How to use SQLAlchemy ORM to access Quickbase Data in Python

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

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

Connecting to Quickbase Data

Connecting to Quickbase 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.

User Authentication Method

To authenticate with user credentials, specify the following connection properties:

1. Set the User and Password.
2. If your application requires an ApplicationToken;, you must provide it otherwise an error will be thrown. You can find the ApplicationToken under SpecificApp > Settings > App management > App properties > Advanced settings > Security options > Manage Application Token.

User Token Authentication

To authenticate with a user token, specify the following connection properties:

1. Set UserToken and you are ready to connect. You can find the UserToken under Quick Base > My Preferences > My User Information > Manage User Tokens.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Quickbase 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("quickbase:///[email protected]&Password=password&Domain=myinstance.quickbase.com&ApplicationToken=bwkxrb5da2wn57bzfh9xn24")

Declare a Mapping Class for Quickbase 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 SampleTable_1 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 SampleTable_1(base):
	__tablename__ = "SampleTable_1"
	Id = Column(String,primary_key=True)
	Column1 = Column(String)
	...

Query Quickbase 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("quickbase:///[email protected]&Password=password&Domain=myinstance.quickbase.com&ApplicationToken=bwkxrb5da2wn57bzfh9xn24")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(SampleTable_1).filter_by(Column2="100"):
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	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

SampleTable_1_table = SampleTable_1.metadata.tables["SampleTable_1"]
for instance in session.execute(SampleTable_1_table.select().where(SampleTable_1_table.c.Column2 == "100")):
	print("Id: ", instance.Id)
	print("Column1: ", instance.Column1)
	print("---------")

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

Insert Quickbase Data

To insert Quickbase 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 Quickbase.

new_rec = SampleTable_1(Id="placeholder", Column2="100")
session.add(new_rec)
session.commit()

Update Quickbase Data

To update Quickbase 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 Quickbase.

updated_rec = session.query(SampleTable_1).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Column2 = "100"
session.commit()

Delete Quickbase Data

To delete Quickbase 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(SampleTable_1).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 Quickbase to start building Python apps and scripts with connectivity to Quickbase data. Reach out to our Support Team if you have any questions.