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

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How to use SQLAlchemy ORM to access Google Cloud Storage Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Google Cloud Storage 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 Cloud Storage and the SQLAlchemy toolkit, you can build Google Cloud Storage-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Google Cloud Storage data to query Google Cloud Storage data.

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

Connecting to Google Cloud Storage Data

Connecting to Google Cloud Storage 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.

Authenticate with a User Account

You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

Authenticate with a Service Account

Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.

You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:

  • InitiateOAuth: Set this to GETANDREFRESH.
  • OAuthJWTCertType: Set this to "PFXFILE".
  • OAuthJWTCert: Set this to the path to the .p12 file you generated.
  • OAuthJWTCertPassword: Set this to the password of the .p12 file.
  • OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
  • OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
  • OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
  • ProjectId: Set this to the Id of the project you want to connect to.

The OAuth flow for a service account then completes.

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

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

Declare a Mapping Class for Google Cloud Storage 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 Buckets 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 Buckets(base): __tablename__ = "Buckets" Name = Column(String,primary_key=True) OwnerId = Column(String) ...

Query Google Cloud Storage 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("googlecloudstorage:///?ProjectId='project1'&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Buckets).filter_by(Name="TestBucket"): print("Name: ", instance.Name) print("OwnerId: ", instance.OwnerId) 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

Buckets_table = Buckets.metadata.tables["Buckets"] for instance in session.execute(Buckets_table.select().where(Buckets_table.c.Name == "TestBucket")): print("Name: ", instance.Name) print("OwnerId: ", instance.OwnerId) 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 Python Connector for Google Cloud Storage to start building Python apps and scripts with connectivity to Google Cloud Storage data. Reach out to our Support Team if you have any questions.