Scale-Out Query Optimization With Data Movement Cost Modeling
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Solution Overview
Problem
Existing query optimizers fail to account for the costs of data movement between nodes during scale-out query execution, leading to inefficient resource utilization and improper prioritization of relational operations versus data movement.
Innovation Solution
A unified query optimization process that evaluates both relational and data movement costs simultaneously, generating query plans that include physical and data movement operators for distributed node execution, ensuring accurate analysis of resource costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard query optimizers are used for scale-out query execution, then query execution can be performed on distributed nodes, but data movement costs are not accounted for leading to inefficient resource utilization
Solution Approach 1:
The query optimizer extends the cost model by introducing new parameters for data movement costs (network bandwidth, latency, node-to-node transfer costs) alongside traditional computational cost parameters. This allows the optimizer to evaluate and compare different query execution plans based on both computational efficiency and data movement expenses, selecting plans that minimize total cost rather than just computational operations.
2Productivity
If data is moved across multiple nodes for parallel query execution, then query processing capacity is increased, but resource intensive and inefficient data movement occurs
Solution Approach 1:
The query execution plan is segmented into distinct phases: local data processing phases that can be executed independently on individual nodes without data movement, and coordination phases that require data exchange between nodes. The optimizer identifies and maximizes the local processing segments, minimizing the need for inter-node data movement while maintaining parallel execution benefits.
Solution Approach 2:
The system introduces intermediary aggregation nodes or buffer zones that collect intermediate results from multiple worker nodes before final aggregation. This intermediary layer reduces the total volume of data that needs to be moved across the distributed network by performing partial aggregations locally, thereby reducing overall network bandwidth consumption.
3Adaptability or versatility
If scale-out execution is implemented without considering data movement costs, then distributed query processing is achieved, but cost-effective execution planning is compromised
Solution Approach 1:
The query optimizer dynamically adjusts the degree of parallelization and data distribution strategies based on real-time or pre-computed cost estimates. For queries where data movement costs are high, the optimizer may dynamically choose less parallel execution plans or reposition data beforehand. For queries where parallel execution benefits outweigh movement costs, aggressive parallelization is selected, making the execution strategy adaptive rather than static.
Data Source
AI summary
Example aspects include techniques for unified query optimization for scale-out query processing. These techniques may include receiving a query including one or more query operators, and determining, based on the one or more query operators, a query representation including one or more logical operators. In addition, the techniques may include generating, based on the query representation and metadata, a query plan including one or more physical operators implementing the one or more logical operators and a data movement operator corresponding to data movement to a plurality of nodes for parallel execution of the one or more physical operators over data. Further, the techniques may include selecting the query plan based on individual cost values of the one or more physical operators and the data movement operator, and executing, based on the selecting, the query plan to generate a query result.