transformed)

    # Perform the transformation
    results = PyChecksumCache().transform(input_files, output_folder, output_extension, transform_func)

    # See which files were processed
    for output_file, was_transformed in results:
        assert not was_transformed, f"Skipped (unchanged): {output_file}"
    assert os.path.exists("checksum_cache.json") is True