Hazelcast

What is Hazelcast

The world’s leading companies trust Hazelcast to modernize applications and take instant action on data in motion to create new revenue streams, mitigate risk, and operate more efficiently. Businesses use Hazelcast’s unified real-time data platform to process streaming data, enrich it with historical context and take instant action with standard or ML/AI-driven automation - before it is stored in a database or data lake.

Hazelcast is named in the Gartner Market Guide to Event Stream Processing and a leader in the GigaOm Radar Report for Streaming Data Platforms. To join our community of CXOs, architects and developers at brands such as Lowe’s, HSBC, JPMorgan Chase, Volvo, New York Life, and others, visit hazelcast.com.

When to use Hazelcast

Hazelcast provides a platform that can handle multiple types of workloads for building real-time applications.

Stateful data processing over streaming data or data at rest
Querying streaming and batch data sources directly using SQL
Ingesting data through a library of connectors and serving it using low-latency SQL queries
Pushing updates to applications on events
Low-latency queue-based or pub-sub messaging
Fast access to contextual and transactional data via caching patterns such as read/write-through and write-behind
Distributed coordination for microservices
Replicating data from one region to another or between data centers in the same region

Key Features

Stateful and fault-tolerant data processing and querying over data streams and data at rest using SQL or dataflow API
A comprehensive library of connectors such as Kafka, Hadoop, S3, RDBMS, JMS and many more
Distributed messaging using pub-sub and queues
Distributed, partitioned, queryable key-value store with event listeners, which can also be used to store contextual data for enriching event streams with low latency
Tight integration for deploying machine learning models with Python to a data processing pipeline
Cloud-native, run everywhere architecture
Zero-downtime operations with rolling upgrades
At-least-once and exactly-once processing guarantees for stream processing pipelines
Data replication between data centers and geographic regions using WAN
Microsecond performance for key-value point lookups and pub-sub
Unique data processing architecture results in 99.99% latency of under 10ms for streaming queries with millions of events per second.
Client libraries in Java, Python, Node.js, .NET, C++ and Go

Stateful Data Processing

Hazelcast has a built-in data processing engine called Jet, which can be used to build both streaming/real-time and batch/static data pipelines that are elastic. A single node of Hazelcast has been proven to aggregate 10 million events per second with latency under 10 milliseconds. A cluster of Hazelcast nodes can process billion events per second.

Get Started

Follow the Getting Started Guide to install and start using Hazelcast.

Documentation

Read the documentation for in-depth details about how to install Hazelcast and an overview of the features.

Get Help

You can use Slack for getting help with Hazelcast.

How to Contribute

Thanks for your interest in contributing! The easiest way is to just send a pull request.

Building From Source

Building Hazelcast requires at minimum JDK 17. Pull the latest source from the repository and use Maven install (or package) to build:

$ git pull origin master
$ ./mvnw clean package -DskipTests

It is recommended to use the included Maven wrapper script. It is also possible to use local Maven distribution with the same version that is used in the Maven wrapper script.

Additionally, there is a quick build activated by setting the -Dquick system property that skips validation tasks for faster local builds (e.g. tests, checkstyle validation, javadoc, source plugins etc) and does not build extensions and distribution modules.

Testing

Take into account that the default build executes thousands of tests which may take a considerable amount of time. Hazelcast has 3 testing profiles:

Default:
```
./mvnw test
```

to run quick/integration tests (those can be run in parallel without using network by using -P parallelTest profile).

Slow Tests:
```
./mvnw test -P nightly-build
```

to run tests that are either slow or cannot be run in parallel.

All Tests:
```
./mvnw test -P all-tests
```

to run all tests serially using network.

Some tests require Docker to run. Set -Dhazelcast.disable.docker.tests system property to ignore them.

When developing a PR it is sufficient to run your new tests and some related subset of tests locally. Our PR builder will take care of running the full test suite.