A walk through caching patterns
Nicolas Frankel (~nicolas71) |
When your application starts slowing down, the reason is probably a bottleneck somewhere in the execution chain. Sometimes, this bottleneck is due to a bug. Sometimes, somebody didn’t set up the optimal configuration. And sometimes, the process of fetching the data is the bottleneck.
One option would be to change your whole architecture. Before moving to such a drastic, and probably expensive measure, one can consider a trade-off: instead of getting remote data every time, you can store the data locally after the first read. This is the trade-off that caching offers: stale data vs. speed.
Deciding to use caching is just the first step in a long journey. The next step is to think about how your application and the cache will interact. This talk focuses on options available regarding those interactions.
None beside the obvious (Python dev), as I try to be as didactic as possible
Developer Advocate with 15+ years experience consulting for many different customers, in a wide range of contexts (such as telecoms, banking, insurances, large retail and public sector). Usually working on Java/Java EE and Spring technologies, but with focused interests like Rich Internet Applications, Testing, CI/CD and DevOps. Currently working for Hazelcast. Also double as a trainer and triples as a book author.