TIBCO DataSynapse GridServer and Apache Ignite are both distributed computing solutions that provide high-performance capabilities.
The TIBCO DataSynapse GridServer deployment consists of Managers and multiple Engine hosts to scale any application at any time. TIBCO designed this highly scalable software infrastructure for enterprise organizations. It enables users to submit multiple…
GridGain Blog
Apache Ignite's default storage engine is sophisticated enough to enable us to use the database for various use cases, ranging from transactional workloads to real-time analytics. The multi-tiered storage architecture allows you to configure Ignite as a distributed, in-memory cache without persistence or to have Ignite function as a hybrid transactional/analytical database that scales beyond…
In my previous blog posts—Ignite 3 Alpha: A Sneak Peek into the Future of Apache Ignite and Just Released: Apache Ignite 3, Alpha 2—I talked about the Apache Ignite community's journey toward Ignite 3, the next generation of the Apache Ignite database.It's a tradition within the Ignite community to provide an Ignite 3 alpha release every few months. This tradition enables everyone interested in…
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,…
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…
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…
In this third article of the three-part series “Getting Started with Ignite Data Loading,” we continue to review data loading into Ignite tables and caches, but now we focus on using the Ignite Data Streamer facility to load data in large volume and with highest speed.
Apache Ignite Data-Loading Facilities
In the first article of this series, we discussed the facilities that are available to…
In this second article of the three-part “Getting Started with Ignite Data Loading” series, we continue our review of data loading into Ignite tables and caches. However, we now focus on Ignite CacheStore.
CacheStore Load Facility
Background
Let’s review what was discussed about CacheStore in “Article 1: Loading Facilities.”
The CacheStore interface of Ignite is the primary vehicle used in…
With this first part of “Getting Started with Ignite Data Loading” series we will review facilities available to developers, analysts and administrators for data loading with Apache Ignite. The subsequent two parts will walk through the two core Apache Ignite data loading techniques, the CacheStore and the Ignite Data Streamer.
We are going to review these facilities in relation to specific…