GridGain Systems Expands its Platform with Automated Benchmarking

GridGain Systems, provider of in-memory computing platform solutions based on Apache Ignite, is releasing GridGain Professional Edition 1.9, improving performance and automated benchmarking.

This update version fully supports Apache Ignite 1.9 and introduces Kubernetes support for deploying containerized Ignite clusters, expands support for data manipulation language (DML), and more.

“GridGain Professional Edition 1.9 adds additional deployment and management capabilities that make in-memory computing easier to use for mission-critical, demanding applications,” said Abe Kleinfeld, President and CEO of GridGain Systems. “With its easily deployed package, professional support and timely bug fixes, the GridGain Professional Edition helps make in-memory computing the only sensible solution for organizations to solve their most processing-intensive data challenges.”

Apache Ignite 1.9 outperforms Apache Ignite 1.8 by 20 to 40 percent for most core cache and SQL operations, according to the vendor. In addition, the Apache 1.9 release automates benchmarks execution. Users can now deploy the Apache Ignite 1.9 binary and execute benchmarks within the Ignite environment.

Additionally, Kubernetes is an open source solution that automates the deployment, scaling, and management of containerized applications.

The integration of Apache Ignite with Kubernetes simplifies the deployment of an Ignite cluster in a Kubernetes container by allowing Kubernetes to manage resources and scale the Ignite cluster.

The update expands support for data manipulation language (DML) to include Ignite.NET and Ignite.C++ APIs, which extends DML capabilities to Apache Ignite users who run on .NET or C++.

Ignite.NET, the .NET version of Apache Ignite, now includes the .NET TransactionScope API which allows users to work with distributed Ignite transactions by fully relying on standard interfaces available in .NET. For C++ users, Ignite.C++, the C++ version of Apache Ignite, now includes support for the most popular continuous queries API, which allows users to monitor the data modifications that occur on Ignite-distributed caches within their C++ applications.

Apache Ignite is now integrated with Apache Spark 2.1, enabling the use of Ignite Shared RDDs in applications using the most current version of Apache Spark.

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