Composite Union Enumeration for Database Query Optimization
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Solution Overview
Problem
Existing database systems face inefficiencies in combining disjoint data sets from multiple sources into a single set, leading to increased economic and computational costs due to high storage and management requirements, particularly when dealing with large datasets.
Innovation Solution
The implementation of a composite union enumeration that encodes enumeration values and source identifiers, allowing for efficient storage and decoding of data values, thereby reducing storage needs and query execution costs by using a look-up table to map enumeration values to actual data values only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple disjoint data sets are combined into a single set using traditional methods, then the capability to process combined data is improved, but storage requirements and computational costs increase
Solution Approach 1:
The patent combines multiple separate enumeration data structures into a single composite enumeration structure that stores both the enumeration value and source identifier together. This merging reduces the total storage space by eliminating redundant storage of actual data values across multiple separate structures, while maintaining the capability to process and retrieve data from all combined data sets.
Solution Approach 2:
The composite enumeration structure serves multiple functions simultaneously: it stores enumeration values, identifies source data sets, and enables efficient retrieval for different source tables. This multi-functional design eliminates the need for separate data structures for each source, reducing overall storage requirements while maintaining adaptability to combine any number of disjoint data sets.
2Measurement precision
If actual data values are stored for all enumeration values in combined data sets, then data retrieval accuracy is improved, but storage costs and management overhead increase
Solution Approach 1:
Instead of storing actual data values in the composite enumeration structure, the patent uses enumeration values that reference the actual data in the source data sets. This copying approach maintains data retrieval accuracy by preserving the reference relationship, while significantly reducing storage costs by avoiding duplication of large actual data values in the combined structure.
Solution Approach 2:
The patent adds a new dimension to the storage structure by including source identifiers alongside enumeration values. This dimensional change allows the system to retrieve accurate data by combining the enumeration value with its source identifier to locate the actual value in the original data set, rather than storing redundant copies of actual values in the combined structure.
3Productivity
If traditional union operations are used to combine data sets, then query processing capability is improved, but query execution time increases due to handling of actual values
Solution Approach 1:
The patent extracts only the essential identifying information (enumeration values and source identifiers) from the combined data sets, leaving the actual data values in their original source tables. This extraction reduces the amount of data that needs to be processed during query execution, significantly decreasing query execution time while maintaining full query processing capability through efficient reference resolution.
Data Source
AI summary
A method may include accepting a database query including an operator requesting two or more incoming tuple streams be combined into a result tuple stream. At least one data value in the incoming tuple streams may be represented by an enumeration value. The method may include generating a query execution plan for the database query. The query execution plan may include encoding the enumeration value and a corresponding source identifier into a composite union enumeration. The source identifier may identify which of the two or more tuple streams corresponds to the enumeration value. The method may further include executing the database query according to the query execution plan to obtain the data value and providing the data value in response to the database query.