> Rate limiting is one of the most common Redis use cases. Traditionally, users implemented rate limiters using server-side Lua scripts combined with client logic. In Redis 8.8, we introduce a window counter rate limiter (by @raffertyyu, together with the Redis team).
I had a look for this and it turns out it's slightly mis-described there - it's not a window counter, it's a "GCRA (Generic Cell Rate Algorithm)" - a leaky bucket algorithm. Code here: https://github.com/redis/redis/blob/unstable/src/gcra.c
It's a neat algorithm (I just learned about it today) - it only needs to store a single integer for each rate-limited key, which is the "Theoretical Arrival Time" when the bucket would next be empty.
While I love Redis as a versatile tool for external data structures, it's still lacking in two areas IMHO:
One, it would be cool to be able to embed it, similar to sqlite, directly into applications.
Two, the HA story is so much more complicated than it should be. I totally acknowledge that concurrency and distributed computing is hard, but it should not require reading heaps of documentation and understanding two entirely separate multi-node approaches only to figure out there are lots of subtle strings attached that make it impractical for many applications.
What would be the point of embedding Redis into an application? What's the advantage of using Redis over using the builtin (or third party) data structures of the language the application is developed in?
I'm asking as a non-webdev who never quite got what Redis actually does, but would love to learn.
To me the thing I like about Redis is that it gives you a storage engine very suitable for caches; it handles TTLs and memory pressure, as well as built-in serialization with the ability to get better performance by allowing for some data loss. At the same time, many users will be deploying small programs to individual machines. If you could just have Redis be embedded this would make it very operationally simple: no additional daemons and a single file to backup if you want to.
It would also be useful because of the ability to switch modalities. When running a multi node service, you can use Redis to share data between nodes and use Redis pubsub as a communication bus. If you wanted to support a simple single node configuration too, then it wouldn't need to be a special case, it could just go through the same mechanism but with an embedded Redis instance.
It's pretty similar to SQLite: being able to embed more or less a complete storage engine into your app can be very convenient and powerful.
Probably because Redis gives you a very well-defined/understood set of rich data structures with built-in behavior like TTL, atomic operations, eviction, and persistence. These things are otherwise usually scattered across native types, helper classes, or entirely separate libraries.
Language's own native data-structures are generally much more capable and vast. 99%+ developers use only a very limited set of those capabilities. This approach packages those most used ones into a nice, consistent DSL.
It's similar in effect to what busybox does to shell utilities, though the motives are different.
In practice, mostly scaling sessions and ephemeral data (caching) across multiple intances of a microservice on multiple machines. Seperating the kv store and the application allows upgrading each application while retaining availability and avoiding loss of session data.
For simple cases, it is probably a total overkill to even consider it, but for something heavier, embedding the database gives you a chance to trivially migrate later to a separate database server.
A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary.
A typical (meaningful) example might be communication between threads or actors in a single process, or idempotent tests.
As with SQLite, an external xxx that does this for you is certainly better, etc. but it’s convenient sometimes, to have an application that doesn’t go “now before you run this install Postgres…”.
It’s seldom useful for a web app where you control everything.
> One, it would be cool to be able to embed it, similar to sqlite, directly into applications.
I've found myself wanting this on several occasions too. I.e. wanting all my rust backend processes (k8s pods) to have some minimal shared state, without having to spin up a Redis cluster. I've talked to Claude about it a couple of times, and it descends into something like, "you gotta use Raft or CRDTs, and pick 2 out of 3 from CAP". Which honestly seems pretty fair, and indicates to me that I'm dreaming for something magical.
Nonetheless, it is nice to hear someone else asking for this. If this is indeed feasible (even if simple/limited), then I'd be interested to try it.
Genuinely interested why we need HA in redis, just not read round robin from multiple non-HA instances?
Redis (and memcache) are memory caches and should be treated like that, not like highly consistent distributed session store.
> Redis (and memcache) are memory caches and should be treated like that
If you haven't come across Kvrocks yet, it may be worth a look: https://github.com/apache/kvrockshttps://kvrocks.apache.org/ . It's a database with a Redis-compatible wire protocol, but the database is stored on disk. This means your working set is not limited by RAM and can be a few orders of magnitude larger! On modern SSDs this is still very fast. I think it improves the durability story as well. But the big win is the orders of magnitude larger database space.
As I've been improving my side project https://totalrealreturns.com/ recently I've ended up using both Redis and Kvrocks together. Redis is great for small global state that needs to be super fast. Kvrocks is great for larger bulk data storage (large precomputed datasets), but also supports a lot of the Redis data structures as well as Lua scripts.
