Databricks v1.63.0, Mar 13 25

Databricks v1.63.0 published on Thursday, Mar 13, 2025 by Pulumi

databricks.Cluster

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Databricks v1.63.0 published on Thursday, Mar 13, 2025 by Pulumi

Import

The resource cluster can be imported using cluster id.

bash

$ pulumi import databricks:index/cluster:Cluster this <cluster-id>

Create Cluster Resource

Resources are created with functions called constructors. To learn more about declaring and configuring resources, see Resources.

new Cluster(name: string, args: ClusterArgs, opts?: CustomResourceOptions);

@overload
def Cluster(resource_name: str,
            args: ClusterArgs,
            opts: Optional[ResourceOptions] = None)

@overload
def Cluster(resource_name: str,
            opts: Optional[ResourceOptions] = None,
            spark_version: Optional[str] = None,
            idempotency_token: Optional[str] = None,
            custom_tags: Optional[Mapping[str, str]] = None,
            aws_attributes: Optional[ClusterAwsAttributesArgs] = None,
            azure_attributes: Optional[ClusterAzureAttributesArgs] = None,
            init_scripts: Optional[Sequence[ClusterInitScriptArgs]] = None,
            cluster_mount_infos: Optional[Sequence[ClusterClusterMountInfoArgs]] = None,
            cluster_name: Optional[str] = None,
            instance_pool_id: Optional[str] = None,
            data_security_mode: Optional[str] = None,
            is_pinned: Optional[bool] = None,
            driver_instance_pool_id: Optional[str] = None,
            driver_node_type_id: Optional[str] = None,
            enable_elastic_disk: Optional[bool] = None,
            enable_local_disk_encryption: Optional[bool] = None,
            gcp_attributes: Optional[ClusterGcpAttributesArgs] = None,
            apply_policy_default_values: Optional[bool] = None,
            cluster_log_conf: Optional[ClusterClusterLogConfArgs] = None,
            autotermination_minutes: Optional[int] = None,
            docker_image: Optional[ClusterDockerImageArgs] = None,
            is_single_node: Optional[bool] = None,
            kind: Optional[str] = None,
            libraries: Optional[Sequence[ClusterLibraryArgs]] = None,
            no_wait: Optional[bool] = None,
            node_type_id: Optional[str] = None,
            num_workers: Optional[int] = None,
            policy_id: Optional[str] = None,
            runtime_engine: Optional[str] = None,
            single_user_name: Optional[str] = None,
            spark_conf: Optional[Mapping[str, str]] = None,
            spark_env_vars: Optional[Mapping[str, str]] = None,
            autoscale: Optional[ClusterAutoscaleArgs] = None,
            ssh_public_keys: Optional[Sequence[str]] = None,
            use_ml_runtime: Optional[bool] = None,
            workload_type: Optional[ClusterWorkloadTypeArgs] = None)

func NewCluster(ctx *Context, name string, args ClusterArgs, opts ...ResourceOption) (*Cluster, error)

public Cluster(string name, ClusterArgs args, CustomResourceOptions? opts = null)

public Cluster(String name, ClusterArgs args)
public Cluster(String name, ClusterArgs args, CustomResourceOptions options)

type: databricks:Cluster
properties: # The arguments to resource properties.
options: # Bag of options to control resource's behavior.

Parameters

name string: The unique name of the resource.
args ClusterArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

resource_name str: The unique name of the resource.
args ClusterArgs: The arguments to resource properties.
opts ResourceOptions: Bag of options to control resource's behavior.

ctx Context: Context object for the current deployment.
name string: The unique name of the resource.
args ClusterArgs: The arguments to resource properties.
opts ResourceOption: Bag of options to control resource's behavior.

name string: The unique name of the resource.
args ClusterArgs: The arguments to resource properties.
opts CustomResourceOptions: Bag of options to control resource's behavior.

name String: The unique name of the resource.
args ClusterArgs: The arguments to resource properties.
options CustomResourceOptions: Bag of options to control resource's behavior.

Constructor example

The following reference example uses placeholder values for all input properties.

