Distributed Query Workload Management With Real-Time Execution Feedback
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Solution Overview
Problem
Conventional estimate-driven workload managers in distributed Big Data Analytics services face inaccuracies in resource estimation, leading to subpar performance, increased operational costs, and reduced concurrency due to under/overestimation, noisy neighbors, infrastructure issues, and node bottlenecks, which hinder optimal workload management.
Innovation Solution
A system that leverages live execution feedback from nodes to detect resourcing issues and anomalies, enabling real-time corrective measures by adjusting resource allocations during query execution using a distributed query processor and query optimizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If estimate-driven workload management is used to allocate resources, then resource allocation can be performed without real-time monitoring, but resource estimation inaccuracies lead to subpar performance and increased operational costs
Solution Approach 1:
The system implements feedback by monitoring actual resource consumption of executing tasks and using this information to adjust resource estimates for dependent tasks. The workload manager receives feedback from nodes about actual resource usage and dynamically updates estimates, resolving the contradiction between simple allocation and accurate performance by adding adaptive feedback loops.
Solution Approach 2:
The system performs preliminary resource estimation based on available statistics before task execution, allowing initial resource allocation to be made in advance. This preliminary action enables quick deployment while the system later refines estimates based on actual consumption patterns.
2Loss of energy
If underestimation of resources is used to reduce operational costs, then resource allocation is optimized, but query performance deteriorates due to insufficient resources
Solution Approach 1:
The system dynamically adjusts resource estimates based on actual consumption feedback rather than using static underestimation. This allows the system to start with conservative estimates to control costs, then adaptively increase resource allocation for subsequent tasks based on observed performance and consumption patterns, resolving the trade-off between cost and performance.
Solution Approach 2:
By implementing feedback loops that monitor actual resource usage and use this information to adjust estimates for dependent tasks, the system learns from actual performance to optimize the balance between operational cost and query execution speed over time.
3Productivity
If overestimation of resources is used to ensure sufficient capacity, then query performance is maintained, but operational costs increase and concurrency is reduced
Solution Approach 1:
The system performs preliminary resource estimation that can be conservative initially, allowing queries to execute with sufficient resources to maintain performance. The estimates are then refined downward based on actual consumption feedback, reducing overestimation and associated costs while preserving necessary performance capability.
Solution Approach 2:
The system changes the resource estimation parameters dynamically based on actual consumption data. Instead of using fixed overestimation margins, the system adjusts estimation parameters upward or downward based on observed performance and resource usage patterns, optimizing the balance between performance assurance and cost reduction.
4Device complexity
If static resource estimation is used to simplify workload management, then system complexity is reduced, but the system cannot adapt to node anomalies or performance variations
Solution Approach 1:
The system adds feedback mechanisms that monitor actual resource consumption and node performance, enabling automatic detection and adaptation to node anomalies. This feedback-driven approach maintains relatively simple workload management logic while significantly improving reliability through adaptive response to actual system conditions.
Solution Approach 2:
The workload manager performs self-service by automatically adjusting resource estimates based on feedback from actual task execution without requiring complex external intervention. The system self-corrects estimation errors and adapts to node variations through automated feedback processing.
Data Source
AI summary
Systems and methods are provided that introduce an approach for executing a multi-query workload that leverages live execution feedback from nodes to detect resourcing issues and anomalies, and deploy real-time corrective measures for the multi-query workload. Leveraging live execution feedback from the nodes as the queries are executing make it possible to detect various resourcing issues and anomalies, and enable the system to perform corrective actions “live” or in “real-time” during an execution of a query, and more specifically during execution of the tasks within a query.