System and Methodology for Parallel Query Optimization Using Semantic-Based Partitioning

In a database system, a method for optimization of a query requesting data from a database, the method comprising:

• constructing a tree of relational operators based on the query, each relational operator for performing a given relational operation;
• determining whether dividing data processed by a particular relational operator in said tree is useful for executing a relational operation in parallel;
• if partitioning of a particular relational operator is determined to be useful, creating a revised tree by adding operators to said tree for dividing data processed by the particular relational operator and executing the particular relational operator in parallel over the divided data;
• and
• generating a plan for execution of the query based on said revised tree.

A database system for dynamically partitioning data during query processing, the system comprising:

• a module for generating a plurality of plan fragments for obtaining data requested by a query from database tables of the database system;
• a partitioning module for creating additional plan fragments by adding operators for dynamically partitioning data and processing the partitioned data in parallel to at least some of said plan fragments;
• and
• a module for constructing a final plan for execution of the query based, at least in part, upon selecting plan fragments having operators for dynamically partitioning data and processing the partitioned data in parallel when dynamically partitioning data is determined to be advantageous.

In a database system comprising a database storing data in database tables, a method for improving query performance comprising:

• receiving a query specifying a join of two or more database tables;
• as data is retrieved from the database during processing of the query, partitioning said data into separate memory buffers;
• and
• processing said query in parallel by concurrently processing said data in said memory buffers.