公开(公告)号：US11971793B2

公开(公告)日：2024-04-30

申请号：US16292990

申请日：2019-03-05

Applicant: ENTIT Software LLC

Inventor： Yuanzhe Bei ,  Zhihao Zheng

IPC: G06F16/00 ,  G06F7/00 ,  G06F11/30 ,  G06F16/2453 ,  G06F16/901

CPC classification number: G06F11/3006 ,  G06F16/24542 ,  G06F16/9027

Abstract: Current physical resources utilization of a computing system as a whole is monitored. The number of queries concurrently being executed against a database by a database management system (DBMS) running on a computing system is monitored. A query plan for a received query to be executed against the database is generated. The query plan includes operators; the generation of the query plan includes generation of query-based statistics for the received query on a per-operator basis without consideration of the queries concurrently being executed. An estimated execution time of the received query is dynamically predicted using a machine-learning model based on the query-based statistics generated for the received query on the per-operator basis, the current physical resources utilization of the computing system, and the number of queries concurrently being executed. The received query is executed against the database based on the dynamically predicted estimated execution time for the received query.

公开(公告)号：US20200285642A1

公开(公告)日：2020-09-10

申请号：US16292990

申请日：2019-03-05

Applicant: ENTIT Software LLC

Inventor： Yuanzhe Bei ,  Zhihao Zheng

IPC: G06F16/2453 ,  G06N20/00 ,  G06F11/30 ,  G06K9/62 ,  G06F16/901

Abstract: Current physical resources utilization of a computing system as a whole is monitored. The number of queries concurrently being executed against a database by a database management system (DBMS) running on a computing system is monitored. A query plan for a received query to be executed against the database is generated. The query plan includes operators; the generation of the query plan includes generation of query-based statistics for the received query on a per-operator basis without consideration of the queries concurrently being executed. An estimated execution time of the received query is dynamically predicted using a machine-learning model based on the query-based statistics generated for the received query on the per-operator basis, the current physical resources utilization of the computing system, and the number of queries concurrently being executed. The received query is executed against the database based on the dynamically predicted estimated execution time for the received query.