Redis is a great piece of tech but it suffers from trying to be good at two different jobs (persistent data structures, volatile cache) which should not be combined. And indeed in Redis itself they don't combine well - persistence is globally on or off.
Personally I'd use memcached or some equivalent for strictly cacheing, and then bring on Redis with persistence if you need its data structures for e.g scoreboards.
At $WORK we never imported either, our cache layer for slow operations keeps its data in both the filesystem and a db table (used as a k/v store). The database helps coordinate thundering herd problems - this operation is being calculated by another thread, so just wait for it. Reads from the same server just hit the filesystem, and reads from another server hit the db once and then keep it in the filesystem. We could change the fs layer to memcached but so far it's working great.
> We could change the fs layer to memcached but so far it's working great.
This so much. (Ab)Using a db table as a k/v store + the FS can do so much before even considering paying the price of setting up a dedicated caching store. I’ve fought countless foes in the engineering world when proposing solutions like yours just because (incompetent) people feel like caching should live in its dedicated store.
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.
Once someone decides they want to use redis as something other than a cache, you sort of do have 2 cache technologies anyway. You can't use a redis instance that is configured for caching for any other purpose (caching instance must have eviction, non-caching instance must not have eviction). You need a second redis with a different configuration.
Honestly designing your app to have a "memcache-friedly cache layout" is the same thing as designing it to have a redis-friendly cache layout. The pattern for this kind of application cache is identical: "get, and if not there, calculate and set".
This kind of thing tends to happen a lot with open source projects or programs that are maintained long term. As the codebase grows, it inevitably starts supporting things that weren't part of the original plan.
More features mean more users. Some stick to the old stuff, some embrace the new, and eventually certain values become the de facto default, not really optional anymore.
Take Redis. Turn off AOF and it works as a volatile in memory cache. But most of us don't even think about it that way. So there is this argument that fewer features and simpler is better.(Memcached is such an example in this context) The so called 'straitjacket' approach. That makes total sense for big teams. But on the other hand, open source projects need regular updates to keep getting funding or contributions, so there is a built in tension.
And sometimes that leads to specialized forks or spin offs that excel in one niche area. My personal take? There is no right answer. It all depends on the context. Communication itself isn't free, after all
One other feature of memcache that is rarely mentioned is that all operations are O(1) by design, which is a conscious design choice from the authors: yes, it is limiting, but it also ensures no random stalls on simple operations, whereas Redis with its single-threaded core design can't guarantee that since you can run operations of arbitrary complexity (which surely as a developer make you feel very smart about it) and everything else will be waiting for them to complete
memcached is about a bazillion times faster than redis at doing the simple KV cache job. it’s got threads. it’s highly optimized to do its one job super well, where redis is more a arbitrary shared Python heap kinda thing with all the data structures and single thread and whatnot.
at notion we use redis for a lot of things, but actual caching we leave to memcached
At many companies (I want to say most), Redis is seen as an actual durable production database and operated that way, not just as a cache that can disappear at any time. It's not unreasonable for a new dev to assume this unless told otherwise.
Ultimately though, regardless of whether you’re experience is true for the wider industry or not, if you’re letting a junior dev who refuses to read product documentation the responsibility of architecting production systems, then your problem isn’t Redis.
I've done a bunch of Flask work over the past couple of years - not full time but as part of the tech stack for my small eCommerce business. Have run into all kinds of footguns and weirdness with MongoEngine, SQLAlchemy, Celery (seriously, if you value your sanity, don't use Celery!), the Python stacks for Google, eBay and Shopify but never Redis.
Perhaps that's because I'm not giving admin access to random people who think that Redis is a persistent storage, but honestly it's one of those technologies I'd describe as absolutely rock solid and well designed. The API is dead basic and every time I need to do something slightly weird, there's a sensible and well thought out way to achieve it.
No, you're right. Nothing crazy. But things like counting API usage across threads with INCRBY, or debounced HTML cache clears, or even an actual light db with persistence (AOF), and everything just working.
So does that mean you are tracking how many times data is being entered into redis, and rejecting it if the entry rate is too high?
Why would you not track this before, at the point of calculating the data to enter into redis, rather than querying redis to see how much data is entered in a given timeframe?
Again, genuinely curious as to the reason for architectural decisions.
Things like chaining, groups, named queues just don't work the way you'd think they would. There's a lot of footguns and things require weird workarounds. Error reporting is misleading.
It's not bad enough where I had to pull it from the old project that used it, but going forward the new ones used a vibecoded queueing system that was genuinely more reliable than Celery but consumed a lot of memory (RSS inflation). Have then shifted to rq and at least for now it seems to "just work". You're better off doing anything custom/complex (like dependencies, or progress updates across multiple tasks) directly yourself in Redis anyway; since half the time Celery's less-well-trodden inbuilt features don't work the way they should anyway.
