Cloud-Scale Query Execution with Runtime Statistics Feedback
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Solution Overview
Problem
Existing systems struggle to determine the most efficient sequence of operations for processing distributed data across multiple computing devices due to the difficulty in predicting the effects of user-defined operations and data characteristics, leading to inefficient and error-prone processing.
Innovation Solution
Collect runtime statistics from actual data processing operations to dynamically modify and optimize the sequence of operations, using statistical information to refine and adjust processing strategies in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static operation sequences are used for distributed data processing, then system complexity is reduced, but processing efficiency deteriorates due to inability to adapt to actual data characteristics
Solution Approach 1:
The patent implements dynamic operation sequences that adapt during execution based on runtime statistics. The system transitions from static pre-defined sequences to dynamic sequences that are modified based on actual data characteristics observed during processing, allowing the operation sequence to evolve and optimize itself during execution.
Solution Approach 2:
The system incorporates feedback mechanisms where runtime statistics from executed operations are collected and used to inform subsequent operation selections. This feedback loop enables the system to learn from actual data processing performance and adjust the operation sequence accordingly, improving efficiency while managing complexity through automated adaptation.
2Productivity
If runtime statistics collection is implemented, then processing optimization is improved, but system overhead increases
Solution Approach 1:
The system collects runtime statistics selectively rather than comprehensively monitoring all operations. It focuses on collecting statistics for operations that provide the most valuable information for optimization decisions, avoiding the overhead of complete monitoring while still achieving effective optimization through targeted data collection.
3Adaptability or versatility
If dynamic modification of operation sequences is performed, then adaptability to data characteristics is improved, but processing time increases due to continuous optimization decisions
Solution Approach 1:
The system performs preliminary analysis of runtime statistics to identify optimization opportunities before making modification decisions. By pre-processing and analyzing the collected statistics, the system prepares optimization candidates in advance, reducing the time required for actual modification decisions and minimizing disruption to the processing flow.
Data Source
AI summary
Runtime statistics from the actual performance of operations on a set of data are collected and utilized to dynamically modify the execution plan for processing a set of data. The operations performed are modified to include statistics collection operations, the statistics being tailored to the specific operations being quantified. Optimization policy defines how often optimization is attempted and how much more efficient an execution plan should be to justify transitioning from the current one. Optimization is based on the collected runtime statistics but also takes into account already materialized intermediate data to gain further optimization by avoiding reprocessing.