Datastore Management Framework Configuration Parser
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Solution Overview
Problem
Current datastore management systems lack flexibility and extendibility, leading to a cumbersome and lengthy configuration process, with limited reusability and unsuitable metadata handling for large-scale, categorized connectivity.
Innovation Solution
A flexible, reusable, and extendible framework for datastore management that allows easy addition of new datastores, supports different configurations, and provides efficient metadata handling through a configuration parser and user interface for creating and managing datastores, with each datastore type associated with a specific configuration file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a traditional datastore management system is used, then the system structure is simple, but the flexibility and extendibility are poor, resulting in a lengthy configuration process
Solution Approach 1:
The system is divided into distinct modules: a configuration file storage module that maintains separate configuration files for each datastore type, a parser module that processes these files, and a user interface module. This segmentation allows the system to handle multiple datastore types flexibly while keeping each component's complexity manageable.
Solution Approach 2:
The framework implements a universal configuration file format that can accommodate multiple datastore types (relational, non-relational, structured, unstructured). The parser and user interface are designed to work with any datastore type through this common format, providing multi-functionality without requiring separate handling logic for each type.
2Productivity
If a traditional datastore management system is used, then the configuration process is simple in structure, but it is lengthy and cumbersome
Solution Approach 1:
Datastore configuration templates and metadata are prepared in advance and stored in configuration files. When a user needs to configure a datastore, the system retrieves and parses these pre-prepared configurations, significantly reducing the time required compared to creating configurations from scratch.
Solution Approach 2:
The configuration parser acts as an intermediary between the stored configuration files and the datastore creation process. It automatically processes and validates configuration data, reducing manual intervention and accelerating the configuration process while maintaining accuracy.
3Adaptability or versatility
If a traditional metadata handling approach is used, then the system is easier to implement, but it is unsuitable for large-scale categorized connectivity
Solution Approach 1:
The system introduces a new dimension of organization by categorizing metadata according to datastore types (relational, non-relational, structured, unstructured). This dimensional categorization allows the system to handle large-scale metadata efficiently by grouping similar items together, making the complexity manageable through structured organization.
Data Source
AI summary
Systems and methods for a datastore management framework are disclosed. In some example embodiments, a computer-implemented method comprises storing each one of a plurality of datastore configuration files in association with a corresponding one of a plurality of datastore types, receiving a request to create a datastore of a datastore type of the plurality of datastore types, selecting, from the plurality of datastore configuration files, a datastore configuration file associated with the datastore type, parsing the selected datastore configuration file, rendering a user interface for display based on the parsing of the selected datastore configuration file, receiving a user input via the rendered user interface, and creating the datastore based on the received user input, with the datastore being of the datastore type indicated by the request.