I think I've seen all of the Redis/Valkey issues the author mentioned in production.
* Outages where Valkey had no memory policy, ate all the memory, and then caused write errors to its append-only file. Bonus points for another one where the disk itself was full, and AOF writes failed.
* 500s where Redis was fully expected to be live, running, and populated with data for every user, and no fallback to a slower path.
* Creative uses of sorted sets and other data structures which depended on the sets never being evicted.
Despite the observations from the field, I think it's still hard to recommend memcache ahead of Redis. It can be difficult to architect an app to have a memcache-friendly cache layout.
I'd almost guarantee a large enough team using memcache will find a way to need Redis. And then we're maintaining 2 cache technologies.
Redis works great as a cache, but there are a few things you need to do in order to use it reliably as a cache.
1) Wrap your client library so that it's impossible to store anything without an expiry date. You don't want 6-months-old data suddenly coming up in your app!
2) Either turn off persistence, or use a separate database for the cache. In other words, don't mix volatile data with stuff you actually care about.
3) Set up a reasonable maxmemory value with an appropriate maxmemory-policy, so that Redis doesn't eat up all your RAM.
4) Resist the urge to use complex data structures. If you try to update a single field on an expired hash, you will end up with an incomplete object.
If you don't want all that hassle, then yes, Memcached probably works better out of the box.
> 1) Wrap your client library so that it's impossible to store anything without an expiry date. You don't want 6-months-old data suddenly coming up in your app!
No need for this client-side complexity, as you should be using `allkeys-lru`. FWIW, should likely be doing this anyway, as (generally speaking) all data stored in Redis is usually regarded as volatile because of what Redis actually is.
> as (generally speaking) all data stored in Redis is usually regarded as volatile because of what Redis actually is.
If you know this already, then you didn't need to read OP or any of this thread. :)
The problem is that Redis tries very hard to position itself as a persistent data store, with defaults that lean toward persistence (no default eviction policy). Beginners need to fight the defaults every step of the way if all they want is a cache.
Memcached is meant to be a lightweight memory cache, which makes sense, but contrary to the article's claim that "Redis is brought into a stack as a cache, and it is run with the assumption that people treat it that way"—I have very very rarely experienced this. Redis is brought into a stack because (most importantly!) it's fast and (almost as importantly!) because it's simple. I don't think this article is written by AI, but (and I'm trying to be charitable here), it's just like.. dumb.
> Dealing with memcached downtime is incredibly easy, because client libraries generally ignore connection exceptions. For instance, a simple get will just return the default value (or none) if the server is down.
This is a terrible idea in the context of things that might use Redis. If you use Redis with some kind of complex state (say, a document if you're working on a Notion clone, for instance), wtf even is a "default value"? In fact, I actually also want to know when the thing is down.
> Clustering memcached is wonderful, because memcached actually has no clustering built-in.
Yeah bro, this is yet another one of the reasons people use Redis: it handles consensus and clustering for you. What even is this article? It's a master class in straw-manning architectural decisions: most people use hammers as hammers, but screwdrivers make great hammers too, especially if you also need to screw stuff in! I mean.. technically true?
The article mentions the default value is a null, which would be the cue to run whatever computationally expensive op or query the db or hit the disk etc... that you would normally run if you had no cache to begin with.
> but screwdrivers make great hammers too
I don't know what your screwdrivers look like but that sounds like a rough time.
I'm not really sure memcached optimizes for operational simplicity. The only time I've run it at scale, it would have low cpu utilization then unexpectedly hit lock contention and fall over without warning.
“Anyways, Redis homepage aside, you deploy it, and off you go - your trusty cache. You hand the connection string to the people who asked for it, and off you go.”
“None of these things are impossible with Redis, it’s just that memcached’s architecture in general more leans towards these directions, which makes it much, much more straightforward from an operations point of view.”
It's become de rigueur on HN to accuse any article one thinks is trite, obvious, or simply disagreeable of being AI-written. ("That comment has three items in a list! No human would ever put three items in a list! Checkmate, bot!")
* Outages where Valkey had no memory policy, ate all the memory, and then caused write errors to its append-only file. Bonus points for another one where the disk itself was full, and AOF writes failed.
* 500s where Redis was fully expected to be live, running, and populated with data for every user, and no fallback to a slower path.
* Creative uses of sorted sets and other data structures which depended on the sets never being evicted.
Despite the observations from the field, I think it's still hard to recommend memcache ahead of Redis. It can be difficult to architect an app to have a memcache-friendly cache layout.
I'd almost guarantee a large enough team using memcache will find a way to need Redis. And then we're maintaining 2 cache technologies.
Never felt the need to go back to memcached except when a legacy dependency needed it.
What do you think of the argument made in the article?
Clustering redis is not that hard even if you do it manually and I have only had to do it once.
I never use redis persistence and have a max size set with LRU or whatever the application requires.
With memcached I remember having to mess around the LD_LIBRARY path to link whatever python module I was using at the time
1) Wrap your client library so that it's impossible to store anything without an expiry date. You don't want 6-months-old data suddenly coming up in your app!
2) Either turn off persistence, or use a separate database for the cache. In other words, don't mix volatile data with stuff you actually care about.
3) Set up a reasonable maxmemory value with an appropriate maxmemory-policy, so that Redis doesn't eat up all your RAM.
4) Resist the urge to use complex data structures. If you try to update a single field on an expired hash, you will end up with an incomplete object.
If you don't want all that hassle, then yes, Memcached probably works better out of the box.
No need for this client-side complexity, as you should be using `allkeys-lru`. FWIW, should likely be doing this anyway, as (generally speaking) all data stored in Redis is usually regarded as volatile because of what Redis actually is.
If you know this already, then you didn't need to read OP or any of this thread. :)
The problem is that Redis tries very hard to position itself as a persistent data store, with defaults that lean toward persistence (no default eviction policy). Beginners need to fight the defaults every step of the way if all they want is a cache.
> Dealing with memcached downtime is incredibly easy, because client libraries generally ignore connection exceptions. For instance, a simple get will just return the default value (or none) if the server is down.
This is a terrible idea in the context of things that might use Redis. If you use Redis with some kind of complex state (say, a document if you're working on a Notion clone, for instance), wtf even is a "default value"? In fact, I actually also want to know when the thing is down.
> Clustering memcached is wonderful, because memcached actually has no clustering built-in.
Yeah bro, this is yet another one of the reasons people use Redis: it handles consensus and clustering for you. What even is this article? It's a master class in straw-manning architectural decisions: most people use hammers as hammers, but screwdrivers make great hammers too, especially if you also need to screw stuff in! I mean.. technically true?
Considering how complex and error prone this is, I don’t want it in my stack.
The article mentions the default value is a null, which would be the cue to run whatever computationally expensive op or query the db or hit the disk etc... that you would normally run if you had no cache to begin with.
> but screwdrivers make great hammers too
I don't know what your screwdrivers look like but that sounds like a rough time.
It works pretty well when you need to hit something with a solid object a couple times.
“Anyways, Redis homepage aside, you deploy it, and off you go - your trusty cache. You hand the connection string to the people who asked for it, and off you go.”
“None of these things are impossible with Redis, it’s just that memcached’s architecture in general more leans towards these directions, which makes it much, much more straightforward from an operations point of view.”