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

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

Connecting to Guru Data

Connecting to Guru 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 Guru Profile on disk (e.g. C:\profiles\Guru.apip). Next, set the ProfileSettings connection property to the connection string for Guru (see below).

Guru API Profile Settings

In Guru, navigate to Settings > API and click Create New API Token. Use your email address as the User and the generated token as the Password.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Guru 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\Guru.apip&ProfileSettings='User=your_email&Password=your_api_token'")

Declare a Mapping Class for Guru 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 Analytics 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 Analytics(base):
	__tablename__ = "Analytics"
	User = Column(String,primary_key=True)
	Type = Column(String)
	...

Query Guru 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\Guru.apip&ProfileSettings='User=your_email&Password=your_api_token'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Analytics).filter_by(TeamId="team-12345"):
	print("User: ", instance.User)
	print("Type: ", instance.Type)
	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

Analytics_table = Analytics.metadata.tables["Analytics"]
for instance in session.execute(Analytics_table.select().where(Analytics_table.c.TeamId == "team-12345")):
	print("User: ", instance.User)
	print("Type: ", instance.Type)
	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

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