Hazelcast
Modern applications are changing very rapidly. Traditional keyword search is no longer sufficient for many real-world use cases, including AI-Powered recommendations, semantic search, chatbots, fraud detection, and product similarity engines. These systems require the ability to understand meaning and relationships between pieces of data rather than just matching exact keywords. This is how and where […]
Introduction OutOfMemoryException (often seen as java.lang.OutOfMemoryError) is one of the most frustrating problems Java developers face in production. When this happens inside a Hazelcast cluster, the impact becomes even more serious nodes crash, data partitions get redistributed, performance drops, and entire applications may become unstable. Hazelcast unlike a in memory data grid, it is fast
Overview In contemporary AI and ML environments, vectors are the primary way to encode and search complex, high-dimensional data such as text, images, audio, and user preferences. Starting with Hazelcast 5.4, Vector Collections let developers implement semantic search, recommendation engines, and other AI features directly inside a distributed in-memory data grid, removing the need for
Introduction In a modern application that interacts with different database and app services, handling the distributed transactions are very efficient and crucial. To manage the data consistency across the multiple resources XA transaction(eXtended Architecture) is a standard way. In this article, we will explore how to use the XA Transactions in Java using Hazelcast data
Introduction In a distributed system, maintaining data integrity and consistency is very challenging. To implement Hazelcast transaction Management, Hazelcast provides features that make it easy to manage transactions and data consistency. Hazelcast is a high-performance, in-memory data grid(IMDG) which supports distributed caching, computation, and messaging. In this article, we will explore the features of Hazelcast
Introduction In modern applications, building a data pipeline with Hazelcast is important and data pipelines are one of the most critical systems in the whole applications architecture. Data pipelines are responsible for processing and transforming raw data into a format that can be used for analysis. However, it is not that simple to build a useful data
1. Introduction In today’s time, efficient querying and management of complex data structures is a necessity for data-driven applications. Hazelcast, a powerful in-memory data grid, is known for handling large-scale distributed data and provides robust querying features. One unique feature of Hazelcast JSON SQL JOIN Query is HazelcastJsonValue, which natively supports JSON data. SQL (Structured
1. Introduction to Hazelcast and JSON Support Hazelcast is one of the in-memory data grid and computing platform, which is designed to process the large volume of data into quickly and reliably. Hazelcast distribute the data into multiple clusters of nodes, which enables to access the data significantly faster and with low latency, compared to
Overview Hazelcast work with distributed cluster with nodes. If you need to implement counter which need to shared accross the distributed nodes for the testing and fundamental. Hazelcast gives a few incredible assets to deal with these difficulties. Among them is the Hazelcast PN Counter, a profoundly helpful design for overseeing dispersed information in a
Overview As distributed systems and applications are gaining traction, the role of unique identifiers across numerous nodes becomes essential. This issue is relevant when working with a distributed data platform. A popular in-memory data grid, Hazelcast has a perfect remedy for this problem in the form of the Hazelcast Flake ID Generator. In essence, the
