International Business Machines Corporation
Permutation-based machine learning for database query optimization

Abstract:

A DBMS query-optimization module receives a corpus of training data that contains data-access requests, such as SQL queries. Each request specifies data to be accessed but does not specify a query plan that the database should use to generate the requested data. The module identifies, in each received request, parameters, such as join methods and access methods, that can vary among query plans; and variables that cannot be assigned values until the query is actually processed. The system generates a set of queries, each of which implements a distinct query plan, that specify every viable permutation of values of the parameters and of the variables. The generated queries are added to the received corpus, which is forwarded to a machine-learning module in order to train the query-optimizer to select query plans that incur the lowest resource costs when servicing a particular type of query.