How to deploy SQL Server big data clusters on Kubernetes
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:
- Azure Data Studio
- SQL Server 2019 extension
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.
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.
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.json||Azure Kubernetes Service (AKS)|
|kubeadm-dev-test.json||Multiple machines (kubeadm)|
You can deploy a big data cluster by running mssqlctl cluster create. This prompts you to choose one of the default configurations and then guides you through the deployment.
mssqlctl cluster 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
It is also possible to customize your own deployment configuration file. You can do this with the following steps:
Start with one of the standard deployment profiles that match your Kubernetes environment. You can use the mssqlctl cluster config list command to list them:
mssqlctl cluster config list
To customize your deployment, create a copy of the deployment profile with the mssqlctl cluster config init command. For example, the following command creates a copy of the aks-dev-test.json deployment configuration file in the current directory:
mssqlctl cluster config init --src aks-dev-test.json --target custom.json
To customize settings in your deployment configuration file, you can edit it in a tool that is good for editing json docs like VS Code. For scripted automation, you can edit the custom configuration file using mssqlctl cluster config section set command. For example, the following command alters a custom configuration file to change the name of the deployed cluster from the default (mssql-cluster) to test-cluster:
mssqlctl cluster config section set --config-file custom.json --json-values "metadata.name=test-cluster"
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.
Then pass the custom configuration file to mssqlctl cluster create. Note that you must set the required environment variables, otherwise you will be prompted for the values:
mssqlctl cluster create --config-file custom.json --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.
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.
|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 cluster 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_USERNAME=admin export CONTROLLER_PASSWORD=<password> export MSSQL_SA_PASSWORD=<password> export KNOX_PASSWORD=<password> export DOCKER_USERNAME=<docker-username> export DOCKER_PASSWORD=<docker-password>
SET CONTROLLER_USERNAME=admin SET CONTROLLER_PASSWORD=<password> SET MSSQL_SA_PASSWORD=<password> SET KNOX_PASSWORD=<password> SET DOCKER_USERNAME=<docker-username> SET DOCKER_PASSWORD=<docker-password>
Upon setting the environment variables, you must run
mssqlctl cluster create to trigger the deployment. This example uses the cluster configuration file created above:
mssqlctl cluster create --config-file custom.json --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
'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.
For an unattended deployment, you must set all required environment variables, use a configuration file, and call
mssqlctl cluster 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:
2019-04-12 14:40:10.0129 UTC | INFO | Waiting for controller pod to be up...
In less than 15 to 30 minutes, you should be notified that the controller pod is running:
2019-04-12 15:01:10.0809 UTC | INFO | Waiting for controller pod to be up. Checkthe mssqlctl.log file for more details. 2019-04-12 15:01:40.0861 UTC | INFO | Controller pod is running. 2019-04-12 15:01:40.0884 UTC | INFO | Controller Endpoint: https://<ip-address>:30080
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:
2019-04-12 15:37:18.0271 UTC | INFO | Monitor and track your cluster at the Portal Endpoint: https://<ip-address>:30777/portal/ 2019-04-12 15:37:18.0271 UTC | INFO | Cluster deployed successfully.
Note the URL of the Portal Endpoint in the previous output for use in the next section.
The default name for the deployed big data cluster is
mssql-cluster unless modified by a custom configuration.
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.
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-clusterin the previous command. mssql-cluster is the default name for the big data cluster.
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.
Run mssqlctl cluster endpoint list to get a list with a description of each endpoint and their corresponding IP address and port values.
mssqlctl cluster endpoint list
The following list shows sample output from this command:
Name Description Endpoint Ip Port Protocol ----------------- ------------------------------------------------------ --------------------------------------------------------- -------------- ------ ---------- gateway Gateway to access HDFS files, Spark https://126.96.36.199:30443 188.8.131.52 30443 https spark-history Spark Jobs Management and Monitoring Dashboard https://184.108.40.206:30443/gateway/default/sparkhistory 220.127.116.11 30443 https yarn-ui Spark Diagnostics and Monitoring Dashboard https://18.104.22.168:30443/gateway/default/yarn 22.214.171.124 30443 https app-proxy Application Proxy https://126.96.36.199:30778 188.8.131.52 30778 https management-proxy Management Proxy https://184.108.40.206:30777 220.127.116.11 30777 https portal Management Portal https://18.104.22.168:30777/portal 22.214.171.124 30777 https log-search-ui Log Search Dashboard https://126.96.36.199:30777/kibana 188.8.131.52 30777 https metrics-ui Metrics Dashboard https://184.108.40.206:30777/grafana 220.127.116.11 30777 https controller Cluster Management Service https://18.104.22.168:30080 22.214.171.124 30080 https sql-server-master SQL Server Master Instance Front-End 126.96.36.199,31433 188.8.131.52 31433 tcp webhdfs HDFS File System Proxy https://184.108.40.206:30443/gateway/default/webhdfs/v1 220.127.116.11 30443 https livy Proxy for running Spark statements, jobs, applications https://18.104.22.168:30443/gateway/default/livy/v1 22.214.171.124 30443 https
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.
Irrespective of the platform you are running your Kubernetes cluster on, to get all the service endpoints deployed for the cluster, run following command:
kubectl get svc -n <your-big-data-cluster-name>
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.
To learn more about big data cluster deployment, see the following resources:
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