Query Optimizer Lineage Recording for Plan Replay
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Solution Overview
Problem
Current database systems face significant processing resource consumption and exponential query optimization time due to the large search space of possible query execution plans, leading to inefficient query optimization and memory usage, especially when query plans need to be recomputed.
Innovation Solution
A query optimizer records the lineage of transformations used to generate a query execution plan, allowing for optimization replay by limiting the application of only those transformations that resulted in the original plan, thereby reducing the search space and optimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If query optimization explores all possible transformations to find optimal execution plans, then query execution plan quality is improved, but processing time and resource consumption increase exponentially
Solution Approach 1:
The system performs preliminary recording of transformation lineage during the first query optimization process. This recorded lineage information is then utilized in subsequent query optimizations to guide the search process, eliminating the need to re-explore the entire transformation space and thereby reducing optimization time while maintaining plan quality.
Solution Approach 2:
The system uses feedback from the recorded transformation lineage to inform and constrain subsequent query optimization processes. By feeding back the successful transformation paths from previous optimizations, the system can efficiently reproduce optimal plans without redundant exploration, directly addressing the time-quality tradeoff.
2Adaptability or versatility
If query optimization applies all available transformations with multiple invocations, then the completeness of query execution plans is improved, but the set of possible combinations becomes very large consuming more memory
Solution Approach 1:
The system extracts and records only the essential transformation lineage information from the complete set of possible transformations. This extracted lineage data captures the critical paths without needing to maintain the entire exponential space of possible combinations, thereby reducing memory requirements while preserving plan completeness.
Solution Approach 2:
Instead of maintaining the full original search space, the system creates a compressed representation (copy) of the transformation lineage. This lineage copy contains sufficient information to reconstruct optimal plans without replicating the entire exponential combination space, thus reducing memory consumption while preserving completeness.
3Speed
If query execution plans are cached to avoid reoptimization, then query processing speed is improved, but cache management complexity and replacement strategies increase
Solution Approach 1:
The system enables self-service query optimization by using recorded lineage information to automatically guide subsequent optimizations. This self-service mechanism allows the optimizer to efficiently reproduce optimal plans without requiring complex cache management strategies, as the lineage data itself provides the guidance needed for rapid reoptimization when necessary.
Data Source
AI summary
A database system includes a query optimizer which applies transformations to a logical representation of an input query in a declarative query language to generate expressions for a query execution plan. The query optimizer stores information, for each rule application, indicating a transformation applied and bindings for the transformation, the bindings indicating expressions input to the transformation. When a new query execution plan is to be generated for the input query, the query optimizer uses this stored information, for expressions in an original query execution plan, to guide the query optimizer to produce a current query execution plan while avoiding transformations not used to generate expressions in the original query execution plan.