WHEN do you move to Redis/Memcached? None of my projects have exceeded 1000 rps at peak, and in none of them have I felt the need to move from unlogged PostgreSQL tables to Redis.
Just trying to get a sense of where people draw the line.
Mostly is no rule, adding a cache can just save you from having to buy a bigger database instance in many cases.
The most common first thing to cache is getting the current user, because this ends up being a very hot path for most stateless systems. Because you need to get the current user for almost every request, it's quite easy for getting the current user to be 50% of database load: first you get the user, then you do the thing. tada, user lookup is now half your app by volume
Perhaps you should try aerospike which provides a data-in-memory mode and reliable persistence and of course, automatic scale-out. Your mum will stop worrying about you and your job once and for all.
Mature ops would be tracking cache hit ratios right?
It sounds like memcached would be really good in a use case where you really just need an optional stateless pure cache with absolutely zero rope to hang yourself on. A use case where "cache hit ratio" is the goal, not "fiddly in-memory data store".
There's basically zero reason to use redis. Pretty much every rdbms like mariadb, postgres, etc is just as fast. So then why redis? It's basically needless complexity in your system.
Modern rdbms databases already have an in-memory cache. For 99% of projects there's no actual difference. The round trip will end up around 12-22 ms in all best possible cases.
If you're getting 12-22ms latency for your cache reads, the network is your bottleneck. If stored locally, you would get many orders of magnitude faster than that.
The network IS your bottleneck. That's exactly what I'm saying.
APCu count=1000 min=0.000290 avg=0.000318 p50=0.000320 p95=0.000331 max=0.000992 ms
Memcached count=1000 min=0.032422 avg=0.039714 p50=0.037211 p95=0.053261 max=0.091343 ms
MariaDB count=1000 min=0.015680 avg=0.019541 p50=0.018485 p95=0.023855 max=0.103867 ms
Don't even start a socket if possible.
Now then do a traceroute. Even to my router it costs 0.547 ms but that's only 1 direction. And a cloud space is hosting many servers, many routers, many switches, with lots of moving pieces so you're realistically adding 1.1 ms per subnet hop and in pretty much every data center that's probably 3-5 hops inside the LAN.
To me the only difference that mattered is that Redis allows to do range queries, while Memcached only by key. Aka TreeMap vs HashMap. Or B-tree index vs Hash index.
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 these defaults every step of the way if all they want is a cache.
> The problem is that Redis tries very hard to position itself as a persistent data store
What are you talking about? On their website, the top 3 use cases (under the Platform menu) are: caching, streaming, and session management. Literally all of these three are volatile.
I haven encountered a _shocking_ number of people who treat Redis as a persistent store. There mere mention that it has some kind of persistence machinery is enough to convince some that is therefore durable and stable and should be treated like a DB.
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?
> Considering how complex and error prone this is, I don’t want it in my stack.
Have you ever used Redis before? I've literally never had to manage clustering or had any issues with it, and I've been using Redis for like 15 years (including for games where state had to live in multiple regions and could change on a 30- or 60-tick basis).
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.
“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!")
Redis is a great piece of tech but it suffers from trying to be good at two different jobs (persistent data structures, volatile cache) which should not be combined. And indeed in Redis itself they don't combine well - persistence is globally on or off.
Personally I'd use memcached or some equivalent for strictly cacheing, and then bring on Redis with persistence if you need its data structures for e.g scoreboards.
At $WORK we never imported either, our cache layer for slow operations keeps its data in both the filesystem and a db table (used as a k/v store). The database helps coordinate thundering herd problems - this operation is being calculated by another thread, so just wait for it. Reads from the same server just hit the filesystem, and reads from another server hit the db once and then keep it in the filesystem. We could change the fs layer to memcached but so far it's working great.
> We could change the fs layer to memcached but so far it's working great.
This so much. (Ab)Using a db table as a k/v store + the FS can do so much before even considering paying the price of setting up a dedicated caching store. I’ve fought countless foes in the engineering world when proposing solutions like yours just because (incompetent) people feel like caching should live in its dedicated store.
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.
Once someone decides they want to use redis as something other than a cache, you sort of do have 2 cache technologies anyway. You can't use a redis instance that is configured for caching for any other purpose (caching instance must have eviction, non-caching instance must not have eviction). You need a second redis with a different configuration.
Honestly designing your app to have a "memcache-friedly cache layout" is the same thing as designing it to have a redis-friendly cache layout. The pattern for this kind of application cache is identical: "get, and if not there, calculate and set".
Not maintaining 2 cache technologies is always a winning argument.
