How to deploy SQL Server big data clusters on Kubernetes

THIS TOPIC APPLIES TO:yesSQL Server noAzure SQL DatabasenoAzure SQL Data Warehouse noParallel Data Warehouse

SQL Server big data cluster is deployed as docker containers on a Kubernetes cluster. This is an overview of the setup and configuration steps:

  • Set up a Kubernetes cluster on a single VM, cluster of VMs, or in Azure Kubernetes Service (AKS).
  • Install the cluster configuration tool mssqlctl on your client machine.
  • Deploy a SQL Server big data cluster in a Kubernetes cluster.


SQL Server big data clusters is first available as a limited public preview through the SQL Server 2019 Early Adoption Program. To request access, register here, and specify your interest to try SQL Server big data clusters. Microsoft will triage all requests and respond as soon as possible.

Install SQL Server 2019 big data tools

Before deploying a SQL Server 2019 big data cluster, first install the big data tools:

  • mssqlctl
  • kubectl
  • Azure Data Studio
  • SQL Server 2019 extension

Kubernetes prerequisites

SQL Server big data clusters require a minimum Kubernetes version of at least v1.10 for both server and client (kubectl).


Note that the client and server Kubernetes versions should be within +1 or -1 minor version. For more information, see Kubernetes supported releases and component skew.

Kubernetes cluster setup

If you already have a Kubernetes cluster that meets the above prerequisites, then you can skip directly to the deployment step. This section assumes a basic understanding of Kubernetes concepts. For detailed information on Kubernetes, see the Kubernetes documentation.

You can choose to deploy Kubernetes in any of three ways:

Deploy Kubernetes on: Description Link
Azure Kubernetes Services (AKS) A managed Kubernetes container service in Azure. Instructions
Multiple machines (kubeadm) A Kubernetes cluster deployed on physical or virtual machines using kubeadm Instructions
Minikube A single-node Kubernetes cluster in a VM. Instructions


For a sample python script that deploys both AKS and a SQL Server big data cluster in one step, see Quickstart: Deploy SQL Server big data cluster on Azure Kubernetes Service (AKS).

Verify Kubernetes configuration

Run the kubectl command to view the cluster configuration. Ensure that kubectl is pointed to the correct cluster context.

kubectl config view

After you have configured your Kubernetes cluster, you can proceed with the deployment of a new SQL Server big data cluster. If you are upgrading from a previous release, please see How to upgrade SQL Server big data clusters.

Deployment overview

Starting in CTP 2.5, most big data cluster settings are defined in a JSON deployment configuration file. You can use a default deployment profile for AKS, kubeadm, or minikube or you can customize your own deployment configuration file to use during setup. For security reasons, authentication settings are passed via environment variables.

The following sections provide more details on how to configure your big data cluster deployments as well as examples of common customizations. Also, you can always edit the custom deployment configuration file using an editor like VSCode for example.

Default configurations

Big data cluster deployment options are defined in JSON configuration files. There are three standard deployment profiles with default settings for dev/test environments:

Deployment profile Kubernetes environment
aks-dev-test Azure Kubernetes Service (AKS)
kubeadm-dev-test Multiple machines (kubeadm)
minikube-dev-test minikube

You can deploy a big data cluster by running mssqlctl bdc create. This prompts you to choose one of the default configurations and then guides you through the deployment.

mssqlctl bdc create

In this scenario, you are prompted for any settings that are not part of the default configuration, such as passwords. Note that the Docker information is provided to you by Microsoft as part of the SQL Server 2019 Early Adoption Program.


The default name of the big data cluster is mssql-cluster. This is important to know in order to run any of the kubectl commands that specify the Kubernetes namespace with the -n parameter.

Custom configurations

It is also possible to customize your own deployment configuration profile. You can do this with the following steps:

  1. Start with one of the standard deployment profiles that match your Kubernetes environment. You can use the mssqlctl bdc config list command to list them:

    mssqlctl bdc config list
  2. To customize your deployment, create a copy of the deployment profile with the mssqlctl bdc config init command. For example, the following command creates a copy of the aks-dev-test deployment configuration file in a target directory named custom:

    mssqlctl bdc config init --source aks-dev-test --target custom


    The --target specifies a directory that contains the configuration file based on the --source parameter.

  3. To customize settings in your deployment configuration profile, you can edit the deployment configuration file in a tool that is good for editing JSON files, such as VS Code. For scripted automation, you can also edit the custom deployment profile using mssqlctl bdc config section set command. For example, the following command alters a custom deployment profile to change the name of the deployed cluster from the default (mssql-cluster) to test-cluster:

    mssqlctl bdc config section set --config-profile custom --json-values ""


    The --config-profile specifies a directory name for your custom deployment profile, but the actual modifications happen on the deployment configuration JSON file within that directory. A useful tool for finding JSON paths is the JSONPath Online Evaluator.

    In addition to passing key-value pairs, you can also provide inline JSON values or pass JSON patch files. For more information, see Configure deployment settings for big data clusters.

  4. Then pass the custom configuration file to mssqlctl bdc create. Note that you must set the required environment variables, otherwise you will be prompted for the values:

    mssqlctl bdc create --config-profile custom --accept-eula yes


For more information on the structure of a deployment configuration file, see the Deployment configuration file reference. For more configuration examples, see Configure deployment settings for big data clusters.

Environment variables

The following environment variables are used for security settings that are not stored in a deployment configuration file. Note that Docker settings except credentials can be set in the configuration file.

