The GridGain Systems In-Memory Computing Blog

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…
Apache Ignite has the ability to scale horizontally, allowing you to handle the data generated by your applications and services. When your Apache Ignite cluster is using excessive memory, you can utilize horizontal scaling, which is one of the fundamental architectural capabilities of Ignite. While the common advice is to "throw more resources into the cluster," it is often not practical or…
There are two significant categories in in-memory computing: In-Memory Database and In-Memory Data Grids. This post aims to present a concise version of thoughts on this topic, with insights gained from a recent analyst call aiding in organizing the information. Nomenclature of In-Memory Database vs In-Memory Data Grid Let's start by clarifying the terminology and buzzwords. The term "In-…
In this post, we will be discussing the GridGain Nebula managed service offering for Apache Ignite, available on the GridGain portal. The main benefits of GridGain Nebula are that it simplifies cloud provisioning and provides managed service support. What is Apache Ignite? Before we begin, let’s take a look at Apache Ignite. At a very high level, Apache Ignite and GridGain are both in-memory…
Businesses today are increasingly complex leading to slow performance which negatively impacts customer experiences, productivity, and ultimately revenue. An in-memory data fabric addresses data complexity head-on. Companies today are facing a significant problem – their reliance on disk-based data stores are slowing down performance and costing them valuable time and money. Enter GridGain’s…
The Apache Ignite native persistence storage engine follows a classic database approach based on the ARIES architecture. However, the Ignite developers needed to make some adjustments to the architecture in order to improve development speed and support memory-only storage. In this blog, I will provide an overview of the Ignite native persistence storage engine and discuss the tradeoffs that…
The Apache Ignite community is maintained by dedicated volunteers who manage a highly informative and well-designed website. This website is regularly updated with the latest news and information about the open-source project. A section of this content, found on the GridGain blog, provides an overview of Apache Ignite's basic concepts, facts, tips, and tricks. The goal of this blog post is to…
After five days (and eleven meetings) with new customers in Europe and the Middle East, I think the time is right for another refinement of in-memory computing’s definition. To me, it is clear that our industry is lagging when it comes to explaining in-memory computing to potential customers and defining what in-memory computing is really about. We struggle to come up with a simple,…
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.…
Apache® Ignite™ is a very versatile product that supports a wide-range of integrated components. These components include a Machine Learning (ML) library that supports popular ML algorithms, such as Linear Regression, k-NN Classification, and K-Means Clustering. The ML capabilities of Ignite provide a wide-range of benefits, as shown in Figure 1. For example, Ignite can work on the data in place…