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Kafka vs Kafka Manager: What are the differences?

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; Kafka Manager: A tool for managing Apache Kafka, developed by Yahoo. This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

Kafka and Kafka Manager 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, Kafka Manager provides the following key features:

  • Manage multiple clusters
  • Easy inspection of cluster state (topics, brokers, replica distribution, partition distribution)
  • Run preferred replica election

Kafka and Kafka Manager are both open source tools. It seems that Kafka with 12.7K GitHub stars and 6.81K forks on GitHub has more adoption than Kafka Manager with 7.55K GitHub stars and 1.84K GitHub forks.

According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Kafka Manager, which is listed in 8 company stacks and 5 developer stacks.

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 Kafka Manager?

This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

Want advice about which of these to choose?Ask the StackShare community!

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      What are some alternatives to Kafka and Kafka Manager?
      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 Kafka Manager
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      How developers use Kafka and Kafka Manager
      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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