In-Memory Computing Platform

In-Memory Computing is characterized by using high-performance, integrated, distributed memory systems to compute and transact on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies.

Open Source

GridGain open-source project is licensed under Apache 2.0 and is hosted on GitHub where you can review code, learn GridGain internals, and file and review issues. GridGain has almost a million lines of code and it is sometimes beneficial to look under the hood to understand details, programming style or specifics of implementations.

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GitHub repo:
Open source website:


GridGain’s products are designed to deliver uncompromised performance by providing developers with a comprehensive set of APIs. Developed for the most demanding use cases, including sub-millisecond SLAs, core platform products allow you to programmatically fine-tune performance on large and super-large topologies with hundreds to thousands of nodes:

In-Memory Data Grid

Natively distributed, ACID transactional, MVCC-based, SQL+NoSQL, in-memory object key-value store. The only in-memory data grid proven to scale to billions of transactions per second on commodity hardware.

In-Memory Streaming

Massively distributed processing meets Complex Event Processing (CEP) and Streaming Processing with advanced workflow support, windowing, user-defined indexes and more.

In-Memory Accelerator for Apache Hadoop

Combination of In-Memory File System 100% compatible with Hadoop HDFS and In-Memory MapReduce delivering 100x performance increase. Minimal integration, plug-n-play acceleration with any Hadoop distro.

Management & Monitoring

Every GridGain product comes with GridGain Visor that provides a single unified operations, management and monitoring console across all GridGain products and for any applications and systems built with GridGain.


Learn more about GridGain Visor.