Property Graph Query Parser with Schema-Based Error Detection
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Solution Overview
Problem
Current graph query processing systems lack effective feedback mechanisms, making it difficult for users to identify and resolve issues, such as empty results or type mismatches, in property graph queries before execution.
Innovation Solution
A parser for property graph queries that analyzes queries before execution, using a property graph schema to provide precise error reporting and auto-completion suggestions, ensuring that queries are valid and will return non-empty results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph queries are executed immediately without prior analysis, then query processing speed is maintained, but error detection capability deteriorates
Solution Approach 1:
The patent implements a parser that performs preliminary analysis of graph queries before execution. The parser validates query syntax, checks data type compatibility, verifies property existence, and detects potential errors in advance. This preliminary action enables precise error detection without compromising execution speed, as invalid queries are identified and corrected before being sent to the graph database engine.
2Loss of information
If query execution is attempted first, then processing efficiency is maintained, but user feedback quality deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the parser provides detailed error messages and suggestions to users before query execution. When syntax errors, type mismatches, or invalid property references are detected, the system returns specific feedback indicating the nature and location of errors. This high-quality feedback loop enables users to correct queries immediately without wasting execution resources on invalid queries.
3Measurement precision
If pre-execution analysis is performed, then error detection precision is improved, but system complexity increases
Solution Approach 1:
The patent segments the graph query processing system into distinct components: a parser module for syntax validation and error detection, a query execution module for running valid queries, and an autocomplete module for assisting users. The parser is further divided into sub-components for syntax checking, data type validation, and property verification. This segmentation isolates the complexity of pre-execution analysis to the parser module while keeping the execution module simple and efficient.
4Reliability
If comprehensive query validation is performed, then query accuracy is improved, but processing overhead increases
Solution Approach 1:
The patent implements partial validation by focusing the parser on detecting the most common and critical errors: syntax errors, data type mismatches, and invalid property references. The parser performs targeted checks based on the query structure without exhaustively validating every possible aspect of the query. This partial action approach provides sufficient error detection precision while minimizing processing overhead and maintaining query submission speed.
Data Source
AI summary
A method, apparatus, and product to provide a parser for property graph queries with precise error reporting and auto-completion based on information from property graph schemas. The approach generally comprises analysis of graph queries prior to their execution to identify issues prior to execution. In some embodiments, the approach includes any of: use of a property graph schema to determine whether names in a received property graph query exist within a corresponding property graph; determining whether the property graph query includes a comparison of mismatched data types; providing an autocomplete suggestion feature for assistance in resolving errors or corresponding to a cursor position within a query string; or evaluation of a property graph query to determine whether it would return an empty result. In some embodiments, property graph query analysis is performed using a context aware approach.