Use Cases

JCache Provider

Hazelcast and JCache

Today’s applications, whether they are newly developed or long-proven, need predictable latency and fast response times to reach their growing mass of users.

JCache standardizes caching for the Java platform. It is a common mechanism to create, access, update, and remove information from caches. It accelerates mainstream adoption of in-memory computing by giving all Java developers an easy way to access memory from within Java. Enterprises greatly benefit from the increased speed and scalability of applications that take advantage of JCache, and can change providers without rewriting their applications or maintaining a proprietary bespoke cache abstraction layer.

Hazelcast enables organizations to seamlessly integrate with JCache. The JCache caching layer API—specified by the Java Community Process (JCP) as Java Specification Request (JSR) 107—provides a standard set of operations specialized for caching use cases. Organizations can use these operations to scale out applications and manage high-speed access to frequently used data. Hazelcast smoothly achieves its caching potential with a 100 percent compliant implementation that transparently registers with the JCache subsystem.

Using Hazelcast with JCache

Hazelcast provides multiple ways to use JCache, depending on your deployment strategies, security considerations, and usage patterns. You can use Hazelcast as a client-server or a cluster-only architecture.

Client-server architectures are used for high-security environments where:

  • Different clients have diverging security policies.
  • Multiple applications access the same cache pool.
  • The environment is deployed on frontend and backend server clusters, such as Tomcat and JBoss.
  • Cluster-only architectures are used with embedded caches. These caches are kept in the memory of the application cluster for the highest possible access speed.

With both client-server and cluster-only, Hazelcast offers:

Hazelcast JCache Architecture

Hazelcast JCache Architecture


for the highest throughput and lowest latency to accelerate your applications.

Elasticity and Scalability

so the cache can be sized up and down.

Transparent Integration

with backend systems such as databases using JCache CacheStore and CacheLoader interfaces.

Cluster Management

through your web browser with Hazelcast Management Center.

Solutions: Hazelcast In-Memory Computing Platform

The most comprehensive solution for data at rest and data in motion.

Hazelcast Platform

The Hazelcast Platform is a software technology that unifies transactional, operational, and analytical workloads by combining stream processing with in-memory computing. Hazelcast is the fastest solution for merging real-time streaming and historical data from any sources.

In-memory data grids are designed to provide high-availability and scalability by distributing data across multiple machines. Hazelcast enriches applications by providing capabilities to quickly process, store, and access data with the speed of RAM.

The platform also leverages an application embeddable, distributed stream processing platform for building IoT and microservices-based applications. The Hazelcast architecture is high-performance and low-latency-driven, based on a parallel, distributed core engine enabling data-intensive applications to operate at real-time speeds.

Hazelcast Cloud

The benefits of moving to the cloud are well known and applicable to virtually every industry. Hazelcast offers our customers the flexibility to deploy to the cloud on their terms, whether it's a dedicated cloud, on-premise cloud, hybrid cloud, or private cloud.

In-Memory Store and Cache

High-Density Memory Store adds the ability for Hazelcast Enterprise to store very large amounts of cached data in Hazelcast members (servers) and in the Hazelcast Client (near cache), limited only by available RAM for extreme scale-up.

Stream Processing

Stream processing is how Hazelcast processes data on-the-fly, prior to storage, rather than batch processing, where the data set has to be stored in a database before processing. This approach is vital when the value of the information contained in the data decreases rapidly with age. The faster information is extracted from data, the better.