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Hazelcast vs Kafka: What are the differences?

Developers describe Hazelcast as "Clustering and highly scalable data distribution platform for Java". With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution. 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.

Hazelcast and Kafka are primarily classified as "In-Memory Databases" and "Message Queue" tools respectively.

Some of the features offered by Hazelcast are:

  • Distributed implementations of java.util.{Queue, Set, List, Map}
  • Distributed implementation of java.util.concurrent.locks.Lock
  • Distributed implementation of java.util.concurrent.ExecutorService

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)

"High Availibility" is the primary reason why developers consider Hazelcast over the competitors, whereas "High-throughput" was stated as the key factor in picking Kafka.

Hazelcast and Kafka are both open source tools. Kafka with 12.5K GitHub stars and 6.7K forks on GitHub appears to be more popular than Hazelcast with 3.15K GitHub stars and 1.15K GitHub forks.

Slack, Shopify, and SendGrid are some of the popular companies that use Kafka, whereas Hazelcast is used by Yammer, Seat Pagine Gialle, and Stormpath. Kafka has a broader approval, being mentioned in 500 company stacks & 451 developers stacks; compared to Hazelcast, which is listed in 25 company stacks and 15 developer stacks.

What is Hazelcast?

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

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 Hazelcast and Kafka?
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.
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.
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.
Memcached
Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.
Apache Ignite
It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale
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Decisions about Hazelcast 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)

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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 121.4K 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 Hazelcast and Kafka
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How developers use Hazelcast 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 Vital Labs, Inc.
Vital Labs, Inc. uses HazelcastHazelcast

HazelCast is the foundation for the distributed system that hosts our APIs and intelligent workflows. We wrap the core HazelCast functions in Clojure protocols to implement micro-services on top of a coherent, single-process instance per virtual node.

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.

How much does Hazelcast cost?
How much does Kafka cost?
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