How To Generate Test Data for Your Database Project With Python

by Mario García

If you need test data for the database of your project, you can get a dataset from Kaggle or use a data generator. In the first case, if you need to process the data before inserting it into the database, you can use Pandas, a widely used Python library for data analysis. This library supports different formats, including CSV and JSON, and it also provides a method for inserting data into a SQL database.

If you choose a data generator instead, you can find one for MySQL in one of the repositories on our Percona Lab GitHub account. Are you using other database technologies? You can follow the guides I already published where I explain how to create your own data generator for MySQL (it could work for PostgreSQL) and MongoDB.

If you create you’re own data generator, this is the process you must follow:

  • Generate fake data using Faker
  • Store generated data in a Pandas DataFrame
  • Establish a connection to your database
  • Insert the content of the DataFrame into the database

Requirements

Dependencies

Make sure all the dependencies are installed before creating the Python script that will generate the data for your project.

You can create a requirements.txt file with the following content:

pandas
tqdm
faker

Or if you’re using Anaconda, create an environment.yml file:

name: percona
dependencies:
  - python=3.10
  - pandas
  - tqdm
  - faker

You can change the Python version as this script has been proven to work with these versions of Python: 3.7, 3.8, 3.9, 3.10, and 3.11.

Depending on the database technology you’re using, you must add the corresponding package to your requirements.txt or environment.yml file:

  • MySQL → PyMySQL
  • PostgreSQL → psycopg2
  • MongoDB → pymongo

Run the following command if you’re using pip:

pip install -r requirements.txt

Or run the following statement to configure the project environment when using Anaconda:

conda env create -f environment.yml

Database

Now that you have the dependencies installed, you must create a database named company, for MySQL or PostgreSQL.

Log into MySQL:

$ mysql -u root -p

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

Or log into PostgreSQL:

$ sudo su postgres
$ psql

and create the company database:

create database company;

You don’t need to create the MongoDB database previously.

Creating a Pandas DataFrame

Before creating the script, it’s important to know that we need to implement multiprocessing for optimizing the execution time of the script.

Multiprocessing is a way to take advantage of the CPU cores available in the computer where the script is running. In Python, single-CPU use is caused by the global interpreter lock, which allows only one thread to carry the Python interpreter at any given time. With multiprocessing, all the workload is divided into every CPU core available. For more information see this blog post.

Now, let’s start creating our own data generator. First, a modules directory needs to be created, and inside the directory, we will create a module named dataframe.py. This module will be imported later into our main script, and this is where we define the method that will generate the data.

You need to import the required libraries and methods:

from multiprocessing import cpu_count
import pandas as pd
from tqdm import tqdm
from faker import Faker
  • pandas. Data generated with Faker will be stored in a Pandas DataFrame before being imported into the database.
  • tqdm(). This method is required for adding a progress bar to show the progress of the DataFrame creation.
  • Faker(). It’s the generator from the faker library.
  • cpu_count(). This is a method from the multiprocessing module that will return the number of cores available.

Then, a faker generator will be created and initialized, by calling the Faker() method. This is required to generate data by accessing the properties in the Faker library.

And we determine the number of cores of the CPU available, by calling the cpu_count() method and assigning this value to the num_cores variable.

fake = Faker()
num_cores = cpu_count() - 1

num_cores is a variable that stores the value returned after calling the cpu_count() method. We use all the cores minus one to avoid freezing the computer.

def create_dataframe(arg):
    x = int(60000/num_cores)
    data = pd.DataFrame()
    for i in tqdm(range(x), desc='Creating DataFrame'):
        data.loc[i, 'first_name'] = fake.first_name()
        data.loc[i, 'last_name'] = fake.last_name()
        data.loc[i, 'job'] = fake.job()
        data.loc[i, 'company'] = fake.company()
        data.loc[i, 'address'] = fake.address()
        data.loc[i, 'city'] = fake.city()
        data.loc[i, 'country'] = fake.country()
        data.loc[i, 'email'] = fake.email()
    return data

Then, we define the create_dataframe() function, where:

  • x is the variable that will determine the number of iterations of the for loop where the DataFrame is created.
  • data is an empty DataFrame that will later be fulfilled with data generated with Faker.
  • Pandas DataFrame.loc attribute provides access to a group of rows and columns by their label(s). In each iteration, a row of data is added to the DataFrame and this attribute allows assigning values to each column.

The DataFrame that is created after calling this function will have the following columns:

 #   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   company     60000 non-null  object
 4   address     60000 non-null  object
 5   country     60000 non-null  object
 6   city        60000 non-null  object
 7   email       60000 non-null  object

Note: The script is generating 60 thousand records but it can be adapted to your project, you can modify this value in the x variable.

Connection to the Database

MySQL and PostgreSQL

Before inserting the data previously generated with Faker, we need to establish a connection to the database, and for doing this the SQLAlchemy library will be used.

SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
 
engine = create_engine("mysql+pymysql://user:password@localhost/company")
Session = sessionmaker(bind=engine)

From SQLAlchemy, we import the create_engine() and the sessionmaker() methods. The first one is for connecting to the database, and the second one is for creating a session bond to the engine object.

