IronMQ vs Kafka: What are the differences?
What is IronMQ? Message Queue for any deployment. An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.
What is Kafka? Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
IronMQ and Kafka can be categorized as "Message Queue" tools.
Some of the features offered by IronMQ are:
- Instant High Availability- Runs on top cloud infrastructures and uses multiple high-availability data centers. Uses reliable datastores for message durability and persistence.
- Easy to Use- IronMQ is super easy to use. Simply connect directly to the API endpoints and you're ready to create and use queues. There are also client libraries available in any language you want – Ruby, Python, PHP, Java, .NET, Go, Node.JS, and more
- Scalable / High Performance- Built using high-performance languages designed for concurrency and runs on industrial-strength clouds. Push messages and stream data at will without worrying about memory limits or adding more servers.
On the other hand, Kafka provides the following key features:
- Written at LinkedIn in Scala
- Used by LinkedIn to offload processing of all page and other views
- Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)
"Great Support" is the primary reason why developers consider IronMQ over the competitors, whereas "High-throughput" was stated as the key factor in picking Kafka.
Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas IronMQ is used by HotelTonight, Coinbase, and Hubble. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to IronMQ, which is listed in 9 company stacks and 5 developer stacks.
What is IronMQ?
What is Kafka?
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I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).
Downsides of using Kafka are: - you have to deal with Zookeeper - you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)
The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.
I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.
For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).
I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.
So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.
I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.
I deploy to Heroku. However, my applications require full linux applications that cannot be deployed to Heroku. I deploy them to Rackspace.
Then Heroku and Rackspace communicate over IronMQ. Problem solved.
Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.