Scale Up with Parallel Query
Version 9.6 adds support for parallelizing some query operations, enabling utilization of several or all of the cores on a server to return query results faster. This release includes parallel sequential (table) scan, aggregation, and joins. Depending on details and available cores, parallelism can speed up big data queries by as much as 32 times faster.
“I migrated our entire genomics data platform – all 25 billion legacy MySQL rows of it – to a single Postgres database, leveraging the row compression abilities of the JSONB datatype, and the excellent GIN, BRIN, and B-tree indexing modes. Now with version 9.6, I expect to harness the parallel query functionality to allow even greater scalability for queries against our rather large tables,” said Mike Sofen, Chief Database Architect, Synthetic Genomics.