High rate of database modifications

Do not use django-cachalot if your project has more than 50 database modifications per second on most of its tables. There will be no problem, but django-cachalot will become inefficient and will end up slowing your project instead of speeding it. Read the introduction for more details.


By default, Redis will not evict persistent cache keys (those with a None timeout) when the maximum memory has been reached. The cache keys created by django-cachalot are persistent by default, so if Redis runs out of memory, django-cachalot and all other cache.set will raise ResponseError: OOM command not allowed when used memory > 'maxmemory'. because Redis is not allowed to delete persistent keys.

To avoid this, 2 solutions:

  • If you only store disposable data in Redis, you can change maxmemory-policy to allkeys-lru in your Redis configuration. Be aware that this setting is global; all your Redis databases will use it. If you don’t know what you’re doing, use the next solution or use another cache backend.
  • Increase maxmemory in your Redis configuration. You can start by setting it to a high value (for example half of your RAM) then decrease it by looking at the Redis database maximum size using redis-cli info memory.

For more information, read Using Redis as a LRU cache.


By default, memcached is configured for small servers. The maximum amount of memory used by memcached is 64 MB, and the maximum memory per cache key is 1 MB. This latter limit can lead to weird unhandled exceptions such as Error: error 37 from memcached_set: SUCCESS if you execute queries returning more than 1 MB of data.

To increase these limits, set the -I and -m arguments when starting memcached. If you use Ubuntu and installed the package, you can modify /etc/memcached.conf, add -I 10m on a newline to set the limit per cache key to 10 MB, and if you want increase the already existing -m 64 to something like -m 1000 to set the maximum cache size to 1 GB.


Locmem is a just a dict stored in a single Python process. It’s not shared between processes, so don’t use locmem with django-cachalot in a multi-processes project, if you use RQ or Celery for instance.


Filebased, a simple persistent cache implemented in Django, has a small bug (#25501): it cannot cache some objects, like psycopg2 ranges. If you use range fields from django.contrib.postgres and your Django version is affected by this bug, you need to add the tables using range fields to CACHALOT_UNCACHABLE_TABLES.


This database software already provides by default something like django-cachalot: MySQL query cache. Unfortunately, this built-in query cache has no significant effect since at least MySQL 5.7. However, in MySQL 5.5 it was working so well that django-cachalot was not improving performance. So depending on the MySQL version, django-cachalot may be useless. See the current django-cachalot benchmark and compare it with an older run of the same benchmark to see the clear difference: MySQL became 4 × slower since then!

Raw SQL queries


Don’t worry if you don’t understand what follow. That probably means you don’t use raw queries, and therefore are not directly concerned by those potential issues.

By default, django-cachalot tries to invalidate its cache after a raw query. It detects if the raw query contains UPDATE, INSERT, DELETE, ALTER, CREATE or DROP and then invalidates the tables contained in that query by comparing with models registered by Django.

This is quite robust, so if a query is not invalidated automatically by this system, please send a bug report. In the meantime, you can use the API to manually invalidate the tables where data has changed.

However, this simple system can be too efficient in some very rare cases and lead to unwanted extra invalidations.

Multiple servers clock synchronisation

Django-cachalot relies on the computer clock to handle invalidation. If you deploy the same Django project on multiple machines, but with a centralised cache server, all the machines serving Django need to have their clocks as synchronised as possible. Otherwise, invalidations will happen with a latency from one server to another. A difference of even a few seconds can be harmful, so double check this!

To get a rough idea of the clock synchronisation of two servers, simply run python -c 'import time; print(time.time())' on both servers at the same time. This will give you a number of seconds, and it should be almost the same, with a difference inferior to 1 second. This number is independent of the time zone.

To keep your clocks synchronised, use the Network Time Protocol.

Replication server

If you use multiple databases where at least one is a replica of another, django-cachalot has no way to know that the replica is modified automatically, since it happens outside Django. The SQL queries cached for the replica will therefore not be invalidated, and you will see some stale queries results.

To fix this problem, you need to tell django-cachalot to also invalidate the replica when the primary database is invalidated. Suppose your primary database has the 'default' database alias in DATABASES, and your replica has the 'replica' alias. Use the signal and cachalot.api.invalidate() this way:

from cachalot.api import invalidate
from cachalot.signals import post_invalidation
from django.dispatch import receiver

def invalidate_replica(sender, **kwargs):
    if kwargs['db_alias'] == 'default':
        invalidate(sender, db_alias='replica')

Multiple cache servers for the same database

On large projects, we often end up having multiple Django servers on several physical machines. For performance reasons, we generally decide to have a cache per server, while the database stays on a single server. But the problem with django-cachalot is that it only invalidates the cache configured using CACHALOT_CACHE. So all caches end up serving stale data.

To avoid this, you need inside each Django server to be able to communicate with the rest of the servers in order to invalidate other caches when an invalidation occurs. If this is not possible in your situation, you must not use django-cachalot. But if you can, each Django server must also have all other caches in the CACHES setting. Then, you need to manually invalidate all other caches when an invalidation occurs. Add this to a file of an installed application:

import threading

from cachalot.api import invalidate
from cachalot.signals import post_invalidation
from django.dispatch import receiver
from django.conf import settings

SIGNAL_INFO = threading.local()

def invalidate_other_caches(sender, **kwargs):
    if getattr(SIGNAL_INFO, 'was_called', False):
    db_alias = kwargs['db_alias']
    for cache_alias in settings.CACHES:
        if cache_alias == settings.CACHALOT_CACHE:
        SIGNAL_INFO.was_called = True
            invalidate(sender, db_alias=db_alias, cache_alias=cache_alias)
            SIGNAL_INFO.was_called = False