OSC Databricks SQL Connector: Python Example

Nov 8, 2025 by Admin 45 views

Hey data enthusiasts! Ever wanted to seamlessly connect your Python code to Databricks SQL? Well, buckle up, because we're diving headfirst into the OSC Databricks SQL Connector for Python! This guide is your go-to resource for understanding how to leverage this powerful tool. We'll walk through setup, connection, and execution, with plenty of real-world examples to get you up and running in no time. Get ready to unlock the full potential of your Databricks SQL data with Python. Let's get started!

Setting the Stage: Prerequisites for Using the OSC Databricks SQL Connector

Alright, before we jump into the fun stuff, let's make sure we're all on the same page. You'll need a few things in place to follow along with this tutorial. First off, you'll need Python installed on your system. We recommend using the latest stable version. If you don't have it already, go ahead and download it from the official Python website (https://www.python.org/downloads/).

Next up, you'll want to make sure you have pip, the Python package installer, installed. Pip usually comes bundled with Python, so you're probably good to go. But, just to be sure, open up your terminal or command prompt and type pip --version. If you see a version number, you're golden! If not, you might need to reinstall Python, making sure to check the box that says, "Add Python to PATH."

Now, for the main event – the Databricks SQL Connector. You'll need access to a Databricks workspace and a Databricks SQL endpoint. This is where your data lives! If you don't have one set up yet, you'll need to create a Databricks workspace and then configure a SQL endpoint. This involves setting up your compute resources and ensuring you have the necessary permissions. Don't worry, it's not as scary as it sounds. The Databricks documentation is super helpful, and there are tons of tutorials available online.

Finally, we'll need to install the databricks-sql-connector package itself. Open your terminal or command prompt and run the following command: pip install databricks-sql-connector. This command downloads and installs the necessary package and its dependencies. If you're using a virtual environment (which is always a good practice, by the way), make sure you activate it before running this command. That keeps your project dependencies nice and tidy. After the installation, you should be all set to connect to Databricks SQL from your Python scripts. We have now covered the prerequisites for using the OSC Databricks SQL Connector, and we can now get into the core of the tutorial.

Connecting to Databricks SQL: Your First Python Script

Alright, let's get down to the nitty-gritty and write some code! Connecting to Databricks SQL from Python involves a few key steps: importing the necessary modules, setting up your connection parameters, and establishing a connection. Let's break it down step by step and create a simple Python script to connect to your Databricks SQL endpoint. The core of your connection will depend on the databricks-sql-connector library.

First, open your favorite code editor or IDE and create a new Python file (e.g., connect_to_databricks.py). At the beginning of your script, you'll need to import the connect function from the databricks module and sql to perform SQL queries. You will also need to import os to fetch the environment variables. Then, let's define your connection parameters. These parameters are crucial for telling your Python script how to connect to your Databricks SQL endpoint. You'll need the following information:

Server Hostname: This is the hostname of your Databricks SQL endpoint. You can find this in your Databricks workspace under "SQL Endpoints." It usually looks something like xxxxxx.us-east-1.azuredatabricks.net.
HTTP Path: This is the HTTP path for your SQL endpoint. You'll also find this in your Databricks workspace, under the SQL endpoint details. It will look like a path, e.g., /sql/1.0/endpoints/xxxxxxxxxxxxxxxx.
Personal Access Token (PAT): You'll need a personal access token to authenticate your connection. You can generate a PAT in your Databricks user settings. Make sure to keep this token secure.

For security reasons, it's best practice to store these connection parameters as environment variables rather than hardcoding them in your script. That way, you don't accidentally expose your credentials. You can set environment variables in your operating system or use a .env file for your project. Here is an example to make your own environment variables:

import os
from databricks import sql

# Retrieve connection details from environment variables
server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_ACCESS_TOKEN")

# Create a connection
with sql.connect(
    server_hostname=server_hostname,
    http_path=http_path,
    access_token=access_token
) as connection:
    print("Connection successful!")

