Parallel Task Execution Plan Optimization via Runtime Statistics
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Solution Overview
Problem
Current map-reduce data processing systems rely on fixed apriori estimates for execution plans, leading to poor data processing performance due to the difficulty in estimating properties in highly distributed environments with arbitrary code operations.
Innovation Solution
The use of statistical data collected during task execution to optimize execution plans by adjusting the sequence of operations, degree of parallelism, and data partitioning, allowing for on-the-fly optimization and improved resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed apriori estimates are used to generate execution plans, then the system can operate without real-time statistical data, but the data processing performance deteriorates due to inability to adapt to actual distributed environment conditions
Solution Approach 1:
The system performs preliminary statistical data collection during the initial execution of tasks in the distributed data parallel computation system. This statistical information about resource usage, computation cost, and cardinality is gathered in advance and stored for later use in optimizing execution plans for recurring jobs, eliminating the need to rely solely on fixed apriori estimates.
Solution Approach 2:
The system implements a feedback mechanism where statistical data collected from actual task execution is fed back into the execution plan generation process. The optimizer uses this real-world performance data to adjust and improve execution plans for subsequent recurring jobs, creating a continuous improvement loop that enhances data processing performance over time.
2Adaptability or versatility
If statistical data collection and optimization processes are implemented, then execution plans can be dynamically optimized, but the system complexity increases due to additional data collection and processing requirements
Solution Approach 1:
The statistical data collection mechanism is designed to be universal and reusable across multiple jobs and tasks. The same data collection infrastructure serves both initial execution and subsequent optimization of recurring jobs, reducing the need for separate specialized systems and minimizing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The system performs self-optimization by automatically collecting its own execution statistics and using that data to improve its own execution plans. The optimizer component uses the collected statistical data to autonomously generate optimized execution plans without requiring external intervention, reducing the complexity of manual configuration and management.
3Use of energy by moving object
If fixed execution plans are used for recurring jobs, then system operation is simple and predictable, but computation resources are wasted due to inability to optimize based on actual performance data
Solution Approach 1:
The system performs optimization preparation in advance by collecting statistical data during the first execution of jobs. This preliminary data collection enables rapid optimization of recurring jobs without requiring extensive analysis time at execution time, thus reducing computation resource waste while minimizing the time loss associated with optimization.
Data Source
AI summary
The use of statistics collected during the parallel distributed execution of the tasks of a job may be used to optimize the performance of the task or similar recurring tasks. An execution plan for a job is initially generated, in which the execution plan includes tasks. Statistics regarding operations performed in the tasks are collected while the tasks are executed via parallel distributed execution. Another execution plan is then generated for another recurring job, in which the additional execution plan has at least one task in common with the execution plan for the job. The additional execution plan is subsequently optimized based at least on the statistics to produce an optimized execution plan.