Database Range Search Optimization via Bit-Based Operation Selection
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Solution Overview
Problem
Database systems face inefficiencies in processing range searches due to the lack of optimized operations based on the number of bits used to represent values, leading to suboptimal retrieval of records within specified value ranges.
Innovation Solution
A system that determines the number of bits used to represent values in a database field and performs specific operations, such as logical and comparison operations, based on this determination to efficiently identify records within a range, using bit arrays and SIMD operations for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard search operations are used for range searches in database systems, then the search can be performed without optimizing for specific bit representations, but the search performance is suboptimal and retrieval efficiency is reduced
Solution Approach 1:
The patent changes the operational parameters of the search based on the number of bits used to represent values. Different bit representations (e.g., 1-bit, 2-bit, 3-bit, 4-bit) trigger different search operations and optimization strategies. This allows the system to adapt the search algorithm to the specific data characteristics, improving performance without requiring a single complex universal approach.
Solution Approach 2:
The system dynamically selects search operations based on the determined bit representation of the data. The search strategy is not fixed but adapts runtime based on the actual data characteristics discovered during query processing. This dynamic adaptation enables optimal performance for each specific case while maintaining a unified search interface.
2Productivity
If the system determines and adapts operations based on the number of bits used to represent values, then search efficiency is improved, but the system complexity increases due to multiple operation sets
Solution Approach 1:
The patent segments the search operations into distinct sets based on bit representation categories (1-bit, 2-bit, 3-bit, 4-bit). Each segment handles specific bit widths with optimized operations tailored to that category. This segmentation reduces the complexity of managing all possible variations by grouping them into manageable segments with representative optimization strategies.
Solution Approach 2:
Different search operations are applied to different bit representation categories based on their specific characteristics. Each bit category receives locally optimized operations suited to its properties rather than applying a uniform approach. This local optimization improves overall efficiency while keeping each local operation set relatively simple and focused.
3Speed
If range searches are performed without bit-based optimization, then the system maintains simplicity in operation selection, but the retrieval time increases and performance is suboptimal
Solution Approach 1:
The system performs preliminary determination of the bit representation before executing the search operations. By identifying the bit width upfront (1-bit, 2-bit, 3-bit, or 4-bit), the system can pre-select the appropriate optimized operation set. This preliminary action avoids the overhead of dynamic decision-making during the search itself and enables faster execution of the actual search operations.
Data Source
AI summary
Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a query for a set of records in a database system having values in a field of a table that fall within a range of values. The program also determines a number of bits used to represent the values in the field of the table. The program further determines a set of operations to perform on the values in the field of the table based on the determined number of bits. The program also performs the determined set of operations on the values in the field of the table in order to identify the set of records in the database.