Parallel Query Execution Across Distributed Database Nodes
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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 execution of complex database functions.
Innovation Solution
A large-scale data processing network architecture that includes parallelized data input, storage, and query systems, utilizing a distributed computing approach with multiple computing devices and nodes to process and store data efficiently, optimizing query plans, and employing a database overriding operating system for independent parallel execution of operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sequential processing is used in database systems, then hardware complexity is reduced, but processing speed and query execution time deteriorate
Solution Approach 1:
The patent divides the database system into multiple independent computing nodes, each capable of processing queries in parallel. The data is segmented across these nodes, allowing simultaneous processing of different query components without requiring complex centralized hardware coordination.
Solution Approach 2:
The patent transitions from single-dimensional sequential processing to multi-dimensional parallel processing by distributing compute resources across multiple nodes and layers. This dimensional expansion enables concurrent execution of queries without proportionally increasing hardware complexity.
2Speed
If data is stored in traditional hierarchical structures, then storage simplicity is maintained, but data retrieval speed deteriorates
Solution Approach 1:
The patent segments data into distributed chunks stored across multiple nodes rather than in a single hierarchical structure. This segmentation enables parallel data retrieval operations, where different data portions can be accessed simultaneously from different nodes, dramatically improving retrieval speed.
Solution Approach 2:
The patent implements local data caching and indexing at each node, optimizing data storage characteristics locally to enable faster retrieval. Each node maintains data in formats optimized for its specific query workload, improving overall system retrieval performance without requiring a globally complex storage hierarchy.
3Productivity
If co-processing options are restricted to maintain system simplicity, then device complexity is reduced, but query execution capability deteriorates
Solution Approach 1:
The patent makes each computing node universal and multi-functional, capable of executing various types of queries and processing different data formats independently. This eliminates the need for specialized co-processing units, maintaining system simplicity while enhancing query execution capability through parallel universal processing.
Solution Approach 2:
The patent enables each node to autonomously process queries without requiring complex centralized coordination or specialized co-processing assistance. Nodes independently manage their own data storage, query execution, and result compilation, improving productivity while keeping system complexity manageable through decentralized self-service operations.
4Loss of time
If parallel processing is implemented to improve processing speed, then query execution time is reduced, but system complexity increases
Solution Approach 1:
The patent segments both data and processing tasks across multiple independent nodes, enabling parallel query execution. By dividing the workload into independent segments that can be processed concurrently, the system reduces query execution time without requiring complex inter-node coordination mechanisms.
Solution Approach 2:
The patent performs preliminary data organization and indexing at each node before query execution. This preliminary action enables faster parallel processing by pre-positioning data in query-ready formats, reducing execution time while avoiding the need for complex real-time coordination during query processing.
Data Source
AI summary
A computing device includes a computing device controller hub and a plurality of parallelized nodes coupled to the computing device controller hub. Each node of the plurality of parallelized nodes includes a central processing module, a main memory, and at least one disk memory. The plurality of computing devices is operable to collectively execute query requests against at least one database table stored by the plurality of computing devices based on each node of each computing device performing corresponding operations independently from other nodes of the plurality of parallelized nodes.