https://github.com/slott56/unlearning-sql

The examples from the Unlearning SQL book
https://github.com/slott56/unlearning-sql

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The examples from the Unlearning SQL book

• Host: GitHub
• URL: https://github.com/slott56/unlearning-sql
• Owner: slott56
• License: mit
• Created: 2022-07-06T20:23:21.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-19T19:12:04.000Z (3 months ago)
• Last Synced: 2024-08-20T22:55:53.829Z (3 months ago)
• Language: Python
• Size: 19.3 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
################

unlearning-sql

################

Components and examples from the Unlearning SQL book.

Installation

=============

There are several steps to getting this ready for experimentation.

1.  Create a virtual environment with Python 3.12.

    Use venv or Poetry or hatch or conda.

    Conda example:

    ..  code-block:: bash

        conda create -n unlearning-sql python=3.12

        conda activate unlearning-sql

2.  Clone or download this https://github.com/slott56/unlearning-sql repository.

3.  Change to the downloaded directory.

    ..  code-block:: bash

        cd unlearning-sql

3.  Install required packages to run the demo applications.

    ..  code-block:: bash

        python -m pip install -r requirements.txt

4.  Install the packages to do unit testing.

    ..  code-block:: bash

        python -m pip install -r requirements-test.txt

Upgrades

--------

To get **all** the latest and greatest versions of package, use pip-tools.

..  code-block:: bash

    pip-compile --upgrade

    pip-compile --upgrade requirements-test.in

    python -m pip install --upgrade -r requirements.txt

    python -m pip install --upgrade -r requirements-test.txt

General Operation

=================

In order to play with the examples, it's helpful

to have a file named ``activate_source.csv``.

This file contains the raw data to be bulk-loaded

into the database.

It's also helpful, of course, to have a database.

In this case, it will be ``unlearning_sql.db``. 

To build the file, and the database, execute

the following two steps:

..  code-block:: bash

    python src/fake_data.py

    python src/sql_db_preparation.py

The ``fake_data.py`` program builds the ``activate_source.data`` file.

The ``sql_db_preparation.py`` program loads the database with some (but not all) of the fake data.

This omission of data permits some of the data validation rules to spot 

bad data in the input.

Makefile

=========

The ``Makefile`` has targets to help create files and databases.

..  code-block:: bash

    make sql_load

The ``sql_load`` target will build the fake data and populate the database if needed.

It will run the SQL-based loader application.

..  code-block:: bash

    make python_load

The ``python_load`` target will build the fake data and populate the database if needed.

It will run the Python-based loader application.

Diagrams

========

The diagrams were built with PlantUML. See https://plantuml.com for more information.

This is not required to run the demonstration applications.

It's handy for creating entity-relationship diagrams.