Lakehouse Query Execution Planning for Energy and Performance
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Solution Overview
Problem
Existing database systems face limitations in processing speed due to hardware constraints, data storage methods, and restricted co-processing options, leading to inefficiencies in data processing and query execution.
Innovation Solution
A database system architecture that employs parallelized data input, storage, and query processing systems, utilizing a network of computing devices with independent processing core resources to optimize data partitioning, storage, and query execution, while incorporating energy utilization monitoring and optimization for efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallelized data input, storage, and query processing systems are employed, then processing speed and efficiency are enhanced, but device complexity increases
Solution Approach 1:
The database system is divided into multiple independent processing core resources, each capable of executing operations autonomously. Data is partitioned across multiple storage devices, and query processing is distributed across multiple computing devices. This segmentation enables concurrent execution of operations without locks, thereby enhancing processing speed while managing complexity through modular design.
Solution Approach 2:
The system transitions from sequential single-threaded processing to parallel multi-threaded processing across multiple dimensions. Multiple computing devices operate simultaneously on different data partitions, and multiple operations can execute concurrently within each device. This dimensional expansion from single-core to multi-core distributed architecture resolves the contradiction by providing scalability.
2Use of energy by moving object
If energy utilization monitoring and optimization are incorporated, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The system incorporates energy utilization monitoring that provides feedback to the query optimizer. The monitoring component tracks energy consumption metrics of executing operations, and this feedback is used to adjust query execution plans dynamically. The optimizer selects execution strategies that minimize energy consumption based on real-time and historical energy data, achieving energy efficiency without requiring complete system redesign.
Data Source
AI summary
A data storage 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. Based on the first 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 first 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 from the set of optimization factors. Based on the second optimization factor selection data indicating selection of the energy efficiency optimization factor, an optimized energy utilization-based operation execution plan is generated for execution of a second operation.