Database Query Processing Task Graphs for Compression-Aware Cost Optimization
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Solution Overview
Problem
Existing database systems struggle to efficiently optimize query processing due to inflexible handling of data compression and expansion, leading to suboptimal performance and resource utilization.
Innovation Solution
A method and system that generate a task graph to analyze and optimize query processing by considering various task sequences, including different compression states, and determine the most cost-effective sequence for execution, utilizing a management system to coordinate servers with diverse capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression processing is performed in database systems, then storage efficiency and data transfer efficiency are improved, but processing time and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing compression ratios and processing costs for different compression methods before query execution. The management system pre-evaluates multiple compression schemes and their associated costs, so that during query processing, the optimal compression method can be selected without real-time computation overhead, thus reducing processing time while maintaining storage efficiency
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting compression ratios and methods based on query characteristics, data types, and resource availability. The system evaluates different compression parameters (compression level, algorithm selection) and selects the optimal combination that balances storage efficiency with processing time requirements for each specific query scenario
2Productivity
If multiple task sequences with different compression states are considered for query processing, then query processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling the management system to automatically generate, evaluate, and select optimal task sequences without requiring manual intervention or complex external orchestration. The system self-evaluates multiple compression states and task sequences, computes their costs, and autonomously selects the optimal execution path, thereby improving query processing efficiency while keeping system complexity manageable through automation
Solution Approach 2:
The patent utilizes dynamics by creating flexible, adaptable task sequences that can dynamically adjust compression states based on real-time conditions. The system generates multiple possible task sequences with varying compression levels and selectively executes those that are most appropriate for the current query and system state, allowing the system to adapt efficiently without rigid complex structures
3Use of energy by moving object
If compression processing location and timing are made flexible, then resource utilization is improved, but control complexity and coordination overhead increase
Solution Approach 1:
The patent applies local quality by allowing different compression processing locations and timings for different data segments and query operations. Instead of applying a uniform compression strategy system-wide, the management system evaluates and determines optimal compression locations and timing for each specific data set and query type, thereby improving resource utilization while managing control complexity through localized decision-making
Solution Approach 2:
The patent utilizes segmentation by dividing query processing into discrete task sequences that can be independently evaluated and optimized. Each task sequence represents a segmented unit of work with specific compression operations at specific locations and timings, allowing the system to optimize resource utilization at the segment level without requiring complex system-wide coordination for the entire query processing workflow
Data Source
AI summary
A method for executing query processing includes, in response to a query from a host, generating a task graph indicating a plurality of task sequences, each of the task sequences capable f performing query processing corresponding to the query. The sequences includes a first sequence that outputs data in a first compression state to a one of output targets, and a second sequence of tasks that outputs the data in a second compression state different from the first compression state to the one of the output targets. The method further includes determining a processing cost for each of the task sequences, selecting one of the task sequences in accordance with the determined processing cost, and performing the query processing corresponding to the query in accordance with the selected task sequence.