Query Join Reordering Using Fact-Dimension Table Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing join reordering algorithms in relational database systems, such as heuristics-based, cost-based, and machine learning-based algorithms, often fail to achieve optimal query execution efficiency due to suboptimal table classification and join ordering, leading to inefficient data processing and increased computational resources.
Innovation Solution
A join optimizer that classifies database tables as fact or dimension tables based on normalized cardinality statistics and z-scores, generating deconstructed query join trees and optimizing the join order by sequentially joining smaller dimension tables with fact tables, adjusting for filter selectivity, and incorporating residual tables as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional join reordering algorithms are used, then query execution can be performed, but query execution efficiency is suboptimal due to inadequate table classification and join ordering
Solution Approach 1:
The patent segments the join optimization process into distinct phases: table classification (categorizing tables as fact or dimension based on cardinality), join tree deconstruction (breaking down the query into individual join trees), and sequential join execution (processing joins in an optimized order). This segmentation allows each phase to be optimized independently, improving overall query execution efficiency while reducing processing time.
2Productivity
If more computational resources are allocated to join optimization, then better join ordering can be achieved, but computational overhead increases
Solution Approach 1:
The system performs self-service by automatically classifying tables into fact or dimension categories based on their cardinality characteristics, and autonomously determining the optimal join sequence without requiring manual intervention or complex external optimization resources. This self-service approach reduces computational overhead while maintaining high data processing efficiency.
Solution Approach 2:
The patent changes key parameters such as table classification (from unclassified to fact/dimension categories), join order (from arbitrary to sequential optimized), and intermediate result sizes (from uncontrolled to managed). These parameter changes improve data processing efficiency while keeping computational overhead manageable through systematic optimization rather than brute-force computation.
3Manufacturing precision
If complex join ordering is implemented to optimize query results, then query accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by classifying all tables into fact or dimension categories before the actual join execution, and pre-determining the optimal join sequence based on cardinality analysis. This preliminary classification and planning ensures query result accuracy is maintained while avoiding the need for complex real-time decision-making during join execution, thus reducing system complexity.
Data Source
AI summary
In various examples, table classification based query join reordering for relational database systems and applications are provided. In some embodiments, a relational database system is provided that includes a join optimizer that evaluates a join clause of a query and categorizes relational database tables as either fact tables or dimension tables based on a normalized cardinality statistic. The join optimizer uses the fact and dimension tables to deconstruct the query into a plurality of deconstructed query join trees. Individual deconstructed query join trees may be generated for each respective fact table. The deconstructed query join trees may be joined to generate a reordered join solution representing a sequential join of the plurality of deconstructed query join trees. An updated query may be generated based on the reordered join solution, and a query response generated that answers the query based at least on the updated query.