Professional Development for Databricks with Visual Studio Code

When working with Databricks you will usually start developing your code in the notebook-style UI that comes natively with Databricks. This is perfectly fine for most of the use cases but sometimes it is just not enough. Especially nowadays, where a lot of data engineers and scientists have a strong background also in regular software development and expect the same features that they are used to from their original Integrated Development Environments (IDE) also in Databricks.

For those users Databricks has developed Databricks Connect (Azure docs) which allows you to work with your local IDE of choice (Jupyter, PyCharm, RStudio, IntelliJ, Eclipse or Visual Studio Code) but execute the code on a Databricks cluster. This is awesome and provides a lot of advantages compared to the standard notebook UI. The two most important ones are probably the proper integration into source control / git and the ability to extend your IDE with tools like automatic formatters, linters, custom syntax highlighting, …

While Databricks Connect solves the problem of local execution and debugging, there was still a gap when it came to pushing your local changes back to Databricks to be executed as part of a regular ETL or ML pipeline. So far you had to either “deploy” your changes by manually uploading them via the Databricks UI again or write a script that uploads it via the REST API (Azure docs).

NOTE: I also published a PowerShell module that eases the automation/scripting of these tasks also as part of CI/CD pipeline. It is available from the PowerShell gallery DatabricksPS and integrates very well with this VSCode extension too!

However, this is not really something you would call a “seamless experience” so I also started working on an extension for Visual Studio Code to work more efficiently with Databricks. It has been in the VS Code gallery (Databricks VSCode) for about a month now and I received mostly positive feedback so far. Now I am at a stage where I want to get more people to use it – hence this blog post to announce it officially. The extension is currently published under GPLv3 license and is free to use for everyone. The GIT repository is also linked in the VS Code gallery if you want to participate or have any issues with the extension.

It currently supports the following features:

  • Workspace browser
    • Up-/download of notebooks and whole folders
    • Compare/Diff of local vs online notebook (currently only supported for raw files but not for notebooks)
    • Execution of local code and notebooks against a Databricks Cluster (via Databricks-Connect)
  • Cluster manager
    • Start/stop clusters
    • Script cluster definition as JSON
  • Job browser
    • Start/stop jobs
    • View job-run history + status
    • Script job definition as JSON
    • Script job-run output as JSON
  • DBFS browser
    • Upload files
    • Download files
    • (also works with mount points!)
  • Secrets browser
    • Create/delete secret scopes
    • Create/delete secrets
  • Support for multiple Databricks workspaces (e.g. DEV/TEST/PROD)
  • Easy configuration via standard VS Code settings

More features to come in the future but these will be mainly based on the requests that come from users or my personal needs. So your feedback is highly appreciated – either directly here or using the feedback section in the GIT repository.

I will also write some follow up post to show you how to work in the most efficient way using this new VSCode extension in combination with your Databricks workspace so stay tuned!

VS Code gallery: paiqo.Databricks-VSCode
Github repository: Databricks-VSCode

How-To: Migrating Databricks workspaces

Foreword:
The approach described in this blog post only uses the Databricks REST API and therefore should work with both, Azure Databricks and also Databricks on AWS!

It recently had to migrate an existing Databricks workspace to a new Azure subscription causing as little interruption as possible and not loosing any valuable content. So I thought a simple Move of the Azure resource would be the easiest thing to do in this case. Unfortunately it turns out that moving an Azure Databricks Service (=workspace) is not supported:

Resource move is not supported for resource types ‘Microsoft.Databricks/workspaces’. (Code: ResourceMoveNotSupported)

I do not know what is/was the problem here but I did not have time to investigate but instead needed to come up with a proper solution in time. So I had a look what needs to be done for a manual export. Basically there are 5 types of content within a Databricks workspace:

  • Workspace items (notebooks and folders)
  • Clusters
  • Jobs
  • Secrets
  • Security (users and groups)

For all of them an appropriate REST API is provided by Databricks to manage and also exports and imports. This was fantastic news for me as I knew I could use my existing PowerShell module DatabricksPS to do all the stuff without having to re-invent the wheel again.
So I basically extended the module and added new Import and Export functions which automatically process all the different content types:

  • Export-DatabricksEnvironment
  • Import-DatabricksEnvironment

They can be further parameterized to only import/export certain artifacts and how to deal with updates to already existing items. The actual output of the export looks like this and of course you can also modify it manually to your needs – all files are in JSON except for the notebooks which are exported as .DBC file by default:

A very simple sample code doing and export and an import into a different environment could look like this:

Having those scripts made the whole migration a very easy task.
In addition, these new cmdlets can also be used in your Continuous Integration/Continuous Delivery (CI/CD) pipelines in Azure DevOps or any other CI/CD tool!

So just download the latest version from the PowerShell gallery and give it a try!

PowerShell module for Databricks on Azure and AWS

Avaiilable via PowerShell Gallery: DatabricksPS

Over the last year I worked a lot with Databricks on Azure and I have to say that I was (and still am) very impressed how well it works and how it integrates with other services of the Microsoft Azure Data Platform like Data Lake Store, Data Factory, etc.

Some of the projects I worked on also included CI/CD like pipelines using Azure DevOps where Databricks did not really shine so bright in the beginning. There are no native tasks for it or anything. But this is OK as for those scenarios, where you need to automate/script something, Databricks offers a REST API (Azure, AWS).

As most of our deployments use PowerShell I wrote some cmdlets to easily work with the Databricks API in my scripts. These included managing clusters (create, start, stop, …), deploying content/notebooks, adding secrets, executing jobs/notebooks, etc. After some time I ended up having 20+ single scripts which was not really maintainable any more. So I packed them into a PowerShell module and also published it to the PowerShell Gallery (https://www.powershellgallery.com/packages/DatabricksPS) for everyone to use!

The module works for Databricks on Azure and also if you run Databricks on AWS – fortunately the API endpoints are almost identical.
The usage is quite simple as for any other PowerShell module:

  1. Install it using Install-Module cmdlet
  2. Setup the Databricks environment using API key and endpoint URL
  3. run the actual cmdlets (e.g. to start a cluster)


Here is the same code for you to copy&paste:

At the moment, the module supports the following APIs:

These APIs are not yet implemented but will be added in the near future:

All the cmdlets are documented and contain links to official documentation of the Rest API call used by the cmdlet. Some API endpoints support different variations of parameters – this was implemented using different parameter sets in PowerShell. There are still some ongoing tests (especially on AWS) and improvements but I general all cmdlets work as expected. I hope this helps anyone else who also has to deal with the Databricks APIs frequently or has to integrate it in a CI/CD pipeline.

The whole source code is also available from my Git-repository (https://github.com/gbrueckl/Databricks.API.PowerShell). If you want to provide any feedback, please use the Git-repository to do so.