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ColumnCore is a high-performance analytical database system designed for beginners or small projects. It supports a rich SQL dialect, runs within the same process as the application, has a vectorized query execution engine, and uses a columnar storage format.

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ColumnCore

ColumnCore is a high-performance analytical database system designed for beginners or small projects. It supports a rich SQL dialect, runs within the same process as the application, has a vectorized query execution engine, and uses a columnar storage format. It also has a flexible data import interface that performs various tasks such as schema inference, format detection, data validation, data conversion, and data loading.

Features

  • Rich SQL dialect
  • Vectorized query execution engine
  • Columnar storage format
  • Flexible data import interface
  • Schema inference
  • Format detection
  • Data validation
  • Data conversion
  • Data loading

Getting Started

To get started with ColumnCore, follow these steps:

  1. Clone the repository: git clone https://github.com/fx2y/ColumnCore.git
  2. Install the dependencies: pip install -r requirements.txt
  3. Run the tests: pytest
  4. Start the server: python server.py

Usage

To use ColumnCore, follow these steps:

  1. Import your data using the data import interface
  2. Write your SQL queries using the supported SQL dialect
  3. Execute your queries using the vectorized query execution engine
  4. Analyze your results using the output interface

Contributing

Contributions to ColumnCore are welcome and encouraged! To contribute, follow these steps:

  1. Fork the repository
  2. Create a new branch: git checkout -b my-feature-branch
  3. Make your changes and commit them: git commit -m "Add new feature"
  4. Push your changes to your fork: git push origin my-feature-branch
  5. Create a pull request

License

ColumnCore is licensed under the MIT License. See LICENSE for more information.

About

ColumnCore is a high-performance analytical database system designed for beginners or small projects. It supports a rich SQL dialect, runs within the same process as the application, has a vectorized query execution engine, and uses a columnar storage format.

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