c-susan / cloud_db_mgmt_pooling_migrations

HHA504 / Cloud Computing / Assignment 4c / Cloud DB Management

cloud_db_mgmt_pooling_migrations

HHA504 / Cloud Computing / Assignment 4c / Cloud DB Management

This assignment aims to gain practical experience into managing a cloud-based MySQL database with a focus on implementing connection pooling and performing database migrations.

This repo contains the following:

• A code folder contain code for creating the database tables in both gcp and azure and for creating a Flask app to dsiplay the tables.
• A screenshots folder containing screenshots for the entity relationship diagram and the tables displayed on Flask.
• README.md file with an overview of the repo.

Documentation

1. MySQL Cloud Instance Setups

Google Cloud Platform (GCP) Setup

In GCP, I created a MySQL instance with the following customizations:

• Database version: MySQL 8.0
• Cloud SQL edition: Enterprise
• Preset: Sandbox
• Machine Configuration: Shared core; 1 vCPU, 0.614 GB
• In Conections>Authorized Networks, added an allow all network: 0.0.0.0/0.

Azure Setup

Using the Azure MySQL service, I created a flexible MySQL server with the following customizations:

• Burstable compute tier with Standard_B1s (1 vCore, 1GiB memory, 400 max iops) compute size
  • max_connections: 20
  • connect_timeout: 3
• In the Networking section, added a new Firewall rule: 0.0.0.0 - 255.255.255.255.

2. Database Schema and Data

I created a new database the MySQL instances:

1. In the google shell terminal, signed into MySQL with: mysql -u <username> -h <host IP address/servername> -p.
2. Created a database called 'susan': create database `susan`;
3. Ran show databases; to confirm the database was created and then signed out of MySQL.
4. In order to create a new tables, I did the following to create a connection to MySQL:

• Created a .env file to contain my database credentials that contains the following:

DB_HOST = <host IP address / server name>
DB_DATABASE = <database name>
DB_USERNAME = <username>
DB_PASSWORD = <password>
DB_PORT = 3306
DB_CHARSET = utf8mb4

5. Created a .gitignore file to hide the contents of my .env.
6. In both my .py files, I had to include the following code in order to connect to MySQL instances:

from dotenv import load_dotenv 
import os

load_dotenv()

## Database credentials 
DB_HOST = os.getenv("DB_HOST")
DB_DATABASE = os.getenv("DB_DATABASE")
DB_USERNAME = os.getenv("DB_USERNAME")
DB_PASSWORD = os.getenv("DB_PASSWORD")
DB_PORT = int(os.getenv("DB_PORT", 3306))
DB_CHARSET = os.getenv("DB_CHARSET", "utf8mb4")

# Connection string and creating the engine 
connect_args={'ssl':{'fake_flag_to_enable_tls': True}}
connection_string = (f'mysql+pymysql://{DB_USERNAME}:{DB_PASSWORD}@{DB_HOST}:{DB_PORT}/{DB_DATABASE}'
                    f"?charset={DB_CHARSET}")
engine = create_engine(
        connection_string,
        connect_args=connect_args)

7. For both the azure and gcp MySQL instances, I created the same database name and the same tables in a .py file. The first is a doctors table that includes data on doctor IDs, their names, the department they work in, and their phone number. The second table is a patients table and contains patient IDs, their names, date of birth, and their primary doctor ID, which references the doctor ID in the doctors table. I populated data into the tables using another .py using sample data generated using Faker.
8. In the doctors table, there is a column for phone number. In order to populate correct data into that column, I first had to create a function into order to put the sample data in correct format using the following code:

def phone_number():
    # Generate random numbers for each part of the phone number
    p1 = str(random.randint(1, 999)).zfill(3)
    p2 = str(random.randint(0, 999)).zfill(3)
    p3 = str(random.randint(0, 9999)).zfill(4)

    format = f"{p1}-{p2}-{p3}"
    return format

9. In the patients table the primary_doctor_id is a foreign key that references the doctor_id in the doctors table. In order fo the column to populate with data from the doctors tables, I had to use the following code: primary_doctor_id=session.query(Doctor).order_by(func.rand()).first().doctor_id.

3. SQLAlchemy and Flask Integration

A Flask app was created to connect to the MySQL instance wo display the database tables. A python file was created to contain the flask app code. A templates folder with an html file was created to contain code to display the tables. They can be foound in the code/flask folder. No errors were encountered in this step.

4. Database Migration with Alembic

To set up Alembic for database migrations, I did the following:

1. To check if alembic was installed/enabled in my google shell environment, I ran alembic in the terminal.
2. Ran alembic init migrations in the terminal. An alembic.ini file and a migrations folder appeared in the workspace.
3. In the alembic.ini file:
   • Located line 63: sqlalchmey.url is where we will edit to connect to the database
   • Changed line 63 to: sqlalchemy.url = mysql+mysqlconnector://username:password@host/database_name and included my database credentials.
4. Included alembic.ini in my .gitignore file to hide my database connection data.
5. In the env.py file located in the migrations folder:
   • uncommented lines 19 & 20 and commented out line 21
     • In line 19, changed to: from <file name> import Base. Changed <file name> to the name of the file where I created the tables.
     • In line 20, changed to: target_metadata = Base.metadata.

To create a migration:

1. Ran alembic revision --autogenerate -m "create tables" in the terminal.
2. Ran alembic upgrade head to run the migrations.
3. Ran alembic upgrade head --sql > migration.sql to save it.