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DistributedLog
DistributedLog

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Kafka

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DistributedLog vs Kafka: What are the differences?

DistributedLog: High-performance replicated log service, by Twitter. DistributedLog (DL) is a high-performance, replicated log service, offering durability, replication and strong consistency as essentials for building reliable distributed systems; 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.

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

Some of the features offered by DistributedLog are:

  • High Performance
  • Durable and Consistent
  • Efficient Fan-in and Fan-out

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)

DistributedLog and Kafka are both open source tools. Kafka with 12.7K GitHub stars and 6.81K forks on GitHub appears to be more popular than DistributedLog with 2.25K GitHub stars and 283 GitHub forks.

What is DistributedLog?

DistributedLog (DL) is a high-performance, replicated log service, offering durability, replication and strong consistency as essentials for building reliable distributed systems.

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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Why do developers choose DistributedLog?
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        What are some alternatives to DistributedLog 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.
        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.
        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.
        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.
        ZeroMQ
        The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.
        See all alternatives
        Decisions about DistributedLog and Kafka
        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)

        See more
        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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        Fr茅d茅ric MARAND
        Fr茅d茅ric MARAND
        Core Developer at OSInet | 2 upvotes 121.8K 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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        Interest over time
        Reviews of DistributedLog and Kafka
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        How developers use DistributedLog 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 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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