Apache Ignite 2.4 rolls out with Machine Learning and Spark DataFrames capabilities

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The Apache Ignite community has announced the latest version of Apache Ignite, its open-source distributed database. Apache Ignite 2.4 features new machine learning capabilities, Spark DataFrames support, and the introduction of a low-level binary client protocol.

Machine Learning APIs were first teased at the launch of Apache Ignite 2.0, approximately eight months ago. Now with Apache Ignite 2.4, the ML Grid is production ready. With new ML features, Ignite users can deal with fraud detection, predictive analytics, and for building recommendation systems. The ML grid can also solve regression and classification tasks,  and avoid ETL from Ignite to other systems.

ML Grid in the future releases of Ignite 2.4, will also incorporate a genetic algorithm software, donated by NetMillennium Inc. This software will help in solving optimization problems by simulating the process of biological evolution. These in turn can be applied to real-world applications including automotive design, computer gaming, robotics, investments, traffic/shipment routing and more.

There is also a good news for Spark users. Dataframes is now officially supported for Apache Spark. In addition, Apache Ignite can also be installed from the official RPM repository.


Apache Ignite 2.4 also has a new low-level binary client protocol. This would allow all developers, including but not limited to Java, C#, and C++ developers, to utilize Ignite APIs in their applications. The protocol communicates with an existing Ignite cluster without starting a full-fledged Ignite node. An application can connect to the cluster through a raw TCP socket from any programming language.

Apache Ignite 2.4 took five months in total for development. Normally, a new version is rolled out every three months. You can read the complete list of addition in the release notes.

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