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My First Open-Source Project: A Python CLI for Database Inspection

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My First Open-Source Project: A Python CLI for Database Inspection

This article chronicles the journey of creating my first open-source project, a Python command-line interface (CLI) tool for quickly inspecting databases. Throughout this exploration, we'll delve into the core concepts, techniques, and tools used, providing a step-by-step guide with practical examples. This project, while seemingly simple, served as a valuable learning experience, fostering my understanding of Python, CLI development, and open-source contributions.

The Need for a Database Inspection Tool

As a developer, I frequently found myself needing to inspect databases, whether for troubleshooting issues, analyzing data, or simply understanding schema structure. Existing tools often felt cumbersome, requiring extensive configuration or lacking the flexibility to cater to specific needs. This realization sparked the idea of building a lightweight and customizable CLI tool for database inspection, tailored to my workflow and potentially useful for others.

Project Setup and Core Libraries

The first step was to set up the project environment. I chose Python as my language of choice due to its versatility, extensive libraries, and ease of use. Here's a basic project structure:



database_inspector/

├── init.py

├── cli.py

├── database.py

├── utils.py

└── tests/

    └── test_database.py

Key libraries used:

click: For building a user-friendly CLI interface.
https://click.palletsprojects.com/en/8.1.x/
sqlalchemy: For interacting with different database systems in a consistent manner.
https://www.sqlalchemy.org/
tabulate: For presenting data in a readable table format.
https://pypi.org/project/tabulate/

Installation

To get started, you can create a new virtual environment and install the necessary libraries:



python3 -m venv .venv

source .venv/bin/activate

pip install click sqlalchemy tabulate

Designing the CLI Interface with Click

Click provides a simple yet powerful way to define CLI commands and options. The
cli.py
file defines the main entry point and various commands:



import click

from database_inspector.database import connect_to_database, execute_query

from database_inspector.utils import format_table

@click.group()

def cli():

    """Database Inspector CLI"""

    pass

@cli.command()

@click.option('--host', prompt='Database host', help='Host address of the database')

@click.option('--user', prompt='Database user', help='Username for the database')

@click.option('--password', prompt='Database password', help='Password for the database', hide_input=True)

@click.option('--database', prompt='Database name', help='Name of the database')

def connect(host, user, password, database):

    """Connects to a database."""

    connection = connect_to_database(host, user, password, database)

    click.echo(f'Successfully connected to {database} database.')

    click.echo(f'Connection details: {connection}')

@cli.command()

@click.pass_context

@click.argument('query')

def query(ctx, query):

    """Executes a SQL query."""

    connection = ctx.obj

    if not connection:

        click.echo('Please connect to a database first using connect command.')

        return

    results = execute_query(connection, query)

    if results:

        click.echo(format_table(results))

    else:

        click.echo('No results found.')

if name == 'main':

    cli(obj=None)

In this code,
click.group()
defines the main CLI group, and
click.command()
creates individual commands. Options (
--host
,
--user
, etc.) are defined using
click.option()
, and arguments are declared with
click.argument()
. The
pass_context
decorator allows passing the connection object (if it exists) to subsequent commands.

Interacting with Databases using SQLAlchemy

The
database.py
file houses the core database interaction logic using SQLAlchemy:



from sqlalchemy import create_engine

from sqlalchemy.orm import sessionmaker

def connect_to_database(host, user, password, database):

    """Connects to a database using SQLAlchemy."""

    # Replace with your specific database dialect

    engine = create_engine(f'postgresql://{user}:{password}@{host}/{database}')

    Session = sessionmaker(bind=engine)

    session = Session()

    return session

def execute_query(session, query):

    """Executes a SQL query and returns the results."""

    results = session.execute(query).fetchall()

    return results

This code utilizes SQLAlchemy's
create_engine()
to create an engine specific to the chosen database dialect (e.g., PostgreSQL in this case).
sessionmaker()
is used to create a session factory, allowing for consistent interactions with the database.

Formatting Results with Tabulate

The
utils.py
file contains helper functions, including one for formatting query results using Tabulate:



from tabulate import tabulate

def format_table(results):

    """Formats a list of tuples into a readable table."""

    headers = [column.name for column in results[0].keys()]

    data = [list(row) for row in results]

    return tabulate(data, headers=headers, tablefmt='grid')

This function takes a list of tuples (representing query results) and uses Tabulate's
tabulate()
function to create a formatted table with headers and grid formatting for better readability.

Testing the Functionality

Writing unit tests is crucial for ensuring code quality and stability. In the
tests/test_database.py
file, we can write tests to verify core database functionalities:



import unittest

from database_inspector.database import connect_to_database, execute_query

class DatabaseTestCase(unittest.TestCase):

def test_connection(self):
    # Replace with your actual database credentials
    session = connect_to_database(host='localhost', user='your_user', password='your_password', database='your_database')
    self.assertIsNotNone(session)

def test_query_execution(self):
    session = connect_to_database(host='localhost', user='your_user', password='your_password', database='your_database')
    query = 'SELECT * FROM users LIMIT 1'
    results = execute_query(session, query)
    self.assertIsNotNone(results)


if name == 'main':


    unittest.main()

These tests cover basic functionalities like connecting to the database and executing queries. We can extend them to cover more specific scenarios and edge cases.

Running the Database Inspector

After completing the code and tests, we can run the Database Inspector from the command line:




python database_inspector/cli.py connect

This will prompt for database connection details, establishing a connection. Then, we can execute queries:




python database_inspector/cli.py query 'SELECT * FROM users'

The results will be presented in a formatted table, providing a convenient way to inspect data and schema.

Deployment and Open-Source Contribution

Once the project is functional and tested, we can prepare it for deployment. This involves creating a setup.py file for packaging and publishing the project on platforms like PyPI. The following is an example of a setup.py file:




from setuptools import setup

setup(


    name='database_inspector',


    version='0.1.0',


    description='A simple CLI tool for database inspection',


    author='Your Name',


    author_email='your_email@example.com',


    url='https://github.com/your_username/database_inspector',


    packages=['database_inspector'],


    install_requires=[


        'click',


        'sqlalchemy',


        'tabulate',


    ],


    entry_points={


        'console_scripts': [


            'database-inspector = database_inspector.cli:cli',


        ],


    },


)

After creating a

setup.py

file, you can use

python setup.py sdist bdist_wheel

to create source and wheel distributions. Then, you can upload the distributions to PyPI using

twine upload dist/*

. This process will make your project available for others to install via

pip install database_inspector

.

Finally, publishing the project on a platform like GitHub allows others to access the source code, collaborate, and contribute. The open-source model fosters community engagement and promotes continuous improvement.

Conclusion

Building my first open-source project, a Python CLI tool for database inspection, has been a rewarding journey. It has solidified my understanding of Python, CLI development, and the open-source ecosystem. This project serves as a practical example of creating a useful tool and contributing to the community. Key takeaways include:

Leverage existing libraries:

Libraries like Click, SQLAlchemy, and Tabulate provide excellent building blocks for CLI development and database interaction.
Prioritize testing:

Writing unit tests ensures code quality and stability, enabling confident development and maintenance.
Embrace open-source:

Contributing to open-source projects allows for collaboration, learning, and creating tools that benefit a wider community.

This journey has just begun. I plan to expand the Database Inspector with more features, such as support for different database dialects, query logging, and interactive data exploration. By sharing this project, I hope to inspire others to embark on their own open-source adventures, leveraging the power of Python to create impactful tools.

My first open-source project built with Python to inspect databases through CLI fast