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A fast thread-safe Python dictionary implementation designed to act as an in-memory RAM constrained LRU TTL cache dict for FaaS environments. Though it has many valuable use cases outside FaaS.

This is a Pythonic dict implementation with all the typical methods working .get .keys .values .items len etc. This package uses only core Python stdlib + objsize.

If used in a serverless FaaS environment then this package works best by supporting an existing caching strategy, as there is no guarantee that any in-memory data will persist between calls.

This was originally designed to be a performant in-memory cache dict for AWS Lambda, preventing repeated invocations making "slow" network calls to a connected ElastiCache Redis cluster.

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 cache dict. It also allows setting an optional max items length, and a TTL for each item.

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 dict is out of space.

Several dimensions exist to constrain the longevity of the data the cache dict stores. These can all be combined as your use case demands. You can also use none, if you so wish.

A max memory (RAM) size the cache dict can use before it starts deleting the LRU values. This can be expressed in bytes (1024) or "human" format 1K (kibibyte). Supported "human" expressions are K, M, G, T.

from faas_cache_dict import FaaSCacheDict

cache = FaaSCacheDict(max_size_bytes='128M')
cache.change_byte_size('64M')  # If data is too large, LRU will be trimmed until it fits

cache.get_byte_size()  # Returns actual size of data and cache dict structure (bytes)

The number of *seconds* to hold a data point before making it unavailable and then later purging it. This can be sub-second by using float values. This can be configured as a default across the cache dict, or on a per key basis.

from faas_cache_dict import FaaSCacheDict

cache = FaaSCacheDict(default_ttl=60)  # Setting it to None (default) means no expiry

cache['key'] = 'value'  # Will expire in 60 seconds
cache.set_ttl('key', 120)  # Will now expire in 120 seconds from now
cache.get_ttl('key')
>>> 119.9
cache.set_ttl('key', None)  # Will now never expire

from datetime import time
cache.expire_at('key', time.time() + 5) # Expire in 5 seconds time (epoch)

cache.default_ttl = 30  # Now all *new* keys will expire in 30 seconds by default
cache['another_key'] = 'value'  # Expires in 30 seconds as per new default

<Wait 31 seconds>

cache['another_key']
>>> KeyError  # Expired

cache.is_expired('another_key')
>>> True

A max list length constraint which deletes the least recently accessed item once the max size is reached.

from faas_cache_dict import FaaSCacheDict

cache = FaaSCacheDict(max_items=10)  # Setting it to None (default) means sys.maxsize
cache.change_max_items(5)  # If data is too large, LRU will be trimmed until it fits

A hook exists enabling post-deletion lifecycle events, for example if a networked resource is deleted from the cache dict you may wish to perform dependency clean up.

from faas_cache_dict import FaaSCacheDict

def post_deletion_hook(key, value):
    pass  # do stuff here

cache = FaaSCacheDict(max_items=5, on_delete_callable=post_deletion_hook)

Note that even if the post_deletion_callable fails, the item will still be purged from the cache dict. You are responsible for implementing your own error handling.

Note that lifecycle hooks are run synchronously, so time costly operations will degrade the performance of the faas cache dict.

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

• The memory constraint applies to the whole cache dict object not just its contents. The cache dict itself consumes a small amount of memory in overheads, so eg. 1K of requested memory will yield slightly less than 1K of available internal storage.
• Due to extra processing, performance does slowly degrade with size (item count), you will need to test this for your situation. In 99% of use cases this will still be an order of magnitude faster than doing network calls to an external cache (and more reliable).

CPython 3.8 or greater.

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

Thanks to mobilityhouse/ttldict for their implementation which served as a proof of concept, which has since been much extended.