The GridGain in-memory computing platform is installed between the application and data layers and uploads data from the underlying RDBMS, NoSQL or Hadoop datastores from disk into RAM. The results is massive scalability and a 1,000x or greater improvement in processing speed. This is accomplished by creating what is effectively a distributed database across the nodes in the GridGain cluster. The system accelerates compute by moving data from disk into RAM and also by leveraging the GridGain In-Memory Compute Grid to use all of the compute power of the GridGain cluster for massive parallel processing. The system is highly scalable because new nodes can be added to the GridGain cluster at any time and the automatic rebalancing feature will balance the in-memory data between nodes.
GridGain supports fully ACID compliant transactions so data changes at the application layer are written to the underlying RDBMS. The GridGain In-Memory SQL Grid provides in-memory database-like features which allow you to interact with the GridGain cluster using standard SQL commands, powering OLTP, OLAP and hybrid transactional/analytical processing (HTAP) use cases.
GridGain removes the scalability limitations of many common relational databases and allows you to create:
- A Distributed MySQL Database
- A Distributed PostgreSQL Database
- A Distributed Oracle Database
- A Distributed Version of Any Popular RDBMS
GridGain is ANSI SQL-99 compliant with full data indexing so it can support distributed NoSQL and HDFS while also providing a number of database-specific advantages such as: