Thoughts about Caching

Caching is a key component of any significant Web or REST backend so as to avoid performance issues when accessing the storage tier, in term of latency, throughput and resource usage.

Shared Cache

A convenient setup is to have one shared cache storage tier at the application level, which is accessed through wrappers to avoid collisions between cache functions, basically by prepending keys with some prefix.

Depending on the access pattern, it may or may not be useful to put a multiple-level caching strategy in place, with a local in-process cache and a higher-level inter-process and inter-host cache such as Redis or MemCached.

When using a global shared cache, it should be clear that the cache may hold sensitive data and its manipulation may allow to change the behavior of the application, including working around security by tampering with the application authentication and authorization guards.

Although a cache may allow to improve performance, an important side effect is that it reduces the query load on the actual database backend.


In order to reduce latency, as most time should be spent in network accesses, reducing the number of trips is a key strategy. This suggests combining data transfers where possible through higher-level interfaces and queries, both at the HTTP level and at the database level.

Denormalizing the relational data model may help. Having an application-oriented view of the model (eg JSON objects rather than attributes and tables) can help performance, at the price of losing some of the consistency warranties provided by a database. The best of both word may be achieved, to some extent, by storing JSON data into a database such as Postgres.

Invalidating data from the cache requires a detailed knowledge of internal cache operations and are not very easy to manage at the application level, so devops should want to avoid this path if possible, possibly by relying on a time-based cache expiration aka TTL (time-to-live). Note that the extended cached decorator provided with this module includes a the convenient cached_del method to help implement cache invalidation at the application level.


Write operations need to be sent to storage. Depending on transaction requirements, i.e. whether some rare data loss is admissible, various strategies can be applied, such as updating in parallel the cache and the final storage. Yet again, this strategy requires a deep knowledge of the underlying cache implementation, thus is best avoided most of the time.

Read operations can be cached, at the price of possibly having inconsistent data shown to users. LRU/LFU/ARC cache strategies mean that inconsistent data can be kept in cache for indefinite time, which is annoying. A TLL expiration on top of that makes such discrepancies bounded in time, so that after some time the data shown are eventually up to date.

Basically the application should aim at maximizing throughput for the available resources whilst keeping the latency under control, eg 90% of queries under some limit.