Database Power Strategy via Segmented Parallel Processing
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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 operations and queries.
Innovation Solution
A scalable database system architecture that utilizes a parallelized data input, storage, and processing sub-system, along with a query and response sub-system, to divide data into segments, restructure it for efficient storage, and optimize query plans based on storage instructions and resource availability, enabling concurrent processing across multiple nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing is used to extend CPU capabilities and perform computing functions, then processing capability is improved, but hardware constraints still limit the speed of database operations
Solution Approach 1:
The database is divided into multiple segments that are distributed across different cloud computing resources. Each segment can be processed independently, allowing parallel execution of database operations across multiple nodes, thereby overcoming single-machine hardware constraints while maintaining high processing capability and speed
2Ease of manufacture
If data is stored in traditional formats, then storage is simple, but processing speed is limited by hardware constraints
Solution Approach 1:
The system dynamically restructures data storage formats based on query patterns and workload requirements. Data is organized in flexible structures that can be optimized for specific operations, allowing the storage system to adapt to different processing needs and achieve high speeds without sacrificing storage manageability
3Device complexity
If co-processing options are restricted, then system complexity is reduced, but execution speed is limited
Solution Approach 1:
Multiple co-processing nodes are merged into a unified distributed processing system. These nodes work together to execute database operations in parallel, combining their computational power to achieve high execution speeds while the distributed architecture manages complexity through standardized communication protocols and coordination mechanisms
4Speed
If parallel processing is implemented across multiple nodes, then query execution speed is improved, but device complexity increases
Solution Approach 1:
The parallel processing nodes are designed with universal functionality to handle various database operations. Each node can execute multiple types of queries and processing tasks, reducing the need for specialized hardware configurations and simplifying the overall system architecture while maintaining high query execution speeds through flexible resource allocation
Data Source
AI summary
A database system is operable to perform a power supply strategy selection function based on first energy utilization-based operation optimizer input data to generate first energy utilization-based power supply strategy data denoting, for each power supply type of a plurality of power supply types, a corresponding first proportion of power supply resources that be implemented as the each power supply type. A first set of power supply resources is selected to execute a first database operation based on the first energy utilization-based power supply strategy data. The first set of power supply resources are utilized to power a first set of computing devices to enable the first set of computing devices to participate in execution of the first database operation in accordance with the first energy utilization-based power supply strategy data.