Cluster Views for Query Compute Scaling and Cache Preservation
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Solution Overview
Problem
Cloud computing platforms face challenges in balancing cache preservation and scalability, as maintaining active clusters for cache benefits leads to high costs and managing volatile compute clusters impacts cache health and resource efficiency.
Innovation Solution
Implementing separate cluster views with distinct scaling policies, segregating compute nodes into locality and utility types to optimize cache reuse and elasticity, and using an operator analyzer and scheduler to assign operators to appropriate node sets based on their characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If compute clusters are maintained active to preserve cache, then query performance is improved, but operational costs increase
Solution Approach 1:
The compute cluster is segmented into two distinct node sets: a first node set for executing operators requiring cache preservation and a second node set for operators where elasticity is prioritized. This segmentation allows independent scaling and management of each node set, enabling the system to maintain only the necessary compute resources for cache preservation rather than keeping the entire cluster active, thus reducing operational costs while maintaining query performance for cache-dependent operations.
2Productivity
If compute clusters are scaled aggressively to meet demand, then resource utilization is improved, but cache health deteriorates
Solution Approach 1:
By dividing the cluster into specialized node sets, the system can scale the first node set independently to meet cache preservation requirements while keeping the second node set sized for elasticity. This prevents over-scaling that would waste resources and under-scaling that would compromise cache availability, achieving optimal resource utilization while maintaining cache health through targeted scaling of the appropriate node set.
Solution Approach 2:
Different node sets are assigned different scaling characteristics tailored to their specific functions. The first node set has scaling policies optimized for maintaining cache availability, while the second node set has policies optimized for resource elasticity. This local differentiation of scaling quality allows each node set to be optimized for its specific purpose, preventing cache health deterioration from aggressive scaling while still achieving high resource utilization when needed.
3Device complexity
If a single cluster view is used for all operators, then system simplicity is maintained, but resource efficiency decreases
Solution Approach 1:
The system introduces a multi-view cluster architecture where operators are classified and assigned to different node sets based on their characteristics. This segmentation enables differentiated resource allocation and scaling policies for different operator types, significantly improving resource efficiency by ensuring that compute resources are allocated only where needed rather than uniformly across the entire cluster, while the operator analyzer and scheduler provide automated management to keep the system approachable.
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.