How to use SQLAlchemy ORM to access Postmark Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData API Driver for Python and the SQLAlchemy toolkit, you can build Postmark-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Postmark data to query Postmark data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Postmark data in Python. When you issue complex SQL queries from Postmark, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Postmark and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Postmark Data

Connecting to Postmark data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Using API Key Authentication

Postmark uses server API tokens to authenticate requests. Each Postmark server has its own API token, which controls access to messages, bounces, templates, and statistics associated with that server.

To obtain your Server API Token, log in to your Postmark account and navigate to the server you want to connect to. Go to API Tokens under the server settings and copy the token labeled Server API token.

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Postmark Server API Token. This value is sent as the X-Postmark-Server-Token header on every request.

Example connection string:

Profile=C:\profiles\Postmark.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your-server-api-token"

Connecting to Postmark

Once the authentication is configured, you can connect to Postmark and query data from any of the available tables such as OutboundMessages, Bounces, and Templates.

Follow the procedure below to install SQLAlchemy and start accessing Postmark through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Postmark Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Postmark data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("api:///?Profile=C:\profiles\Postmark.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your-server-api-token"")

Declare a Mapping Class for Postmark Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Bounces table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Bounces(base):
	__tablename__ = "Bounces"
	 = Column(String,primary_key=True)
	 = Column(String)
	...

Query Postmark Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("api:///?Profile=C:\profiles\Postmark.apip&AuthScheme=APIKey&ProfileSettings="APIKey=your-server-api-token"")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Bounces).filter_by(=""):
	print(": ", instance.)
	print(": ", instance.)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Bounces_table = Bounces.metadata.tables["Bounces"]
for instance in session.execute(Bounces_table.select().where(Bounces_table.c. == "")):
	print(": ", instance.)
	print(": ", instance.)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Postmark data. Reach out to our Support Team if you have any questions.