The GridGain Systems In-Memory Computing Blog
Metrics in Distributed Systems Monitoring
Metrics change over time and, at any particular time, indicate the current state of a system. For example, you can determine whether everything is good with your computer by checking the processor load level, the amount of memory, and the used disk space. Also, for example, a graph that identifies numbers of business operations describes the system from…
Overview
The task of monitoring complex distributed systems can be a headache, from the configuration and updating point of view and from the performance point of view. The easiest way to avoid problems is to prevent them at the design stage. In this post, we describe how to implement monitoring of a complex distributed system by using Zabbix as the monitoring tool and Apache Ignite as the…
Overview
Prometheus is a popular monitoring tool that is supported by the Cloud Native Computing Foundation, the group who support Kubernetes. We often see Apache Ignite and GridGain users trying to integrate Prometheus with their clusters. This post provides hints about how to integrate Prometheus with Apache Ignite and GridGain.
Prometheus Configuration
This post isn’t about how best to…
It’s been a while since we published a major release of GridGain In-Memory Computing Platform. There is a reason for that. We’ve been advancing our multi-tier database engine, powered by Apache Ignite. And, with GridGain 8.8, we are rolling out the first set of advancements (yep, more to come) that enable you to leverage the disk tier of the database to query larger datasets, reduce the total…
What Is Ignite 3?
Apache Ignite has existed for more than six years. During those years, Ignite evolved incredibly and, with thousands of deployments worldwide, became a top-5 project of the Apache Software foundation. 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…
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,…
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…