Hazelcast
Overview Hazelcast has in-memory data platform, it offers distributed data structures and stream data processing capabilities. One of its important features is the ability to perform SQL queries over distributed data. Hazelcast SQL makes it easier to interact with distributed data structures, such as maps, in a manner similar to relational databases. Understanding Hazelcast SQL […]
Overview Hazelcast, a leading in-memory data grid, offers seamless integration with SQL queries, making it a powerful tool for developers who need both scalability and performance. The Hazelcast SQL Query Example provides a structured way to query data stored within Hazelcast clusters, combining the flexibility of SQL with the speed of in-memory computing. In this
Overview Monitoring is essential for ensuring the health and performance of your Hazelcast cluster, especially in distributed environments. One of the most effective ways to monitor Hazelcast is through Hazelcast JMX Monitoring (Java Management Extensions), which allows you to gather detailed metrics about your system, JVM, and Hazelcast-specific components. This blog will explore how you
Overview Monitoring a distributed system like Hazelcast is crucial to maintaining high availability and performance. While Hazelcast provides robust tools like Management Center, one of the most flexible methods for monitoring is through the REST API. This approach offers real-time, customizable insights into the health and performance of your Hazelcast cluster. In this blog, we’ll
Overview Hazelcast is a powerful in-memory data grid that helps manage data across distributed systems. One of its key features is listeners, which allow you to handle events efficiently in real-time. Whether you’re adding entries to a map, publishing messages to a topic, or updating a distributed queue, Hazelcast listeners ensure that these events trigger
Overview Hazelcast Serialization and Deserialization are key processes that convert objects to and from binary formats so they can be transmitted across nodes in a distributed environment, cached, or stored. Hazelcast provides multiple serialization mechanisms tailored for performance and flexibility. Pre-Requisite Before start learning about Hazelcast Serialization and Deserialization process you need to understand basics
Overview In today’s software application, it is essential to manage the data, especially when your system is getting scaled. Hazelcast is one of the distributed in-memory data grids that works with distributed systems and clusters of nodes. Hazelcast allows the distributed map with Spring Boot, which manages the distributed cache, session, and various cloud patterns
Overview Hazelcast has provided feature intefaces MapStore, MapLoader and QueueStore to store and load the data from/into NoSQL or RDBMS Database store. Hazelcast has internal dependency of zaxxer.hikari Hazelcast Hikari connection pooling feature. You can implement the Hikari connection pooling feature using these classes. Let’s implement the Hazelcast Hikari connection pooling using H2 database with
Hazelcast is well known for its in-memory data and platform, which include various distributed data structures such as maps, sets, and queues. Among these, Hazelcast’s distributed queues offer a strong mechanism for managing data and messages across a cluster of nodes. To enhance the performance of these queues, Hazelcast provides an important interface called Hazelcast QueueStore,
What is Hazelcast MapLoader? Hazelcast distributed map implementation is an in-memory data store, but it can be backed by any type of data store such as RDBMS, OODBMS, NOSQL, or simply a file-based data store. MapLoader implementation provides feature to load data from external filesystem or storage to Hazelcast in-memory map. Normally returns the value
