Kafka
Kafka

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Kestrel

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Kafka vs Kestrel: What are the differences?

Developers describe Kafka as "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. On the other hand, Kestrel is detailed as "Simple, distributed message queue system". 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.

Kafka and Kestrel belong to "Message Queue" category of the tech stack.

Some of the features offered by Kafka are:

  • 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)

On the other hand, Kestrel provides the following key features:

  • Written by Robey Pointer
  • Starling clone written in Scala (a port of Starling from Ruby to Scala)
  • Queues are stored in memory, but logged on disk

Kafka and Kestrel are both open source tools. It seems that Kafka with 12.7K GitHub stars and 6.81K forks on GitHub has more adoption than Kestrel with 2.8K GitHub stars and 326 GitHub forks.

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.

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        What are some alternatives to Kafka and Kestrel?
        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.
        RabbitMQ
        RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
        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.
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        Decisions about Kafka and Kestrel
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        How developers use Kafka and Kestrel
        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 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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