Kafka
Kafka

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NSQ

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Kafka vs NSQ: 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, NSQ is detailed as "A realtime distributed messaging platform". NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

Kafka and NSQ 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, NSQ provides the following key features:

  • support distributed topologies with no SPOF
  • horizontally scalable (no brokers, seamlessly add more nodes to the cluster)
  • low-latency push based message delivery (performance)

"High-throughput" is the primary reason why developers consider Kafka over the competitors, whereas "It's in golang" was stated as the key factor in picking NSQ.

Kafka and NSQ are both open source tools. NSQ with 15.6K GitHub stars and 2.03K forks on GitHub appears to be more popular than Kafka with 12.7K GitHub stars and 6.81K GitHub forks.

According to the StackShare community, Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to NSQ, which is listed in 21 company stacks and 8 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 NSQ?

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

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

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    What are some alternatives to Kafka and NSQ?
    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 NSQ
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    How developers use Kafka and NSQ
    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 InsideSales.com
    InsideSales.com uses NSQNSQ

    The built-in Gamification that comes with our Playbooks application uses NSQ for work queues and microservice communication.

    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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