Dynamic Query Feedback for Complex Database Optimization
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Solution Overview
Problem
Traditional query optimizers rely on static plans generated before execution, which may not account for real-time data and can lead to suboptimal performance, especially with complex queries and heterogeneous data sources, limiting their ability to adapt and optimize query execution efficiently.
Innovation Solution
The implementation of dynamic result feedback mechanisms within the query optimizer, which allows for on-the-fly query simplification and optimization by applying result feedback to trigger rewrite rules such as block simplification, predicate push-up/down, and elimination, enabling adaptive query processing and improving query execution plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static query plan is generated before execution, then the query optimization process is simple and fast, but the query execution may be suboptimal because it does not account for real-time data
Solution Approach 1:
The patent implements dynamic query optimization by allowing the query plan to be modified during execution based on actual result feedback. The system transitions from a static plan to a dynamic one where operators can be simplified, eliminated, or reorganized based on runtime data characteristics, thereby achieving better query execution optimality without excessive complexity
Solution Approach 2:
The patent employs feedback mechanisms where intermediate query results are analyzed during execution and used to trigger query rewrite rules. The feedback from actual data patterns enables the optimizer to apply simplifications such as block elimination, predicate push-up, and join reordering, improving execution efficiency while maintaining manageable complexity through rule-based automation
2Productivity
If dynamic result feedback is used to simplify queries during execution, then query performance is improved, but the complexity of the query processing system increases
Solution Approach 1:
The patent segments the query execution into phases where feedback is collected at specific points and applied selectively. Rather than continuously analyzing and rewriting the entire query, the system divides the query plan into manageable blocks and applies simplification rules incrementally, improving efficiency while controlling system complexity through phased processing
Solution Approach 2:
The query optimization system performs self-service by automatically analyzing its own intermediate results and applying rewrite rules without external intervention. The feedback-driven simplification mechanisms autonomously identify optimization opportunities and execute transformations, enhancing query execution efficiency while keeping the system architecture relatively simple through self-optimization
3Loss of time
If complex queries are executed without dynamic optimization, then the system architecture remains simple, but unnecessary operations increase execution time
Solution Approach 1:
The patent applies partial optimization by selectively simplifying only those portions of complex queries where feedback indicates unnecessary operations. Rather than fully re-optimizing the entire query plan, the system applies targeted simplifications to specific blocks or operators, reducing execution time for complex queries while maintaining simple architecture through selective rather than comprehensive adaptation
Data Source
AI summary
Techniques for improving complex database queries are provided. A determination is made whether to adopt a static or dynamic query execution plan for a received database query based on metrics. When a dynamic query execution plan is adopted, the database query is separated into query fragments. A plan fragment is generated for each query fragment and executed to generate feedback for the plan fragment. The feedback from the execution of each plan fragment is used to initiate query rewrite rules to simplify the corresponding query fragments. The rewritten query fragments are combined to generate the dynamic query plan.