Database Accelerator Skip Information Generation
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Solution Overview
Problem
As databases grow in size, processing queries becomes increasingly time-consuming due to the need to examine large amounts of irrelevant data.
Innovation Solution
An accelerator generates skip information that identifies columns in a database not relevant to a query, allowing these columns to be skipped during processing, thereby reducing the time required to execute queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all columns in the database are processed to ensure complete query accuracy, then query result accuracy is maintained, but query processing time increases significantly
Solution Approach 1:
The patent extracts and processes only the relevant columns identified by the query rather than processing all columns in the database. The system identifies which columns are actually needed for the query result and processes only those, leaving irrelevant columns unprocessed. This extraction approach maintains query accuracy while significantly reducing processing time by eliminating unnecessary column processing operations.
Solution Approach 2:
The patent segments the database processing into distinct phases: first identifying relevant columns through query analysis, then processing only those specific columns separately from the rest of the database. This segmentation allows the system to isolate and process only the necessary data portions, maintaining accuracy for required columns while avoiding time-consuming processing of irrelevant data segments.
2Reliability
If all columns in the database are examined during query processing, then complete data coverage is achieved, but processing efficiency decreases
Solution Approach 1:
The system extracts and processes only the relevant columns identified through query analysis, rather than examining all columns. This extraction maintains reliable data coverage for the specific columns needed by the query while dramatically improving processing efficiency by excluding irrelevant columns from the processing pipeline.
Solution Approach 2:
The patent applies partial action by processing only the necessary subset of columns required for the query rather than performing excessive processing on all columns. This partial processing approach ensures sufficient data coverage for query accuracy while enhancing processing efficiency by avoiding redundant operations on unnecessary columns.
3Quantity of substance
If the database processes increasing amounts of data to maintain comprehensive information, then data completeness is improved, but query processing time increases
Solution Approach 1:
The system extracts and processes only the relevant columns that contribute to query results, rather than processing the entire database. This extraction maintains data completeness for the specific query requirements while reducing query processing time by eliminating unnecessary processing of irrelevant data portions.
Solution Approach 2:
The patent applies local quality by providing different processing treatments to different columns based on their relevance to the query. Relevant columns receive thorough processing to ensure data completeness, while irrelevant columns are excluded from processing. This localized approach maintains necessary data completeness while improving processing efficiency.
Data Source
AI summary
An accelerator is disclosed. The accelerator may include a connection to a first storage, which may store a database. The database may include four columns. The accelerator may also include a second storage to store information about the columns of the database. The accelerator may also include a circuit to process the information about the columns and a query to generate a skip information.