Dynamic Operator Pruning from Intermediate Query Results
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Solution Overview
Problem
Existing database query optimization techniques fail to efficiently prune query plans based on dynamic and intermediate results, leading to unnecessary resource utilization and inefficiencies in processing large datasets.
Innovation Solution
Implement dynamic pruning of query plans by detecting shared empty state objects between join operators and applying pruning conditions, utilizing direct, forward, and backward pruning rules to mark and propagate pruned operators, thereby optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic pruning based on intermediate results is implemented, then query execution efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining operator pruning conditions and state object dependencies before query execution. The query plan is generated with embedded pruning rules that automatically trigger based on intermediate results, eliminating the need for complex runtime analysis and reducing overall system complexity while maintaining high efficiency
Solution Approach 2:
The system implements feedback mechanisms by monitoring intermediate query results and using them to dynamically adjust the query plan execution. When intermediate results indicate that certain operators will not contribute to the final outcome, the system feeds this information back to prune those operators, improving efficiency without requiring complex external control systems
2Loss of energy
If comprehensive operator pruning conditions are defined, then resource utilization is optimized, but query plan complexity increases
Solution Approach 1:
The system segments the query plan into distinct operators with defined input-output relationships and state object dependencies. Each operator is analyzed independently for pruning conditions based on its specific role in the query pipeline, allowing for targeted optimization without requiring complex analysis of the entire query plan as a monolithic structure
Solution Approach 2:
The system changes parameters by defining specific pruning conditions based on intermediate result states (e.g., empty results, null values, cardinality thresholds). These parameter-based conditions allow the system to optimize resource utilization dynamically without hardcoding complex decision logic, maintaining query plan simplicity while achieving efficient resource usage
Data Source
AI summary
In some implementations, there is provided a method including generating a query plan including in a first pipeline a first join operator and in a second pipeline a second join operator; executing at least a portion of the query plan including the first pipeline and the first join operator; detecting, based on the at least one operator usage state and the at least one operator pruning condition, an empty state object shared between the first join operator and the second join operator in the second pipeline; and processing, by the at least one operator pruning condition, an indication of the empty state object, wherein the least one operator pruning condition is associated with the second join operator and includes at least a first rule to mark the second join operator for pruning.