Rust - from PostgreSQL to Databricks using Diesel

Discover the synergy of Rust, Diesel ORM, and Databricks for efficient data pipelines, from PostgreSQL to Databricks integration


Jatin Solanki

Updated on

May 5, 2024

Decube utilizes Rust to abstract and standardize metadata gathered from various sources. We firmly believe that Rust will become an essential component of the data stack in the coming months/years.

In this tutorial, we'll explore the unlikely love triangle between Rust, Diesel ORM, and Databricks. These three technologies might seem like an odd combination, but when they come together, they can create powerful data processing pipelines. So, let's dive in!

Section 1: Setting up the project

To get started with using Rust, we need to set up a Rust project using Cargo - Rust's package manager. If you're not already familiar with Rust, don't worry - it's a modern programming language that's been designed to be both safe and performant. Rust has many similarities to C++, but with less of the headaches associated with that language.

Before we get started with creating a Rust project, make sure that Rust is installed on your machine. If it isn't already installed, you can download it from the official Rust website: Once Rust is installed, open up your terminal and run the following commands to create a new Rust project:

Section 2: Adding dependencies

Now that we have our Rust project set up, we need to add some dependencies to it. As mentioned earlier, we'll be using Diesel ORM to interact with our database, dotenv to load environment variables, and serde for JSON serialization.

If you're not familiar with these libraries, don't worry! They're widely used and have great documentation to help you get started.

To add these dependencies to our project, open up your Cargo.toml file and add the following lines:

  • diesel: This is the main library we'll use for interacting with our database. It includes query building, schema migrations, and more. We're specifying the postgres feature here, as we'll be using PostgreSQL as our database.
  • dotenv: This library allows us to load environment variables from a .env file in our project directory. This makes it easy to keep sensitive information (like our database credentials) out of our source code.
  • serde: This library provides serialization and deserialization of Rust data structures. We're using the derive feature, which allows us to automatically generate serialization code for our User struct.
  • serde_json: This library provides serialization and deserialization of JSON data.

Now, let's move on to the fun stuff.

Section 3: Configuring the database connection

To connect to our database, we need to set the DATABASE_URL environment variable. This might sound like a daunting task, but it's actually as easy as pie. Just create a .env file in the root of your project and add the following line:

Replace username, password, and database_name with the appropriate values for your database. If you're using a different database, the connection string will be different.

Now that we've set up our environment variables, we can move on to the next step.

Section 4: Running a query with Diesel

With the database connection configured, we can now use Diesel to run a query against the database. In this example, we'll retrieve a list of users from a users table and serialize the data to JSON format.

But first, we need to define a struct that represents the data we want to retrieve. Create a new file in the src directory called, and add the following code:

This defines a User struct with id, name, and email fields. Now, we're ready to run our query:


This function establishes a connection to the database using the DATABASE_URL environment variable and retrieves the list of users from the users table using Diesel's query API. Finally, the data is returned as a vector of User structs.

Section 5: Processing data to Databricks

Now that we've retrieved our data, it's time to process it in Databricks. But first, we need to authenticate with the Databricks API. Don't worry if this sounds intimidating - it's as easy as pie.

Create a new file in the root of your project called, and add the following code:

This code sends a POST request to the Databricks API to authenticate and obtain an access token. Replace <YOUR-DATABRICKS-INSTANCE> with the URL of your Databricks instance.

Now that we're authenticated, we can create a new notebook in Databricks and upload the data as a JSON file. Here's the code to do that:

This code sends a POST request to the Databricks API to create a new notebook in the user's home directory, with the name rust-diesel-databricks. The data is uploaded as a JSON file using the content field. Replace <YOUR-USERNAME> with your Databricks username.

Section 6: Wrapping up

That's it! We've successfully used Rust with Diesel ORM to retrieve data from a database, and then processed that data in Databricks using the Databricks REST API. I hope you've enjoyed this tutorial, and that it's inspired you to explore the possibilities of Rust and Databricks further.

In conclusion, Rust, Diesel ORM, and Databricks might seem like an unlikely combination, but when they come together, they can create powerful data processing pipelines. And if you're ever feeling overwhelmed, just remember - it's as easy as pie!

That's it for this tutorial on using Rust with Diesel ORM and Databricks. It's amazing to see how these technologies can come together to create powerful data processing pipelines. If you're interested in exploring the possibilities of Rust and data governance, check out Decube, a unified platform for data observability, catalog, and governance that leverages Rust and now supports integration with Databricks. You can sign up for Decube. Thanks for reading!

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