Semantic Query Equivalence Detection for Redundant Workload Elimination
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Solution Overview
Problem
Existing solutions for detecting equivalence in large-scale analytics engines are inadequate, particularly in identifying semantic equivalence between subexpressions, leading to computational redundancy and inefficiencies due to the complexity and scalability issues of current syntactic and verification-based approaches.
Innovation Solution
The GEqO engine employs a series of equivalence filters, including a schema filter, vector matching filter, equivalence model filter, and automated verifier, to efficiently identify semantically equivalent subexpressions by converting query plan trees into matrices and vectors for comparison, using machine learning models to predict equivalence and a semi-supervised feedback loop to improve model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If syntactic-level equivalence detection is used, then detection speed is improved, but detection precision deteriorates due to inability to identify semantic equivalence
Solution Approach 1:
The patent introduces query plan trees as an intermediary representation between the original SQL queries and the equivalence detection process. The queries are first parsed into query plan trees, which capture the semantic structure and execution logic. This intermediary representation enables both fast processing (through structured representation) and accurate semantic comparison (through structure-based equivalence checking), resolving the contradiction between speed and precision.
Solution Approach 2:
The patent transforms the equivalence detection problem from comparing raw SQL text strings to comparing structured query plan tree parameters. By changing the representation parameters from syntactic text to semantic structure parameters (nodes, edges, operations in the query plan tree), the system achieves both efficient computation and accurate semantic equivalence detection.
2Measurement precision
If verification-based equivalence detection is used, then detection precision is improved, but computational complexity and execution time worsen
Solution Approach 1:
The patent segments the equivalence detection process into multiple hierarchical levels: first comparing query structure at the top level, then progressively examining nested sub-queries and operations. This segmentation allows the system to quickly eliminate non-equivalent queries through structure mismatch detection, avoiding the need for complete verification of all equivalent queries, thus reducing computational complexity while maintaining precision.
Solution Approach 2:
The patent performs preliminary structure validation and canonicalization of query plan trees before conducting detailed equivalence verification. By preprocessing the queries into a standardized form and checking basic structural equivalence first, the system eliminates many false candidates early in the process, reducing the computational burden of subsequent verification steps.
3Loss of energy
If existing equivalence detection solutions are applied to large-scale analytics engines, then operational costs are reduced through redundancy elimination, but scalability deteriorates due to complexity
Solution Approach 1:
The patent implements a self-service equivalence detection mechanism that automatically parses, compares, and identifies equivalent sub-queries without requiring manual intervention or complex configuration. The system autonomously builds query plan trees, performs structural comparison, and eliminates redundant computations, making it easily scalable to large-scale analytics engines with diverse query workloads.
Solution Approach 2:
The patent creates a universal equivalence detection framework that handles multiple types of queries (SQL, analytical queries, complex joins) through a unified query plan tree representation. This multi-functional approach allows the same detection mechanism to work across different query types and engine configurations, enhancing scalability without requiring separate specialized solutions for each query type.
Data Source
AI summary
Examples detect equivalent subexpressions within a computational workload. Examples include converting a query plan tree associated with a first subexpression into a matrix. The first subexpression is a portion of a database query from the computational workload. Each node in the query plan tree is represented as a row of the matrix. The matrix is converted into a first vector. The first subexpression is determined to be equivalent to a second subexpression by comparing the first vector to a second vector associated with the second subexpression. The comparison includes computing a distance between the first and second vectors that is lower than a distance threshold. The computational workload is modified, based on the determining, to perform the first subexpression and exclude performance of the second subexpression as duplicative.