Distributed Query Scheduling for Cache-Stable Cluster Scaling
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Solution Overview
Problem
Cloud data warehouses face challenges in balancing cache preservation and elasticity in compute clusters, leading to inefficiencies and high operational costs due to volatile resource demands.
Innovation Solution
Implementing separate cluster views within compute clusters, segregating nodes into locality and utility types based on caching and computational demands, with distinct scaling policies to optimize cache reuse and elasticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If compute clusters dynamically scale up and down based on workload demand, then resource utilization and cost efficiency improve, but cache preservation and query performance deteriorate
Solution Approach 1:
The compute cluster is segmented into two distinct node sets: a first node set for executing operators that benefit from cache preservation (scan operators), and a second node set for executing operators that prioritize computation (join, aggregate, etc.). This segmentation allows independent scaling policies for each node set, resolving the contradiction between cluster elasticity and cache preservation.
Solution Approach 2:
Different node sets are assigned different characteristics and scaling behaviors tailored to their specific functions. The first node set maintains stability and cache warmth for scan operations, while the second node set implements aggressive scaling for computational operations. This local differentiation allows each part of the system to optimize for its specific requirements.
2Reliability
If all nodes are scaled uniformly to meet peak demand, then query performance is maintained, but operational costs increase due to idle resources during low demand
Solution Approach 1:
By segmenting nodes into function-specific sets, the system can scale each set independently based on actual workload patterns. Scan operators (first node set) maintain smaller, stable clusters that preserve cache, while computational operators (second node set) utilize larger, more volatile clusters that scale only when needed, reducing overall resource waste.
Solution Approach 2:
The system applies partial scaling to each node set rather than uniform scaling to the entire cluster. Each node set scales to the extent necessary for its specific workload characteristics, avoiding the excessive provisioning required if the entire cluster were scaled uniformly to peak demand.
3Loss of energy
If nodes are removed from the cluster to reduce costs during low demand, then operational costs decrease, but cache is lost and query performance deteriorates
Solution Approach 1:
The segmentation allows selective node removal from only the second node set (computational operators) during low demand periods, while maintaining the first node set (scan operators) with sufficient nodes to preserve cache. This targeted approach reduces costs without sacrificing the cache necessary for performance-critical operations.
Solution Approach 2:
Different retention policies are applied locally to different node sets. The first node set maintains stability and cache warmth as a local priority, while the second node set implements aggressive cost optimization. This local quality differentiation allows the system to balance cost reduction with performance preservation in the right places.
Data Source
AI summary
A distributed query processor in a server is configured for compute scale and cache preservation to enable efficient cluster usage for query processing. The query processor includes an operator analyzer and an operator scheduler. The operator analyzer determines a first operator, of a graph of operators representative of a user query, to have a first characteristic and assigns the first operator to a first node set of a plurality of node sets. The first node set is associated with the first characteristic. A second node set of the node sets is associated with a second characteristic different from the first characteristic. The operator scheduler is configured to cause the first operator to be executed in the assigned first node set to generate a first operator result, and a query result to be generated based at least on the first operator result.