In this code snippet, we import the necessary modules, retrieve the connection parameters from environment variables, and then create a connection using the sql.connect() function. The with statement ensures that the connection is automatically closed when you're finished with it, which is good practice. Also, it’s always better to make sure your credentials are safe, and stored appropriately. Now, run your script, and if everything is set up correctly, you should see the "Connection successful!" message printed to your console. Awesome, you've successfully connected to Databricks SQL from Python! This establishes the base for your OSC Databricks SQL Connector.

Querying Databricks SQL: Executing Your First SQL Query

Now that we've established a connection, let's move on to the fun part: running SQL queries! Executing queries is the heart of interacting with your data in Databricks. We'll walk through how to execute a simple query, retrieve the results, and print them to your console. This section will demonstrate how to harness the power of your OSC Databricks SQL Connector. Let's keep the code from the previous section and just add the querying part.

First, you'll need to open the connection in your connect_to_databricks.py file. Assuming you've already created the connection as described in the previous section, you can use the connection object to create a cursor. A cursor is an object that allows you to execute SQL statements and fetch results. The connection.cursor() method creates a cursor object.

Next, let's define your SQL query. For this example, we'll use a simple SELECT statement to retrieve data from a table. The SQL query will depend on the tables available in your Databricks SQL endpoint. Here's a basic example that will show the version of the Databricks SQL Connector:

import os
from databricks import sql

# Retrieve connection details from environment variables
server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_ACCESS_TOKEN")

# Create a connection
with sql.connect(
    server_hostname=server_hostname,
    http_path=http_path,
    access_token=access_token
) as connection:
    with connection.cursor() as cursor:
        try:
            # Execute a SQL query
            cursor.execute("SELECT current_version()")

            # Fetch the results
            result = cursor.fetchall()

            # Print the results
            for row in result:
                print(row)

        except Exception as e:
            print(f"An error occurred: {e}")

In this example, we execute a SELECT query using the cursor.execute() method. Then, we use cursor.fetchall() to fetch the results. fetchall() retrieves all the results from the query. You can also use other methods like fetchone() to retrieve a single row or fetchmany(size) to retrieve a specific number of rows. Finally, we loop through the results and print each row to the console. When you run this script, it should print the Databricks SQL version. This showcases the ability of the OSC Databricks SQL Connector to perform queries.

Handling Query Results: Fetching and Displaying Data

Once you've executed a SQL query, the next step is to retrieve and display the results. There are several methods you can use to fetch the data, depending on your needs. Let's explore how to use fetchall(), fetchone(), and fetchmany() to get different results and display them. This will make working with the OSC Databricks SQL Connector much easier.

As we saw in the previous example, fetchall() retrieves all the rows returned by your query. This is great for small datasets, but it might not be the best choice for large tables, as it could consume a lot of memory. To use fetchall(), execute your query and then call cursor.fetchall(). The result will be a list of tuples, where each tuple represents a row, and each element in the tuple represents a column value.

import os
from databricks import sql

# Retrieve connection details from environment variables
server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_ACCESS_TOKEN")

# Create a connection
with sql.connect(
    server_hostname=server_hostname,
    http_path=http_path,
    access_token=access_token
) as connection:
    with connection.cursor() as cursor:
        # Execute a SQL query
        cursor.execute("SELECT * FROM samples.nyctaxi.trips LIMIT 5")

        # Fetch all results
        results = cursor.fetchall()

        # Print the results
        for row in results:
            print(row)

This snippet retrieves the first five rows from the samples.nyctaxi.trips table. fetchone() retrieves the next row of a query result. This is useful when you only need to process one row at a time. After executing a query, call cursor.fetchone() to retrieve the next row. It returns a tuple representing the row or None if there are no more rows. Using this method is beneficial if you want to avoid loading the whole dataset into the memory.

import os
from databricks import sql

# Retrieve connection details from environment variables
server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_ACCESS_TOKEN")

# Create a connection
with sql.connect(
    server_hostname=server_hostname,
    http_path=http_path,
    access_token=access_token
) as connection:
    with connection.cursor() as cursor:
        # Execute a SQL query
        cursor.execute("SELECT * FROM samples.nyctaxi.trips LIMIT 5")

        # Fetch one result at a time
        row = cursor.fetchone()
        while row:
            print(row)
            row = cursor.fetchone()

