Configure deployment settings for Big Data Cluster resources and services
Applies to: 
SQL Server 2019 (15.x)
Important
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.
Starting from a pre-defined set of configuration profiles that are built into the Azure Data CLI (azdata) management tool, you can easily modify the default settings to better suit your BDC workload requirements. The structure of the configuration files enables you to granularly update settings for each service of the resource.
Note
Big Data Clusters version CU9+ have support for configuration management functionality. This feature enables post-deployment configurations and provides increased visibility and configurability of the cluster. Versions CU8 and lower do not have this functionality and configurations can only be done at deployment time.
Watch this 13-minute video for an overview of big data cluster configuration:
Tip
Please reference the articles on how to configure high availability for mission critical components like SQL Server master or HDFS name node, for details on how to deploy highly available services.
Tip
Reference the SQL Server Big Data Clusters Configuration Properties article to see what settings are configurable. For versions CU8 or lower, reference SQL Server Master Instance Configuration Properties - Pre CU9 Release for configurations available for the SQL Server master instance and Apache Spark & Apache Hadoop (HDFS) configuration properties for Apache Spark and Hadoop properties.
You can also set resource level configurations or update the configurations for all services in a resource. Here is a summary of the structure for bdc.json:
{
"apiVersion": "v1",
"metadata": {
"kind": "BigDataCluster",
"name": "mssql-cluster"
},
"spec": {
"resources": {
"nmnode-0": {...
},
"sparkhead": {...
},
"zookeeper": {...
},
"gateway": {...
},
"appproxy": {...
},
"master": {...
},
"compute-0": {...
},
"data-0": {...
},
"storage-0": {...
},
"services": {
"sql": {
"resources": [
"master",
"compute-0",
"data-0",
"storage-0"
]
},
"hdfs": {
"resources": [
"nmnode-0",
"zookeeper",
"storage-0",
"sparkhead"
],
"settings": {...
},
"spark": {
"resources": [
"sparkhead",
"storage-0"
],
"settings": {...
}
}
}
}
For updating resource level configurations like instances in a pool, you will update the resource spec. For example, to update the number of instances in the compute pool you will modify this section in bdc.json configuration file:
"resources": {
...
"compute-0": {
"metadata": {
"kind": "Pool",
"name": "default"
},
"spec": {
"type": "Compute",
"replicas": 4
}
}
...
}
Similarly for changing the settings of a single service within a specific resource. For example, if you want to change the Spark memory settings only for the Spark component in the Storage pool, you will update the storage-0 resource with a settings section for spark service in the bdc.json configuration file.
"resources":{
...
"storage-0": {
"metadata": {
"kind": "Pool",
"name": "default"
},
"spec": {
"type": "Storage",
"replicas": 2,
"settings": {
"spark": {
"spark-defaults-conf.spark.driver.memory": "2g",
"spark-defaults-conf.spark.driver.cores": "1",
"spark-defaults-conf.spark.executor.instances": "3",
"spark-defaults-conf.spark.executor.memory": "1536m",
"spark-defaults-conf.spark.executor.cores": "1",
"yarn-site.yarn.nodemanager.resource.memory-mb": "18432",
"yarn-site.yarn.nodemanager.resource.cpu-vcores": "6",
"yarn-site.yarn.scheduler.maximum-allocation-mb": "18432",
"yarn-site.yarn.scheduler.maximum-allocation-vcores": "6",
"yarn-site.yarn.scheduler.capacity.maximum-am-resource-percent": "0.3"
}
}
}
}
...
}
If you want to apply same configurations for a service associated with multiple resources, you will update the corresponding settings in the services section. For example, if you would like to set same settings for Spark across both storage pool and Spark pools, you will update the settings section in the spark service section in the bdc.json configuration file.
"services": {
...
"spark": {
"resources": [
"sparkhead",
"storage-0"
],
"settings": {
"spark-defaults-conf.spark.driver.memory": "2g",
"spark-defaults-conf.spark.driver.cores": "1",
"spark-defaults-conf.spark.executor.instances": "3",
"spark-defaults-conf.spark.executor.memory": "1536m",
"spark-defaults-conf.spark.executor.cores": "1",
"yarn-site.yarn.nodemanager.resource.memory-mb": "18432",
"yarn-site.yarn.nodemanager.resource.cpu-vcores": "6",
"yarn-site.yarn.scheduler.maximum-allocation-mb": "18432",
"yarn-site.yarn.scheduler.maximum-allocation-vcores": "6",
"yarn-site.yarn.scheduler.capacity.maximum-am-resource-percent": "0.3"
}
}
...
}
To customize your cluster deployment configuration files, you can use any JSON format editor, such as VSCode. For scripting these edits for automation purposes, use the azdata bdc config command. This article explains how to configure big data cluster deployments by modifying deployment configuration files. It provides examples for how to change the configuration for different scenarios. For more information about how configuration files are used in deployments, see the deployment guidance.
Prerequisites
Each of the examples in this section assume that you have created a copy of one of the standard configurations. For more information, see Create a custom configuration. For example, the following command creates a directory called
custom-bdcthat contains two JSON deployment configuration files,bdc.jsonandcontrol.json, based on the defaultaks-dev-testconfiguration:azdata bdc config init --source aks-dev-test --target custom-bdc
Warning
The parameter imagePullPolicy is required to be set as "Always" in the deployment profile control.json file.
Change default Docker registry, repository, and images tag
The built-in configuration files, specifically control.json includes a docker section where container registry, repository, and images tag are pre-populated. By default, images required for big data clusters are in the Microsoft Container Registry (mcr.microsoft.com), in the mssql/bdc repository:
{
"apiVersion": "v1",
"metadata": {
"kind": "Cluster",
"name": "mssql-cluster"
},
"spec": {
"docker": {
"registry": "mcr.microsoft.com",
"repository": "mssql/bdc",
"imageTag": "2019-GDR1-ubuntu-16.04",
"imagePullPolicy": "Always"
},
...
}
}
Before deployment, you can customize the docker settings by either directly editing the control.json configuration file or using azdata bdc config commands. For example, following commands are updating a custom-bdc control.json configuration file with a different <registry>, <repository> and <image_tag>:
azdata bdc config replace -c custom-bdc/control.json -j "$.spec.docker.registry=<registry>"
azdata bdc config replace -c custom-bdc/control.json -j "$.spec.docker.repository=<repository>"
azdata bdc config replace -c custom-bdc/control.json -j "$.spec.docker.imageTag=<image_tag>"
Tip
As a best practice, you must use a version specific image tag and avoid using latest image tag, as this can result in version mismatch that will cause cluster health issues.
Tip
Big data clusters deployment must have access to the container registry and repository from which to pull container images. If your environment does not have access to the default Microsoft Container Registry, you can perform an offline installation where the required images are first placed into a private Docker repository. For more information about offline installations, see Perform an offline deployment of a SQL Server big data cluster. Note that you must set the DOCKER_USERNAME and DOCKER_PASSWORD environment variables before issuing the deployment to ensure the deployment workflow has acces to your private repository to pull the images from.
Change cluster name
The cluster name is both the name of the big data cluster and the Kubernetes namespace that will be created on deployment. It is specified in the following portion of the bdc.json deployment configuration file:
"metadata": {
"kind": "BigDataCluster",
"name": "mssql-cluster"
},
The following command sends a key-value pair to the --json-values parameter to change the big data cluster name to test-cluster:
azdata bdc config replace --config-file custom-bdc/bdc.json --json-values "metadata.name=test-cluster"
Important
The name of your big data cluster must be only lower case alpha-numeric characters, no spaces. All Kubernetes artifacts (containers, pods, statefull sets, services) for the cluster will be created in a namespace with same name as the cluster name specified.
Update endpoint ports
Endpoints are defined for the controller in the control.json and for gateway and SQL Server master instance in the corresponding sections in bdc.json. The following portion of the control.json configuration file shows the endpoint definitions for the controller:
{
"endpoints": [
{
"name": "Controller",
"serviceType": "LoadBalancer",
"port": 30080
},
{
"name": "ServiceProxy",
"serviceType": "LoadBalancer",
"port": 30777
}
]
}
The following example uses inline JSON to change the port for the controller endpoint:
azdata bdc config replace --config-file custom-bdc/control.json --json-values "$.spec.endpoints[?(@.name==""Controller"")].port=30000"
Configure scale
The configurations of each resource, such as the storage pool, is defined in the bdc.json configuration file. For example, the following portion of the bdc.json shows a storage-0 resource definition:
"storage-0": {
"metadata": {
"kind": "Pool",
"name": "default"
},
"spec": {
"type": "Storage",
"replicas": 2,
"settings": {
"spark": {
"spark-defaults-conf.spark.driver.memory": "2g",
"spark-defaults-conf.spark.driver.cores": "1",
"spark-defaults-conf.spark.executor.instances": "3",
"spark-defaults-conf.spark.executor.memory": "1536m",
"spark-defaults-conf.spark.executor.cores": "1",
"yarn-site.yarn.nodemanager.resource.memory-mb": "18432",
"yarn-site.yarn.nodemanager.resource.cpu-vcores": "6",
"yarn-site.yarn.scheduler.maximum-allocation-mb": "18432",
"yarn-site.yarn.scheduler.maximum-allocation-vcores": "6",
"yarn-site.yarn.scheduler.capacity.maximum-am-resource-percent": "0.3"
}
}
}
}
You can configure the number of instances in a storage, compute and/or data pool by modifying the replicas value for each pool. The following example uses inline JSON to change these values for the storage, compute and data pools to 10, 4 and 4 respectively:
azdata bdc config replace --config-file custom-bdc/bdc.json --json-values "$.spec.resources.storage-0.spec.replicas=10"
azdata bdc config replace --config-file custom-bdc/bdc.json --json-values "$.spec.resources.compute-0.spec.replicas=4"
azdata bdc config replace --config-file custom-bdc/bdc.json --json-values "$.spec.resources.data-0.spec.replicas=4"
Note
The maximum number of instances validated for compute and data pools is 8 each. There is no enforcement of this limit at deployment time, but we do not recommend configuring a higher scale in production deployments.