This kind of thing tends to happen a lot with open source projects or programs that are maintained long term. As the codebase grows, it inevitably starts supporting things that weren't part of the original plan.
More features mean more users. Some stick to the old stuff, some embrace the new, and eventually certain values become the de facto default, not really optional anymore.
Take Redis. Turn off AOF and it works as a volatile in memory cache. But most of us don't even think about it that way. So there is this argument that fewer features and simpler is better.(Memcached is such an example in this context) The so called 'straitjacket' approach. That makes total sense for big teams. But on the other hand, open source projects need regular updates to keep getting funding or contributions, so there is a built in tension.
And sometimes that leads to specialized forks or spin offs that excel in one niche area. My personal take? There is no right answer. It all depends on the context. Communication itself isn't free, after all
"Communication itself isn't free, after all"
Off topic, but that's my problem with microservices, devs seem to be totally unaware of this.
I think the clearest example of that is that people think Redis can only function as a cache that loses data on crash or shutdown.
I think that’s because people replaced Memcached with Redis, and expect the same from it.
One other feature of memcache that is rarely mentioned is that all operations are O(1) by design, which is a conscious design choice from the authors: yes, it is limiting, but it also ensures no random stalls on simple operations, whereas Redis with its single-threaded core design can't guarantee that since you can run operations of arbitrary complexity (which surely as a developer make you feel very smart about it) and everything else will be waiting for them to complete
memcached is about a bazillion times faster than redis at doing the simple KV cache job. it’s got threads. it’s highly optimized to do its one job super well, where redis is more a arbitrary shared Python heap kinda thing with all the data structures and single thread and whatnot.
at notion we use redis for a lot of things, but actual caching we leave to memcached
I like memcached, but its really not redis's fault if you set it up as a volatile cache but people treat it as a persistent data store.
The comparison is especially weird as memcached is also not persistent.
At many companies (I want to say most), Redis is seen as an actual durable production database and operated that way, not just as a cache that can disappear at any time. It's not unreasonable for a new dev to assume this unless told otherwise.
That’s not been my experience.
Ultimately though, regardless of whether you’re experience is true for the wider industry or not, if you’re letting a junior dev who refuses to read product documentation the responsibility of architecting production systems, then your problem isn’t Redis.
Sure, but that is an internal documentation failure not a redis failure. It feels incredibly unfair to blame redis for that.
I've done a bunch of Flask work over the past couple of years - not full time but as part of the tech stack for my small eCommerce business. Have run into all kinds of footguns and weirdness with MongoEngine, SQLAlchemy, Celery (seriously, if you value your sanity, don't use Celery!), the Python stacks for Google, eBay and Shopify but never Redis.
Perhaps that's because I'm not giving admin access to random people who think that Redis is a persistent storage, but honestly it's one of those technologies I'd describe as absolutely rock solid and well designed. The API is dead basic and every time I need to do something slightly weird, there's a sensible and well thought out way to achieve it.
> every time I need to do something slightly weird, there's a sensible and well thought out way to achieve it.
In my world cache systems like memcached and redis are just that, a cache to put and get from. Possibly use some invalidation system like tagging.
What can you do with a cache system that is 'wierd'? What are people doing with caches other than just caching data?
Genuinely interested.
No, you're right. Nothing crazy. But things like counting API usage across threads with INCRBY, or debounced HTML cache clears, or even an actual light db with persistence (AOF), and everything just working.
We had Rails writing to memcached, and nginx pulling from memcached for full page caching.
At some point someone decided to gzip all writes into memcached, and our site looked really fun for a while.
I’ve done moving window rate limiting using redis to do atomic rate calculations etc.
That requires some weirdness
> moving window rate limiting
So does that mean you are tracking how many times data is being entered into redis, and rejecting it if the entry rate is too high?
Why would you not track this before, at the point of calculating the data to enter into redis, rather than querying redis to see how much data is entered in a given timeframe?
Again, genuinely curious as to the reason for architectural decisions.
I am currently in the process of starting a project with Flask, SQLAlchemy, Celery. Say more about why I should avoid Celery and what to use instead.
Things like chaining, groups, named queues just don't work the way you'd think they would. There's a lot of footguns and things require weird workarounds. Error reporting is misleading.
It's not bad enough where I had to pull it from the old project that used it, but going forward the new ones used a vibecoded queueing system that was genuinely more reliable than Celery but consumed a lot of memory (RSS inflation). Have then shifted to rq and at least for now it seems to "just work". You're better off doing anything custom/complex (like dependencies, or progress updates across multiple tasks) directly yourself in Redis anyway; since half the time Celery's less-well-trodden inbuilt features don't work the way they should anyway.
