A Python package for using disk-based or object-bashed caching, perfect for simple scripts or Jupyter Notebooks.
✨ Flexible Storage Options
- Multiple cache storage modes (Memory, Disk, or Hybrid)
- Support for multiple independent cache stores
- Configurable cache directory location
⚡ Performance
- In-memory caching for fast access
- Disk persistence for data durability
- Hybrid mode for optimal performance and reliability
⚙️ Advanced Cache Control
- Automatic cache expiration
- Manual cache item removal
- Configurable default values for cache misses
- Non-expiring cache items support
🛠️ Developer Friendly
- Simple, intuitive API
- Perfect for scripts and Jupyter notebooks
- No external dependencies
- Lightweight alternative to Redis for simple use cases
Use pip to install:
pip install light-cacheTo get started, you need to set up a cache store:
from light_cache import CacheStore
cache = CacheStore(persist_cache=True, keep_cache_in_memory=True)There are three main modes for this cache:
- Disk cache: Setting
persist_cacheto True andkeep_cache_in_memoryto False, the cache will read and write to a local cache file on each get/put operation. If you have many instances of a script running and need to keep the cache in sync, this is likely the right option. - Memory cache: Setting
persist_cacheto False andkeep_cache_in_memoryto True, keeps the cache in memory during the execution of the script or while the notebook is running. The cache will not persist between sessions. If you do not need the cache to exist after the session ends and have many computationally intensive functions during the execution, this is likely the right option. - Hybrid (default and recommended): Setting both to True will allow the cache to stay in memory during execution but save changes to a file to persist between sessions. For most use-cases of working in a notebook or creating small scripts that do not require full Redis or similar cache, this is likely the right option.
You can set up different cache stores for different information. For example, if you need one store for just movies and another for podcasts, you could:
from light_cache import CacheStore
movie_cache = CacheStore(store='movies')
podcast_cache = CacheStore(store='podcasts')The main methods are get(), put(), has(), and forget().
from light_cache import CacheStore
cache = CacheStore()
cache.put('some-key', 'some-value')
my_value = cache.get('some-key')
# `get()` returns None if cache doesn't exist. This can be changed using the `default` parameter
my_value = cache.get('some-nonexistent-key', default=0)
if cache.has('some-key'):
# do something
# Expiration is in seconds (defaults to 5 minutes)
cache.put('new-key', 'new-value', expires=1200)
# Set to None for items that should not expire
cache.put('never-expire-key', 'never-expire-value', expires=None)
# Items that do not expire will remain in cache until the item is removed using `forget()`
cache.forget('never-expire-key')When using disk-based cache, the files are stored in .cache/ but this can be changed:
from light_cache import CacheStore
cache = CacheStore(cache_directory='src/app/cache')Community made feature requests, patches, bug reports, and contributions are always welcome.
Please review our contributing guidelines if you decide to make a contribution.
This project uses Poetry for managing dependencies and environments.
This project uses pytest for its tests.
To run tests locally, use:
poetry run pytestThis project uses black for formatting. To run the formatter, use:
poetry run black .This project is licensed under the MIT License. See LICENSE for more details.