Dynamic Instruction Generation for Database Querying
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Solution Overview
Problem
Current backend development processes for database operations are complex and time-consuming, requiring extensive effort to define payloads, code, test, and implement, often necessitating the creation of new applications for even minor changes, such as adding new objects or operations.
Innovation Solution
A data retrieval system that generates dynamic instructions and execution steps based on user input, including database connections, credentials, and models, allowing for dynamic path variables in URLs to modify data retrieval models without building new applications, enabling parallel execution and caching of retrieved data for future use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional backend development processes are used for database operations, then data retrieval can be achieved, but development time and complexity increase significantly
Solution Approach 1:
The system generates executable code by copying and adapting templates based on user-defined models and instructions. Instead of writing code from scratch, the system retrieves pre-defined models (e.g., User, Product, Order models) and generates execution steps by copying their structure and adapting them to specific query requirements, significantly reducing development time and complexity
Solution Approach 2:
The system creates a universal code generation platform that handles multiple database operations (SELECT, INSERT, UPDATE, DELETE) and various data retrieval scenarios through a single unified interface. Users can define models once and reuse them across different applications and queries, making the system multi-functional and highly adaptable
2Adaptability or versatility
If traditional development approaches are used, then applications can be built, but modifications require creating entirely new applications
Solution Approach 1:
The system enables dynamic modification of applications by allowing users to update models and instructions at runtime. Instead of deploying new applications for changes, the system dynamically generates new execution steps based on updated models and instructions, enabling flexible adaptation without time-consuming rewrites
Solution Approach 2:
The system segments applications into independent models and instructions that can be modified separately. Each model (e.g., User model, Product model) and its associated instructions can be updated independently, allowing selective modifications without affecting the entire application, thus reducing modification time and increasing flexibility
3Adaptability or versatility
If dynamic path variables are used in URLs, then data retrieval can be customized without new applications, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer between the URL and the database execution. The path variable in the URL (e.g., /user/{userId}) serves as input to the code generation system, which translates it into specific execution steps. This intermediary abstraction layer handles the complexity of parsing, validating, and translating URL parameters into executable queries without exposing the complexity to the user
Data Source
AI summary
A data retrieval system can include a computing device that is configured to, in response to receiving a uniform resource locator from a user device, parse the uniform resource locator to identify a dynamic path variable. The computing device can be configured to generate a set of execution steps based on the received uniform resource locator. The computing device can also be configured to execute the set of execution steps to retrieve a set of data, the set of execution steps being executed based on the dynamic path variable in response to the dynamic path variable being identified. The computing device can further be configured to transform a user interface of the user device based on the set of data.