How to deploy an app on SQL Server Big Data Clusters

Applies to: SQL Server 2019 (15.x)


The Microsoft SQL Server 2019 Big Data Clusters add-on will be retired. Support for SQL Server 2019 Big Data Clusters will end on February 28, 2025. All existing users of SQL Server 2019 with Software Assurance will be fully supported on the platform and the software will continue to be maintained through SQL Server cumulative updates until that time. For more information, see the announcement blog post and Big data options on the Microsoft SQL Server platform.

Applications deployed on SQL Server Big Data Clusters not only benefit from many advantages such as the computational power of the cluster but also access massive data that is available on the cluster. It dramatically improve the performance since your app sits in the same cluster where the data lives.

Applications are deployed and managed using Azure Data CLI (azdata).

This article provides examples of how to deploy apps from the command line inside a SQL Server Big Data Cluster. To learn how to use this in Visual Studio Code refer to Visual Studio Code Extension.



In SQL Server 2019 you can create, delete, describe, initialize, list run and update your application. The following table describes the application deployment commands that you can use with azdata.

Command Description
azdata login Sign into a SQL Server big data cluster
azdata app create Create application.
azdata app delete Delete application.
azdata app describe Describe application.
azdata app init Kickstart new application skeleton.
azdata app list List application(s).
azdata app run Run application.
azdata app update Update application.

You can get help with the --help parameter as in the following example:

azdata app create --help

The following sections describe these commands in more detail.

Sign in

Before you deploy or interact with applications, first sign in to your SQL Server big data cluster with the azdata login command. Specify the external IP address of the controller-svc-external service (for example: https://ip-address:30080) along with the user name and password to the cluster.

azdata login --controller-endpoint https://<ip-address-of-controller-svc-external>:30080 --controller-username <user-name>

Azure Kubernetes Service (AKS)

If you're using AKS, you need to run the following command to get the IP address of the controller-svc-external service by running this command in a bash or cmd window:

kubectl get svc controller-svc-external -n <name of your big data cluster>

Kubernetes clusters created with kubeadm

Run the following command to get the IP address to sign in to the cluster

kubectl get node --selector=''

Create an app skeleton

The azdata app init command provides a scaffold with the relevant artifacts that is required for deploying an app. The example below creates add-app you can do this by running the following command.

azdata app init --name add-app --version v1 --template python

This will create a folder called hello. You can use cd command into the directory and inspect the generated files in the folder. spec.yaml defines the app, such as name, version, and source code. You can edit the spec to change name, version, input, and outputs.

Here is a sample output from the init command that you'll see in the folder

Create an app

To create an application, you use Azure Data CLI (azdata) with the app create command. These files reside locally on the machine that you're creating the app from.

Use the following syntax to create a new app in big data cluster:

azdata app create --spec <directory containing spec file>

The following command shows an example of what this command might look like:

azdata app create --spec ./addpy

This assumes that you have your application stored in the addpy folder. This folder should also contain a specification file for the application, called spec.yaml. For more information, see the Application Deployment page on the spec.yaml file.

To deploy this app sample app, create the following files in a directory called addpy:

  • Copy the following Python code into this file:
    def add(x, y):
      result = x+y
      return result
  • spec.yaml. Copy the following code into this file:
    name: add-app #name of your python script
    version: v1  #version of the app
    runtime: Python #the language this app uses (R or Python)
    src: ./ #full path to the location of the app
    entrypoint: add #the function that will be called upon execution
    replicas: 1  #number of replicas needed
    poolsize: 1  #the pool size that you need your app to scale
    inputs:  #input parameters that the app expects and the type
       x: int
       y: int
    output: #output parameter the app expects and the type
       result: int

Then, run the command below:

azdata app create --spec ./addpy

You can check if the app is deployed using the list command:

azdata app list

If the deployment isn't completed, you should see the state show WaitingforCreate as the following example:

    "name": "add-app",
    "state": "WaitingforCreate",
    "version": "v1"

After the deployment is successful, you should see the state change to Ready status:

    "name": "add-app",
    "state": "Ready",
    "version": "v1"

List an app

You can list any apps that were successfully created with the app list command.

The following command lists all available applications in your big data cluster:

azdata app list

If you specify a name and version, it lists that specific app and its state (Creating or Ready):

azdata app list --name <app_name> --version <app_version>

The following example demonstrates this command:

azdata app list --name add-app --version v1

You should see output similar to the following example:

    "name": "add-app",
    "state": "Ready",
    "version": "v1"

Delete an app

To delete an app from your big data cluster, use the following syntax:

azdata app delete --name add-app --version v1

Next steps

Explore how to integrate apps deployed on SQL Server Big Data Clusters in your own applications at Run applications on big data clusters and Consume applications on big data clusters for more information. You can also check out additional samples at App Deploy Samples.

For more information about SQL Server Big Data Clusters, see Introducing SQL Server 2019 Big Data Clusters.