Configure storage
You can also change the storage class and characteristics that are used for each pool. The following example assigns a custom storage class to the storage and data pools and updates the size of the persistent volume claim for storing data to 500 Gb for HDFS (storage pool) and 100 Gb for master and data pool.
Tip
For more information about storage configuration, see Data persistence with SQL Server big data cluster on Kubernetes.
First create a patch.json file as below that adjust the storage settings
{
"patch": [
{
"op": "add",
"path": "spec.resources.storage-0.spec.storage",
"value": {
"data": {
"size": "500Gi",
"className": "default",
"accessMode": "ReadWriteOnce"
},
"logs": {
"size": "30Gi",
"className": "default",
"accessMode": "ReadWriteOnce"
}
}
},
{
"op": "add",
"path": "spec.resources.master.spec.storage",
"value": {
"data": {
"size": "100Gi",
"className": "default",
"accessMode": "ReadWriteOnce"
},
"logs": {
"size": "30Gi",
"className": "default",
"accessMode": "ReadWriteOnce"
}
}
},
{
"op": "add",
"path": "spec.resources.data-0.spec.storage",
"value": {
"data": {
"size": "100Gi",
"className": "default",
"accessMode": "ReadWriteOnce"
},
"logs": {
"size": "30Gi",
"className": "default",
"accessMode": "ReadWriteOnce"
}
}
}
]
}
You can then use the azdata bdc config patch command to update the bdc.json configuration file.
azdata bdc config patch --config-file custom-bdc/bdc.json --patch ./patch.json
Note
A configuration file based on kubeadm-dev-test does not have a storage definition for each pool, but you can use above process to added if needed.
Configure storage pool without spark
You can also configure the storage pools to run without spark and create a separate spark pool. This configuration enables you to scale spark compute power independent of storage. To see how to configure the spark pool, see the Create a spark pool section in this article.
Note
Deploying a big data cluster without Spark is not supported. So you must either have includeSpark set to true or you must create a separate spark pool with at least one instance. You can also have Spark running both in storage pool (includeSpark is true) and have a separate Spark pool.
By default, the includeSpark setting for the storage pool resource is set to true, so you must edit the includeSpark field into the storage configuration in order to make changes. The following command shows how to edit this value using inline json.
azdata bdc config replace --config-file custom-bdc/bdc.json --json-values "$.spec.resources.storage-0.spec.settings.spark.includeSpark=false"
Create a spark pool
You can create a Spark pool in addition, or instead of Spark instances running in the storage pool. Following example shows how to create a spark pool with two instances by patching the bdc.json configuration file.
First, create a spark-pool-patch.json file as below:
{
"patch": [
{
"op": "add",
"path": "spec.resources.spark-0",
"value": {
"metadata": {
"kind": "Pool",
"name": "default"
},
"spec": {
"type": "Spark",
"replicas": 2
}
}
},
{
"op": "add",
"path": "spec.services.spark.resources/-",
"value": "spark-0"
},
{
"op": "add",
"path": "spec.services.hdfs.resources/-",
"value": "spark-0"
}
]
}
Then run the azdata bdc config patch command:
azdata bdc config patch -c custom-bdc/bdc.json -p spark-pool-patch.json
Configure pod placement using Kubernetes labels
You can control pod placement on Kubernetes nodes that have specific resources to accommodate various types of workload requirements. Using Kubernetes labels, you can customize which are the nodes in your Kubernetes cluster will be used for deploying big data cluster resources, but also restrict which nodes are used for specific resources.
For example, you might want to ensure the storage pool resource pods are placed on nodes with more storage, while SQL Server master instances are placed on nodes that have higher CPU and memory resources. In this case, you will first build a heterogeneous Kubernetes cluster with different types of hardware and then assign node labels accordingly. At the time of deploying big data cluster, you can specify same labels at cluster level to indicate which nodes are used for big data cluster using the clusterLabel attribute in the control.json file. Then, different labels will be used for pool level placement. These labels can be specified in the big data cluster deployment configuration files using nodeLabel attribute. Kubernetes assigns the pods on nodes that match the specified labels. The specific label keys that needs to be added to the nodes in the kubernetes cluster are mssql-cluster (for indicating which nodes are used for big data cluster) and mssql-resource (to indicate which specific nodes the pods are placed on for various resources). The values of these labels can be any string that you choose.
Note
Due to the nature of the pods that do node level metrics collection, metricsdc pods are deployed on all nodes with the mssql-cluster label, and the mssql-resource will not apply to these pods.
The following example shows how to edit a custom configuration file to include a node label bdc for the entire big data cluster, a label bdc-master for placing SQL Server master instance pods on a specific node, bdc-storage-pool for storage pool resources, bdc-compute-pool for compute pool and data pool pods, and bdc-shared for rest of the resources.
First label the Kubernetes nodes:
kubectl label node <kubernetesNodeName1> mssql-cluster=bdc mssql-resource=bdc-shared --overwrite=true
kubectl label node <kubernetesNodeName2> mssql-cluster=bdc mssql-resource=bdc-master --overwrite=true
kubectl label node <kubernetesNodeName3> mssql-cluster=bdc mssql-resource=bdc-compute-pool --overwrite=true
kubectl label node <kubernetesNodeName4> mssql-cluster=bdc mssql-resource=bdc-compute-pool --overwrite=true
kubectl label node <kubernetesNodeName5> mssql-cluster=bdc mssql-resource=bdc-storage-pool --overwrite=true
kubectl label node <kubernetesNodeName6> mssql-cluster=bdc mssql-resource=bdc-storage-pool --overwrite=true
kubectl label node <kubernetesNodeName7> mssql-cluster=bdc mssql-resource=bdc-storage-pool --overwrite=true
kubectl label node <kubernetesNodeName8> mssql-cluster=bdc mssql-resource=bdc-storage-pool --overwrite=true
Then update the cluster deployment configuration files to include the label values. This example assumes that you are customizing configuration files in a custom-bdc profile. By default, there are no nodeLabel and clusterLabel keys in the built-in configurations so you will need to either edit a custom configuration file manually or use the azdata bdc config add commands to make the necessary edits.
azdata bdc config add -c custom-bdc/control.json -j "$.spec.clusterLabel=bdc"
azdata bdc config add -c custom-bdc/control.json -j "$.spec.nodeLabel=bdc-shared"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.master.spec.nodeLabel=bdc-master"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.compute-0.spec.nodeLabel=bdc-compute-pool"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.data-0.spec.nodeLabel=bdc-compute-pool"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.storage-0.spec.nodeLabel=bdc-storage-pool"
# below can be omitted in which case we will take the node label default from the control.json
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.nmnode-0.spec.nodeLabel=bdc-shared"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.sparkhead.spec.nodeLabel=bdc-shared"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.zookeeper.spec.nodeLabel=bdc-shared"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.gateway.spec.nodeLabel=bdc-shared"
azdata bdc config add -c custom-bdc/bdc.json -j "$.spec.resources.appproxy.spec.nodeLabel=bdc-shared"
Note
Best practice avoids giving the Kubernetes master any of the above BDC roles. If you plan on assigning these roles to the Kubernetes master node anyway, you'll need to remove its master:NoSchedule taint. Be aware that this could overload the master node and inhibit its ability to perform its Kubernetes management duties on larger clusters. It's normal to see some pods scheduled to the master on any deployment: they already tolerate the master:NoSchedule taint, and they're mostly used to help manage the cluster.
Other customizations using JSON patch files
JSON patch files configure multiple settings at once. For more information about JSON patches, see JSON Patches in Python and the JSONPath Online Evaluator.
The following patch.json files perform the following changes:
- Update the port of single endpoint in
control.json.
{
"patch": [
{
"op": "replace",
"path": "$.spec.endpoints[?(@.name=='Controller')].port",
"value": 30000
}
]
}
- Update all endpoints (
portandserviceType) incontrol.json.
{
"patch": [
{
"op": "replace",
"path": "spec.endpoints",
"value": [
{
"serviceType": "LoadBalancer",
"port": 30001,
"name": "Controller"
},
{
"serviceType": "LoadBalancer",
"port": 30778,
"name": "ServiceProxy"
}
]
}
]
}
- Update the controller storage settings in
control.json. These settings are applicable to all cluster components, unless overridden at pool level.
{
"patch": [
{
"op": "replace",
"path": "spec.storage",
"value": {
"data": {
"className": "managed-premium",
"accessMode": "ReadWriteOnce",
"size": "100Gi"
},
"logs": {
"className": "managed-premium",
"accessMode": "ReadWriteOnce",
"size": "32Gi"
}
}
}
]
}
- Update the storage class name in
control.json.
{
"patch": [
{
"op": "replace",
"path": "spec.storage.data.className",
"value": "managed-premium"
}
]
}
- Update pool storage settings for storage pool in
bdc.json.
{
"patch": [
{
"op": "replace",
"path": "spec.resources.storage-0.spec",
"value": {
"type": "Storage",
"replicas": 2,
"storage": {
"data": {
"size": "100Gi",
"className": "myStorageClass",
"accessMode": "ReadWriteOnce"
},
"logs": {
"size": "32Gi",
"className": "myStorageClass",
"accessMode": "ReadWriteOnce"
}
}
}
}
]
}
- Update Spark settings for storage pool in
bdc.json.
{
"patch": [
{
"op": "replace",
"path": "spec.services.spark.settings",
"value": {
"spark-defaults-conf.spark.driver.memory": "2g",
"spark-defaults-conf.spark.driver.cores": "1",
"spark-defaults-conf.spark.executor.instances": "3",
"spark-defaults-conf.spark.executor.memory": "1536m",
"spark-defaults-conf.spark.executor.cores": "1",
"yarn-site.yarn.nodemanager.resource.memory-mb": "18432",
"yarn-site.yarn.nodemanager.resource.cpu-vcores": "6",
"yarn-site.yarn.scheduler.maximum-allocation-mb": "18432",
"yarn-site.yarn.scheduler.maximum-allocation-vcores": "6",
"yarn-site.yarn.scheduler.capacity.maximum-am-resource-percent": "0.3"
}
}
]
}
Tip
For more information about the structure and options for changing a deployment configuration file, see Deployment configuration file reference for big data clusters.
Use azdata bdc config commands to apply the changes in the JSON patch file. The following example applies the patch.json file to a target deployment configuration file custom-bdc/bdc.json.
azdata bdc config patch --config-file custom-bdc/bdc.json --patch-file ./patch.json
Disable ElasticSearch to run in privileged mode
By default, ElasticSearch container runs in privilege mode in big data cluster. This setting ensures that at container initialization time, the container has enough permissions to update a setting on the host required when ElasticSearch processes higher amount of logs. You can find more information about this topic in this article.
For disabling the container that runs ElasticSearch to run in privileged mode, you must updated the settings section in the control.json and specify the value of vm.max_map_count to -1. Here is a sample of how this section would look like:
{
"apiVersion": "v1",
"metadata": {...},
"spec": {
"docker": {...},
"storage": {...},
"endpoints": [...],
"settings": {
"ElasticSearch": {
"vm.max_map_count": "-1"
}
}
}
}
You can manually edit the control.json and add the above section to the spec, or you can create a patch file elasticsearch-patch.json like below and use Azure Data CLI (azdata) to patch the control.json file:
{
"patch": [
{
"op": "add",
"path": "spec.settings",
"value": {
"ElasticSearch": {
"vm.max_map_count": "-1"
}
}
}
]
}
Run this command to patch the configuration file:
azdata bdc config patch --config-file custom-bdc/control.json --patch-file elasticsearch-patch.json
Important
We recommend as a best practice to manually update the max_map_count setting manually on each host in the Kubernetes cluster as per instructions in this article.
Turn pods and nodes metrics collection on/off
SQL Server 2019 CU5 enabled two feature switches to control the collection of pods and nodes metrics. In case you are using different solutions for monitoring your Kubernetes infrastructure, you can turn off the built-in metrics collection for pods and host nodes by setting allowNodeMetricsCollection and allowPodMetricsCollection to false in control.json deployment configuration file. For OpenShift environments, these settings are set to false by default in the built-in deployment profiles, since collecting pod and node metrics requires privileged capabilities. Run this command to update the values of these settings in your custom configuration file using azdata CLI:
azdata bdc config replace -c custom-bdc/control.json -j "$.security.allowNodeMetricsCollection=false"
azdata bdc config replace -c custom-bdc/control.json -j "$.security.allowPodMetricsCollection=false"
Next steps
For more information about using configuration files in big data cluster deployments, see How to deploy SQL Server Big Data Clusters on Kubernetes.