How to use SQLAlchemy ORM to access SparkPost 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 SparkPost-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to SparkPost data to query SparkPost data.

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

Connecting to SparkPost Data

Connecting to SparkPost 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.

Start by setting the Profile connection property to the location of the SparkPost Profile on disk (e.g. C:\profiles\SparkPost.apip). Next, set the ProfileSettings connection property to the connection string for SparkPost (see below).

SparkPost API Profile Settings

Generate an API key by navigating to Configuration > API Keys > Create API Key in your SparkPost account.

Follow the procedure below to install SQLAlchemy and start accessing SparkPost 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 SparkPost Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with SparkPost 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\SparkPost.apip&ProfileSettings='APIKey=your_api_key'")

Declare a Mapping Class for SparkPost 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 ABTests 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 ABTests(base):
	__tablename__ = "ABTests"
	Id = Column(String,primary_key=True)
	Name = Column(String)
	...

Query SparkPost 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\SparkPost.apip&ProfileSettings='APIKey=your_api_key'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(ABTests).filter_by(Status="completed"):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	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

ABTests_table = ABTests.metadata.tables["ABTests"]
for instance in session.execute(ABTests_table.select().where(ABTests_table.c.Status == "completed")):
	print("Id: ", instance.Id)
	print("Name: ", instance.Name)
	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 SparkPost data. Reach out to our Support Team if you have any questions.