TypeScript
Python
Go
C#
Java
YAML

var clusterResource = new Databricks.Cluster("clusterResource", new()
{
    SparkVersion = "string",
    IdempotencyToken = "string",
    CustomTags = 
    {
        { "string", "string" },
    },
    AwsAttributes = new Databricks.Inputs.ClusterAwsAttributesArgs
    {
        Availability = "string",
        EbsVolumeCount = 0,
        EbsVolumeIops = 0,
        EbsVolumeSize = 0,
        EbsVolumeThroughput = 0,
        EbsVolumeType = "string",
        FirstOnDemand = 0,
        InstanceProfileArn = "string",
        SpotBidPricePercent = 0,
        ZoneId = "string",
    },
    AzureAttributes = new Databricks.Inputs.ClusterAzureAttributesArgs
    {
        Availability = "string",
        FirstOnDemand = 0,
        LogAnalyticsInfo = new Databricks.Inputs.ClusterAzureAttributesLogAnalyticsInfoArgs
        {
            LogAnalyticsPrimaryKey = "string",
            LogAnalyticsWorkspaceId = "string",
        },
        SpotBidMaxPrice = 0,
    },
    InitScripts = new[]
    {
        new Databricks.Inputs.ClusterInitScriptArgs
        {
            Abfss = new Databricks.Inputs.ClusterInitScriptAbfssArgs
            {
                Destination = "string",
            },
            File = new Databricks.Inputs.ClusterInitScriptFileArgs
            {
                Destination = "string",
            },
            Gcs = new Databricks.Inputs.ClusterInitScriptGcsArgs
            {
                Destination = "string",
            },
            S3 = new Databricks.Inputs.ClusterInitScriptS3Args
            {
                Destination = "string",
                CannedAcl = "string",
                EnableEncryption = false,
                EncryptionType = "string",
                Endpoint = "string",
                KmsKey = "string",
                Region = "string",
            },
            Volumes = new Databricks.Inputs.ClusterInitScriptVolumesArgs
            {
                Destination = "string",
            },
            Workspace = new Databricks.Inputs.ClusterInitScriptWorkspaceArgs
            {
                Destination = "string",
            },
        },
    },
    ClusterMountInfos = new[]
    {
        new Databricks.Inputs.ClusterClusterMountInfoArgs
        {
            LocalMountDirPath = "string",
            NetworkFilesystemInfo = new Databricks.Inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs
            {
                ServerAddress = "string",
                MountOptions = "string",
            },
            RemoteMountDirPath = "string",
        },
    },
    ClusterName = "string",
    InstancePoolId = "string",
    DataSecurityMode = "string",
    IsPinned = false,
    DriverInstancePoolId = "string",
    DriverNodeTypeId = "string",
    EnableElasticDisk = false,
    EnableLocalDiskEncryption = false,
    GcpAttributes = new Databricks.Inputs.ClusterGcpAttributesArgs
    {
        Availability = "string",
        BootDiskSize = 0,
        GoogleServiceAccount = "string",
        LocalSsdCount = 0,
        UsePreemptibleExecutors = false,
        ZoneId = "string",
    },
    ApplyPolicyDefaultValues = false,
    ClusterLogConf = new Databricks.Inputs.ClusterClusterLogConfArgs
    {
        Dbfs = new Databricks.Inputs.ClusterClusterLogConfDbfsArgs
        {
            Destination = "string",
        },
        S3 = new Databricks.Inputs.ClusterClusterLogConfS3Args
        {
            Destination = "string",
            CannedAcl = "string",
            EnableEncryption = false,
            EncryptionType = "string",
            Endpoint = "string",
            KmsKey = "string",
            Region = "string",
        },
        Volumes = new Databricks.Inputs.ClusterClusterLogConfVolumesArgs
        {
            Destination = "string",
        },
    },
    AutoterminationMinutes = 0,
    DockerImage = new Databricks.Inputs.ClusterDockerImageArgs
    {
        Url = "string",
        BasicAuth = new Databricks.Inputs.ClusterDockerImageBasicAuthArgs
        {
            Password = "string",
            Username = "string",
        },
    },
    IsSingleNode = false,
    Kind = "string",
    Libraries = new[]
    {
        new Databricks.Inputs.ClusterLibraryArgs
        {
            Cran = new Databricks.Inputs.ClusterLibraryCranArgs
            {
                Package = "string",
                Repo = "string",
            },
            Egg = "string",
            Jar = "string",
            Maven = new Databricks.Inputs.ClusterLibraryMavenArgs
            {
                Coordinates = "string",
                Exclusions = new[]
                {
                    "string",
                },
                Repo = "string",
            },
            Pypi = new Databricks.Inputs.ClusterLibraryPypiArgs
            {
                Package = "string",
                Repo = "string",
            },
            Requirements = "string",
            Whl = "string",
        },
    },
    NoWait = false,
    NodeTypeId = "string",
    NumWorkers = 0,
    PolicyId = "string",
    RuntimeEngine = "string",
    SingleUserName = "string",
    SparkConf = 
    {
        { "string", "string" },
    },
    SparkEnvVars = 
    {
        { "string", "string" },
    },
    Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
    {
        MaxWorkers = 0,
        MinWorkers = 0,
    },
    SshPublicKeys = new[]
    {
        "string",
    },
    UseMlRuntime = false,
    WorkloadType = new Databricks.Inputs.ClusterWorkloadTypeArgs
    {
        Clients = new Databricks.Inputs.ClusterWorkloadTypeClientsArgs
        {
            Jobs = false,
            Notebooks = false,
        },
    },
});

