Kafka is dead, long live Kafka

What Kafka is not for is dealing with enormous number of events or enormous amount of data. Not that it is particularly slow at it (actually, quite fast) but I see a lot of people needlessly try to push more data and events than they need and then complain they have problems.

One easy trick to deal with enormous numbers of events and amounts of data is batching. Just batch some number of events into a single event so that a consumer can pick it up in one go. Removes a lot of cost of transferring the event.

Too much data to transfer? Don't be stupid and try to transfer those huge documents through the Kafka topic -- there is no need for this. Just upload them to S3 and pass through a reference. Or even better, take 10k events, extract data, zip it up, push to S3, and THEN pass a single event that describes 10k large events.

And the author made a very interesting point about message queues. Simply, any problem that could be resolved by a message queue could be resolved by load balancing or persistence, and, therefore, messages queues were actually kind of a bad idea.

There were two basic issues.

The first is that because of the nature of message queues, they're either empty or full. The second is that for many of the ways that queue are used, the unit putting the request on the queue in the first place may well be waiting for the response related to the request. So you've just turned an every day synchronous request into a more complicated, out of band, multi-party asynchronous request.

If your queues are not empty, they they are filling up. And queue capacity is a two fold problem. One, is that you simply run out of space. But, more likely, referring to the earlier point about waiting for a response, is that you run out of time. The response does not return fast enough to manage your response window to the unit making the request.

This is a load balancing problem. If the queue is filling you simply don't have the capacity to handle the current traffic. It's also a mechanically simpler thing to send out a request and wait for the response than to do the dance via a message queue.

The second part is that if you're throwing items onto a message queue, and you "don't care" about them, that is it's a true "fire and forget" kind of request and direct response time is not a concern, then what does the queue gain you over simply posting it to a database table? If the request is Important, you certainly don't want to trust it to a message queue, a device not really designed for the storage of messages. Messages in a queue are kind of trapped in no mans land, where the easiest way to get to a message is to dig through the ones piled in front of it.

They're interesting insights and worth scratching your chin and going "Hmmm" over.

More refined to this, in my experience at the last two jobs, the queue problem is there, but the Kafka solution is based solely on "enterpriseyness" of Kafka, not any practical reason. RabbitMQ is highly performant, SQS is really easy. Both are great queues. Kafka is muuch more, yet, Kafka is chosen because "it's enterprise."

Yeah, this is simpler for the requester, but not for the counterpart that has to respond. Because now, the responder has to have 100% uptime and better not fail during the request, otherwise things get lost.

Let's take sending emails as an example. You have a server A that can send emails, you have a server B that fulfills requests/actions by a user. Let's just assume that this is the (legacy) setting we are dealing with.

Now, what do you do if you want to send the user an email on a certain action, e.g. a password reset or changing a payment information etc.? Is B then making a synchronous request to A? What if server A is currently down due to maintenance? What if A uses another entity to send the emails, which itself is down? How do you observe the current state of the system and e.g. detect that A is overloaded?

With a message queue you get all those things for free and A doesn't even have to have any persistence layer for "retries" in case of crashes etc.

While it's true that those issues can all be resolved by "by load balancing or persistence" it just means that you now traded one issue (having a message queue) for multiple issues (having database(s), having load balancer(s) and essentially re-implementing a part of a message queue).

In most cases a message queue seems like a good trade-off.