Quickstart: Use Python to connect and query data in Azure Database for PostgreSQL - Flexible Server

APPLIES TO: Azure Database for PostgreSQL - Flexible Server

In this quickstart, you connect to an Azure Database for PostgreSQL flexible server instance by using Python. You then use SQL statements to query, insert, update, and delete data in the database from macOS, Ubuntu Linux, and Windows platforms.

The steps in this article include two authentication methods: Microsoft Entra authentication and PostgreSQL authentication. The Passwordless tab shows the Microsoft Entra authentication and the Password tab shows the PostgreSQL authentication.

Microsoft Entra authentication is a mechanism for connecting to Azure Database for PostgreSQL using identities defined in Microsoft Entra ID. With Microsoft Entra authentication, you can manage database user identities and other Microsoft services in a central location, which simplifies permission management. To learn more, see Microsoft Entra authentication with Azure Database for PostgreSQL - Flexible Server.

PostgreSQL authentication uses accounts stored in PostgreSQL. If you choose to use passwords as credentials for the accounts, these credentials will be stored in the user table. Because these passwords are stored in PostgreSQL, you need to manage the rotation of the passwords by yourself.

This article assumes that you're familiar with developing using Python, but you're new to working with Azure Database for PostgreSQL flexible server.

Prerequisites

• An Azure account with an active subscription. Create an account for trial.
• An Azure Database for PostgreSQL flexible server instance. To create Azure Database for PostgreSQL flexible server instance, refer to Create an Azure Database for PostgreSQL - Flexible Server instance using Azure portal.
• Python 3.8+.
• Latest pip package installer.

Add firewall rules for your client workstation

• If you created your Azure Database for PostgreSQL flexible server instance with Private access (VNet Integration), you will need to connect to your server from a resource within the same VNet as your server. You can create a virtual machine and add it to the VNet created with your Azure Database for PostgreSQL flexible server instance. Refer to Create and manage Azure Database for PostgreSQL - Flexible Server virtual network using Azure CLI.
• If you created your Azure Database for PostgreSQL flexible server instance with Public access (allowed IP addresses), you can add your local IP address to the list of firewall rules on your server. Refer to Create and manage Azure Database for PostgreSQL - Flexible Server firewall rules using the Azure CLI.

Configure Microsoft Entra integration on the server (passwordless only)

If you're following the steps for passwordless authentication, Microsoft Entra authentication must be configured for your server instance, and you must be assigned as a Microsoft Entra administrator on the server instance. Follow the steps in Configure Microsoft Entra integration to ensure that Microsoft Entra authentication is configured and that you're assigned as a Microsoft Entra administrator on your server instance.

Prepare your development environment

Change to a folder where you want to run the code and create and activate a virtual environment. A virtual environment is a self-contained directory for a particular version of Python plus the other packages needed for that application.

Run the following commands to create and activate a virtual environment:

py -3 -m venv .venv
.venv\Scripts\activate

python3 -m venv .venv
source .venv/bin/activate

Install the Python libraries

Install the Python libraries needed to run the code examples.

pip install azure-identity

pip install psycopg

Add authentication code

In this section, you add authentication code to your working directory and perform any additional steps required for authentication and authorization with your server instance.

How to run the Python examples

For each code example in this article:

1. Create a new file in a text editor.

2. Add the code example to the file.

3. Save the file in your project folder with a .py extension, such as postgres-insert.py. For Windows, make sure UTF-8 encoding is selected when you save the file.

4. In your project folder type python followed by the filename, for example python postgres-insert.py.

Create a table and insert data

The following code example connects to your Azure Database for PostgreSQL flexible server database using the psycopg.connect function, and loads data with a SQL INSERT statement. The cursor.execute function executes the SQL query against the database.

import psycopg
from get_conn import get_connection_uri

conn_string = get_connection_uri()

conn = psycopg.connect(conn_string) 
print("Connection established")
cursor = conn.cursor()

# Drop previous table of same name if one exists
cursor.execute("DROP TABLE IF EXISTS inventory;")
print("Finished dropping table (if existed)")

# Create a table
cursor.execute("CREATE TABLE inventory (id serial PRIMARY KEY, name VARCHAR(50), quantity INTEGER);")
print("Finished creating table")

# Insert some data into the table
cursor.execute("INSERT INTO inventory (name, quantity) VALUES (%s, %s);", ("banana", 150))
cursor.execute("INSERT INTO inventory (name, quantity) VALUES (%s, %s);", ("orange", 154))
cursor.execute("INSERT INTO inventory (name, quantity) VALUES (%s, %s);", ("apple", 100))
print("Inserted 3 rows of data")

# Clean up
conn.commit()
cursor.close()
conn.close()

When the code runs successfully, it produces the following output:

Connection established
Finished dropping table (if existed)
Finished creating table
Inserted 3 rows of data

Read data

The following code example connects to your Azure Database for PostgreSQL flexible server database and uses cursor.execute with the SQL SELECT statement to read data. This function accepts a query and returns a result set to iterate over by using cursor.fetchall().

import psycopg
from get_conn import get_connection_uri

conn_string = get_connection_uri()

conn = psycopg.connect(conn_string) 
print("Connection established")
cursor = conn.cursor()

# Fetch all rows from table
cursor.execute("SELECT * FROM inventory;")
rows = cursor.fetchall()

# Print all rows
for row in rows:
    print("Data row = (%s, %s, %s)" %(str(row[0]), str(row[1]), str(row[2])))

# Cleanup
conn.commit()
cursor.close()
conn.close()

When the code runs successfully, it produces the following output:

Connection established
Data row = (1, banana, 150)
Data row = (2, orange, 154)
Data row = (3, apple, 100)

Update data

The following code example connects to your Azure Database for PostgreSQL flexible server database and uses cursor.execute with the SQL UPDATE statement to update data.

import psycopg
from get_conn import get_connection_uri

conn_string = get_connection_uri()

conn = psycopg.connect(conn_string) 
print("Connection established")
cursor = conn.cursor()

# Update a data row in the table
cursor.execute("UPDATE inventory SET quantity = %s WHERE name = %s;", (200, "banana"))
print("Updated 1 row of data")

# Cleanup
conn.commit()
cursor.close()
conn.close()

Delete data

The following code example connects to your Azure Database for PostgreSQL flexible server database and uses cursor.execute with the SQL DELETE statement to delete an inventory item that you previously inserted.

import psycopg
from get_conn import get_connection_uri

conn_string = get_connection_uri()

conn = psycopg.connect(conn_string) 
print("Connection established")
cursor = conn.cursor()

# Delete data row from table
cursor.execute("DELETE FROM inventory WHERE name = %s;", ("orange",))
print("Deleted 1 row of data")

# Cleanup
conn.commit()
cursor.close()
conn.close()

Next steps