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Cassandra vs Kafka: What are the differences?

Cassandra: A partitioned row store. Rows are organized into tables with a required primary key. Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL; 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.

Cassandra belongs to "Databases" category of the tech stack, while Kafka can be primarily classified under "Message Queue".

"Distributed", "High performance" and "High availability" are the key factors why developers consider Cassandra; whereas "High-throughput", "Distributed" and "Scalable" are the primary reasons why Kafka is favored.

Cassandra 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 Cassandra with 5.27K GitHub stars and 2.35K GitHub forks.

Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas Cassandra is used by Uber Technologies, Facebook, and Spotify. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to Cassandra, which is listed in 342 company stacks and 240 developer stacks.

What is Cassandra?

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

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 Cassandra and Kafka?
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    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.
    Couchbase
    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
    MySQL
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    See all alternatives
    Decisions about Cassandra and Kafka
    StackShare Editors
    StackShare Editors
    Kafka
    Kafka
    Scala
    Scala
    Cassandra
    Cassandra
    Akka
    Akka

    To solve the problem of scheduling and executing arbitrary tasks in its distributed infrastructure, PagerDuty created an open-source tool called Scheduler. Scheduler is written in Scala and uses Cassandra for task persistence. It also adds Apache Kafka to handle task queuing and partitioning, with Akka to structure the library鈥檚 concurrency.

    The service鈥檚 logic schedules a task by passing it to the Scheduler鈥檚 Scala API, which serializes the task metadata and enqueues it into Kafka. Scheduler then consumes the tasks, and posts them to Cassandra to prevent data loss.

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    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)

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    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 122.2K 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 Cassandra and Kafka
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    How developers use Cassandra 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 Soundcloud
    Soundcloud uses CassandraCassandra

    Stitch is a wrapper around a Cassandra database. It has a web application that provides read-access to the counts through an HTTP API. The counts are written to Cassandra in two distinct ways, and it's possible to use either or both of them:

    • Real-time: For real-time updates, Stitch has a processor application that handles a stream of events coming from a broker and increments the appropriate counts in Cassandra.

    • Batch: The batch part is a MapReduce job running on Hadoop that reads event logs, calculates the overall totals, and bulk loads this into Cassandra.

    Avatar of Vital Labs, Inc.
    Vital Labs, Inc. uses CassandraCassandra

    Cassandra is our data management workhorse. It handles all our key-value services, supports time-series data storage and retrieval, securely stores all our audit trails, and backs our Datomic database.

    Avatar of SocialCops
    SocialCops uses CassandraCassandra

    While we experimented with Cassandra in the past, we are no longer using it. It is, however, open for consideration in future projects.

    Avatar of ShareThis
    ShareThis uses CassandraCassandra

    We are using Cassandra in a few of our apps. One of them is as a count service application to track the number of shares, clicks.. etc

    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 Kaiko
    Kaiko uses CassandraCassandra
    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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