Note on Hazelcast 5

With the release of Hazelcast 5.0, development of Jet has been moved to the
core Hazelcast Repository - please
follow the repository for details on how to use Hazelcast for building data pipelines.

Hazelcast 5 also comes with extensive documentation, replacing the existing Jet
docs: https://docs.hazelcast.com/hazelcast/latest/index.html

What is Jet

Jet is an open-source, in-memory, distributed
batch and stream processing engine. You can use it to process large
volumes of real-time events or huge batches of static datasets. To give
a sense of scale, a single node of Jet has been proven to aggregate 10
million events per
second with
latency under 10 milliseconds.

It provides a Java API to build stream and batch processing applications
through the use of a dataflow programming
model. After you deploy your
application to a Jet cluster, Jet will automatically use all the
computational resources on the cluster to run your application.

If you add more nodes to the cluster while your application is running,
Jet automatically scales up your application to run on the new nodes. If
you remove nodes from the cluster, it scales it down seamlessly without
losing the current computational state, providing exactly-once
processing
guarantees.

For example, you can represent the classical word count problem that
reads some local files and outputs the frequency of each word to console
using the following API:

java
JetInstance jet = Jet.bootstrappedInstance();

Pipeline p = Pipeline.create();
p.readFrom(Sources.files("/path/to/text-files"))
 .flatMap(line -> traverseArray(line.toLowerCase().split("\\W+")))
 .filter(word -> !word.isEmpty())
 .groupingKey(word -> word)
 .aggregate(counting())
 .writeTo(Sinks.logger());

jet.newJob(p).join();

and then deploy the application to the cluster:

bash
bin/jet submit word-count.jar

Another application which aggregates millions of sensor readings per
second with 10-millisecond resolution from Kafka looks like the
following:

java
Pipeline p = Pipeline.create();

p.readFrom(KafkaSources.<String, Reading>kafka(kafkaProperties, "sensors"))
 .withTimestamps(event -> event.getValue().timestamp(), 10) // use event timestamp, allowed lag in ms
 .groupingKey(reading -> reading.sensorId())
 .window(sliding(1_000, 10)) // sliding window of 1s by 10ms
 .aggregate(averagingDouble(reading -> reading.temperature()))
 .writeTo(Sinks.logger());

jet.newJob(p).join();

Jet comes with out-of-the-box support for many kinds of data sources
and sinks, including:

Apache Kafka
Local Files (Text, Avro, JSON)
Apache Hadoop (Azure Data Lake, S3, GCS)
Apache Pulsar
Debezium
Elasticsearch
JDBC
JMS
InfluxDB
Hazelcast
Redis
MongoDB
Twitter

When Should You Use Jet

Jet is a good fit when you need to process large amounts of data in a
distributed fashion. You can use it to build a variety of
data-processing applications, such as:

Low-latency stateful stream processing. For example, detecting trends
in 100 Hz sensor data from 100,000 devices and sending corrective
feedback within 10 milliseconds.
High-throughput, large-state stream processing. For example,
tracking GPS locations of millions of users, inferring their velocity
vectors.
Batch processing of big data volumes, for example analyzing a
day's worth of stock trading data to update the risk exposure of a
given portfolio.

Key Features

Predictable Latency Under Load

Jet uses a unique execution model with cooperative
multithreading
and can achieve extremely low
latencies while
processing millions of items per second on just a single node:

The engine is able to run anywhere from tens to thousands of jobs
concurrently on a fixed number of threads.

Fault Tolerance With No Infrastructure

Jet stores computational state in a distributed, replicated in-memory
store and
does not require the presence of a distributed file system nor
infrastructure like Zookeeper to provide high-availability and
fault-tolerance.

Jet implements a version of the
Chandy-Lamport
algorithm to provide exactly-once processing under the face of
failures. When interfacing with external transactional systems like
databases, it can provide end-to-end processing guarantees using
two-phase
commit.

Advanced Event Processing

Event data can often arrive out of
order and Jet has
first-class support for dealing with this disorder. Jet implements a
technique called distributed
watermarks
to treat disordered events as if they were arriving in order.

How Do I Get Started

Follow the Get Started
guide to start using Jet.

Download

You can download Jet from
https://jet-start.sh.

Alternatively, you can use the latest docker
image:

java
docker run -p 5701:5701 hazelcast/hazelcast-jet

Use the following Maven coordinates to add Jet to your application:

xml
<groupId>com.hazelcast.jet</groupId>
<artifactId>hazelcast-jet</artifactId>
<version>4.2</version>

Tutorials

See the tutorials for
tutorials on using Jet. Some examples:

Reference

Jet supports a variety of transforms and operators. These include:

Stateless
transforms such
as mapping and filtering.
Stateful
transforms such as
aggregations and stateful mapping.

Community

Hazelcast Jet team actively answers questions on Stack
Overflow and
Hazelcast Community Slack.

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

How Can I Contribute

Thanks for your interest in contributing! The easiest way is to just
send a pull request. Have a look at the issues marked as good first
issue
for some guidance.

Building From Source

To build, use:

bash
./mvnw clean package -DskipTests

Use Latest Snapshot Release

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

Maven snippet:

xml
<repositories>
    <repository>
        <id>snapshot-repository</id>
        <name>Maven2 Snapshot Repository</name>
        <url>https://oss.sonatype.org/content/repositories/snapshots</url>
        <snapshots>
            <enabled>true</enabled>
            <updatePolicy>daily</updatePolicy>
        </snapshots>
    </repository>
</repositories>
<dependencies>
    <dependency>
        <groupId>com.hazelcast.jet</groupId>
        <artifactId>hazelcast-jet</artifactId>
        <version>4.3-SNAPSHOT</version>
    </dependency>
</dependencies>

Trigger Phrases in the Pull Request Conversation

When you create a pull request (PR), it must pass a build-and-test
procedure. Maintainers will be notified about your PR, and they can
trigger the build using special comments. These are the phrases you may
see used in the comments on your PR:

verify - run the default PR builder, equivalent to mvn clean install
run-nightly-tests - use the settings for the nightly build (mvn clean install -Pnightly). This includes slower tests in the run,
which we don't normally run on every PR
run-windows - run the tests on a Windows machine (HighFive is not
supported here)
run-cdc-debezium-tests - run all tests in the
extensions/cdc-debezium module
run-cdc-mysql-tests - run all tests in the extensions/cdc-mysql
module
run-cdc-postgres-tests - run all tests in the
extensions/cdc-postgres module

Where not indicated, the builds run on a Linux machine with Oracle JDK
8.

License

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

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.