It’s not. Imagine a web app that stores your user information in a session store, mapped by your cookie-provided session ID. Your web app searches redis 1 for the session id, but since that key is on redis 2, the lookup fails and the application thinks there is no such session, and rejects the request.
Now you could solve this specific case by sharding by prefix, or by querying all instances, but then you still do not have high availability: if the instance a specific session is on is down, these users cannot authenticate. At that point you’re better off with a single instance.
But that is his point.
If you cannot find the session id in redis, you login again.
If your Redis server crash, you start a new one and everyone just login again. No data is lost.
Redis doesn't necessarily have to be used as a cache. Streams, for example, make it a great message queue; but a single-node message queue is a single point of failure and thus not viable for many setups.
That you do. Until you realise that there is only a single writer in that scenario, it doesn’t address any sharding concerns, you need to use compatible clients that opt into the sentinel protocol, during failover you’ll see client errors… there’s lots of room for improvement on redis HA.
With the amount of problems I had using Redis Sentinel, I really wish there was another way. On multiple occasions, with completely different deployments, it got itself into a non-repairable state where the only option was to drop it and setup the replicas manually. I was hoping someone would do a Patroni-like project for Redis, but I've not found it yet. I've moved all persistent data to PostgreSQL and use a number of Valkeys behind Envoy proxy as a cache.
We switched to Valkey two years ago. I haven't really looked back. I think both projects have done a lot of nice stuff since the split but it's not really impacting anything I use. The feature set was fine five years ago and I don't think we're using anything in Valkey that wouldn't work in Redis. There are probably a lot of projects that never switched over because they had no real need.
But most of the cloud providers now offer Valkey because of the license changes. Of course, cloud providers not offering Redis was the intention of the license change from the Redis point of view. So mission accomplished for Redis.
But the flip side of course is that if you want to deploy on standard infrastructure rather than self hosting Redis, Valkey is now the easy, low risk path that probably should be the default for most companies that target AWS, Azure, GCP, etc. Same with Elasticsearch vs. Opensearch and a few other products where the community forked because of license changes.
Mentioning Elasticsearch because I know people in both communities and I'm deeply familiar with the stack. A few years on, Opensearch has taken a lot of the momentum from Elasticsearch.
Valkey, because our cloud provider is hosting it and that's obviously what they prefer.
I feel like we're using about 1% of its features at this point - really just as a fast K/V store - so it would be easy to switch if needed, but I can't see a case where we would.
We switched to Valkey after the Redis license kerfuffle happened, discovered we were saving money on our AWS bill, and have no motivation to go back to Redis.
We use almost exclusively Valkey now, mostly because we host on AWS and Render, which both use Valkey. It's faster, cheaper and compatible. I'd consider Garnet too but I believe it doesn't support LUA(or didn't at the time we needed it).
The experimental SSD streaming branch https://github.com/antirez/ds4/tree/streaming - author's demo @ https://x.com/antirez/status/2062536214675067322 is great news for that project, allowing for SOTA inference (DeepSeek V4 Flash and Pro!) on RAM-limited machines. Now we need work on large-ish scale batching in order to recover tok/s under the SSD streaming scenario. It's not helpful when running normally (at least not on Apple Silicon) since thermal/power throttling is the constraint in that case, but SSD streaming is a whole other consideration.
Possibly, but the array type code was implemented using GPT/Claude models before DS4 was a thing. I really recommend this write up on how he used LLMs which I think is a more sane/safe way to code with them vs the YOLOing even I'm subject to unfortunately...
There's also a separate blog post that goes into the details of why existing data structures Redis already supported, which could provide array-like behavior, weren't good enough:
@antirez wrote about the development of that data structure last month, which includes how he used LLMs to do it (which was before ds4 for the co-comment mentioning it ;). The PR he linked goes into the motivation.
I had a look for this and it turns out it's slightly mis-described there - it's not a window counter, it's a "GCRA (Generic Cell Rate Algorithm)" - a leaky bucket algorithm. Code here: https://github.com/redis/redis/blob/unstable/src/gcra.c
The code comments say it was heavily influenced by https://github.com/brandur/redis-cell by Brandur Leach.
It's a neat algorithm (I just learned about it today) - it only needs to store a single integer for each rate-limited key, which is the "Theoretical Arrival Time" when the bucket would next be empty.
One, it would be cool to be able to embed it, similar to sqlite, directly into applications.
