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

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

Connecting to Parallel Data

Connecting to Parallel 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.

The Parallel API uses API Key authentication via the x-api-key request header.

Using API Key Authentication

Your Parallel API key is required to create a connection. To obtain your API key:

1. Log into your Parallel account at app.parallel.ai.
2. Navigate to Settings or API Keys in your account dashboard.
3. Generate or copy your API key.

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Parallel API key.

Example connection string:

Profile=C:\profiles\Parallel.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key';

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

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

Declare a Mapping Class for Parallel 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 MonitorEvents 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 MonitorEvents(base):
	__tablename__ = "MonitorEvents"
	 = Column(String,primary_key=True)
	 = Column(String)
	...

Query Parallel 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\Parallel.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(MonitorEvents).filter_by(MonitorId="mon_abc123"):
	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

MonitorEvents_table = MonitorEvents.metadata.tables["MonitorEvents"]
for instance in session.execute(MonitorEvents_table.select().where(MonitorEvents_table.c.MonitorId == "mon_abc123")):
	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 Parallel data. Reach out to our Support Team if you have any questions.