Concurrent Query Execution Plan Segmentation
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Solution Overview
Problem
Conventional database systems face inefficiencies in pattern identification and data retrieval due to the need to operate on numerous individual records within normalized schemas, which requires building complex query execution plans and accessing multiple tables, leading to slower data processing.
Innovation Solution
A computer system utilizing multiple threads and processing cores to concurrently execute portions of a query execution plan, allowing for simultaneous retrieval and storage of data from multiple tables, thereby improving efficiency and reducing the need for extensive data movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional database engines execute queries by processing numerous individual records sequentially, then data retrieval accuracy is maintained, but data processing speed deteriorates
Solution Approach 1:
The patent segments the query execution process into multiple independent threads that can process different portions of data simultaneously. Each thread handles specific table scans or operations, allowing parallel execution of query plan components rather than sequential processing of individual records.
Solution Approach 2:
The patent implements continuous data retrieval by pre-fetching and buffering data from multiple tables concurrently while queries are being executed. This eliminates idle waiting time between operations and maintains continuous productive work across multiple processing cores.
2Measurement precision
If database engines build complex query execution plans to optimize data retrieval, then query accuracy is improved, but system complexity increases
Solution Approach 1:
The complex query execution plan is divided into multiple independent, manageable thread units. Each thread executes a specific portion of the query plan (such as scanning a particular table or applying specific filters), making the overall complex operation decomposable into simpler concurrent tasks that are easier to manage and optimize.
3Loss of information
If multiple tables are accessed to retrieve related data from normalized schemas, then data completeness is improved, but processing overhead increases
Solution Approach 1:
The patent merges the access operations to multiple tables by executing them concurrently through multiple threads rather than sequentially. Data from different tables is retrieved in parallel and then combined, reducing the total time required to access all necessary tables while maintaining complete data retrieval.
Data Source
AI summary
A pattern matching component is provided. The pattern matching component utilizes a plurality of processors to efficiently identify and retrieve information from a plurality of data sources. In some examples, the pattern matching component concurrently executes multiple portions of a data access plan, such as a query execution plan, using a plurality of processors disposed within a multi-core processor. The pattern matching component may concurrently execute multiple portions of the data access plan using a plurality of processors in a distributed system. Concurrent execution of at least some of the operations required to identify and retrieve the information results in increased efficiency over conventional query execution methods.