Query Plan Level Structure for Parallel Database Execution
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Solution Overview
Problem
Database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, which hinder efficient query execution and data management.
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 cost analysis function to optimize query plans and distribute processing across multiple nodes and storage clusters, enabling efficient data storage, retrieval, and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sequential processing is used in database systems, then hardware simplicity is maintained, but processing speed and query execution efficiency deteriorate
Solution Approach 1:
The patent divides the database system into multiple independent nodes that can process queries in parallel. Each node handles a portion of the data or a specific query operation, enabling simultaneous processing across the network without requiring complex centralized hardware architecture.
Solution Approach 2:
The patent transitions from single-dimensional sequential processing to multi-dimensional parallel processing by distributing queries across multiple nodes in a network. This adds spatial and temporal dimensions to processing, allowing concurrent execution of multiple query operations simultaneously.
2Productivity
If data is stored in traditional centralized manner, then storage simplicity is maintained, but retrieval speed and processing efficiency deteriorate
Solution Approach 1:
The patent segments the centralized storage system into distributed storage across multiple nodes. Each node stores portions of the database or specific data tables, allowing parallel access and retrieval operations without requiring a complex centralized storage architecture.
Solution Approach 2:
The patent introduces query optimization software as an intermediary that automatically generates and manages query plans. This software mediator handles the complexity of distributed data retrieval by translating user queries into optimized execution plans that distribute operations across appropriate nodes.
3Productivity
If co-processing options are restricted, then system simplicity is maintained, but processing capability and speed deteriorate
Solution Approach 1:
The patent makes each database node universal and multi-functional, capable of performing various query operations, data storage, and processing tasks. This eliminates the need for specialized co-processing hardware while maintaining processing capability through software-defined node functionality.
Solution Approach 2:
The patent enables nodes to autonomously execute query operations and data processing tasks without requiring external co-processing assistance. Each node independently manages its data and operations, reducing system complexity while maintaining processing capability through distributed autonomy.
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.