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

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How to use SQLAlchemy ORM to access SendGrid Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of SendGrid data.

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

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

Connecting to SendGrid Data

Connecting to SendGrid 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.

To make use of all the available features, provide the User and Password connection properties.

To connect with limited features, you can set the APIKey connection property instead. See the "Getting Started" chapter of the help documentation for a guide to obtaining the API key.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with SendGrid 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("sendgrid:///?User=admin&Password=abc123")

Declare a Mapping Class for SendGrid 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 AdvancedStats 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 AdvancedStats(base): __tablename__ = "AdvancedStats" Name = Column(String,primary_key=True) Clicks = Column(String) ...

Query SendGrid 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("sendgrid:///?User=admin&Password=abc123") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(AdvancedStats).filter_by(Type="Device"): print("Name: ", instance.Name) print("Clicks: ", instance.Clicks) 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

AdvancedStats_table = AdvancedStats.metadata.tables["AdvancedStats"] for instance in session.execute(AdvancedStats_table.select().where(AdvancedStats_table.c.Type == "Device")): print("Name: ", instance.Name) print("Clicks: ", instance.Clicks) print("---------")

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

Insert SendGrid Data

To insert SendGrid 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 SendGrid.

new_rec = AdvancedStats(Name="placeholder", Type="Device") session.add(new_rec) session.commit()

Update SendGrid Data

To update SendGrid 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 SendGrid.

updated_rec = session.query(AdvancedStats).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Type = "Device" session.commit()

Delete SendGrid Data

To delete SendGrid 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(AdvancedStats).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SendGrid to start building Python apps and scripts with connectivity to SendGrid data. Reach out to our Support Team if you have any questions.