Finally, fetchmany(size) retrieves a specified number of rows. This is helpful when you need to process data in batches. After executing a query, call cursor.fetchmany(size), where size is the number of rows you want to retrieve. This method returns a list of tuples, each representing a row. Here is a small example.

import os
from databricks import sql

# Retrieve connection details from environment variables
server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_ACCESS_TOKEN")

# Create a connection
with sql.connect(
    server_hostname=server_hostname,
    http_path=http_path,
    access_token=access_token
) as connection:
    with connection.cursor() as cursor:
        # Execute a SQL query
        cursor.execute("SELECT * FROM samples.nyctaxi.trips LIMIT 10")

        # Fetch the first two rows
        results = cursor.fetchmany(2)

        # Print the results
        for row in results:
            print(row)

By using these methods, you can efficiently retrieve and display data from your Databricks SQL endpoint using the OSC Databricks SQL Connector.

Advanced Techniques: Parameterized Queries and Error Handling

Let's level up your skills! We'll explore two important techniques: using parameterized queries to prevent SQL injection and implementing error handling to make your code more robust. Both are important in the field of the OSC Databricks SQL Connector.

Parameterized Queries: Parameterized queries are a powerful way to safely pass data into your SQL queries. They prevent SQL injection attacks by treating user-provided data as literal values, rather than executable SQL code. The Databricks SQL connector supports parameterized queries using the %s placeholder.

import os
from databricks import sql

# Retrieve connection details from environment variables
server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_ACCESS_TOKEN")

# Create a connection
with sql.connect(
    server_hostname=server_hostname,
    http_path=http_path,
    access_token=access_token
) as connection:
    with connection.cursor() as cursor:
        # Define the query with a placeholder
        query = "SELECT * FROM samples.nyctaxi.trips WHERE passenger_count = %s"
        # Define the parameter
        passenger_count = 1
        # Execute the query with the parameter
        cursor.execute(query, (passenger_count,))
        # Fetch and print the results
        results = cursor.fetchall()
        for row in results:
            print(row)

In this example, the passenger_count variable is passed as a parameter to the query, preventing any potential SQL injection vulnerabilities.

Error Handling: Implementing error handling is critical for creating reliable applications. It helps you catch and handle potential issues that may arise during the connection or query execution. You can use a try-except block to catch exceptions and handle them gracefully.

import os
from databricks import sql

# Retrieve connection details from environment variables
server_hostname = os.getenv("DATABRICKS_SERVER_HOSTNAME")
http_path = os.getenv("DATABRICKS_HTTP_PATH")
access_token = os.getenv("DATABRICKS_ACCESS_TOKEN")

# Create a connection
with sql.connect(
    server_hostname=server_hostname,
    http_path=http_path,
    access_token=access_token
) as connection:
    with connection.cursor() as cursor:
        try:
            # Execute a SQL query
            cursor.execute("SELECT * FROM non_existent_table")
            results = cursor.fetchall()
            for row in results:
                print(row)
        except Exception as e:
            # Handle the exception
            print(f"An error occurred: {e}")

In this example, if the query fails (e.g., due to a table not existing), the except block catches the exception and prints an error message. Good error handling is a key aspect of any professional-grade Python code. By incorporating these techniques, you can make your Databricks SQL interactions more secure and reliable with the OSC Databricks SQL Connector.

Conclusion: Mastering the OSC Databricks SQL Connector

Alright, folks, we've reached the end of our journey through the OSC Databricks SQL Connector for Python! We've covered everything from setting up your environment and establishing a connection to executing queries, handling results, and implementing advanced techniques. You're now equipped with the knowledge and tools to seamlessly integrate your Python code with Databricks SQL. Go forth and explore your data, build amazing applications, and unlock valuable insights. Remember to always prioritize security and best practices when working with databases. Happy coding!

This article provided a practical guide to using the databricks-sql-connector Python library. It covered the installation process, establishing connections, executing queries, handling results, and implementing advanced techniques. By following the examples and best practices outlined in this guide, you can start integrating your Python code with Databricks SQL and leverage the power of this data platform.