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The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for IBM Cloud Object Storage and the SQLAlchemy toolkit, you can build IBM Cloud Object Storage-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to IBM Cloud Object Storage data to query IBM Cloud Object Storage data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live IBM Cloud Object Storage data in Python. When you issue complex SQL queries from IBM Cloud Object Storage, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to IBM Cloud Object Storage and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to IBM Cloud Object 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.
If you do not already have Cloud Object Storage in your IBM Cloud account, follow the procedure below to install an instance of SQL Query in your account:
There are certain connection properties you need to set before you can connect. You can obtain these as follows:
To connect with IBM Cloud Object Storage, you need an API Key. You can obtain this as follows:
If you have multiple accounts, specify the CloudObjectStorageCRN explicitly. To find the appropriate value, you can:
You can now set the following to connect to data:
When you connect, the connector completes the OAuth process.
Follow the procedure below to install SQLAlchemy and start accessing IBM Cloud Object Storage through Python objects.
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
You can now connect with a connection string. Use the create_engine function to create an Engine for working with IBM Cloud Object 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("ibmcloudobjectstorage:///?ApiKey=myApiKey&CloudObjectStorageCRN=MyInstanceCRN&Region=myRegion&OAuthClientId=MyOAuthClientId&OAuthClientSecret=myOAuthClientSecret")
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 Objects 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 Objects(base): __tablename__ = "Objects" Key = Column(String,primary_key=True) Etag = Column(String) ...
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.
engine = create_engine("ibmcloudobjectstorage:///?ApiKey=myApiKey&CloudObjectStorageCRN=MyInstanceCRN&Region=myRegion&OAuthClientId=MyOAuthClientId&OAuthClientSecret=myOAuthClientSecret")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Objects).filter_by(Bucket="someBucket"):
print("Key: ", instance.Key)
print("Etag: ", instance.Etag)
print("---------")
Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.
Objects_table = Objects.metadata.tables["Objects"]
for instance in session.execute(Objects_table.select().where(Objects_table.c.Bucket == "someBucket")):
print("Key: ", instance.Key)
print("Etag: ", instance.Etag)
print("---------")
For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.
Download a free, 30-day trial of the CData Python Connector for IBM Cloud Object Storage to start building Python apps and scripts with connectivity to IBM Cloud Object Storage data. Reach out to our Support Team if you have any questions.
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