Redis is many times faster, so much that it doesn't seem comparable to me.
A lot of data you can get away with just caching in-mem on each node, but when you have many nodes there are valid cases where you really want that distributed cache.
Just use memcache for query cache if you have to. And only if you have to, because invalidation is hard. It's cheap, reliable, mature, fast, scalable, requires little understanding, has decent quality clients in most languages, is not stateful and available off the shelf in most cloud providers and works in-clusetr in kubernetes if you want to do it that way.
I can't find a use case for Redis that postgres or postgres+memcache isn't a simpler and/or superior solution.
Just to give you an idea how good memcache is, I think we had 9 billion requests across half a dozen nodes over a few years without a single process restart.
memcached clients also frequently uses ketama consistent hashing, so it is much easier to do load/clustering, being much simpler than redis clustering (sentinel, etc).
Mcrouter[1] is also great for scaling memcached.
dragonfly, garnet, and pogocache are other alternatives too.
redis i/o is multithreaded, it's just the command loop that's single-threaded. If all you're doing is SET and GET of individual key-value pairs, every time I've seen a redis instance run hot under that sort of load, the bottleneck was the network card, never the CPU.
I ... actually think scaling redis for simple k-v storage is already pretty easy so I dunno that that's much of a concern?
mcrouter ... damn I haven't thought about mcrouter in at least 10 years.
Run benchmarks that show that, for your application under your expected best-case loads, using Redis for caching instead of PostgreSQL provides a meaningful improvement.
If it doesn't provide a meaningful improvement, stick with PostgreSQL.
I like Redis a lot, but for things in the start I'm not sure the juice is always worth the squeeze to get it setup and manage another item in teh stack.
Luckily, search is something that has been thought about and worked on for a while and there's lots of ways to slice it initially.
I'm probably a bit biased though from past experiences from seeing so many different search engines shimmed beside or into a database that there's often an easier way in the start than adding more to the stack.
But As soon as you go outside Postgres you cannot guarantee consistent reads within a transaction.
That’s usually ok, but it’s a good enough reason to keep it in until you absolutely need to.