Use SQLAlchemy ORMs to Access Google Spanner Data in Python

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Python Connector Libraries for Google Cloud Spanner Data Connectivity. Integrate Google Cloud Spanner with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Google Spanner enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Google Spanner data.

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

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

Connecting to Google Spanner Data

Connecting to Google Spanner 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 Spanner uses the OAuth authentication standard. To authenticate using OAuth, you can use the embedded credentials or register an app with Google.

See the Getting Started guide in the CData driver documentation for more information.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Google Spanner 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("googlespanner:///?ProjectId='project1'&InstanceId='instance1'&Database='db1'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Google Spanner 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 Customer 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 Customer(base): __tablename__ = "Customer" Name = Column(String,primary_key=True) TotalDue = Column(String) ...

Query Google Spanner 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("googlespanner:///?ProjectId='project1'&InstanceId='instance1'&Database='db1'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customer).filter_by(Id="1"): print("Name: ", instance.Name) print("TotalDue: ", instance.TotalDue) 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

Customer_table = Customer.metadata.tables["Customer"] for instance in session.execute( == "1")): print("Name: ", instance.Name) print("TotalDue: ", instance.TotalDue) print("---------")

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

Insert Google Spanner Data

To insert Google Spanner 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 Google Spanner.

new_rec = Customer(Name="placeholder", Id="1") session.add(new_rec) session.commit()

Update Google Spanner Data

To update Google Spanner 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 Google Spanner.

updated_rec = session.query(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Id = "1" session.commit()

Delete Google Spanner Data

To delete Google Spanner 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(Customer).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

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