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 to achieve the performance and scale of in-memory computing, the durability of disk, and strong consistency, all in one system. Persistent Store is a distributed ACID and ANSI-99 SQL-compliant disk store that can be deployed on spinning disks, solid state drives (SSDs), 3D XPoint, and other storage-class memory technologies. Persistent store keeps the full data set on disk, which is fully operational, while putting only a subset of user-defined, time-sensitive data in memory. This allows organizations to adjust the amount of data kept in-memory to achieve an optimal trade-off between infrastructure costs and application performance. And because the data on disk is fully operational, there is no need to wait for all the data to be loaded into RAM in the event of a cluster restart. Persistent store will also enable organizations to take advantage of HTAP without the need to keep 100 percent of data in-memory.