Relational Database Query Scheduling Through Shared Partition Analysis
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Solution Overview
Problem
Existing relational database systems face inefficiencies in scheduling query execution plans due to computational costs and resource conflicts when multiple queries access overlapping data sets, leading to increased computational load and potential data inconsistencies.
Innovation Solution
A method and system for scheduling query execution plans by determining partitions of data blocks accessed by each query based on metadata, examining for common partitions, and optimizing execution to avoid overlapping access, thereby reducing computational costs and ensuring data isolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple queries are executed simultaneously without partition analysis, then query processing speed is improved, but computational overhead and resource conflicts increase
Solution Approach 1:
The patent segments the data space into partitions based on metadata analysis. By determining which partitions are accessed by each query and identifying common partitions, the system can schedule queries to execute in parallel when they access different partitions, or serialize them when they share partitions. This segmentation approach enables efficient resource utilization while avoiding unnecessary computational overhead from locking mechanisms.
2Reliability
If locking mechanisms are used to ensure data consistency during concurrent query execution, then data integrity is maintained, but execution time and computational load increase
Solution Approach 1:
The patent performs preliminary analysis of query execution plans to determine partition access patterns before query execution. By examining the metadata and identifying common partitions in advance, the system can make scheduling decisions without requiring locking mechanisms during actual query execution. This preliminary action ensures data integrity while avoiding the time penalty of runtime locking.
3Productivity
If partition analysis is performed for every query pair, then scheduling optimization is achieved, but metadata processing time increases
Solution Approach 1:
The patent changes the parameter of partition analysis from a fine-grained per-query-pair approach to a coarser approach that leverages existing metadata about partition access patterns. By using pre-computed metadata information about which partitions contain which data sets, the system can quickly determine scheduling decisions without performing exhaustive metadata processing for every query pair.
Data Source
AI summary
A method, a computer program product, and a system for scheduling a first query execution plan for executing a first query for accessing first data sets of data of a relational database system (RDBS) and a second query execution plan for executing a second query for accessing second data sets of the data. The data is divided into data blocks of data sets of the data, wherein metadata of the RDBS comprises respective information about at least one property of the data sets of a respective data block of the data blocks. The scheduling may be performed on the basis of an examination if there is a common partition between a first partition of data blocks which may fulfill a first condition according to the first query and a second partition of data blocks which may fulfill a second condition according to the second query.