Database Query Optimization Using Join Cardinality Tags
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Solution Overview
Problem
Conventional database systems perform unnecessary joins during query processing, leading to inefficiencies in query processing time and complexity due to the lack of specific criteria for optimizing join operations.
Innovation Solution
A database management system (DBMS) utilizes join cardinality information to tag join operations, allowing it to selectively remove unnecessary tables from the query processing plan, thereby simplifying and optimizing the query execution plan based on the type of join operation and cardinality tag.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the database system performs all specified join operations in the query, then completeness of data retrieval is ensured, but query processing time and complexity increase due to unnecessary joins
Solution Approach 1:
The patent extracts and removes unnecessary tables from the query processing plan by analyzing join cardinality tags. The optimizer identifies tables that can be safely excluded based on cardinality information (one-to-one, one-to-many, many-to-one relationships) and removes them before query execution, thereby reducing processing time while maintaining result correctness.
Solution Approach 2:
The patent performs preliminary analysis of join cardinality tags during query optimization, before actual query execution. By pre-tagging join operations with cardinality information and pre-identifying removable tables in the query processing plan, the system prepares an optimized execution plan that avoids unnecessary joins from the outset.
2Reliability
If the database system includes all tables in join operations, then data completeness is maintained, but query processing complexity increases
Solution Approach 1:
The patent extracts unnecessary tables from the query processing plan by evaluating join cardinality tags. Tables that do not contribute to the final query result based on their cardinality relationships are removed, simplifying the execution plan while preserving data retrieval accuracy for necessary tables.
Solution Approach 2:
The patent changes the parameter of join operation optimization by introducing cardinality tags (one-to-one, one-to-many, many-to-one) that provide specific guidance to the query optimizer. This parameter change enables the optimizer to make informed decisions about which joins are necessary, reducing processing complexity while maintaining reliability.
3Productivity
If the database system performs unnecessary join operations, then all specified data sources are processed, but processing time and computational resources are wasted
Solution Approach 1:
The patent extracts and removes unnecessary tables from the query processing plan by analyzing join cardinality tags. This extraction eliminates wasted computational resources on processing tables that would not contribute to the final query result, thereby improving processing efficiency and reducing energy consumption.
Solution Approach 2:
The patent enables the query optimizer to automatically analyze join cardinality tags and self-determine which tables are necessary for query execution. This self-service optimization occurs without requiring manual intervention, automatically eliminating unnecessary joins and reducing resource waste while improving efficiency.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for utilizing join cardinality to improve the performance of query processing. An embodiment operates by a database engine receiving a query including a join operation on a first and a second table using a join predicate, and a tag for the join operation indicating a cardinality relationship between the first and the second table when satisfying the join predicate. Then, the database engine generates a query processing plan for performing the query. For the join operation, the database engine selects one of the first or the second tables to remove from the join operation based on a type of the join operation and the tag. Based on the selecting, the database engine may remove may the join operation and the selected table from the query processing plan.