Database Reorganization Task Optimization Under Memory Constraints
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Solution Overview
Problem
Existing database reorganization methods face challenges in determining the optimal number of tasks during reorganization with memory and processor constraints, particularly due to the complexity added by parallel processing and the need to minimize downtime in 24/7 availability systems.
Innovation Solution
A method that identifies memory and processing capabilities to determine an optimal number and combination of reorganization processes, applying a set of rules to minimize reorganization time by managing the distribution of tasks such as unload, reload, sort, and build processes concurrently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel processing is used to speed up database reorganization, then productivity is improved, but device complexity increases due to the need to manage multiple tasks and processes
Solution Approach 1:
The system dynamically determines the optimal number of tasks based on available memory and processor capabilities. The task manager adjusts the number of parallel tasks dynamically according to system resources, using formulas that calculate optimal task counts based on memory size, processor speed, and I/O capabilities. This dynamic adjustment resolves the contradiction by adapting the degree of parallelism to actual system constraints.
Solution Approach 2:
The patent changes key parameters (memory size, processor speed, I/O rate) to determine the optimal number of tasks. By using these parameters in calculation formulas, the system determines the optimal task count that maximizes productivity without exceeding resource constraints. This parameter-based approach allows the system to optimize parallel processing while maintaining manageable complexity.
2Productivity
If more tasks are used during reorganization, then productivity is improved, but loss of time increases due to task coordination and overhead
Solution Approach 1:
The system uses partial parallelism by determining an optimal number of tasks that is less than the maximum possible parallelism. The calculation formulas derive this optimal number based on resource constraints, implementing just enough parallelism to gain productivity benefits while avoiding excessive task coordination overhead. This partial action approach resolves the contradiction by finding the sweet spot between parallelism benefits and coordination costs.
3Manufacturing precision
If database reorganization is performed offline to ensure complete reorganization, then manufacturing precision is improved, but loss of time increases due to database downtime
Solution Approach 1:
The system performs preliminary determination of the optimal task number before starting reorganization. By pre-calculating the optimal task count based on system resources, the system prepares the reorganization process in advance, enabling efficient online execution. This preliminary action allows the system to maintain database availability while ensuring complete reorganization through properly configured parallel tasks.
Solution Approach 2:
The system enables dynamic reorganization by determining optimal task parameters in advance and then executing reorganization online with multiple concurrent tasks. This dynamic approach allows the database to remain available for operations during reorganization, resolving the contradiction between complete reorganization and continuous availability by using pre-calculated optimal task configurations.
Data Source
AI summary
A method, apparatus and program storage device for determining an optimal number of tasks during reorganization of a database system with memory and processor constraints. The reorganization system identifies memory constraints, identifies processing capabilities and determines a number of reorganization processes and a combination of reorganization processes that provides a minimum reorganization time based upon the identified memory and processing constraints. The identified memory and processing capabilities identify a domain representing a set of rules applicable to the identified memory and processing capabilities. The set of rules of the identified domain are applied to determine an optimal number and combination of reorganization processes that minimizes a reorganization time.