Database Query Acceleration via Temporary Enumeration Representation
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Solution Overview
Problem
Database queries involving large data types consume excessive memory and processing resources due to the need for temporary data structures, which are inefficiently managed in traditional database operations.
Innovation Solution
The method involves creating enumerated values and lookup tables to represent and translate data values, allowing for delayed value projection and secondary enumeration to optimize memory usage and processing efficiency during query resolution, while also deleting unnecessary tables to conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data of large data type is propagated through temporary data structures during query processing, then complete data accuracy is maintained, but memory consumption increases and processing resources are exhausted
Solution Approach 1:
The patent creates enumerated copies of large data type values that map to compact integer representations. Instead of propagating actual large data through temporary structures, the system uses these compact enumerated copies that preserve the necessary equality and grouping properties for query processing while consuming minimal memory space.
Solution Approach 2:
The patent transforms the data representation parameter from large data type to compact integer enumeration. By changing how data is represented (from full-value storage to index-based representation), the system maintains query accuracy while dramatically reducing the memory footprint of temporary data structures.
2Productivity
If operations are performed on large data types in temporary data structures, then complete data is processed, but processing time and resource consumption increase
Solution Approach 1:
The system creates lightweight enumerated copies that can be rapidly manipulated in temporary data structures. These copies enable sorting, grouping, and comparison operations to execute much faster than operating on full large data type values, thereby increasing query processing throughput without sacrificing correctness.
Solution Approach 2:
The patent performs preliminary enumeration of distinct data values before query processing begins. This upfront action creates a mapping that enables all subsequent query operations to work with compact integers rather than large data types, significantly reducing processing time for sorting, grouping, and aggregation operations.
3Ease of operation
If temporary data structures are created during query processing, then query calculations can be performed, but memory resources are consumed
Solution Approach 1:
The patent fundamentally changes the parameter of data representation in temporary structures from full large data type values to compact integer enumerations. This parameter change enables temporary data structures to perform all necessary query calculations (sorting, grouping, aggregation) while consuming minimal memory resources.
Data Source
AI summary
A system, method, and computer program product for processing a database query are presented. Steps for identifying a value expression comprising information needed in order to resolve a query, associating enumerated values with the set of distinct values for the value expression, evaluating the query utilizing the enumerated values, and deleting the association are disclosed.