Dynamic Skew Avoidance for MPP Query Processing
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Solution Overview
Problem
Current database management systems face inefficiencies in dynamically avoiding data skew during query processing, particularly in massively parallel processing systems, leading to slow response times and unbalanced workloads due to lack of accurate hot AMP detection and redistribution mechanisms.
Innovation Solution
The system evaluates query selectivity at a checkpoint to identify overloaded processing modules and stops redistributing skewed rows to them, maintaining these rows locally instead, and implements a redistribution adjustment routine to evenly distribute rows among all modules, without requiring pre-known skewed value statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hash redistribution is used to join tables in MPP systems, then query processing can be performed in parallel across multiple AMPs, but data skew causes certain AMPs to receive excessive rows resulting in unbalanced workload and slow response time
Solution Approach 1:
The system dynamically adjusts the redistribution strategy during query execution. It begins with hash redistribution for parallel processing, monitors row distribution across AMPs, and dynamically switches to local retention mode when skew is detected, allowing the system to adapt to actual data distribution patterns rather than relying on static pre-computed statistics
Solution Approach 2:
The system implements a feedback mechanism where AMPs report their row receipt counts to the coordinator, which then determines whether to continue redistribution or switch to local retention. This closed-loop control allows the system to respond to actual workload conditions and prevent hot AMP formation based on real-time feedback rather than predictions
2Quantity of substance
If additional AMPs are added to the system, then overall system capacity increases, but the relative disproportionate distribution of rows to hot AMPs increases reducing parallel efficiency
Solution Approach 1:
Each AMP independently determines whether to redistribute or locally retain rows based on its own row receipt count and the system-wide skew detection threshold. This decentralized decision-making allows AMPs to self-adjust their behavior without centralized coordination overhead, maintaining efficiency even as system size changes
3Adaptability or versatility
If dynamic skew avoidance is implemented without accurate statistics, then the system can handle unknown skewed values, but detection of hot AMPs becomes less accurate potentially causing mistakes
Solution Approach 1:
The system pre-establishes a skew detection threshold based on the number of AMPs and expected uniform distribution, allowing it to detect skew dynamically without requiring pre-computed statistics. This preliminary configuration enables the system to adapt to unknown skewed values while maintaining detection accuracy through mathematical bounds rather than empirical data
Data Source
AI summary
A system, method, and computer-readable medium that facilitate dynamic skew avoidance are provided. The disclosed mechanisms advantageously do not require any statistic information regarding which values are skewed in a column on which a query is applied. Query selectivity is evaluated at a check point and thereby facilitates accurate detection of an overloaded processing module. The successful detection of an overloaded processing module causes other processing modules to stop sending more skewed rows to the overloaded processing module. Detection of an overloaded processing module is made when the overloaded processing module has received more rows than a target number of rows. Further, skewed rows that are maintained locally rather than redistributed to a detected processing module may result in more processing modules becoming overloaded. Advantageously, the disclosed mechanisms provide for a final redistribution adjustment to provide for even distribution of rows among all processing modules.