Niko's Project Corner

Caching and perf. monitoring with Redis and Python

When im­ple­ment­ing real-time APIs most of the time server load can greatly be re­duced by caching fre­quently ac­cessed and rarely mod­ified data, or re-us­able cal­cu­la­tion re­sults. Luck­ily Python has sev­eral fea­tures which make it easy to add new con­structs and wrap­pers to the lan­guage, for ex­am­ple thanks to *args, **kwargs func­tion ar­gu­ments, first-class func­tions, dec­ora­tors and so fort. Thus it doesn't take too much ef­fort to im­ple­ment a @cached dec­ora­tor with business-specific logic on cache invalidation. Redis is the perfect fit for the job thanks to its high performance, binary-friendly key-value store with TTL and different data eviction policies and support for other data structures which make it trivial to store additional key metrics there.