Optimized Query Planning for Parallel Database Execution
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Solution Overview
Problem
Current database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, which hinder efficient data processing and query execution.
Innovation Solution
A parallelized database system architecture that includes a parallelized data input sub-system, query and response sub-system, and data store sub-system, utilizing a network of computing devices with multiple processing core resources to divide and process data in parallel, optimizing query plans, and distributing execution across multiple nodes for enhanced processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a traditional database system processes queries using sequential execution, then the system is simpler to implement, but the processing speed is limited due to hardware constraints
Solution Approach 1:
The patent divides the database system into multiple computing devices, each with multiple processing core resources. Queries are segmented and distributed across these cores for parallel execution, transforming a single sequential processing path into multiple concurrent processing paths, thereby increasing query processing speed while managing complexity through modular architecture
Solution Approach 2:
The patent transitions from single-dimensional sequential execution to multi-dimensional parallel execution by utilizing multiple processing cores across multiple computing devices. This dimensional expansion allows simultaneous execution of query operations across different computational nodes, significantly improving processing speed
2Speed
If data is stored using conventional storage methods, then the storage structure is simpler, but the data retrieval and processing speed is limited
Solution Approach 1:
The patent segments data storage across multiple computing devices and processing cores, allowing data to be distributed and retrieved in parallel. This segmentation enables simultaneous access to different portions of data by different processing cores, significantly improving retrieval speed while organizing complexity through structured distribution
Solution Approach 2:
The patent introduces a query optimization module that acts as an intermediary between the query request and the distributed data storage. This module generates optimized query plans that coordinate data retrieval across multiple storage locations, improving retrieval speed while managing the complexity of distributed access through intelligent intermediation
3Productivity
If the database system uses restricted co-processing options, then the system architecture is simpler, but the processing efficiency is limited
Solution Approach 1:
The patent creates a universal processing architecture where multiple processing core resources can execute various types of database operations simultaneously. Each computing device with multiple cores can handle different query types and data processing tasks, improving overall productivity while managing complexity through standardized multi-functional processing units
Solution Approach 2:
The patent segments processing tasks and distributes them across multiple computing devices and cores, enabling parallel execution of data processing operations. This task segmentation allows simultaneous processing of multiple query components, significantly improving data processing efficiency while organizing complexity through structured task distribution
4Loss of time
If queries are executed without optimization, then the execution process is simpler, but the processing time is longer
Solution Approach 1:
The patent implements a query optimization module that performs preliminary analysis and planning before query execution. This module generates optimized query plans that pre-determine the most efficient execution paths, reducing actual query execution time while managing optimization complexity through advance preparation
Solution Approach 2:
The patent incorporates feedback mechanisms where query execution results and performance metrics are analyzed to refine future query optimization. This feedback loop continuously improves query execution efficiency by learning from past performance, reducing execution time while managing complexity through adaptive optimization based on empirical data
Data Source
AI summary
A method includes receiving, by a first computing entity of a database system, a query request that is formatted in accordance with a generic query format. The method further includes generating, by the first computing entity, an initial query plan based on the query request and a query instruction set. The method further includes determining, by the first computing entity, storage parameters. The method further includes determining, by the first computing entity, processing resources for processing the query request based on the storage parameters. The method further includes generating, by the first computing entity, an optimized query plan from the initial query plan based on the storage parameters, the processing resources, and optimization tools. The method further includes sending, by the first computing entity, the optimized query plan to a second computing entity for distribution and execution of the optimized query plan.