How to use SQLAlchemy ORM to access BigQuery Data in Python

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

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

Getting Started

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Connecting to BigQuery Data

Connecting to BigQuery 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.

Google uses the OAuth authentication standard. To access Google APIs on behalf of individual users, you can use the embedded credentials or you can register your own OAuth app.

OAuth also enables you to use a service account to connect on behalf of users in a Google Apps domain. To authenticate with a service account, register an application to obtain the OAuth JWT values.

In addition to the OAuth values, specify the DatasetId and ProjectId. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with BigQuery 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("googlebigquery:///?DataSetId=MyDataSetId&ProjectId=MyProjectId&InitiateOAuth=GETANDREFRESH")

Declare a Mapping Class for BigQuery 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 Orders 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 Orders(base):
	__tablename__ = "Orders"
	OrderName = Column(String,primary_key=True)
	Freight = Column(String)
	...

Query BigQuery 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("googlebigquery:///?DataSetId=MyDataSetId&ProjectId=MyProjectId&InitiateOAuth=GETANDREFRESH")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Orders).filter_by(ShipCity="New York"):
	print("OrderName: ", instance.OrderName)
	print("Freight: ", instance.Freight)
	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

Orders_table = Orders.metadata.tables["Orders"]
for instance in session.execute(Orders_table.select().where(Orders_table.c.ShipCity == "New York")):
	print("OrderName: ", instance.OrderName)
	print("Freight: ", instance.Freight)
	print("---------")

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

Insert BigQuery Data

To insert BigQuery data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to BigQuery.

new_rec = Orders(OrderName="placeholder", ShipCity="New York")
session.add(new_rec)
session.commit()

Update BigQuery Data

To update BigQuery data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to BigQuery.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.ShipCity = "New York"
session.commit()

Delete BigQuery Data

To delete BigQuery data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided records (rows).

deleted_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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