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Gearman vs Kafka: What are the differences?

Developers describe Gearman as "A generic application framework to farm out work to other machines or processes". Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events. On the other hand, Kafka is detailed 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.

Gearman and Kafka can be primarily classified as "Message Queue" tools.

Some of the features offered by Gearman are:

  • Open Source It’s free! (in both meanings of the word) Gearman has an active open source community that is easy to get involved with if you need help or want to contribute. Worried about licensing? Gearman is BSD
  • Multi-language - There are interfaces for a number of languages, and this list is growing. You also have the option to write heterogeneous applications with clients submitting work in one language and workers performing that work in another
  • Flexible - You are not tied to any specific design pattern. You can quickly put together distributed applications using any model you choose, one of those options being Map/Reduce

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)

"Free" is the primary reason why developers consider Gearman 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 Gearman is used by Instagram, Hootsuite, and Grooveshark. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Gearman, which is listed in 19 company stacks and 5 developer stacks.

- No public GitHub repository available -

What is Gearman?

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

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.

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    What are some alternatives to Gearman and Kafka?
    RabbitMQ
    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
    Celery
    Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.
    Redis
    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
    Beanstalkd
    Beanstalks's interface is generic, but was originally designed for reducing the latency of page views in high-volume web applications by running time-consuming tasks asynchronously.
    Amazon SQS
    Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.
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    Decisions about Gearman and Kafka
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    How developers use Gearman and Kafka
    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 opening.io
    opening.io uses GearmanGearman

    Internal, distributed message queue. Main communication happens via port 4730 and consists of simple json messages. Completely independent of the main website back-end.

    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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