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How to use SQLAlchemy ORM to access Google Search Results in Python

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

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

Connecting to Google Search Results

Connecting to Google 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 search with a Google custom search engine, you need to set the CustomSearchId and ApiKey connection properties.

To obtain the CustomSearchId property, sign into Google Custom Search Engine and create a new search engine.

To obtain the ApiKey property, you must enable the Custom Search API in the Google API Console.

Follow the procedure below to install SQLAlchemy and start accessing Google Search 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 Search Results in Python

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

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("googlesearch:///?CustomSearchId=def456&ApiKey=abc123")

Declare a Mapping Class for Google 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 Google 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("googlesearch:///?CustomSearchId=def456&ApiKey=abc123") 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( == "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 CData Python Connector for Google Search to start building Python apps and scripts with connectivity to Google Search results. Reach out to our Support Team if you have any questions.