WHEN do you move to Redis/Memcached? None of my projects have exceeded 1000 rps at peak, and in none of them have I felt the need to move from unlogged PostgreSQL tables to Redis.
Just trying to get a sense of where people draw the line.
Mostly is no rule, adding a cache can just save you from having to buy a bigger database instance in many cases.
The most common first thing to cache is getting the current user, because this ends up being a very hot path for most stateless systems. Because you need to get the current user for almost every request, it's quite easy for getting the current user to be 50% of database load: first you get the user, then you do the thing. tada, user lookup is now half your app by volume
Perhaps you should try aerospike which provides a data-in-memory mode and reliable persistence and of course, automatic scale-out. Your mum will stop worrying about you and your job once and for all.
I stopped using memcached a decade a go in favour of Redis and now use valkey.
Never felt the need to go back to memcached except when a legacy dependency needed it.
OK.
What do you think of the argument made in the article?
I don't want my cache to silently fail.
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
> silently fail
Mature ops would be tracking cache hit ratios right?
It sounds like memcached would be really good in a use case where you really just need an optional stateless pure cache with absolutely zero rope to hang yourself on. A use case where "cache hit ratio" is the goal, not "fiddly in-memory data store".
> Mature ops would be tracking cache hit ratios right?
Sure, and sentry integrates well with redis in python which is what I use primarily with redis.
I don't think memcached is bad, I just think its old and industry has moved to redis because it offers more while covering the previous use case.
Calling redis fiddly is a mischaracterization. For many use cases I have not had to think more than 30s to setup redis.
(also when I say redis I mean Valkey at this point, even if they are starting to diverge)
There's basically zero reason to use redis. Pretty much every rdbms like mariadb, postgres, etc is just as fast. So then why redis? It's basically needless complexity in your system.
Postgres etc are more complex than Redis, are they not?
Does your argument assume you already have a database, so you might as well use it for your cache mechanism?
Modern rdbms databases already have an in-memory cache. For 99% of projects there's no actual difference. The round trip will end up around 12-22 ms in all best possible cases.
If you're getting 12-22ms latency for your cache reads, the network is your bottleneck. If stored locally, you would get many orders of magnitude faster than that.
The network IS your bottleneck. That's exactly what I'm saying.
Don't even start a socket if possible.Now then do a traceroute. Even to my router it costs 0.547 ms but that's only 1 direction. And a cloud space is hosting many servers, many routers, many switches, with lots of moving pieces so you're realistically adding 1.1 ms per subnet hop and in pretty much every data center that's probably 3-5 hops inside the LAN.
Security. More precisely, the ability to secure access to redis with a password.
Okay but I can do that with any rdbms and I can secure memcached too lol. So what? How is redis better than a fixed length table in MySQL?
To me the only difference that mattered is that Redis allows to do range queries, while Memcached only by key. Aka TreeMap vs HashMap. Or B-tree index vs Hash index.
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 these defaults every step of the way if all they want is a cache.
> The problem is that Redis tries very hard to position itself as a persistent data store
What are you talking about? On their website, the top 3 use cases (under the Platform menu) are: caching, streaming, and session management. Literally all of these three are volatile.
I haven encountered a _shocking_ number of people who treat Redis as a persistent store. There mere mention that it has some kind of persistence machinery is enough to convince some that is therefore durable and stable and should be treated like a DB.
An article praising memcached and no mention of the feral bunny mascots.
> And look at those cute little mascots at the top!
My bad!
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?
> it handles consensus and clustering for you
Considering how complex and error prone this is, I don’t want it in my stack.
> Considering how complex and error prone this is, I don’t want it in my stack.
Have you ever used Redis before? I've literally never had to manage clustering or had any issues with it, and I've been using Redis for like 15 years (including for games where state had to live in multiple regions and could change on a 30- or 60-tick basis).
The memcache slab pools are a leaky abstraction that you may end up having to manage operationally, and it's another way Redis is simpler.
Agreed. Memcache is great until you get into the business of having to configure slabs. Most people just reach for redis at that point.
> wtf even is a "default value"?
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.
Generally you can use the back of a screwdriver like a hammer.
It works pretty well when you need to hit something with a solid object a couple times.
Sounds like a trip to the hospital.
Oh God I'm tired of ai written thought pieces
I see this comment frequently in this site and it doesn't provide any value. If this isn't part of the hackernews rules, I hope it becomes one soon.
I don’t think LLMs would write this:
“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.”
So many it's not X it's Y. It might have been polished, but it was claude
there are only 2 and they don't seem to be AI-written
You do realize that actual humans use that formulation, right? I know Claude is fond of it, but it didn't just invent the practice ex nihilo.
Or this:
“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!")
Somebody call Deckard.
I don't see how this is AI-written?