Given that Kafka books are about impenetrable bureaucratic worlds, why is Kafka called Kafka? Sounds like it’s a tool to make sure your infrastructure is hard to comprehend.
It’s rare a piece of tech has a more fitting name! “Is your orgs politics so complicated that direct team-to-team communication has broken down? Is your business process subject to unannounced violent change? Bogged down by consistent DB schemas and API versioning? Tired of retries on failed messages? Introducing Kafka by Apache: an easy to use, highly configurable, persistently stored ephemeral centralized federated event based messaging data analytics Single Source of Truth as a Service that can scale with any enterprise-sized dysfunction!”
Seriously, the one time I was in a situation where much of the team seemed hellbent on this "just put all in Kafka" idea (without really understanding why, exactly) the arguments they came up with were not too dissimilar from what you've shared with us above. It all seemed to come down to "OMG databases are hard, schemas are hard, our customers don't understand the data they're shoving at us. But Kafka will take care of all of that for us. Because, you know, shiny."
That said I'd still like to have a more ... balanced understanding of why Kafka may not necessarily be The Answer, and/or have more hidden complexity or other negative tradeoffs than we may have bargained for.
I worked for a high profile recently-failed project from a company that rhymes with Brillo, and our data was just beginning to be too big for google sheets (!). However, we were also having organizational problems because the higher ups were seeing the failing project losing money so they of course decided to hire 100 extra engineers. Our communications (both human and programmatic) were failing and the confluent salespeople began circling like buzzards. Of course by the time it was suggested we we use it the project was already 6 months past the point of no return.
My advice is that if your data fits in a database, use a database. Anyone who says that isn’t scalable should have to tell you the actual reason it doesn’t scale and the number of requests/users/GBs/uptime/ etc that is the bottleneck.
E.g., Confluent Replicator vs. Mirror Maker 2, Confluent Platform's tiered storage has been available for quite some time (right now a bunch of people from AirBnB are doing a stellar job bringing tiered storage to FOSS Kafka, I'm hoping 3.1 or 3.2).
Actually, easiest thing to do to see the differences is grep all the subpages of this link for properties that start with "confluent":
https://docs.confluent.io/platform/current/installation/conf...
Of course, when have a few megabytes of data and you route it through Kafka, then all you get is an opaque message queue where you can't see which message went from where to where. Good luck debugging any issues. But, hey, you got to use Kafka.
There's many ways to answer that using data streamed over Kafka - ingest it into your preferred query engine, go query it.
Kafka is a distributed log, that's it.
Kafka sits at roughly the same tier as HTTP, but lacks a lot of the convention we have around HTTP. There's a lot of convention around HTTP that allows people to build generic tooling for any apps that use HTTP. Think visibility, metrics, logging, etc, etc. Those are all things you effectively get for free with HTTP in most languages. Afaict, most of that doesn't exist for Kafka in a terribly helpful. You can absolutely build something that will do distributed tracing for Kafka messages, but I'm not aware of a plug-and-play version like there are for most languages.
The fact that Kafka messages are effectively stateless (in the UDP sense, not the application sense) also trips up a lot of people. If you want to publish a message, and you care what happens to that message downstream, things get complicated. I've seen people do RPC over event buses where they actually want a response back, and it became this complicated system of creating new topics so the host that sent the request would get the response back. Again, in HTTP land, you'd just slap a loadbalancer in front of the app and be done. HTTP is stateful, and lends itself to stateful connections.
Another issues it that when you tell people that they can adjust their schema more often, they tend to go nuts. Schemas start changing left and right, and suddenly you now need a product to orchestrate these schema changes and ensuring you're using the right parser for the right message. Schema validation starts to become a significant hurdle.
It's also architecturally complicated to replace HTTP. An HTTP app can be just a single daemon, or a few daemons with a load balancer or two in front. Kafka is, at minimum, your app, a Kafka daemon, and a Zookeeper daemon (nb I'm not entirely sure Zookeeper is still required). You also have to deal with eventual consistency, which can make coding and reasoning about bugs dramatically harder than it needs to be. What happens when Kafka double-delivers a message?
My pitch is always that you shouldn't use Kafka unless it becomes architecturally simpler than the alternatives. There are problems to which Kafka is a better solution than HTTP, but they don't start with unstable schemas or databases being difficult. Huge volumes of data is a good reason to me, not being sure what your downstreams might be is an option. There are probably more, I'm not an expert.
