The GridGain® in-memory computing platform includes an in-memory database which supports data processing APIs including SQL, key-value, compute, machine learning and more. The GridGain in-memory data grid distributes your dataset across a cluster of servers while the distributed SQL capabilities allows you to read and write to the database using standard database commands through the ODBC/JDBC interface. The system offers ACID transaction support and supports ANSI-99 SQL including DDL and DML.
The GridGain in-memory database delivers 1,000 times faster performance than disk-based databases because your data is stored and processed in RAM. GridGain is deployed on a distributed cluster of servers so you can easily scale out the GridGain in-memory database by adding additional nodes to the cluster - the system rebalances your data automatically.
The GridGain in-memory database can power modern, highly available, high performance applications on a solution which provides in-memory speeds and can be scaled out to hold petabytes of in-memory data. The in-memory database can be used to power OLTP, OLAP or hybrid transactional/analytical processing (HTAP) use cases. You can learn more about the in-memory database capabilities in GridGain and how they fit within the in-memory computing platform architecture by reading the Introducing the GridGain In-Memory Computing Platform white paper. To learn more about the difference between an in-memory database and an in-memory data grid, please read our blog post In-Memory Database vs In-Memory Data Grid Revisited.
In-Memory Database Built on a Memory-Centric Architecture
The memory-centric GridGain architecture allows you to execute distributed SQL, key-value and other operations across different memory layers. If your organization deploys a variety of memory technologies such as DRAM, non-volatile memory, and 3D XPoint, you can tune the configuration of your system to use a combination of memory options which provides the best trade off between price and performance for your organization.
You can use the optional Persistent Store feature to achieve the performance and scale of in-memory computing, the durability of disk, and strong consistency, all in one system. The Persistent Store feature maintains a copy of your in-memory database on disk which serves as a backup for data recovery purposes. GridGain can also transact and query data whether it is in-memory or on disk so you can use your in-memory database immediately upon restart by transacting against the data on disk while your full dataset is being loaded into memory.