Distributed Query Workload Feedback for Real-Time Resource Correction
Find Innovative SolutionsGenerate Solutions
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 underestimation or overestimation, and are prone to anomalies from noisy neighbors and infrastructure issues, with the slowest node acting as a bottleneck.
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 (DQP) and query optimizer (QO).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If estimate-driven workload managers use available statistics to produce query estimates, then resource allocation can be planned in advance, but the estimates are inaccurate leading to subpar performance or increased operational cost
Solution Approach 1:
The system implements feedback by monitoring actual resource consumption during query execution and using this information to adjust resource allocation dynamically. The workload manager receives feedback from nodes about actual resource usage and task execution status, then adjusts subsequent resource allocations based on this real-world data rather than relying solely on initial estimates.
Solution Approach 2:
The system transitions from static estimate-driven resource allocation to dynamic resource management. Resource allocations are adjusted in real-time based on actual execution feedback, allowing the system to adapt to varying workload characteristics and node performance conditions during query execution.
2Device complexity
If the system allocates resources based on estimates, then resource planning is simplified, but underestimation leads to bad performance and overestimation leads to increased operational cost
Solution Approach 1:
The workload manager implements continuous feedback loops where actual resource consumption data from executing tasks is collected and used to adjust resource allocations for subsequent tasks. This feedback mechanism allows the system to optimize resource usage without requiring complex manual intervention, automatically adapting to actual workload demands.
Solution Approach 2:
The system enables self-service resource management where the workload manager autonomously adjusts resource allocations based on observed execution patterns and feedback from nodes. The system learns from actual resource consumption and automatically optimizes allocations without requiring external intervention or complex manual planning.
3Ease of operation
If nodes are assigned tasks based on initial estimates, then task scheduling is straightforward, but nodes may go bad due to noisy neighbor issues or infra problems causing anomalies
Solution Approach 1:
The system implements monitoring and feedback mechanisms where nodes report their actual execution status and resource consumption back to the workload manager. This feedback allows the system to detect node anomalies such as noisy neighbor issues or infrastructure problems and adjust task scheduling accordingly, moving tasks from problematic nodes to healthier nodes.
Solution Approach 2:
The system prepares for potential node failures by implementing redundancy and flexibility in task scheduling. When a node shows signs of deterioration or anomaly, the system can redistribute tasks to other nodes before complete failure occurs, cushioning against the impact of node reliability issues.
4Quantity of substance
If the system caps capacity growth to manage resources, then operational cost is controlled, but reduced concurrency occurs when estimates are inaccurate
Solution Approach 1:
The system implements dynamic resource allocation that adjusts capacity utilization based on actual workload demands and execution feedback. Rather than using fixed capacity caps based on estimates, the system dynamically scales resource usage up or down based on real-time observations, allowing higher concurrency when resources are sufficient and reducing it when resources are constrained.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on execution feedback. Resource allocation ratios, parallelism degrees, and capacity utilization thresholds are adjusted as parameters based on observed performance and resource consumption patterns, allowing the system to optimize concurrency within available capacity constraints.
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.