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How to use SQLAlchemy ORM to access Klipfolio Data in Python
Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Klipfolio data.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData API Driver for Python and the SQLAlchemy toolkit, you can build Klipfolio-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Klipfolio data to query Klipfolio data.
With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Klipfolio data in Python. When you issue complex SQL queries from Klipfolio, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Klipfolio and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
Connecting to Klipfolio Data
Connecting to Klipfolio 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.
Start by setting the Profile connection property to the location of the Klipfolio Profile on disk (e.g. C:\profiles\Klipfolio.apip). Next, set the ProfileSettings connection property to the connection string for Klipfolio (see below).
Klipfolio API Profile Settings
In order to authenticate to Klipfolio, you'll need to provide your API Key. You can generate an API key from the Klipfolio Dashboard app through either the My Profile page or from Users if you are an administrator (you must have the user.manage permission). Set the API Key in the ProfileSettings property to connect.
Follow the procedure below to install SQLAlchemy and start accessing Klipfolio 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 Klipfolio Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Klipfolio 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("api:///?Profile=C:\profiles\Klipfolio.apip&ProfileSettings='APIKey=your_api_key'")
Declare a Mapping Class for Klipfolio 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 DataSources 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 DataSources(base):
__tablename__ = "DataSources"
Id = Column(String,primary_key=True)
Name = Column(String)
...
Query Klipfolio 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("api:///?Profile=C:\profiles\Klipfolio.apip&ProfileSettings='APIKey=your_api_key'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(DataSources).filter_by(IsDynamic="true"):
print("Id: ", instance.Id)
print("Name: ", instance.Name)
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
DataSources_table = DataSources.metadata.tables["DataSources"]
for instance in session.execute(DataSources_table.select().where(DataSources_table.c.IsDynamic == "true")):
print("Id: ", instance.Id)
print("Name: ", instance.Name)
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 API Driver for Python to start building Python apps and scripts with connectivity to Klipfolio data. Reach out to our Support Team if you have any questions.
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