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How to use SQLAlchemy ORM to access Harvest Data in Python


Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Harvest 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 Harvest-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Harvest data to query Harvest data.

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

Connecting to Harvest Data

Connecting to Harvest 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 Harvest Profile on disk (e.g. C:\profiles\Harvest.apip). Next, set the ProfileSettings connection property to the connection string for Harvest (see below).

Harvest API Profile Settings

To authenticate to Harvest, you can use either Token authentication or the OAuth standard. Use Basic authentication to connect to your own data. Use OAuth to allow other users to connect to their data.

Using Token Authentication

To use Token Authentication, set the APIKey to your Harvest Personal Access Token in the ProfileSettings connection property. In addition to APIKey, set your AccountId in ProfileSettings to connect.

Using OAuth Authentication

First, register an OAuth2 application with Harvest. The application can be created from the "Developers" section of Harvest ID.

After setting the following connection properties, you are ready to connect:

  • ProfileSettings: Set your AccountId in ProfileSettings.
  • AuthScheme: Set this to OAuth.
  • OAuthClientId: Set this to the client ID that you specified in your app settings.
  • OAuthClientSecret: Set this to the client secret that you specified in your app settings.
  • CallbackURL: Set this to the Redirect URI that you specified in your app settings.
  • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to manage how the driver obtains and refreshes the OAuthAccessToken.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Harvest 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\Harvest.apip&ProfileSettings='APIKey=my_personal_key&AccountId=_your_account_id'")

Declare a Mapping Class for Harvest 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 Invoices 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 Invoices(base): __tablename__ = "Invoices" Id = Column(String,primary_key=True) ClientName = Column(String) ...

Query Harvest 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\Harvest.apip&ProfileSettings='APIKey=my_personal_key&AccountId=_your_account_id'") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Invoices).filter_by(State="open"): print("Id: ", instance.Id) print("ClientName: ", instance.ClientName) 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

Invoices_table = Invoices.metadata.tables["Invoices"] for instance in session.execute(Invoices_table.select().where(Invoices_table.c.State == "open")): print("Id: ", instance.Id) print("ClientName: ", instance.ClientName) 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 Harvest data. Reach out to our Support Team if you have any questions.