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Use SQLAlchemy ORMs to Access Bing Search Results in Python

The CData Python Connector for Bing Search enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Bing Search results.

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

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

Connecting to Bing Search Results

Connecting to Bing Search results 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.

To connect to Bing, set the ApiKey connection property. To obtain the API key, sign into Microsoft Cognitive Services and register for the Bing Search APIs.

Two API keys are then generated; select either one.

When querying tables, the SearchTerms parameter must be supplied in the WHERE clause.

Follow the procedure below to install SQLAlchemy and start accessing Bing Search through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Bing Search Results in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Bing Search results.

engine = create_engine("bing///?APIKey=MyAPIKey")

Declare a Mapping Class for Bing Search Results

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 VideoSearch 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 VideoSearch(base):
	__tablename__ = "VideoSearch"
	Title = Column(String,primary_key=True)
	ViewCount = Column(String)
	...

Query Bing Search Results

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("bing///?APIKey=MyAPIKey")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(VideoSearch).filter_by(SearchTerms="WayneTech"):
	print("Title: ", instance.Title)
	print("ViewCount: ", instance.ViewCount)
	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

VideoSearch_table = VideoSearch.metadata.tables["VideoSearch"]
for instance in session.execute(VideoSearch_table.select().where(VideoSearch_table.c.SearchTerms == "WayneTech")):
	print("Title: ", instance.Title)
	print("ViewCount: ", instance.ViewCount)
	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 Bing Search Python Connector to start building Python apps and scripts with connectivity to Bing Search results. Reach out to our Support Team if you have any questions.