faas-cache-dict 0.0.6

A Python dictionary implementation designed to act as an in-memory cache for FaaS environments

faas-cache-dict

A Python dictionary implementation designed to act as an in-memory RAM constrained LRU TTL cache dict for FaaS environments.

The use cases are not limited to FaaS, but if used in a serverless FaaS environment then this is designed to support an existing caching strategy, as there is no guarantee that any data will persist between calls.

This implementation uses only core Python stdlib.

Background

This was originally designed to be used as an in-memory cache for AWS Lambda.

In most FaaS environments, successive quick invocations of the function persists variables in the global scope. We can leverage this to cache data in global for future calls.

FaaS runtimes have limited RAM capacities so this library allows you to set a max byte size for the dict. It also allows setting an optional max items length.

Items are kept in order with the LRU at the HEAD of the list.

Items are deleted if they expire, or from the head (LRU) if the cache is out of space.

Usage

Simple usage guide:

from faas_cache_dict import FaaSCacheDict

cache = FaaSCacheDict(default_ttl=10, max_size_bytes='128M', max_items=10)

cache['foo'] = 'bar'
print(cache)
>>> <FaaSCacheDict@0x10a9daec0; default_ttl=10, max_memory=128M, max_items=10, current_memory_bytes=496, current_items=1>

print(cache['foo'])
>>> 'bar'

(wait 10 seconds TTL)

print(cache['foo'])
>>> KeyError

Limitations

• The amount of data storage will be less than the set limit as the dict has internal state data (lru/ttl/etc.) which consumes a small fraction of the limit.
• Performance degrades with size, you will need to test this for your use case. Though in most circumstances this will be much faster than performing a network call to an external cache.
• The library should be thread-safe, but limited testing has gone into this.

Support

CPython 3.7 or greater. No extra external dependencies are required.

Contributions

This code is distributed under an open license. Feel free to fork it or preferably open a PR.

Inspirations

Thanks to mobilityhouse/ttldict for their implementation which served as a starting point.