Hazelcast Jet allows you to write modern Java code that focuses purely on data transformation while it does all the heavy lifting of getting the data flowing and computation running across a cluster of nodes. It supports working with both bounded (batch) and unbounded (streaming) data.

These are some of the concerns Jet handles well:

Scale Up and Out: Parallelize a computation across all CPU cores and cluster nodes Auto-Rescale: Scale out to newly added nodes and recover from nodes that left or failed Correctness Guarantee: at-least-once and exactly-once processing in the face of node failures Jet integrates out of the box with many popular data storage systems such as Apache Kafka, Hadoop, relational databases, message queues and many more.

Jet supports a rich set of data transformations, such as windowed aggregations. For example, if your data is GPS location reports from millions of users, Jet can compute every user's velocity vector by using a sliding window and just a few lines of code.

Jet also comes with a fully-featured, in-memory key-value store. Use it to cache results, store reference data or as a data source itself.

Programming language: Java
Latest version: v4.0

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Hazelcast Jet

GitHub release Join the chat at https://gitter.im/hazelcast/hazelcast-jet

Hazelcast Jet is an open-source, cloud-native, distributed stream and batch processing engine.

Jet is simple to set up. The nodes you start discover each other and form a cluster automatically. You can do the same locally, even on the same machine (your laptop, for example). This is great for quick testing.

With Jet it's easy to build fault-tolerant and elastic data processing pipelines. Jet keeps processing data without loss even if a node fails, and you can add more nodes that immediately start sharing the computation load.

You can embed Jet as a part of your application, it's just a single JAR without dependencies. You can also deploy it standalone, as a stream-processing cluster.

Jet also provides a highly available, distributed in-memory data store. You can cache your reference data and enrich the event stream with it, store the results of a computation, or even store the input data you're about to process with Jet.

Start using Jet

Add this to your pom.xml to get the latest Jet as your project dependency:


Since Jet is embeddable, this is all you need to start your first Jet instance! Read on for a quick example of your first Jet program.

Batch Processing with Jet

Use this code to start an instance of Jet and tell it to perform some computation:

String path = "books";

JetInstance jet = Jet.bootstrappedInstance();

Pipeline p = Pipeline.create();

        .flatMap(line -> Traversers.traverseArray(line.toLowerCase().split("\\W+")))
        .filter(word -> !word.isEmpty())
        .groupingKey(word -> word)


When you run this, point the path variable to some directory with text files in it. Jet will analyze all the files and give you the word frequency distribution in the log output (for each word it will say how many times it appears in the files).

The above was an example of processing data at rest (i.e., batch processing). It's conceptually simpler than stream processing so we used it as our first example.

Stream Processing with Jet

For stream processing you need a streaming data source. A simple example is watching a folder of text files for changes and processing each new appended line. Here's the code you can try out:

String path = "books";

JetInstance jet = Jet.bootstrappedInstance();

Pipeline p = Pipeline.create();

        .flatMap(line -> Traversers.traverseArray(line.toLowerCase().split("\\W+")))
        .filter(word -> !word.isEmpty())
        .groupingKey(word -> word)


Before running this make an empty directory and point the path variable to it. While the job is running copy some text files into it and Jet will process them right away.


  • Constant low latency - predictable latency is a design goal
  • Zero dependencies - single JAR which is embeddable (minimum JDK 8)
  • Cloud Native - with Docker images and Kubernetes support including Helm Charts.
  • Elastic - Jet can scale jobs up and down while running
  • Fault Tolerant - At-least-once and exactly-once processing guarantees
  • In-memory storage - Jet provides robust distributed in-memory storage for caching, enrichment or storing job results
  • Sources and sinks for Apache Kafka, Hadoop, Hazelcast IMDG, sockets, files
  • Dynamic node discovery for both on-premise and cloud deployments.


You can download the distribution package which includes command-line tools from https://jet-start.sh.

Getting Started and Documentation

See the Hazelcast Jet Getting Started Guide.

Code Samples

See [examples folder](examples) for some examples.


See the following architecture pages for more insight into the internals of Jet:


You can see a full list of connectors at the (Sources and Sink)[https://jet-start.sh/docs/api/sources-sinks] section of the docs. A summary is below:

Name Description
Amazon S3 A connector that allows AWS S3 read/write support for Hazelcast Jet.
Apache Avro Source and sink connector for Avro files.
Apache Hadoop A connector that allows Apache Hadoop read/write support for Hazelcast Jet.
Apache Kafka A connector that allows consuming/producing events from/to Apache Kafka.
Debezium A Hazelcast Jet connector for Debezium which enables Hazelcast Jet pipelines to consume CDC events from various databases.
Elasticsearch A Hazelcast Jet connector for Elasticsearch for querying/indexing objects from/to Elasticsearch.
Files Connector for local filesystem.
Hazelcast Cache Journal Connector for change events on caches in local and remote Hazelcast clusters.
Hazelcast Cache Connector for caches in local and remote Hazelcast clusters.
Hazelcast List Connector for lists in local and remote Hazelcast clusters.
Hazelcast Map Journal Connector for change events on maps in local and remote Hazelcast clusters.
Hazelcast Map Connector for maps in local and remote Hazelcast clusters.
InfluxDb A Hazelcast Jet Connector for InfluxDb which enables pipelines to read/write data points from/to InfluxDb.
JDBC Connector for relational databases via JDBC.
JMS Connector for JMS topics and queues.
Kafka Connect A generic Kafka Connect source provides ability to plug any Kafka Connect source for data ingestion to Jet pipelines.
MongoDB A Hazelcast Jet connector for MongoDB for querying/inserting objects from/to MongoDB.
Redis Hazelcast Jet connectors for various Redis data structures.
Socket Connector for TCP sockets.
Twitter A Hazelcast Jet connector for consuming data from Twitter stream sources in Jet pipelines.

See hazelcast-jet-contrib repository for more detailed information on community supported connectors and tools.

Start Developing Hazelcast Jet

Use Latest Snapshot Release

You can always use the latest snapshot release if you want to try the features currently under development.

Maven snippet:

        <name>Maven2 Snapshot Repository</name>

Build From Source


  • JDK 8 or later

To build on Linux/MacOS X use:

./mvnw clean package -DskipTests

for Windows use:

mvnw clean package -DskipTests


We encourage pull requests and process them promptly.

To contribute:


Hazelcast Jet team actively answers questions on Stack Overflow and Gitter.

You are also encouraged to join the hazelcast-jet mailing list if you are interested in community discussions


Source code in this repository is covered by one of two licenses:

  1. [Apache License 2.0](licenses/apache-v2-license.txt)
  2. [Hazelcast Community License](licenses/hazelcast-community-license.txt).

The default license throughout the repository is Apache License 2.0 unless the
header specifies another license. Please see the Licensing section for more information.

Copyright (c) 2008-2020, Hazelcast, Inc. All Rights Reserved.

Visit www.hazelcast.com for more info.

*Note that all licence references and agreements mentioned in the Hazelcast Jet README section above are relevant to that project's source code only.