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

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Use SQLAlchemy ORMs to Access Databricks Data in Python


The CData Python Connector for Databricks enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Databricks data.

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

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

Connecting to Databricks Data

Connecting to Databricks 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 connect to a Databricks cluster, set the properties as described below.

Note: The needed values can be found in your Databricks instance by navigating to Clusters, and selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.

  • Server: Set to the Server Hostname of your Databricks cluster.
  • HTTPPath: Set to the HTTP Path of your Databricks cluster.
  • Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Databricks 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("databricks:///?Server=127.0.0.1&Port=443&TransportMode=HTTP&HTTPPath=MyHTTPPath&UseSSL=True&User=MyUser&Password=MyPassword")

Declare a Mapping Class for Databricks 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 Customers 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 Customers(base): __tablename__ = "Customers" City = Column(String,primary_key=True) CompanyName = Column(String) ...

Query Databricks 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("databricks:///?Server=127.0.0.1&Port=443&TransportMode=HTTP&HTTPPath=MyHTTPPath&UseSSL=True&User=MyUser&Password=MyPassword") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Customers).filter_by(Country="US"): print("City: ", instance.City) print("CompanyName: ", instance.CompanyName) 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

Customers_table = Customers.metadata.tables["Customers"] for instance in session.execute(Customers_table.select().where(Customers_table.c.Country == "US")): print("City: ", instance.City) print("CompanyName: ", instance.CompanyName) print("---------")

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

Insert Databricks Data

To insert Databricks 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 Databricks.

new_rec = Customers(City="placeholder", Country="US") session.add(new_rec) session.commit()

Update Databricks Data

To update Databricks 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 Databricks.

updated_rec = session.query(Customers).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Country = "US" session.commit()

Delete Databricks Data

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