The GridGain Systems In-Memory Computing Blog
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…
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…
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…
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…
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.…
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…
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…
Publisher's Note: the article describes a custom data loading technique that worked best for a specific user scenario. It's neither a best practice nor a generic approach for data loading in Ignite. Explore standard loading techniques first, such as IgniteDataStreamer or CacheStore.loadCache, which can also be optimized for loading large data sets.
Now, in-memory cache technology is becoming…
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…
Building an Event Stream Processing Solution With Apache Ignite
(Note that this is Part 2 of a three-part series on Event Stream Processing. Here are the links for Part 1 and Part 3.)
In the first article of this three part series, we talked about streaming systems, the associated event paradigm inherent in streams and how these concepts are seen at different levels of abstraction, the…