Hazelcast offers a simple solution to multiple problems

Hazelcast is an open-source in-memory data grid that provides distributed computing capabilities and data management services. It’s designed to simplify the development of distributed systems and enable scaling of applications in a distributed environment. The biggest benefit of Hazelcast is that it uses less code to offer various functionalities. Hazelcast is written in Java and provides APIs for Java, .NET, C++, Python and Node.js.

Hazelcast connects a set of networked/clustered computers to pool together their random access memory (RAM) and CPU power to let applications share data structures and run parallelized workloads in the cluster.

In this blog, we will explore the features of Hazelcast and demonstrate its usage with a simple Java example.

Hazelcast solves several problems related to distributed computing and data management in a distributed environment. Some of the key problems that Hazelcast addresses are:

1. Scalability: Hazelcast enables scaling of applications by allowing the addition of more nodes to the cluster. It provides a distributed computing framework that distributes computations across nodes, which can process data in parallel, resulting in improved application performance and scalability.
2. In-Memory Data Grid: Hazelcast allows data to be stored in memory, providing fast access to data and reducing the load on the database. This feature can help improve the performance of applications that need to access frequently accessed data quickly.
3. Distributed Caching: Hazelcast provides a distributed cache that can store frequently accessed data in memory, which can help improve application performance by reducing the number of database calls and improving data access times.
4. High Availability: Hazelcast provides a distributed system that is highly available and resilient to failures. It uses replication and partitioning techniques to ensure that data is available even if a node fails.
5. Distributed Computing: Hazelcast provides a framework for distributed computing that allows developers to write applications that can run across multiple nodes in a cluster. This feature can help improve application performance by distributing computations across nodes.

Now, let’s see how we can use Hazelcast in a simple Java application.

First, we need to add the Hazelcast dependency to our project. We can add the dependency to our project using the following Maven coordinates:

<dependency>
 <groupId>com.hazelcast</groupId>
 <artifactId>hazelcast</artifactId>
 <version>5.2.0</version>
</dependency>

Next, let’s create a simple Java class that demonstrates the usage of Hazelcast.

You can create a Hazelcast instance in your code like this:

Config config = new Config();
HazelcastInstance hazelcastInstance = Hazelcast.newHazelcastInstance(config);

This creates a new Hazelcast instance with default configuration.

Now, let’s say you want to cache some data. You can create a distributed map like this:

IMap<String, String> cache = hazelcastInstance.getMap("my-cache");

This creates a distributed map named “my-cache”. You can put some data in the map like this:

cache.put("key1", "value1");
cache.put("key2", "value2");

And you can retrieve the data like this:

String value1 = cache.get("key1");
String value2 = cache.get("key2");

In this example,

The get method returns null if the key does not exist in the map.

That’s it! Hazelcast takes care of distributing the map across the cluster and ensuring consistency between nodes. You can add more nodes to the cluster by creating more Hazelcast instances with the same configuration.

Note that this is just a simple example. Hazelcast offers many more features for distributed caching, such as eviction policies, backups, and distributed queries. You can find more information in the Hazelcast documentation.

In conclusion,

Hazelcast is a powerful tool for building distributed systems and managing data in a distributed environment. Its features, such as in-memory data grid, distributed computing, clustering, and distributed caching, make it an ideal solution for building high-performance, scalable applications. In this blog, we demonstrated the