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.
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
- 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
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.
Follow the Getting Started Guide to install and start using Hazelcast.
Read the documentation for in-depth details about how to install Hazelcast and an overview of the features.
You can use Slack for getting help with Hazelcast.
Thanks for your interest in contributing! The easiest way is to just send a pull request.
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 -DskipTestsIt 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.
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.
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.
We owe (the good parts of) our CLI tool's user experience to picocli.
Copyright (c) 2008-2024, Hazelcast, Inc. All Rights Reserved.
Visit www.hazelcast.com for more info.
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