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How to use SQLAlchemy ORM to access Greenhouse Data in Python


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

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

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

Connecting to Greenhouse Data

Connecting to Greenhouse 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.

You need an API key to connect to Greenhouse. To create an API key, follow the steps below:

  1. Click the Configure icon in the navigation bar and locate Dev Center on the left.
  2. Select API Credential Management.
  3. Click Create New API Key.
    • Set "API Type" to Harvest.
    • Set "Partner" to custom.
    • Optionally, provide a description.
  4. Proceed to Manage permissions and select the appropriate permissions based on the resources you want to access through the driver.
  5. Copy the created key and set APIKey to that value.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Greenhouse 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("greenhouse:///?APIKey=YourAPIKey")

Declare a Mapping Class for Greenhouse 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 Applications 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 Applications(base): __tablename__ = "Applications" Id = Column(String,primary_key=True) CandidateId = Column(String) ...

Query Greenhouse 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("greenhouse:///?APIKey=YourAPIKey") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Applications).filter_by(Status="Active"): print("Id: ", instance.Id) print("CandidateId: ", instance.CandidateId) 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

Applications_table = Applications.metadata.tables["Applications"] for instance in session.execute(Applications_table.select().where(Applications_table.c.Status == "Active")): print("Id: ", instance.Id) print("CandidateId: ", instance.CandidateId) 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 Greenhouse to start building Python apps and scripts with connectivity to Greenhouse data. Reach out to our Support Team if you have any questions.

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