Table of Contents

1. Generate Test Data for Your Project
   • Built With
   • Prerequisites
2. Getting Started
   • Clone this repository
   • Configure your Python environment
   • Running the script
3. Roadmap
4. Contributing
5. License
6. Contact

Generate Test Data for Your project

For testing purposes, especially if you're building an app that uses any database technology to store information, you may need data to try out your project. In that case, you have two options:

• Find a good dataset (Kaggle)
• Use a library like Faker

With Faker you can generate data through methods defined in the library. For example:

from faker import Faker

fake = Faker()
for _ in range(10):
    print(fake.name())

Above code will print ten namess, and on each call to method name(), it will produce a random value. name() is a property of the generator, it's called a fake. and there are many of them packaged in providers. You can find more information on bundled and community providers in the documentation.

Shannon Larson
Timothy Murray
Courtney Moore
Jody Jones
Sandra Miller
Anne Proctor
Brenda Rubio
Craig Turner
Nicole Barton
Nicholas Gentry

Some properties avaliable in the Faker library include:

• first_name()
• last_name()
• job()
• address()
• city()
• email()

Calling the create_dataframe() method of the dataframe module will create a Pandas DataFrame with data generated with Faker. This data will later be imported into the database. This DataFrame will contain the following columns, with values assigned by calling the methods listed above.

 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   first_name  60000 non-null  object
 1   last_name   60000 non-null  object
 2   job         60000 non-null  object
 3   address     60000 non-null  object
 4   city        60000 non-null  object
 5   email       60000 non-null  object

On this repository, you can find two Python scripts:

• sql.py: Generate test data for MySQL and PostgreSQL.
• mongodb.py: Generate test data for MongoDB.

And you will find the following modules in the modules directory:

• base.py: Establish connection to the database, required for MySQL and PostgreSQL.
• dataframe.py: Contains the create_dataframe() method that will create a Pandas DataFrame with test data. It uses the multiprocessing module.
• schema.py: Contains the schema definition, required by Pandas to_sql() method.

The mongodb.py script uses the dataframe module for generating test data. Connection to the database and data importing are made directly from the script.

Built With

• pandas
• SQLAlchemy
• tqdm
• faker

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Prerequisites

MySQL

Tests were run on Percona Server for MySQL. If you already have MySQL running on your system, skip this step.

For installing Percona Server for MySQL from the repositories on Debian and Ubuntu, follow the instructions below. For other operating systems check the documentation.

Before installing Percona Server for MySQL, make sure curl and gnupg2 are installed.

$ sudo apt install gnupg2 curl

Then, install percona-release, a tool that allows users to automatically configure which Percona Software repositories are enabled or disabled.

Get the repository package:

$ wget https://repo.percona.com/apt/percona-release_latest.$(lsb_release -sc)_all.deb

Install the downloaded package with dpkg:

$ sudo dpkg -i percona-release_latest.$(lsb_release -sc)_all.deb

Enable the ps80 repository:

$ sudo percona-release setup ps80

Install percona-server-server, the package that provides the Percona Server for MySQL:

$ sudo apt install percona-server-server

MySQL Shell is also recommended to be installed.

$ sudo apt install percona-mysql-shell

After installing, make sure MySQL is running.

During installation, root password is assigned. You can log into MySQL with this user or create a new one. Also, movienet database must be created.

Log into MySQL using MySQL Shell:

$ mysqlsh root@localhost

Replace root with your user if necessary, and replace localhost with the IP address or URL for your MySQL server instance if needed.

Change to SQL mode and create the movienet database:

\sql
create database company;

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PostgreSQL

Tests were run on Percona Distribution for PostgreSQL. If you already have PostgreSQL running on your system, skip this step.

For installing Percona Distribution for PostgreSQL from the repositories on Debian and Ubuntu, follow the instructions below. For other operating systems check the documentation.

Before installing Percona Distribution for PostgreSQL, make sure curl and gnupg2 are installed.

$ sudo apt install gnupg2 curl

Then, install percona-release, a tool that allows users to automatically configure which Percona Software repositories are enabled or disabled.

Get the repository package:

$ wget https://repo.percona.com/apt/percona-release_latest.$(lsb_release -sc)_all.deb

Install the downloaded package with dpkg:

$ sudo dpkg -i percona-release_latest.$(lsb_release -sc)_all.deb

Enable the ppg14 repository:

$ sudo percona-release setup ppg-14

Install percona-postgresql-14, the package that provides the Percona Distribution for PostgreSQL:

$ sudo apt install percona-postgresql-14

After installing, make sure PostgreSQL is running.

You can connect to the PostgreSQL server with the postgres user or create a new one.

$ sudo su postgres

For Python you will need to create another user. Run:

$ createuser --interactive --pwprompt

It will ask you to enter new user details:

Enter name of role to add:
Enter password for new role:
Enter it again:
Shall the new role be a superuser? (y/n)

Choose y as this new user required superuser permissions.

Then open the PostgreSQL interactive terminal:

$ psql

And create the company database.

create database company;

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MongoDB

Tests were run on Percona Server for MongoDB. If you already have MongoDB running on your system, skip this step.