Don’t forget to replace user, password, and localhost with your authentication details. Save this code in the modules directory and name it as base.py.

For PostgreSQL, replace:

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

With:

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

Database Schema Definition

For MySQL and PostgreSQL, the schema of the database can be defined through the Schema Definition Language provided by SQLAlchemy, but as we’re only creating one table and importing the DataFrame by calling Pandas to_sql() method, this is not necessary.

When calling Pandas to_sql() method, we define the schema as follows:

from sqlalchemy.types import *
schema = {
    "first_name": String(50),
    "last_name": String(50),
    "job": String(100),
    "company": String(100),
    "address": String(200),
    "city": String(100),
    "country" String(100),
    "email": String(50)
}

Then we pass the schema variable as a parameter to this method.

Save this code in the modules directory with the name schema.py.

MongoDB

Before inserting the data previously generated with Faker, we need to establish a connection to the database, and for doing this the PyMongo library will be used.

from pymongo import MongoClient
 
uri = "mongodb://user:password@localhost:27017/"
client = MongoClient(uri)

From PyMongo, we import the MongoClient() method.

Don’t forget to replace user, password, localhost, and port (27017) with your authentication details. Save this code in the modules directory and name it base.py.

Generating Your Data

MySQL and PostgreSQL

All the required modules are now ready to be imported into the main script, now it’s time to create the sql.py script. First, import the required libraries:

from multiprocessing import Pool
from multiprocessing import cpu_count
import pandas as pd

From multiprocessing, Pool() and cpu_count() are required. The Python Multiprocessing Pool class allows you to create and manage process pools in Python.

Then, import the modules previously created:

from modules.dataframe import create_dataframe
from modules.schema import schema
from modules.base import Session, engine

Now we create the multiprocessing pool, configured to use all available CPU cores minus one. Each core will call the create_dataframe() function and create a DataFrame with 4 thousand records. After each call to the function has finished, all the DataFrames created will be concatenated into a single one.

if __name__ == "__main__":
    num_cores = cpu_count() - 1
    with Pool() as pool:
        data = pd.concat(pool.map(create_dataframe, range(num_cores)))
    data.to_sql(name='employees', con=engine, if_exists = 'append', index=False, dtype=schema)

And finally, we will insert the DataFrame into the MySQL database by calling the to_sql() method. All the data will be stored in a table named employees.

The table employees is created without a primary key, so we execute the following SQL statement to add an id column that is set to be the primary key of the table.

with engine.connect() as conn:
        conn.execute("ALTER TABLE employees ADD id INT NOT NULL AUTO_INCREMENT PRIMARY KEY FIRST;")

For PostgreSQL, replace this line:

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

With:

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

MongoDB

All the required modules are now ready to be imported into the main script, now it’s time to create the mongodb.py script. First, import the required libraries:

from multiprocessing import Pool
from multiprocessing import cpu_count
import pandas as pd

From multiprocessing, Pool() and cpu_count() are required. The Python Multiprocessing Pool class allows you to create and manage process pools in Python.

Then, import the modules previously created:

from modules.dataframe import create_dataframe
from modules.base import client

Now we create the multiprocessing pool, configured to use all available CPU cores minus one. Each core will call the create_dataframe() function and create a DataFrame with 4 thousand records. After each call to the function has finished, all the DataFrames created will be concatenated into a single one.

if __name__ == "__main__":
    num_cores = cpu_count() - 1
    with Pool() as pool:
        data = pd.concat(pool.map(create_dataframe, range(num_cores)))
    data_dict = data.to_dict('records')
    db = client["company"]
    collection = db["employees"]
    collection.insert_many(data_dict)

After logging into the MongoDB server, we specify the database and the collection where the data will be stored.

And finally, we will insert the DataFrame into MongoDB by calling the insert_many() method. All the data will be stored in a collection named employees.

Running the script

Run the following statement to populate the table:

$ python sql.py

Or:

$ python mongodb.py

Multiprocessing

Execution time depends on the CPU cores available on your machine. I’m running this script on an Intel i7 1260P that has 16 cores, but using 15.

CPU Utilization

Query Your Data

Once the script finishes, you can check the data in the database.

MySQL and PostgreSQL

Connect to the company database.

MySQL:

use company;

PostgreSQL:

\c company

Then, get the number of records.

select count(*) from employees;

The count() function returns the number of records in the employees table.

+----------+
| count(*) |
+----------+
|    60000 |
+----------+
1 row in set (0.22 sec)

MongoDB

use company;
db.employees.count()

The count() function returns the number of records in the employees table.

60000

Or you can display the records in the employees table:

db.employees.find().pretty()

The code shown in this blog post can be found on my GitHub account in the data-generator repository.

Mario García

Mario has been an active Open Source contributor for more than 15 years. Technical Evangelist @ Percona. Python developer with experience automating stuff and web development. Speaker at tech and innovation events since 2008. DevOps enthusiast. Member of GitLab Heroes, GitKraken Ambassadors and HashiCorp Ambassadors.

See all posts by Mario García »

Comments

Percona Live 2023 - CFP
✎ Edit this page on GitHub