Query Operator Execution Location Determination in Distributed Databases
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Solution Overview
Problem
Current database systems face challenges in efficiently determining the optimal database nodes for executing query operators, especially in distributed architectures with partitioned tables and multi-level nested queries, as they fail to consider data locations and transfer costs effectively.
Innovation Solution
The solution involves determining the costs of executing partition-wise operations on multiple server nodes and using these costs to decide the execution locations for query operators, taking into account the data distribution across nodes and optimizing data transfer between them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If query operators are executed on distributed database nodes without considering data locations and transfer costs, then the system maintains simple execution logic, but query performance deteriorates due to inefficient data transfer
Solution Approach 1:
The system pre-determines execution locations for query operators by analyzing data locations and calculating transfer costs before query execution. This preliminary analysis creates an optimized execution plan that reduces data transfer during actual query processing, thereby improving query performance without adding complexity during execution
Solution Approach 2:
The system introduces an intermediary optimization layer that analyzes data locations and calculates transfer costs between database nodes. This intermediary component determines optimal execution locations by evaluating multiple factors including data distribution and network costs, resolving the contradiction between simple execution logic and optimized performance
2Power
If data is distributed across multiple database nodes to enable parallel processing, then system capacity increases, but data transfer costs increase for query execution
Solution Approach 1:
The system optimizes query execution by selecting local database nodes that store the required data, thereby minimizing data transfer across the network. By analyzing data location information and matching it with query requirements, the system executes operations locally where possible, reducing energy loss from data transfer while maintaining the benefits of distributed storage
Solution Approach 2:
The system balances data distribution and execution location selection to minimize transfer costs. By calculating and comparing transfer costs across different nodes and selecting execution locations that minimize data movement, the system creates an equipotential state where no unnecessary energy is spent on data transfer, thus reducing overall energy loss while maintaining system capacity
Data Source
AI summary
A system includes determination of a first partition-wise operation on a first database table partition of a first table located at a first server node and a first database table partition of a second table located at a second server node, determination of a first cost to execute the first partition-wise operation on the first server node, and a second cost to execute the first partition-wise operation on the second server node, determination of a second partition-wise operation on a result of the first partition-wise operation, determination of a third cost to execute the second partition-wise operation on the first server node based on the first cost and the second cost, and a fourth cost to execute the second partition-wise operation on the second server node based on the first cost and the second cost, determination of one of the first server node and the second server node to execute the second partition-wise operation based on the third cost and the fourth cost, and determination of one of the first server node and the second server node to execute the first partition-wise operation based on the third cost and the fourth cost.