XML Query Rewrite Diagnosability for Database Performance
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Solution Overview
Problem
Database query processing systems face inefficiencies in executing XPath queries over XML data due to the inability to rewrite all queries, leading to suboptimal performance and increased memory usage when constructing in-memory DOM trees for navigation, especially for large XML fragments.
Innovation Solution
A query developer tool that employs query tree construction and optimization techniques, including XML rewrite and conditional query plan execution, to identify and eliminate expensive operators, providing feedback on optimization failures and allowing developers to restructure queries for better performance by leveraging relational database constructs and indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If in-memory DOM trees are constructed for XPath query navigation, then hierarchical navigation capability is improved, but memory usage and construction cost increase significantly
Solution Approach 1:
The patent extracts only the necessary navigation information from the full XML document and stores it in specialized index structures (XML indexes). Instead of loading the entire DOM tree into memory, the system extracts and indexes specific path information, element hierarchies, and navigation relationships into compact relational representations that can be queried efficiently without reconstructing the full DOM structure in memory.
Solution Approach 2:
The patent introduces XML indexes as intermediary structures between the stored XML data and the query processing system. These indexes serve as mediators that provide efficient navigation capability without requiring full DOM construction. The indexes contain pre-computed path information and hierarchical relationships that enable XPath navigation while avoiding the memory overhead of in-memory DOM trees.
2Quantity of substance
If XML data is stored in object-relational or binary storage formats, then storage efficiency is improved, but hierarchical navigation performance deteriorates
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing navigation paths, element hierarchies, and structural relationships in XML indexes during the indexing phase. This preliminary processing creates optimized data structures that enable fast navigation queries without requiring complex runtime processing of the stored XML format, thus resolving the performance issue while maintaining efficient storage.
Solution Approach 2:
The patent changes the parameter representation by transforming XML structural information into different data formats suitable for efficient querying. The XML indexes store navigation information in relational or semi-structured formats with optimized schemas that allow rapid access to hierarchical relationships, element paths, and parent-child connections without requiring full XML parsing during query execution.
3Reliability
If query rewrite is not applied, then query correctness is maintained, but query execution performance deteriorates
Solution Approach 1:
The patent implements feedback mechanisms that provide detailed information to query developers about why query optimization or rewrite failed. The system analyzes the query against the XML schema and index structures, identifies specific reasons for rewrite inapplicability (such as unsupported XPath expressions or missing indexes), and returns diagnostic information that helps developers modify their queries to enable optimization while maintaining correctness.
Solution Approach 2:
The patent introduces XML schemas and type information as intermediary elements between the XML data and the query processing system. These schemas provide the necessary structural information that enables the query optimizer to determine whether rewrite is applicable and to generate correct optimized queries. The schema acts as a mediator that bridges the gap between the flexible XML format and the rigid requirements of query optimization rules.
Data Source
AI summary
A method and apparatus for automatically analyzing and providing feedback regarding the optimizability of a relational database query. A query developer's primary goal is to ensure that queries and DML operations are rewritten for the most efficient execution. Rewrite diagnosability captures metadata for each attempted query optimization including success or failure and the reasons for failure. The metadata is stored in association with the operators that were not removed through rewriting. Once all optimizations have been attempted and rewriting is complete, the metadata is selectively displayed based on the cost to perform the associated operation. The context of performing the operation may affect the cost. The cost may be based at least on the type of operation and where within the query tree the operation is located. A query developer may configure the database system not to execute the resulting query plan based on one or more criteria.