Apache Ignite is a horizontally scalable, fault-tolerant distributed in-memory computing platform for building real-time applications that can process terabytes of data with in-memory speed.
Apache Ignite can be used as an in-memory cache, an in-memory data grid, and an in-memory database.
Apache Ignite is free and open source software.
- Distributed high-performance in-memory layer. Keep underlying databases in sync, using Ignite as an in-memory data grid.
- Cache and persist data sets scattered across many disjointed back-end databases and/or SaaS platforms to make them instantly available to your applications through Apache Ignite’s functionally rich, unified and consistent APIs.
- Multi-tier storage – designed to work with memory, disk, and Intel Optane as active storage tiers. Ignite takes full control of its memory tier by allocating and managing off-heap regions. Each Ignite server node allocates memory regions during bootstrap, splits the regions into pages, and keeps data records with indexes in those pages. Java heap is used to keep temporary objects such as query result sets, metrics samples, and objects generated by your application code. All these objects are garbage collected eventually.
- Distributed SQL – comes with a ANSI-99 compliant, horizontally scalable, and fault-tolerant SQL engine that allows you to interact with Ignite as with a regular SQL database using JDBC, ODBC drivers, or native SQL APIs available for Java, C#, C++, Python, and other programming languages.
- Distributed Key-Value – distributed key-value store that stores data in memory and on disk. Ignite functions as a distributed partitioned hash map with every cluster node owning a portion of the overall data set in this deployment mode.
- ACID Transactions – operate in a strongly consistent mode with full support for distributed ACID transactions. The consistency guarantees are met for both memory and disk tiers.
- Co-located Processing – supports co-located processing technique for compute-intensive and data-intensive calculations as well as machine learning algorithms. This technique increases performance by eliminating the impact of network latency.
- Machine Learning – a set of simple, scalable, and efficient tools that allow building predictive machine learning models without costly data transfers.
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