Query Plan Pipeline Bypass for Complete Result Set Operators
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing database query optimization techniques do not efficiently handle operators that require a complete result set as input, leading to suboptimal performance and increased memory usage.
Innovation Solution
The proposed solution involves generating a query plan that bypasses pipelining between operators that require a complete result set and those that can provide it using a state identified by a state reference, or continues to use chunks for data exchange if the second operator does not support this state-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pipelining is used between operators to enable continuous data flow, then processing throughput is improved, but memory usage increases and execution efficiency deteriorates for operators requiring complete result sets
Solution Approach 1:
The system dynamically determines whether to apply pipelining based on operator characteristics. The query optimizer analyzes each operator to identify if it requires a complete result set, and selectively disables pipelining for such operators while maintaining it for others, creating a dynamic adaptation to different operator requirements
2Speed
If pipelining is used between operators to enable continuous data flow, then processing speed is improved, but query execution efficiency deteriorates for operators requiring complete result sets
Solution Approach 1:
The system applies different data exchange strategies to different segments of the query plan based on local operator requirements. Operators requiring complete result sets receive non-pipelined data exchange, while operators that can handle chunked data receive pipelined data exchange, optimizing each local segment according to its specific needs
3Productivity
If pipelining is used between operators to enable continuous data flow, then data exchange efficiency is improved, but complexity of data exchange management increases
Solution Approach 1:
Operators self-identify their data exchange requirements by implementing interfaces that indicate whether they need complete result sets or can process chunked data. The query optimizer automatically detects these requirements and configures appropriate data exchange strategies without requiring manual intervention or complex management overhead
Data Source
AI summary
In some implementations, there is provided pipeline bypassing of certain operators. In some implementations, a method includes generating a query plan including at least one pipeline of operators; determining whether the at least one pipeline of operators includes a first operator that requires a complete result set as an input and further includes a second operator that supports providing the complete result set using a state identified by a state reference; and in response to determining the at least one pipeline of operators includes the first operator that requires the complete result set as the input and further includes the second operator that supports providing the complete result set using the state identified by the state reference, bypassing in the query plan pipelining between the first operator and the second operator.