Parallel Database Architecture for Faster Query Execution
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Solution Overview
Problem
Existing database systems are limited by hardware, data storage methods, and co-process options, leading to inefficient processing speeds and execution times.
Innovation Solution
A database system architecture that employs parallelized data input, storage, retrieval, and query processing systems, utilizing a network of computing devices with independent processing cores and optimized query plans to manage large data sets efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sequential processing is used, then system complexity is low, but processing speed and execution time are insufficient
Solution Approach 1:
The patent segments the database system into multiple independent computing devices, each with its own processing cores. These devices work in parallel to handle different portions of data processing tasks, thereby increasing overall processing speed while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent transitions from single-threaded sequential processing to multi-threaded parallel processing by utilizing multiple processing cores across multiple computing devices. This dimensional shift from one-dimensional sequential execution to multi-dimensional parallel execution significantly enhances processing speed
2Productivity
If more computing resources are allocated, then processing capacity increases, but resource utilization efficiency decreases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors workload distribution across computing devices and dynamically adjusts resource allocation. This feedback loop ensures that computing resources are allocated efficiently based on actual demand, increasing processing capacity while maintaining high resource utilization efficiency
Solution Approach 2:
The patent employs dynamic resource allocation where computing resources are not statically assigned but are instead dynamically adjusted based on workload conditions. This allows the system to scale processing capacity up or down as needed while optimizing resource utilization and avoiding waste
Data Source
AI summary
A database system includes a data ingest subsystem, a store and compute subsystem, and a query and response subsystem interconnected through a system communication network. Each subsystem includes a hierarchy of computing resources defined by an “a” number of computing clusters, a “b” number of computing entities per cluster providing an “a”*“b” number of computing entities, a “c” number of computing devices per entity providing an “a”*“b”*“c” number of computing devices, a “d” number of computing nodes per device providing an “a”*“b”*“c”*“d” number of computing nodes, and an “e” number of processing core resources per node providing an “a”*“b”*“c”*“d”*“e” number of processing core resources, wherein an asterisk (*) denotes multiplication. The network operably couples the subsystems to enable distributed data ingestion, storage, and query execution across scalable processing resources.