Query Translation Layer for Graph Database SQL Compatibility
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Solution Overview
Problem
Existing database management systems face challenges in efficiently processing and executing SQL queries across different database systems, particularly when converting SQL queries into compatible formats for graph-based database systems that lack built-in functionality for processing SQL queries.
Innovation Solution
A method and system that involve receiving a database query, creating a structural representation of the database table without its data elements, and using a plan generator to produce a query plan, which is then used to generate a dataflow graph for executing the query, allowing for the conversion and execution of SQL queries within graph-based database management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SQL queries are directly executed in graph-based database systems, then query execution can proceed, but the system lacks built-in functionality for processing SQL queries导致incompatibility and inefficiency
Solution Approach 1:
The patent introduces a query translation layer that acts as an intermediary between SQL queries and the graph-based database system. This translation layer converts SQL queries into graph query language, enabling compatibility without requiring the core graph-based system to natively support SQL, thus resolving the contradiction between adaptability and complexity
Solution Approach 2:
The patent creates a structural representation (copy) of the database table schema that mirrors the logical structure needed for SQL processing. This structural copy allows the system to handle SQL queries by mapping them onto the graph structure, achieving SQL compatibility while maintaining the integrity of the graph-based system
2Measurement precision
If complete database tables with data elements are processed, then query accuracy is maintained, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the structural information (schema, data types, constraints) from the complete database tables, creating a streamlined structural representation. This extraction eliminates unnecessary data elements while preserving the essential structure needed for accurate query processing, thereby reducing processing time without sacrificing query result accuracy
Solution Approach 2:
The patent segments the database processing into two distinct phases: structural analysis phase (using only schema information) and data retrieval phase (accessing actual data elements). This segmentation allows the system to perform query planning and optimization using minimal structural data, significantly reducing processing time while maintaining accuracy in the subsequent data retrieval phase
3Productivity
If SQL queries are converted to graph query format, then execution efficiency improves, but the conversion process adds processing overhead
Solution Approach 1:
The patent performs query conversion in advance before actual query execution. By pre-converting SQL queries into graph query format and caching the converted queries, the system eliminates the need for repeated conversion during execution, thereby improving execution efficiency while managing conversion complexity through one-time preprocessing
Solution Approach 2:
The patent changes the parameter representation from SQL syntax to graph query syntax systematically. By establishing mapping rules and transformation algorithms between the two query languages, the conversion process becomes automated and efficient, reducing the complexity overhead while maintaining high execution efficiency
Data Source
AI summary
One method includes receiving a database query, receiving information about a database table in data storage populated with data elements, producing a structural representation of the database table that includes a formatted data organization reflective of the database table and is absent the data elements of the database table, and providing the structural representation and the database query to a plan generator capable of producing a query plan representing operations for executing the database query on the database table. Another method includes receiving a query plan from a plan generator, the plan representing operations for executing a database query on a database table, and producing a dataflow graph from the query plan, wherein the dataflow graph includes at least one node that represents at least one operation represented by the query plan, and includes at least one link that represents at least one dataflow associated with the query plan.