Environment variable Description
DOCKER_USERNAME The username to access the container images in case they are stored in a private repository.
DOCKER_PASSWORD The password to access the above private repository.
CONTROLLER_USERNAME The username for the cluster administrator.
CONTROLLER_PASSWORD The password for the cluster administrator.
KNOX_PASSWORD The password for Knox user.
MSSQL_SA_PASSWORD The password of SA user for SQL master instance.

These environment variables must be set prior to calling mssqlctl bdc create. If any variable is not set, you are prompted for it.

The following example shows how to set the environment variables for Linux (bash) and Windows (PowerShell):

export CONTROLLER_PASSWORD=<password>
export MSSQL_SA_PASSWORD=<password>
export KNOX_PASSWORD=<password>
export DOCKER_USERNAME=<docker-username>
export DOCKER_PASSWORD=<docker-password>
SET DOCKER_USERNAME=<docker-username>
SET DOCKER_PASSWORD=<docker-password>

After setting the environment variables, you must run mssqlctl bdc create to trigger the deployment. This example uses the cluster configuration profile created above:

mssqlctl bdc create --config-profile custom --accept-eula yes

Please note the following guidelines:

  • At this time, credentials for the private Docker registry will be provided to you upon triaging your Early Adoption Program registration. Early Adoption Program registration is required to test SQL Server big data clusters.
  • Make sure you wrap the passwords in double quotes if it contains any special characters. You can set the MSSQL_SA_PASSWORD to whatever you like, but make sure the password is sufficiently complex and don't use the !, & or ' characters. Note that double quotes delimiters work only in bash commands.
  • The SA login is a system administrator on the SQL Server master instance that gets created during setup. After creating your SQL Server container, the MSSQL_SA_PASSWORD environment variable you specified is discoverable by running echo $MSSQL_SA_PASSWORD in the container. For security purposes, change your SA password as per best practices documented here.

Unattended install

For an unattended deployment, you must set all required environment variables, use a configuration file, and call mssqlctl bdc create command with the --accept-eula yes parameter. The examples in the previous section demonstrate the syntax for an unattended installation.

Monitor the deployment

During cluster bootstrap, the client command window will output the deployment status. During the deployment process, you should see a series of messages where it is waiting for the controller pod:

Waiting for cluster controller to start.

In less than 15 to 30 minutes, you should be notified that the controller pod is running:

Cluster controller endpoint is available at
Cluster control plane is ready.


The entire deployment can take a long time due to the time required to download the container images for the components of the big data cluster. However, it should not take several hours. If you are experiencing problems with your deployment, see Monitoring and troubleshoot SQL Server big data clusters.

When the deployment finishes, the output notifies you of success:

Cluster deployed successfully.


The default name for the deployed big data cluster is mssql-cluster unless modified by a custom configuration.

Retrieve endpoints

After the deployment script has completed successfully, you can obtain the IP addresses of the external endpoints for the big data cluster using the following steps.

  1. After the deployment, find the IP address of the controller endpoint by looking at the EXTERNAL-IP output of the following kubectl command:

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


    If you did not change the default name during deployment, use -n mssql-cluster in the previous command. mssql-cluster is the default name for the big data cluster.

  2. Log in to the big data cluster with mssqlctl login. Set the --controller-endpoint parameter to the external IP address of the controller endpoint.

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

    Specify the username and password that you configured for the controller (CONTROLLER_USERNAME and CONTROLLER_PASSWORD) during deployment.

  3. Run mssqlctl bdc endpoint list to get a list with a description of each endpoint and their corresponding IP address and port values.

    mssqlctl bdc endpoint list -o table

    The following list shows sample output from this command:

    Description                                             Endpoint                                                   Ip              Name               Port    Protocol
    ------------------------------------------------------  ---------------------------------------------------------  --------------  -----------------  ------  ----------
    Gateway to access HDFS files, Spark                                  gateway            30443   https
    Spark Jobs Management and Monitoring Dashboard  spark-history      30443   https
    Spark Diagnostics and Monitoring Dashboard      yarn-ui            30443   https
    Application Proxy                                                    app-proxy          30778   https
    Management Proxy                                                     mgmtproxy          30777   https
    Log Search Dashboard                                          logsui             30777   https
    Metrics Dashboard                                            metricsui          30777   https
    Cluster Management Service                                           controller         30080   https
    SQL Server Master Instance Front-End          ,31433                               sql-server-master  31433   tcp
    HDFS File System Proxy                          webhdfs            30443   https
    Proxy for running Spark statements, jobs, applications  livy               30443   https

You can also get all the service endpoints deployed for the cluster by running the following kubectl command:

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


If you are using minikube, you need to run the following command to get the IP address you need to connect to. In addition to the IP, specify the port for the endpoint you need to connect to.

minikube ip

Verify the cluster status

After deployment, you can check the status of the cluster with the mssqlctl bdc status show command.

mssqlctl bdc status show -o table


To run the status commands, you must first log in with the mssqlctl login command, which was shown in the previous endpoints section.

The following shows sample output from this command:

Kind     Name           State
-------  -------------  -------
BDC      mssql-cluster  Ready
Control  default        Ready
Master   default        Ready
Compute  default        Ready
Data     default        Ready
Storage  default        Ready

In addition to this summary status, you can also get more detailed status with the following commands:

The output from these commands contain URLs to Kibana and Grafana dashboards for more detailed analysis.

In addition to using mssqlctl, you can also use Azure Data Studio to find both endpoints and status information. For more information about viewing cluster status with mssqlctl and Azure Data Studio, see How to view the status of a big data cluster.

Connect to the cluster

For more information on how to connect to the big data cluster, see Connect to a SQL Server big data cluster with Azure Data Studio.

Next steps

To learn more about big data cluster deployment, see the following resources: