Dynamic Query Plan Reordering for Database Efficiency
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Solution Overview
Problem
Query execution plans in database systems can be inefficient due to incorrect estimates of intermediate query results and design errors, leading to computational inefficiencies and human errors in manual planning, while automated methods may also generate suboptimal plans.
Innovation Solution
Dynamic re-sequencing of operations in query execution plans based on shared sort orders and policies that optimize the execution order of join and aggregation operations, allowing for improved efficiency and workload management without wasted effort, including pause and resume functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the query execution plan is generated using traditional optimization techniques, then the plan can be created based on current database state, but the plan may include inefficiencies due to dynamic database state variations between optimization and execution
Solution Approach 1:
The system dynamically re-sequences operations in the query execution plan during runtime based on actual database state and intermediate result characteristics, rather than relying solely on static optimizer estimates. This allows the execution plan to adapt to dynamic database state changes between optimization and execution phases.
Solution Approach 2:
The system monitors intermediate query results during execution and uses this feedback to dynamically adjust the sequencing of operations. When intermediate results exhibit certain characteristics (e.g., sorted data), the system feedback-triggered re-sequencing to optimize subsequent operations like joins and aggregations.
2Productivity
If operations are executed strictly in the sequence specified by the query plan, then the plan structure is maintained, but computational inefficiencies occur when intermediate results have shared sort orders that could be exploited
Solution Approach 1:
The execution engine dynamically determines the sequencing of operations based on runtime conditions such as shared sort orders in intermediate results. This dynamic approach allows the system to exploit optimization opportunities without requiring complex static plan transformations.
Solution Approach 2:
The system changes the execution parameter (operation sequence) based on detected characteristics of intermediate results. When shared sort orders are detected, the system adjusts the execution sequence to leverage this property, thereby improving join and aggregation performance without fundamental changes to the query plan structure.
3Reliability
If the query execution plan is optimized for initial database state, then the plan works correctly for that state, but it may become suboptimal when database state changes during execution
Solution Approach 1:
The system maintains reliability by preserving the logical correctness of the query plan while dynamically adjusting the physical execution sequence. The re-sequencing operates within the constraints of the original plan's data flow requirements, ensuring correctness while adapting to runtime conditions for improved efficiency.
Solution Approach 2:
The system performs preliminary analysis of intermediate results during execution to identify opportunities for optimization. By detecting shared sort orders early in the execution process, the system can prepare and apply optimized sequencing strategies before executing costly operations like joins and aggregations.
Data Source
AI summary
There is provided a computer-implemented method of dynamically reordering operations in a query plan. An exemplary method comprises processing a first set of tuples according to a first operation. The query plan is pipelined and specifies that the first operation generates input for a second operation. The query plan further specifies that the second operation is executed after the first operation. The computer-implemented method further includes determining that the second operation is to precede the first operation based on a specified policy. The computer-implemented method further includes executing the second operation for a second set of tuples before executing the first operation for the second set of tuples. The second operation generates an input for the first operation. The first operation is executed after the second operation.