I had a conversation this morning, where I just (maybe I’m slow) realized how Apache Kafka has inverted the responsibility in the world of message passing.
Traditional enterprise services busses ( Wikipedia: Enterprise Service Bus ) typically have some smarts built in. The bus itself routes messages, transforms messages and orchestrates actions based on message attributes. This was the first attempt at building a great mediation layer in an enterprise. Some advantages of the traditional ESB were:
- Producer/Consume Language Agnostic
- Input/Output format changes (XML, JSON, etc)
- Defined routing and actions on messages
The challenges were typical for traditional enterprise software. Scaling was a mess and licenses could be cost prohibitive to scale. This meant lower adoption and general loss of the advantages for smaller projects or customers.
Talk about a huge and complex stack! Look at this picture for the ‘core’ capabilities of an Enterprise Service Bus:
ESB Component Hive
Now let’s take a look at Apache Kafka.
Ok, that’s a lot of arrows, and lines and block, oh my.
BUT, The thing to notice here that’s SUPER important, is that they’re all outside the Kafka box. Kafka isn’t smart. In fact, Kafka was designed to be dumb. There is no message routing, there’s no message format changes, nothing. The big box in the middle is dumb. It scales really well, and stays dumb.
In fact, the only ‘type’ of communication that Kafka has is publish/subscribe. One(to-many) clients produce messages to a topic. They send in data. Doesn’t matter if it’s JSON, XML, yiddish, etc. It goes to the topic. Kafka batches them up, and ‘persists’ them as a log file. That’s it. A big old data file on disk. The smarts of Kafka comes next… One Consumer Group (which may be MANY actual instances of software, but with the same group ID) subscribe to a topic… or more than one topic. Kafka (Zookeeper help) remembers which client in the client group has seen which block of messages. Ok, that sounds confusing. I’ll try again.
Kafka coordinates which blocks of data get to which client. If the clients are in the same client group, then data is only sent out once to a member of the client group. More than one client group can subscribe to a topic, so you can have multiple consumer processes for each topic.
Now, instead of the message bus sending messages from one function to another, that work is left up to the clients. For instance, let’s say you have to ingest an email from a mail server and test it to see if there’s a malicious reply-to address.
First, the message comes in as plain text to the ‘email_ingest‘ topic. This can be published to by many clients reading data from many servers. Let’s assume Logstash. Logstash will send the message in as plain text. After the message is in the ‘email_ingest‘ topic, another program will transform that message to JSON. This program subscribes to ‘email_ingest‘, pulls each message, transforms to JSON, and publishes it back to another topic ‘email_jsonified‘.
The last piece of the puzzle is the code that calls the email hygiene function. This piece of code takes the longest, due to calling an external API, so needs to scale horizontally the most. This function reads from ‘email_jsonified‘, calls the external API, and if there’s a malicious IP or reply-to detected, publishes the message on the last topic ’email_alert’. ‘email_alert‘ is subscribed to by another Logstash instance, to push the message into Elasticsearch for visualization in Kibana.
Sounds complicated right?
The big difference here, is that the intelligence moved into the clients. The clients need to handle the orchestration, error handling, reporting, etc. That has some pros and cons. It’s great that clients can now be written in many technologies, and there is more ‘freedom’ for a development group to do their own thing in a language or framework they’re best suited for. That can also be bad. Errors add a new challenge. Dead letter queues can be a pain to manage, but, again, it puts the onus on the client organization (in the case of a distributed set of teams) to handle their own errors.
Kafka scales horizontally on a small footprint really easily. It’s mostly a network IO bound system, instead of a CPU or memory bound system. It’s important to keep an eye on disk space, memory and CPU, but they tend not to be an issue if you set up your retention policies in an environment appropriate manner.
Reach out if you have any questions
Do you prefer RabbitMQ? ActiveMQ? Kafka? (They’re not the same, but similar!)