It happens that Twitter not only forked and extended 1 year old Redis version, but looks like it doesn’t have plans to upgrade. Redis and its latencies are much-much-much faster than Twitter infrastructure written in Java because of GC in JVM. This allows to put a bunch of proxies on top of Redis caching cluster to do cluster management, the thing Redis misses for a while.
Also Twitter uses Redis only to cache data, doesn’t care about consistency issues, doesn’t use persistent caching, at least article says data is being thrown away when server goes offline.
It is client responsibility to read data from disk storage if there is no data in the cache.
Article desribes Twitter timeline architecture, and that’s quite weird to me: instead of having list of semifixed (or limited by size) chunks of timeline which are loaded on demand, they created a bunch of realtime updated structures in Redis, found non-trivial consistency issues and eventually ended up with the same simple approach of having ‘chunks’ of timeline stored in cache.
I started to compare cache management in Twitter using Redis with what we have in Reverbrain for caching: our Elliptics SLRU cache. It uses persistent caching system (which was also described a bit in article in comparison with memcache), but also uses persistent storage to backup cache, and while cache is actually segmented LRU, its backing store can be arbitrary large at size compared to Redis.
Although article is written as ‘set of facts’ somehow cut out of context (it was interview with the twitter employee), it is a good reading to think about caching, JVM, Redis and cache cluster architecture.