Cross-Tier Query Translation for Reusable Data Validation
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Solution Overview
Problem
Existing data validation systems require separate development and maintenance of client-side, server-side, and database-level validation processes, leading to duplication and maintenance issues, and existing SQL queries cannot be reused across different data stores.
Innovation Solution
A query processor that translates a single SQL-like statement (mvSQL) to execute on both in-memory data and persistent data stores, allowing data validation to be written once and applied across multiple tiers, including client, server, and database environments, and supports translation into various proprietary SQL dialects and non-SQL interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate validation processes are developed for client-side, server-side, and database-level, then each tier can be optimized independently, but development and maintenance costs increase due to duplication
Solution Approach 1:
The patent implements a universal validation framework where a single validation logic can be executed across multiple tiers (client-side, server-side, database-level). The validation rules are defined once and can be applied to different data stores including relational databases, NoSQL databases, and in-memory data structures, eliminating the need for separate validation processes for each tier while maintaining optimization capabilities.
Solution Approach 2:
The patent segments the validation system into distinct components: validation rule definitions, translation layer to different query languages (SQL, NoSQL, in-memory), and execution engine. This segmentation allows each component to be optimized independently while maintaining overall system simplicity and reducing duplication across tiers.
2Adaptability or versatility
If separate validation processes are maintained for different data tiers, then each can be customized for its specific requirements, but maintenance effort and costs increase
Solution Approach 1:
The validation framework provides adaptability to different data tiers through a universal rule execution engine that can translate and execute the same validation rules across relational databases, NoSQL databases, and in-memory data structures. This maintains customization capabilities for each tier while reducing maintenance time through centralized rule management.
Solution Approach 2:
The patent introduces a translation layer as an intermediary between the universal validation rules and different data store-specific query languages. This mediator handles the conversion of generic validation rules into tier-specific queries, allowing customization for each data tier while centralizing maintenance efforts in the translation layer rather than in multiple separate validation processes.
3Manufacturing precision
If SQL queries are written specifically for relational databases, then they can be optimized for relational data structures, but they cannot be reused for NoSQL databases or in-memory data
Solution Approach 1:
The patent employs a translation layer that acts as an intermediary between universal validation rules and data store-specific query languages. This mediator translates generic validation rules into optimized queries for relational databases (SQL), NoSQL databases, and in-memory data structures, maintaining query optimization precision for each data type while achieving broad data store compatibility through a single rule set.
Solution Approach 2:
The system changes the parameters of the validation rules to adapt them for different data stores. The same validation logic can be executed with different query language parameters (SQL for relational databases, NoSQL-specific queries for document or key-value stores, and in-memory data structure queries), allowing optimization for each data type while maintaining versatility across different storage systems.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a source independent query language. One of the methods includes receiving a first executable statement, the first executable statement referencing a second set of fields in a table in a relational database, the first executable statement having instructions to cause the database to perform operations on data in the table. The method includes generating a second executable statement based on the first set of fields and the first executable statement. The method includes determining a mapping between the first set of fields and the second set of fields. The method includes specifying a derived table using the corresponding values and the mapping. The method includes generating instructions to cause the database to perform the operations on the derived table. The method includes sending the second executable statement to the database.