Automatic Sub-Query Generation for Database Efficiency
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Solution Overview
Problem
Large databases with extensive data require significant time and memory resources to process queries, making them inefficient for data retrieval and analysis.
Innovation Solution
Automatically transforming queries into functionally equivalent sub-queries that process disjoint subsets of the input data, allowing for faster execution by distributing the workload across smaller queries that can be combined to produce the original query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single query is executed on a large database to retrieve a particular set of data, then the query returns the complete result set, but the query processing time and memory resource consumption increase significantly
Solution Approach 1:
The patent divides a single large query into multiple smaller sub-queries by partitioning the input data based on selected attributes (e.g., date ranges, geographic regions, product categories). Each sub-query processes a disjoint subset of the data, reducing the computational burden on any single query execution while maintaining complete result coverage through aggregation of all sub-query results.
2Measurement precision
If a single query is executed on a large database, then the complete result set is obtained, but the memory resources required for query execution increase significantly
Solution Approach 1:
The patent segments the query processing into multiple sub-queries that each handle smaller portions of the data in memory. By processing disjoint subsets of input data separately and aggregating results, the system reduces the peak memory footprint required for any single query execution while still retrieving the complete result set.
3Productivity
If the database is divided into smaller subsets for processing, then query execution time and memory usage are reduced, but the system complexity increases due to query transformation and result aggregation
Solution Approach 1:
The patent performs preliminary actions by automatically selecting attributes for data partitioning and generating the appropriate sub-queries before execution. The system pre-determines the query transformation strategy based on the original query and database structure, then executes multiple simplified sub-queries rather than requiring complex manual query decomposition by the user.
Data Source
AI summary
Example implementations relate to automatic generation of sub-queries. For example, a computing device may include a processor. The processor may receive a query defining a particular set of data stored in at least one database. The processor may access metadata associated with the particular set of data and may select a data attribute associated with the particular set of data based on the metadata. The processor may automatically generate sub-queries of the query using the data attribute selected based on the metadata. The processor may process the sub-queries and provide a query result of the query that is a combination of sub-query results of the sub-queries.