If you happen to find yourself in a sysadmin position, or a position where you just so happen to maintain someone’s infrastructure, chances are that at some point in time the topic “we need a cache” comes up.
You think for a moment and reach out for Redis, because you’re used to it, it’s fully featured, and it works! You remember it being a good, solid cache, and you wonder which new features recent releases have brought, and head to its homepage:
Your agents aren’t failing. Their context is.
Inquiring agents want to know:
How can I use Redis Iris as a real-time context engine for AI apps?1
Right, so probably something with AI2. This is sort of understandable, because Redis is a company that wants to make money.
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.
Few months later
After a while, it turns out that cache.set("key", "value") is a really simple
abstraction, and definitely easier than INSERT INTO table VALUES ('key', 'value'). People start treating the REmote DIctionary Server as something
that’s always there, something that persists data, something that is a
database.
You don’t know this. Your ops colleagues don’t know this. Therefore, your alerting doesn’t know this either, because you assume that people treat the cache as something volatile.
You find out that this has been going on when you happen to do something to Redis. Maybe you upgrade it, maybe you move it to another node, maybe your cat hits the eject button on your RAID0 server’s HDD tray.
The issue isn’t that Redis doesn’t have persistence, the issue is that usually, Redis is brought into a stack as a cache, and it is run with the assumption that people treat it that way.
Usually, by the time you realize this, it is already too late, and Redis is too intertwined in the app to really leave its place. Instead, you have the eternal pleasure of maintaining it and monitoring it like a pet.
Enter memcached
First off - what’s a memcached? Easy, ask its website:
What is Memcached?
Free & open source, high-performance, distributed memory object caching system, generic in nature, but intended for use in speeding up dynamic web applications by alleviating database load.3
Wow, first sentence on the page, even with code examples. And look at those cute little mascots at the top!
Memcached is also a cache, similar to Redis. Chances are that you’re using a framework like Django, which supports pluggable caching and allows you to switch between different caching backends.
But why should you use memcached when it has much less features than Redis? Here are my reasons for why these days, I will always prefer memcached over Redis:
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Dealing with memcached downtime is incredibly easy, because client libraries generally ignore connection exceptions. For instance, a simple
getwill just return the default value (or none) if the server is down. -
Clustering memcached is wonderful, because memcached actually has no clustering built-in. To “cluster” it, you configure the client library with multiple URLs4, and the client will select the target instance based on hashing the key. If a client-side call detects an instance as done, it removes the node from the hasher. After a certain time, the client will automatically attempt to reconnect and use the dead node.
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memcached “solves” the whole persistence issue, because it does not persist to disk. It is therefore a perfect fit to just being scheduled as a stateless workload wherever you desire it.
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.
But because of memcached being such a relatively simple application (plus the fact that you can run dozens instances of it with ~64 MB cache size and close to no overhead), if I need a cache these days, I usually reach out to memcached.
That said, a lot of “database too slow” problems actually begin their life as “query too slow” or “missing indices”, so be a kind person and help your developers with optimizing their queries.
Also, if you’re curious about some of the decisions behind memcached, the blog contains interesting posts, with one published just in May: “How Long Does That Response Take… For Real?”.