Two, the HA story is so much more complicated than it should be. I totally acknowledge that concurrency and distributed computing is hard, but it should not require reading heaps of documentation and understanding two entirely separate multi-node approaches only to figure out there are lots of subtle strings attached that make it impractical for many applications.
I'm asking as a non-webdev who never quite got what Redis actually does, but would love to learn.
It would also be useful because of the ability to switch modalities. When running a multi node service, you can use Redis to share data between nodes and use Redis pubsub as a communication bus. If you wanted to support a simple single node configuration too, then it wouldn't need to be a special case, it could just go through the same mechanism but with an embedded Redis instance.
It's pretty similar to SQLite: being able to embed more or less a complete storage engine into your app can be very convenient and powerful.
It's similar in effect to what busybox does to shell utilities, though the motives are different.
- HyperLogLog, bloom filter, other probabilistic data structures
- Geospatial operations on stored points and polygons
- Expiring keys, for creating caches
These aren't in most standard libraries, and the Redis implementations tend to be fast, robust and well understood.
https://blog.codinghorror.com/the-php-singularity/
A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary.
https://en.wikipedia.org/wiki/Key–value_database
it's not a relational database.
It’s the same use case with a different api.
A typical (meaningful) example might be communication between threads or actors in a single process, or idempotent tests.
As with SQLite, an external xxx that does this for you is certainly better, etc. but it’s convenient sometimes, to have an application that doesn’t go “now before you run this install Postgres…”.
It’s seldom useful for a web app where you control everything.
I've found myself wanting this on several occasions too. I.e. wanting all my rust backend processes (k8s pods) to have some minimal shared state, without having to spin up a Redis cluster. I've talked to Claude about it a couple of times, and it descends into something like, "you gotta use Raft or CRDTs, and pick 2 out of 3 from CAP". Which honestly seems pretty fair, and indicates to me that I'm dreaming for something magical.
Nonetheless, it is nice to hear someone else asking for this. If this is indeed feasible (even if simple/limited), then I'd be interested to try it.
This is entirely different than what Redis is and tries to solve.
Sqlite is embedded. It's not a distributed SQL. Redis is a distributed data structure store and concurrency primitive. These are worlds apart.
> HA story is so much more complicated than it should be
It is precisely as complicated as it needs to be. You don't want data loss.
If you're in the business of high available fault tolerance, you read the manual and learn how to Redis.
If you haven't come across Kvrocks yet, it may be worth a look: https://github.com/apache/kvrocks https://kvrocks.apache.org/ . It's a database with a Redis-compatible wire protocol, but the database is stored on disk. This means your working set is not limited by RAM and can be a few orders of magnitude larger! On modern SSDs this is still very fast. I think it improves the durability story as well. But the big win is the orders of magnitude larger database space.
As I've been improving my side project https://totalrealreturns.com/ recently I've ended up using both Redis and Kvrocks together. Redis is great for small global state that needs to be super fast. Kvrocks is great for larger bulk data storage (large precomputed datasets), but also supports a lot of the Redis data structures as well as Lua scripts.
For example if you use it for session storage, you can't have your application read from a random instance that may or may not contain the session.
Now you could solve this specific case by sharding by prefix, or by querying all instances, but then you still do not have high availability: if the instance a specific session is on is down, these users cannot authenticate. At that point you’re better off with a single instance.
https://aws.amazon.com/blogs/database/reduce-your-amazon-ela...
But most of the cloud providers now offer Valkey because of the license changes. Of course, cloud providers not offering Redis was the intention of the license change from the Redis point of view. So mission accomplished for Redis.
But the flip side of course is that if you want to deploy on standard infrastructure rather than self hosting Redis, Valkey is now the easy, low risk path that probably should be the default for most companies that target AWS, Azure, GCP, etc. Same with Elasticsearch vs. Opensearch and a few other products where the community forked because of license changes.
Mentioning Elasticsearch because I know people in both communities and I'm deeply familiar with the stack. A few years on, Opensearch has taken a lot of the momentum from Elasticsearch.
I feel like we're using about 1% of its features at this point - really just as a fast K/V store - so it would be easy to switch if needed, but I can't see a case where we would.
So we’ve stayed with Valkey.
The website looks like openclaw's website.
https://github.com/antirez/ds4
https://antirez.com/news/164
https://redis.io/blog/diving-deep-into-rediss-new-array-data...
https://antirez.com/news/164
https://github.com/redis/redis/pull/15162
This is awesome!
And arrays look great too. Lots to play with.