Kafka vs Kestrel vs RabbitMQ

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Kafka
Kafka

4.9K
4.4K
+ 1
488
Kestrel
Kestrel

13
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+ 1
0
RabbitMQ
RabbitMQ

5.7K
4.5K
+ 1
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What is Kafka?

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

What is Kestrel?

Kestrel is based on Blaine Cook's "starling" simple, distributed message queue, with added features and bulletproofing, as well as the scalability offered by actors and the JVM.

What is RabbitMQ?

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
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Why do developers choose Kafka?
Why do developers choose Kestrel?
Why do developers choose RabbitMQ?
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      What companies use Kafka?
      What companies use Kestrel?
      What companies use RabbitMQ?

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      What tools integrate with Kafka?
      What tools integrate with Kestrel?
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        What are some alternatives to Kafka, Kestrel, and RabbitMQ?
        ActiveMQ
        Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.
        Amazon Kinesis
        Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
        Apache Spark
        Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
        Akka
        Akka is a toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM.
        Apache Storm
        Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
        See all alternatives
        Decisions about Kafka, Kestrel, and RabbitMQ
        James Cunningham
        James Cunningham
        Operations Engineer at Sentry · | 18 upvotes · 320.1K views
        atSentrySentry
        Celery
        Celery
        RabbitMQ
        RabbitMQ
        #MessageQueue

        As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

        Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

        #MessageQueue

        See more
        Roman Bulgakov
        Roman Bulgakov
        Senior Back-End Developer, Software Architect at Chemondis GmbH · | 3 upvotes · 10.5K views
        Kafka
        Kafka

        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)

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        Kafka
        Kafka
        RabbitMQ
        RabbitMQ

        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).

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        Frédéric MARAND
        Frédéric MARAND
        Core Developer at OSInet · | 2 upvotes · 121.6K views
        atOSInetOSInet
        Beanstalkd
        Beanstalkd
        RabbitMQ
        RabbitMQ
        Kafka
        Kafka

        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.

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        Michael Mota
        Michael Mota
        CEO & Founder at AlterEstate · | 4 upvotes · 78.9K views
        atAlterEstateAlterEstate
        Celery
        Celery
        RabbitMQ
        RabbitMQ
        Django
        Django

        Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.

        We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.

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        Interest over time
        Reviews of Kafka, Kestrel, and RabbitMQ
        Review ofRabbitMQRabbitMQ

        I developed one of the largest queue based medical results delivery systems in the world, 18,000+ queues and still growing over a decade later all using MQSeries, later called Websphere MQ. When I left that company I started using RabbitMQ after doing some research on free offerings.. it works brilliantly and is incredibly flexible from small scale single instance use to large scale multi-server - multi-site architectures.

        If you can think in queues then RabbitMQ should be a viable solution for integrating disparate systems.

        How developers use Kafka, Kestrel, and RabbitMQ
        Avatar of Pinterest
        Pinterest uses KafkaKafka

        http://media.tumblr.com/d319bd2624d20c8a81f77127d3c878d0/tumblr_inline_nanyv6GCKl1s1gqll.png

        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.

        Avatar of Cloudify
        Cloudify uses RabbitMQRabbitMQ

        The poster child for scalable messaging systems, RabbitMQ has been used in countless large scale systems as the messaging backbone of any large cluster, and has proven itself time and again in many production settings.

        Avatar of Chris Saylor
        Chris Saylor uses RabbitMQRabbitMQ

        Rabbit acts as our coordinator for all actions that happen during game time. All worker containers connect to rabbit in order to receive game events and emit their own events when applicable.

        Avatar of Clarabridge Engage
        Clarabridge Engage uses RabbitMQRabbitMQ

        Used as central Message Broker; off-loading tasks to be executed asynchronous, used as communication tool between different microservices, used as tool to handle peaks in incoming data, etc.

        Avatar of Analytical Informatics
        Analytical Informatics uses RabbitMQRabbitMQ

        RabbitMQ is the enterprise message bus for our platform, providing infrastructure for managing our ETL queues, real-time event notifications for applications, and audit logging.

        Avatar of Packet
        Packet uses RabbitMQRabbitMQ

        RabbitMQ is an all purpose queuing service for our stack. We use it for user facing jobs as well as keeping track of behind the scenes jobs.

        Avatar of Coolfront Technologies
        Coolfront Technologies uses KafkaKafka

        Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.

        Avatar of ShareThis
        ShareThis uses KafkaKafka

        We are using Kafka as a message queue to process our widget logs.

        Avatar of Christopher Davison
        Christopher Davison uses KafkaKafka

        Used for communications and triggering jobs across ETL systems

        Avatar of theskyinflames
        theskyinflames uses KafkaKafka

        Used as a integration middleware by messaging interchanging.

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        How much does RabbitMQ cost?
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