A database analytic tool that allows a developer to compare the efficiency of different schemas and queries on a granular level to make better informed architectural decisions regarding SQL databases at various scales.
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SeeQR: A database analytic tool that compares the efficiency of different schemas and queries on a granular level to make better informed architectural decisions regarding SQL databases at various scales.
SeeQR is still in BETA. Additional features, extensions, and improvements will continue to be introduced. If you encounter any issues with the application, please report them in the issues tab or submit a PR. Thank you for your interest!
To get started on contributing to this project:
npm installfor application-specific dependencies.
'cross-env',
'webpack',
'webpack-dev-server',
'electron', and
'typescript'.
npm run devto launch Electron application window and webpack-dev-server.
The whole interface in a nutshell
Schema
.sqlor
.tarfile when prompted by splash page, or hit cancel.
.sqlor
.tarfile becomes the active database.
Query input
Data
Input Schema and Tabs
.sqlor a
.tarfile.
Generate Dummy Data
History
Compare
Visualized Analytics
Sandbox Environment
SeeQR streamlines the process of instantiating postgres databases by leveraging Postgres.app to import a copy of your database in postgres on your local machine. This means instances of databases are automatically created every time new schema data is uploaded or inputted via the SeeQR GUI. Electron communicates with the instantiated database’s URIs by taking advantage of the
'pg'npm package.
Cross-schema Comparisons
One of the key features of SeeQR is to compare the efficiency of executing user-inputted queries against different schemas. This allows customization of table scale, relationship, type, and the queries themselves within the context of each schema. This flexibility affords the user granular adjustments for testing every desired scenario. Please refer to “Interface & Functionality” for more details on execution.
Database:Schema 1:1 Architecture
While it is feasible for a database to house multiple schemas, SeeQR’s default architecture for database:schema relations is 1:1. For every schema inputted, a new database is generated to hold that schema. This architecture serves the application’s central purpose: testing — by enabling the capacity to individually scale data connected to each schema, generating analytics at any user-specified conditions.
Session-based Result Caching
The outcome results from each query, both retrieved data and analytics, are stored in the application’s state, which can be viewed and compared in table and visualizer formats. Note that these results’ persistence is session-based and will be cleared upon quitting the application.
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