Caching

Git-Pandas supports pluggable cache backends to optimize performance for expensive, repetitive operations. This is particularly useful for large repositories or when running multiple analyses.

Overview

The caching system provides: * In-memory caching for temporary results * Redis-based caching for persistent storage * Configurable cache durations * Automatic cache invalidation

Available Cache Backends

In-Memory Cache

The default in-memory cache is ephemeral and will be cleared when the process ends:

from gitpandas import Repository
repo = Repository('/path/to/repo', cache=True)  # Enable in-memory caching

Redis Cache

For persistent caching across sessions, use Redis:

from gitpandas import Repository
repo = Repository(
    '/path/to/repo',
    cache=True,
    cache_backend='redis',
    redis_url='redis://localhost:6379/0'
)

Configuration

You can configure cache behavior through the following parameters:

cache: Enable/disable caching (default: False)
cache_backend: Choose the cache backend (‘memory’ or ‘redis’)
cache_ttl: Time-to-live for cached results in seconds
redis_url: Redis connection URL (for Redis backend)

Example Usage

from gitpandas import Repository

# Create repository with Redis caching
repo = Repository(
    working_dir='/path/to/repo',
    cache=True,
    cache_backend='redis',
    cache_ttl=3600,  # Cache for 1 hour
    redis_url='redis://localhost:6379/0'
)

# First call will compute and cache the result
blame_df = repo.blame()

# Subsequent calls will use cached result if available
blame_df = repo.blame()  # Much faster!

API Reference