> our customers don't understand the data they're shoving at us. But Kafka will take care of all of that for us
Kafka isn't going to help with this at all. If your HTTP app can't parse it, neither will your Kafka app. Kafka does have the ability to do replays, but so does shoving the requests in S3 or a databases for processing later. I promise you that "SELECT * FROM requests WHERE status='failed'" is drastically simpler than any Kafka alternative. It is neat that Kafka lets you "roll back time" like that, but you have to very carefully consider the prospect of re-processing the messages that already succeeded. It's very easy to get a bug where you have double entries in databases or other APIs because you're reprocessing a request.
HTTP definitely has the edge when it comes to library support. In fact, Confluent et al offer HTTP endpoints for Kafka so that you don't have to deal with the vagaries of actually connecting to a broker yourself (the default timeout in python for an unresponsive broker is _criminal_ for consumers. You will spend several minutes wondering when the message will arrive.) We use an in-house one. But that introduces HTTP's problems back into the process; you need to worry about overwhelming your endpoint again...
Regarding application patterns, ideally you're writing applications that read data from one topic (or receive messages, parse a file, etc) and write to another topic. Treating it as a request that will somehow be responded to later in time scares me and I wouldn't do it. What if your application needs to be restarted while some things are in-flight?
I think the biggest drawback to HTTP in this space is that there's typically no coordination between clients and the server. Clients send requests when they want and the server has to respond immediately.
That becomes a big issue when you have an outage and all your clients are in retry loops, spiking your requests per second to 3x what they would normally be, on top of whatever the actual issue is.
Most of the retry stuff seems largely shared; i.e. your code should still have handlers for when Kafka isn't responding right. Kafka will only preserve messages on the queue, it won't help if you lose network connectivity, or your ACLs get messed up, or etc, etc.
> Regarding application patterns, ideally you're writing applications that read data from one topic (or receive messages, parse a file, etc) and write to another topic. Treating it as a request that will somehow be responded to later in time scares me and I wouldn't do it. What if your application needs to be restarted while some things are in-flight?
The pattern I've seen is to make the processing itself idempotent, and only ack messages once they've been successfully processed. So if you restart the app while it's processing, the message will sit there in Kafka as claimed until it hits the ack timeout, and then Kafka will give it to a new node.
As far as RPC, I'm not advocating that it's a good idea, but you could implement timeouts and retries on top of an event bus. Edge cases will abound, and I wouldn't want to be in charge of it, but you could shove that square block into the round hole if you push hard enough.
Redis is an order of magnitude easier to work with but struggles under loads that Kafka has no problem with. Also every once in a while our Amazon managed Redis queue will have a bad failover or melt down because someone runs a bad command on it, but our Amazon managed kafka has been rock solid since we switched to it. When we ran Kafka ourselves though we definitely watched it melt down a few times because we threw too much at one broker or we made obscure config mistakes. And figuring out why a consumer isn't getting messages is always a pain, whereas redis is always a dream to use.
Definitely agree. The basic concept of Kafka is that the publisher doesn't care, so long as data isn't lost. If you need the producer to redo stuff if the consumer failed, then Kafka is the square peg in your round hole.
And yeah, the best use case for Kafka is, IMO, "I have to shift terabytes or more of data daily without risking data loss, and I want to decouple consumers from producers".
But then there are blog posts saying kafka is a terrible job queue because you can only have one worker per partition and it's hard to get more partitions dynamically.
A very basic rule of thumb is, on an X broker cluster, have N partitions, where N / X = 0.
There's no harm in choosing something like 20 - 30 partitions for a topic, and increasing that when you need to scale consumers horizontally.
Dropping partitions is harder, but again, they're cheap, you won't need to for most use cases.
Only caveat to increasing partition count is when you're relying on absolute ordering per partition - key hashing can point to different partitions when you have 10 vs 50. It can still be done, but it requires a careful approach.
The protagonist gets the number of a bureaucrat and wants to contact him at his office in the castle.
The fact that the number he’s given causes all phones in all offices in the castle to ring, and the people who answer don’t know or care about what he’s after, just adds a layer of confusion and difficulty to his goal of getting to the castle.
> hninsight -q kafka
https://www.google.com/search?q=site:news.ycombinator.com%20...
it almost always turns up good stuff.
(Incidentally, at least in my opinion his three novels are his finest work, but they seem to attract much less popular attention than his short stories and novellas.)
The one thing I remember RMS saying after all this time is that “the whole point of writing software is so that you can give it a funny name”.
All things considered, I still think that’s good advice.
Why can't people follow the simple rule of not reusing any word or name that's already in use?
They don't seem to have a problem with racehorses.
In fact, why not get the most comprehensive list of racehorse names to date, and start using them for new software projects - then nobody will have to be creative until they run out.
That's it :(