Graph Database Query Classification via Benchmark Matching
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Solution Overview
Problem
Graph databases face challenges in classifying general-purpose queries that are neither navigation nor analytic, as existing systems lack the ability to determine the optimal query engine for processing such queries, especially when information about the query is limited, leading to inefficient resource allocation and performance.
Innovation Solution
A system that receives and classifies graph queries by matching them against benchmark data, using a query parser to create a canonical internal representation, mapping to rules, and determining a confidence score; if the score is below a threshold, the query is simulated on a synthetic graph to estimate computational cost, thereby determining whether it's a navigation or analytic query and routing it to the appropriate engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph databases use existing query classification systems, then navigation and analytic queries can be processed, but general-purpose queries that are neither navigation nor analytic cannot be effectively classified, leading to inefficient resource allocation
Solution Approach 1:
The system performs preliminary classification by matching queries against a repository of benchmark queries before actual execution. This preliminary action determines the query type (navigation, analytic, or general-purpose) in advance, allowing the system to route queries to appropriate engines and allocate resources efficiently before the queries are executed.
Solution Approach 2:
The system introduces an intermediary classification layer that sits between query submission and engine execution. This intermediary component analyzes queries against benchmark data and rules, determines query types, and routes them to the appropriate engine. This mediator resolves the contradiction by enabling classification of previously unclassifiable general-purpose queries while maintaining efficient resource allocation through structured routing decisions.
2Measurement precision
If the system classifies queries by matching against benchmark data and using confidence scores, then query routing accuracy improves, but computational overhead increases due to parsing, rule mapping, and simulation requirements
Solution Approach 1:
The system performs classification actions preliminarily before query execution. By determining query types through benchmark matching, canonical representation creation, and rule mapping in advance, the system avoids redundant analysis during execution. This preliminary classification reduces overall processing time while maintaining high accuracy through thorough analysis at the classification stage.
Solution Approach 2:
The system creates a canonical internal representation of queries, which is a simplified copy or abstraction of the original query. This canonical form enables efficient matching against benchmark data and rules without repeatedly processing the full original query structure. The copying approach reduces computational overhead during classification while preserving the essential meaning needed for accurate routing decisions.
3Reliability
If the system uses synthetic graph simulation for queries below confidence threshold, then classification reliability improves, but system complexity increases due to additional simulation infrastructure
Solution Approach 1:
The system uses a synthetic graph as a simplified copy or abstraction of the actual graph database structure. This synthetic representation allows reliable classification of uncertain queries through simulation without requiring the full complexity of the actual graph infrastructure. The synthetic graph captures essential characteristics needed for classification while significantly reducing the computational and architectural complexity required for reliable decision-making.
Solution Approach 2:
The system segments the query processing pipeline into distinct stages: benchmark matching, canonical representation creation, rule mapping, and simulation (only when needed). This segmentation allows the complex simulation infrastructure to be activated only for specific queries that fall below the confidence threshold, rather than being always-active. The modular approach manages system complexity by keeping the simulation capability separate and conditional rather than integrated throughout the entire system.
Data Source
AI summary
Examples for graph database query classification include receiving a graph query and determining if the graph query matches benchmark data. In the event that the graph query does not match benchmark data, the query may be parsed, a canonical internal representation of the query may be determined, the representation may be mapped to a rule, and the query may be classified based on the rule. In the event that the confidence score for the query classification does not exceed a threshold, the query may be sent to a synthetic graph or synopsis for simulation. In some examples, the simulation may include selecting computationally expensive graph operators in the query for simulation.