Get Started

Get Started

These guides demonstrate the operational flexibility and speed of the Hazelcast In-Memory Computing Platform. Set-up in seconds, data in microseconds. Operations and developer friendly.

Hazelcast IMDG

Find out for yourself how to get a Hazelcast IMDG cluster up and running. In this Getting Started guide you’ll learn how to:

  • Create a Cluster of 3 Members.
  • Start the Hazelcast Management Center.
  • Add data to the cluster using a sample client in the language of your choice.
  • Add and remove some cluster members to demonstrate the automatic rebalancing of data and back-ups.

Hazelcast Jet

Learn how to run a distributed data stream processing pipeline in Java. In this Getting Started guide you’ll learn how to:

  • Start a Hazelcast Jet cluster in your JVM.
  • Build the Word Count application.
  • Execute the application wit Jet.
  • Push testing data to the cluster and explore results

As a next step, you will be able explore the code samples and extend your setup more features and connectors.

Open Source Storage and Computing at In-Memory Speeds

Use Hazelcast IMDG to store your data in RAM, spread and replicate it across a cluster of machines, and perform data-local computation on it. Replication gives you resilience to failures of cluster nodes.

Use Hazelcast Jet to build data pipelines processing streams of events such as from message queues and database changelogs. The processing state is replicated, allowing you to scale up and down the computation without any loss of data.

Hazelcast IMDG

Open-source distributed In-memory object store supporting a wide variety of data structures such as Map, Set, List, MultiMap, RingBuffer, HyperLogLog. Cloud and Kubernetes friendly.

Hazelcast Jet

Open-source distributed stream and batch processing with embedded in-memory storage and a variety of connectors such as Kafka, Amazon S3, Hadoop, JMS and JDBC.

Why Hazelcast?

Build Distributed Applications

Hazelcast provides tools for building distributed applications. Use Hazelcast IMDG for distributed coordination and in-memory data storage and Hazelcast Jet for building streaming data pipelines. Using Hazelcast allows developers to focus on solving problems rather than data plumbing.

Create a Cluster within Seconds

It’s easy to get started with Hazelcast. The nodes automatically discover each other to form a cluster, both in a cloud environment and on your laptop. This is great for quick testing and simplifies deployment and maintenance. No additional dependencies.

Store Data In-Memory Resiliently

Hazelcast automatically partitions and replicates data in the cluster and tolerates node failures. You can add new nodes to increase storage capacity immediately. You can use it as a cache or to store transactional state and perform data-local computations or queries. As all data is stored in memory, you can access it in sub-millisecond latencies. Clients for Java, Python, .NET, C++ and Go are available.

Build Fault-Tolerant Data Pipelines

Use Hazelcast Jet to build massively parallel data pipelines. You can process data using a rich library of transforms such as windowing, joins and aggregations. Jet keeps processing data without loss even when a node fails, and as soon as you add another node, it starts sharing the computation load. First-class support for Apache Kafka, Hadoop and many other data sources and sinks.

Easy Distributed Coordination

Hazelcast has a full implementation of Raft, allowing a simple API for building linearizable distributed systems. Use tools like FencedLock, Semaphore and AtomicReference to simplify coordination between distributed applications.

Single Binary

Both Hazelcast Jet and Hazelcast IMDG are a single Java archive (JAR) less than 15MB. It’s lightweight enough to run on small devices, you can embed it into your application as just another dependency or deploy it as a standalone cluster. First-class support for Kubernetes is included.

Who is using Hazelcast?

Hazelcast is deployed in the most demanding environments and applications.

Guides

Compare Redis with Hazelcast

Redis and Hazelcast solve many similar use cases, most commonly caching. They are quite different in how they approach things such as cache patterns, clustering & querying.

Build Cloud-Native Microservices

Set up a Hazelcast cluster in Kubernetes, and make use of Hazelcast storage and messaging capabilities in your microservices architectures.

Process Event From Apache Kafka

Use Hazelcast Jet to build a data processing pipeline that will process events from Apache Kafka as they arrive.

Use Distributed Data Structures

Use Hazelcast IMDG for storing and retrieving data from distributed in-memory data structures. You can store your data from one machine and access it from another or perform queries on it.

Open Gitter Chat