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Ignite 3 Alpha: A Sneak Peek into the Future of Apache Ignite

What Is Ignite 3? Apache Ignite has existed for more than six years. During these years, Ignite evolved a lot. The SQL engine became more comprehensive, page-memory architecture and the persistence layer were  introduced, and many features were added. These advancements make Ignite an extremely powerful tool, suitable for a wide variety of use cases from basic caching to complicated, multi-component data integration hubs. However, such power came with a price. The new capabilities were implemented as additions to the existing codebase; that is, Ignite experienced no significant architectural or API changes. As a result, configurations, APIs, and Ignite behaviors are not always consistent and intuitive, and, therefore, Ignite can be quite hard to use, especially for beginners.
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Previous Entries

In this article, we look at how transactions work in Apache Ignite. We begin with an overview of Ignite’s transaction architecture and then illustrate how tracing can be used to inspect transaction logic. Finally, we review a few simple examples that show how transactions work (and why they might not work). Note that we assume you are familiar with the concept of key-value storage. If not,…
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Since our initial launch in mid-2020, GridGain Control Center has strived to bring transparency and flexibility to the monitoring and development of Apache Ignite and GridGain applications. With each monthly update, we introduce new features to make it easier for admins and developers to understand what exactly is happening within their clusters. In our latest update (2020.11.00), we add new…
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Imagine that we need to build a monitoring infrastructure for a distributed database, such as Apache Ignite. Let’s put metrics into Prometheus. And, let’s draw charts in Grafana. Let’s not forget about the notification system—we’ll set up Zabbix for that. Let’s use Jaeger for traces analysis. For state management, the CLI will do. As for SQL queries, let’s use a free JDBC client, such as DBeaver…
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We recently announced the GridGain and Apache Ignite Operator for Kubernetes, which gives GridGain and Apache Ignite users a convenient way to deploy and manage their clusters. The automation provided by the solution simplifies cluster provisioning and minimizes the operational and management burden. In addition, our latest updates to the GridGain thin client and thick client deliver simplified…
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This blog is an abridged version of the talk that I gave at the Apache Ignite community meetup. You can download the slides that I presented at the meetup here. In the talk, I explain how data in Apache Ignite is distributed. Why do you need to distribute anything at all? Inevitably, the evolution of a system that requires data storage and processing reaches a threshold. Either too much data is…
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Where do you store your passwords? Whether you’re integrating Apache Ignite with a relational database, a message queue, or something else, you probably need to manage secrets such as usernames, passwords, and security tokens. In this post, we consider a couple of options to avoid having secrets in your configuration file: using property files and integrating with HashiCorp Vault.…
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Telcos can become a highly data-driven enterprise by leveraging the Digital Integration Hub (DIH) Architecture built on GridGain’s in-memory computing platform. In this blog post, I will discuss how the DIH architecture can help telcos develop better customer insights, generate new revenue streams and be ready to ride the 5G wave. This easy-to-adopt, no rip-replace architecture can meet the needs…
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This tutorial walks you through the process of creating a Spring Cloud-based RESTful web service that uses Apache Ignite as a high-performance, in-memory database. The service is a containerized application that uses HashiCorp Consul for service discovery and interacts with an Apache Ignite cluster via Spring Data repository abstraction. For containerization, we use 🐋 Docker. Apache® Ignite™ is…
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Now, in-memory cache technology is becoming popular, motivating companies to experiment with distributed systems. The technology is advertised to be fast, and data-load speed is often critical for building a successful solution prototype. This blog post provides a technical tutorial on how to populate a distributed Apache Ignite cluster with values that originate from large relational tables. All…
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Using the initial-query, listener, and remote-filter features of Ignite continuous queries to detect, filter, process, and dispatch real-time events (Note that this is Part 3 of a three-part series on Event Stream Processing. Here are the links for Part 1 and Part 2.) Real-time handling of streams of business events is a critical part of modern information-management systems, including online…
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