API Data Query System Resolving Database Complexity
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Solution Overview
Problem
In big data scenarios, users face low query efficiency due to the need for familiarity with database structures, leading to improper query conditions and query exceptions when using traditional data query methods.
Innovation Solution
A data query system and method that provides a data query service through an API, where a central cluster server and data query servers interact with a database server to handle query requests, loading configuration metadata and queryable data, and processing queries without requiring users to construct query statements, thus avoiding query exceptions and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional database query methods are used, then users can query data by providing query statements, but users need to be familiar with database structures which results in low query efficiency
Solution Approach 1:
The patent introduces an intermediary layer (the data query system with API interfaces) between the user and the database. Instead of users directly interacting with database structures and writing SQL queries, they interact with pre-defined API interfaces that automatically generate and execute optimized query statements, thus resolving the contradiction between ease of operation and query efficiency
Solution Approach 2:
The patent segments the query process into distinct components: API interface definition, automatic query statement generation, and execution. By separating the complexity of query optimization from the user, the system allows users to simply call APIs while the system handles the complex database interaction, improving both ease of operation and efficiency
2Measurement precision
If query statements are continuously optimized, then query accuracy can be improved, but the complexity of query statement construction increases requiring users to be familiar with database structures
Solution Approach 1:
The system performs self-service by automatically generating and optimizing query statements based on pre-configured API definitions. The system itself handles the complex task of query optimization without requiring user intervention or knowledge of database structures, thus maintaining high query accuracy while eliminating the complexity burden from users
Solution Approach 2:
The patent applies preliminary action by pre-configuring API interfaces with the necessary query logic and database structure information before user interaction. This advance preparation allows the system to automatically generate optimized queries without requiring users to understand database structures, resolving the contradiction between query accuracy and statement complexity
3Ease of operation
If users provide query statements without familiarity with database structures, then operation simplicity is improved, but query exceptions occur due to improper query conditions
Solution Approach 1:
The API interface acts as an intermediary that validates and transforms user requests into proper database queries. It ensures that query conditions meet database requirements by enforcing pre-defined schemas and validation rules, thus maintaining operation simplicity while preventing query exceptions and improving reliability
Solution Approach 2:
The system applies beforehand cushioning by pre-defining valid query structures and validation rules in the API configurations. This advance preparation cushions against potential query errors by automatically validating user requests before execution, preventing exceptions while keeping the interface simple for users
Data Source
AI summary
A data query method and a data query system are provided. A data query server loads, based on a preset configuration interface, configuration metadata sent by a central cluster server for a target application programming interface (API), to a target storage region being located in a database server and associated with the target API, and loads queryable data associated with the target API to the target storage region. Then, the central cluster server sends a query instruction to the data query server based on query metadata in a data query request sent by a user terminal for the target API. After that, the data query server queries corresponding target query data in the target storage region and sends the target query data to the user terminal through the central cluster server. In this way, a data query service can be provided in a form of an API interface.