Database Query Execution Planning for Energy and Performance Tradeoffs
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Solution Overview
Problem
Existing database systems are limited by hardware, data storage methods, and co-process options, leading to inefficiencies in processing speed and execution.
Innovation Solution
A database system utilizing parallelized data input, storage, and query processing systems, along with energy utilization optimization, to enhance processing efficiency and reduce energy consumption.
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 execution efficiency deteriorate
Solution Approach 1:
The patent divides the database system into multiple independent processing nodes that can operate in parallel. Each node handles specific database operations independently, allowing simultaneous execution of multiple queries without requiring complex centralized coordination, thus improving processing speed while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent transitions from single-threaded sequential processing to multi-threaded parallel processing by adding the dimension of concurrent execution. Multiple query operations are executed simultaneously across different threads and nodes, transforming the system from one-dimensional sequential execution to multi-dimensional parallel execution, thereby significantly enhancing processing speed
2Productivity
If more co-process options are added to enhance processing capability, then execution speed improves, but device complexity increases
Solution Approach 1:
The patent creates universal processing nodes that can handle multiple types of database operations (queries, updates, deletions, etc.) through a standardized interface and common execution engine. Instead of adding specialized co-processors for each operation type, the system uses multi-functional nodes that can dynamically execute different operations, improving execution efficiency while avoiding the complexity of multiple specialized components
Solution Approach 2:
The patent replicates processing nodes across the system rather than adding complex co-process options to single nodes. Each node is a simplified copy of the basic processing unit that can be independently deployed and scaled. This copying approach enables parallel processing and improves execution efficiency without increasing the complexity of individual processing units
3Speed
If data is stored in traditional formats, then storage simplicity is maintained, but processing speed deteriorates
Solution Approach 1:
The patent implements different storage formats and optimization strategies for different types of data and access patterns. Frequently accessed data is stored in optimized formats with higher redundancy, while less accessed data uses more compact storage. This local quality approach tailors storage characteristics to specific access requirements, improving query processing speed for critical operations without unnecessarily complicating the overall storage structure
Solution Approach 2:
The patent performs data preprocessing and transformation during data ingestion and loading phases, organizing data into optimized structures before queries are executed. Indexes, aggregates, and pre-computed results are prepared in advance, so that when queries are executed, the system can retrieve and process data more quickly without requiring complex real-time transformations, thus improving processing speed while maintaining storage simplicity
Data Source
AI summary
A database system is operable to perform an optimization factor selection function based on first operation optimizer input data to generate first optimization factor selection data indicating selection of at least one first optimization factor from a set of optimization factors that includes an energy efficiency optimization factor and a performance efficiency optimization factor, and, based on the first optimization factor selection data indicating selection of the energy efficiency optimization factor, generate an optimized energy utilization-based operation execution plan for execution of a first database operation. The optimization factor selection function is performed based on second operation optimizer input data to generate second optimization factor selection data indicating selection of at least one second optimization factor, and, based on the second optimization factor selection data indicating selection of the performance efficiency optimization factor, an optimized performance-based operation execution plan is generated for execution of a second database operation.