The GridGain In-Memory SQL Grid provides in-memory distributed database capabilities to the GridGain platform. The In-Memory SQL Grid is horizontally scalable, fault tolerant and ANSI SQL-99 compliant. It fully supports SQL and DML commands including SELECT, UPDATE, INSERT, MERGE and DELETE queries. Users can use the DDL capabilities to manage caches and SQL schema with commands like CREATE and DROP table. The In-Memory SQL Grid enables more functionality for a wide variety of use cases including web-scale applications and the Internet of Things (IoT).
The In-Memory SQL Grid allows you to interact with the GridGain platform using standard SQL language through the GridGain JDBC or ODBC APIs without custom coding. This provides true cross-platform connectivity even from languages such as PHP and Ruby which are not natively supported by GridGain. The system supports free-form SQL queries with virtually no limitations, using any SQL function, aggregation, or grouping. It supports distributed SQL joins and allows for cross-cache joins, performing like an in-memory distributed SQL database. Joins between partitioned and replicated caches work without limitations. Joins between partitioned data sets require that the keys are collocated. The system also supports the concept of fields queries to minimize network and serialization overhead.
Geospatial functionality is also available in conjunction with the In-Memory SQL Grid, making the GridGain in-memory computing platform well suited for location-based IoT use cases, such as in transportation, logistics, and wearables.
The GridGain In-Memory SQL Grid empowers big data analytics use cases with ad hoc SQL queries running at in-memory speeds. Existing applications which require a nightly ETL and take hours per SQL query can now potentially yield real-time insights into current operational data. Functioning like an in-memory SQL database, GridGain can address emerging hybrid transactional/analytical processing use cases and eliminate the need for a separate analytics infrastructure that requires nightly ETL.
When GridGain is used as a complement to Apache Spark or Apache Cassandra, the GridGain In-Memory SQL Grid can power major improvements in the underlying technology:
- The GridGain SQL indexing capability can improve Spark query times by 1,000x or more. Spark supports a rich SQL syntax but not data indexing so each query requires a full data scan when run without GridGain.
- GridGain enables ad hoc SQL queries on Cassandra data loaded into GridGain and leveraging the In-Memory SQL Grid. Cassandra has no SQL support and ad hoc queries are not supported
- ANSI SQL-99 Compliance
- Supports SQL and DML commands including SELECT, UPDATE, INSERT, MERGE and DELETE Queries
- Distributed SQL
- Geospatial Support
- SQL Communications Through the GridGain ODBC or JDBC APIs Without Custom Coding