Overview Google Protobuf Timestamp : Google Protocol Buffers (Protobuf) is a robust serialization format used to encode structured data efficiently. One of its key features is the ability to handle time with high precision using the google.protobuf.Timestamp type. This data type is essential for representing specific points in time, making it invaluable for logging events, […]
Overview Data Types in Protocol Buffers – Protocol Buffers (Protobuf), developed by Google, is a highly efficient and language-neutral method of serializing structured data. It’s widely used in distributed systems, APIs, and communication between microservices because of its small footprint, speed, and ease of use. At the heart of Protocol Buffers are the Google protobuf
Overview Protobuf Maven Plugin(maven-compiler-plugin), allows user to working with Google Protocol Buffers aka Protobuff in maven base Java project, and integrate the Maven Plugin build tool is a good approach. To compile protocol buffer .proto files and generate the required Java source code from compiler, user need to configure the Maven proto buffer build process
Overview Google Protocol Buffers (Protobuf) have emerged as a powerful solution designed to enhance the performance, scalability, and maintainability of applications. Initially developed by Google, Protocol Buffers have rapidly gained traction in the developer community due to their efficiency in transmitting and receiving structured data. Understanding how Protocol Buffers work and why they are critical
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
