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