Query Parsing Mechanism for Redundant Predicate Removal
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Solution Overview
Problem
Relational database systems face inefficiencies due to redundant predicates in queries, which are not recognized by optimizers, leading to wasteful use of resources and varying query plans based on expression, hindering performance.
Innovation Solution
A mechanism is introduced to parse queries into multiple object classes stored in tables, allowing for modification and recomposition, identifying and removing redundant predicates, and enabling parallel processing of independent subqueries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an optimizer generates multiple query plans based on different query expressions, then query optimization capability is improved, but query plan consistency deteriorates
Solution Approach 1:
The query is segmented into its core semantic components (predicates, tables, attributes) rather than processing the entire query expression as a single unit. This segmentation allows the system to identify the essential meaning of the query independent of its syntactic expression, enabling consistent query plan generation across different expressions of the same query intent.
Solution Approach 2:
An intermediary semantic representation layer is introduced between the query expression and the query plan generation. This intermediary represents the query's meaning in a standardized form, acting as a mediator that translates various query expressions into a common semantic model, thereby ensuring consistent optimization regardless of the original expression used.
2Measurement precision
If the optimizer processes all predicates in the query, then comprehensive query analysis is improved, but resource efficiency deteriorates due to redundant predicates
Solution Approach 1:
Redundant predicates are extracted and identified from the query before the main optimization process. By taking out these unnecessary conditions, the system avoids processing them during query plan generation, thereby reducing resource consumption while maintaining complete analysis of the essential query requirements.
Solution Approach 2:
Predicate analysis and redundancy detection are performed as preliminary actions before the main query optimization process. This preliminary processing identifies and eliminates redundant predicates in advance, so that the subsequent optimization stages only need to process the essential, non-redundant predicates, improving overall resource efficiency.
3Ease of operation
If the query is processed in its original expression form, then processing simplicity is improved, but recognition of redundant predicates deteriorates
Solution Approach 1:
The query expression is segmented into discrete predicate components that can be individually analyzed. This segmentation transforms the complex original expression into a structured set of manageable units, making it easier to process while simultaneously enabling the detection of redundant predicates through systematic comparison and logical analysis of the segmented components.
Data Source
AI summary
In a database system, a mechanism is provided to enable parsing of a query into plural objects stored in respective plural tables. One or more characteristics of the query is modified using content of the plural tables based on one or more predefined rules for the purpose of recomposing the query.