For installing Percona Server for MongoDB from the repositories on Debian and Ubuntu, follow the instructions below. For other operating systems check the documentation.

Before installing Percona Distribution for PostgreSQL, make sure curl and gnupg2 are installed.

$ sudo apt install gnupg2 curl

Then, install percona-release, a tool that allows users to automatically configure which Percona Software repositories are enabled or disabled.

Get the repository package:

$ wget https://repo.percona.com/apt/percona-release_latest.$(lsb_release -sc)_all.deb

Install the downloaded package with dpkg:

$ sudo dpkg -i percona-release_latest.$(lsb_release -sc)_all.deb

Enable the psmdb-50 repository:

$ sudo percona-release enable psmdb-50 release

Install percona-server-mongodb, the package that provides the Percona Server for MongoDB:

$ sudo apt install percona-server-mongodb

After installing, make sure MongoDB is running.

Run the following script to enable authentication:

$ sudo /usr/bin/percona-server-mongodb-enable-auth.sh

This script creates the dba user with the root role. Don't forget to copy the password.

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Conda

Before running the Python script that imports the data into a MySQL DB, you must install Conda through Anaconda or Miniconda. Here you can find more information on which one is better for you.

To install Anaconda, go to anaconda.com/products/distribution and download the installer for your operating system.

If you're on Linux, run the following command after getting the installer:

$ bash Anaconda3-2022.05-Linux-x86_64.sh

Replacing the filename according to the version you're installing

It will prompt you to confirm some configuration details:

• Accepting license
• Confirming installation folder
• Initializing Anaconda (by the installer)

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Getting Started

Clone this repository

git clone https://github.com/mattdark/json-mysql-importer.git

Configure your Python environment

virtualenv and pip

Install Python runtime and dependencies

When you clone this repository to your local development environment, you will find an environment.yml file that contains information about the Python version and dependencies required by this project. This file is used by Conda to configure the virtual environment and install dependencies.

After installing Anaconda, change to the data-generator directory:

$ cd data-generator

Create the environment:

$ conda env create -f environment.yml

This command will create a virtual environment for your project, install Python 3.10 and dependencies specified in the environment.yml file.

Once the environment is created, you can activate it by running:

$ conda activate percona

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Running the script

Before running the script, don't forget to set user, password and host in the base module, required for connecting to your MySQL or PostgreSQL database.

It creates 60 thousand records by default, but you can change this value by modifying the following line in the dataframe module:

x = int(60000/num_cores)

MySQL

After setting up authentication details in the base module, run the Python script:

Uncomment this line in the base module:

engine = create_engine("mysql+pymysql://user:password@localhost/company")

Uncomment this line in the sql.py script:

conn.execute("ALTER TABLE employees ADD id INT NOT NULL AUTO_INCREMENT PRIMARY KEY FIRST;")

Comment this line in the base module:

#engine = create_engine("postgresql+psycopg2://user:password@localhost:5432/company")

Comment this line in the sql.py script:

#conn.execute("ALTER TABLE employees ADD COLUMN id SERIAL PRIMARY KEY;")

Run the script

$ python sql.py

This script will do the following:

• Generate test data with the Faker library
• Create a Pandas DataFrame
• Import the DataFrame into MySQL
• Add the id column and set it as the primary key

A progress bar was added using tqdm.

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PostgreSQL

After setting up authentication details in the base module, run the Python script:

Uncomment this line in the base module:

engine = create_engine("postgresql+psycopg2://user:password@localhost:5432/company")

Uncomment this line in the sql.py script:

conn.execute("ALTER TABLE employees ADD COLUMN id SERIAL PRIMARY KEY;")

Comment this line in the base module:

#engine = create_engine("mysql+pymysql://user:password@localhost/company")

Comment this line in the sql.py script:

#conn.execute("ALTER TABLE employees ADD id INT NOT NULL AUTO_INCREMENT PRIMARY KEY FIRST;")

Run the script

$ python sql.py

This script will do the following:

• Generate test data with the Faker library
• Create a Pandas DataFrame
• Import the DataFrame into PostgreSQL
• Add the id column and set it as the primary key

A progress bar was added using tqdm.

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MongoDB

Before running the script, don't forget to set user, password and host in the mongodb.py script, required for connecting to your MongoDB database.

Modify this line to set up authentication details:

uri = "mongodb://user:password@localhost:27017/company?authSource=admin"

It creates 60 thousand records by default, but you can change this value by modifying the following line in the dataframe module:

x = int(60000/num_cores)

Run the script

$ python mongodb.py

This script will do the following:

• Generate test data with the Faker library
• Create a Pandas DataFrame
• Import the DataFrame into MongoDB
• Add the id column and set it as the primary key

A progress bar was added using tqdm.

Roadmap

• Set up a Python Env with Anaconda
  • Create Env Config File (environment.yml)
    • Python Runtime
    • Dependencies
• Create requirements.txt
• Test Data created with Faker
• Create Pandas DataFrame
• Import data to MySQL
• Import Data to PostgreSQL
• Import data to MongoDB
• Implement multiprocessing
• Write a tutorial / blog post

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Mario García - @mariogmd - hi@mariog.xyz

Project Link: https://github.com/mattdark/data-generator

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