example, err := databricks.NewCluster(ctx, "clusterResource", &databricks.ClusterArgs{
	SparkVersion:     pulumi.String("string"),
	IdempotencyToken: pulumi.String("string"),
	CustomTags: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	AwsAttributes: &databricks.ClusterAwsAttributesArgs{
		Availability:        pulumi.String("string"),
		EbsVolumeCount:      pulumi.Int(0),
		EbsVolumeIops:       pulumi.Int(0),
		EbsVolumeSize:       pulumi.Int(0),
		EbsVolumeThroughput: pulumi.Int(0),
		EbsVolumeType:       pulumi.String("string"),
		FirstOnDemand:       pulumi.Int(0),
		InstanceProfileArn:  pulumi.String("string"),
		SpotBidPricePercent: pulumi.Int(0),
		ZoneId:              pulumi.String("string"),
	},
	AzureAttributes: &databricks.ClusterAzureAttributesArgs{
		Availability:  pulumi.String("string"),
		FirstOnDemand: pulumi.Int(0),
		LogAnalyticsInfo: &databricks.ClusterAzureAttributesLogAnalyticsInfoArgs{
			LogAnalyticsPrimaryKey:  pulumi.String("string"),
			LogAnalyticsWorkspaceId: pulumi.String("string"),
		},
		SpotBidMaxPrice: pulumi.Float64(0),
	},
	InitScripts: databricks.ClusterInitScriptArray{
		&databricks.ClusterInitScriptArgs{
			Abfss: &databricks.ClusterInitScriptAbfssArgs{
				Destination: pulumi.String("string"),
			},
			File: &databricks.ClusterInitScriptFileArgs{
				Destination: pulumi.String("string"),
			},
			Gcs: &databricks.ClusterInitScriptGcsArgs{
				Destination: pulumi.String("string"),
			},
			S3: &databricks.ClusterInitScriptS3Args{
				Destination:      pulumi.String("string"),
				CannedAcl:        pulumi.String("string"),
				EnableEncryption: pulumi.Bool(false),
				EncryptionType:   pulumi.String("string"),
				Endpoint:         pulumi.String("string"),
				KmsKey:           pulumi.String("string"),
				Region:           pulumi.String("string"),
			},
			Volumes: &databricks.ClusterInitScriptVolumesArgs{
				Destination: pulumi.String("string"),
			},
			Workspace: &databricks.ClusterInitScriptWorkspaceArgs{
				Destination: pulumi.String("string"),
			},
		},
	},
	ClusterMountInfos: databricks.ClusterClusterMountInfoArray{
		&databricks.ClusterClusterMountInfoArgs{
			LocalMountDirPath: pulumi.String("string"),
			NetworkFilesystemInfo: &databricks.ClusterClusterMountInfoNetworkFilesystemInfoArgs{
				ServerAddress: pulumi.String("string"),
				MountOptions:  pulumi.String("string"),
			},
			RemoteMountDirPath: pulumi.String("string"),
		},
	},
	ClusterName:               pulumi.String("string"),
	InstancePoolId:            pulumi.String("string"),
	DataSecurityMode:          pulumi.String("string"),
	IsPinned:                  pulumi.Bool(false),
	DriverInstancePoolId:      pulumi.String("string"),
	DriverNodeTypeId:          pulumi.String("string"),
	EnableElasticDisk:         pulumi.Bool(false),
	EnableLocalDiskEncryption: pulumi.Bool(false),
	GcpAttributes: &databricks.ClusterGcpAttributesArgs{
		Availability:            pulumi.String("string"),
		BootDiskSize:            pulumi.Int(0),
		GoogleServiceAccount:    pulumi.String("string"),
		LocalSsdCount:           pulumi.Int(0),
		UsePreemptibleExecutors: pulumi.Bool(false),
		ZoneId:                  pulumi.String("string"),
	},
	ApplyPolicyDefaultValues: pulumi.Bool(false),
	ClusterLogConf: &databricks.ClusterClusterLogConfArgs{
		Dbfs: &databricks.ClusterClusterLogConfDbfsArgs{
			Destination: pulumi.String("string"),
		},
		S3: &databricks.ClusterClusterLogConfS3Args{
			Destination:      pulumi.String("string"),
			CannedAcl:        pulumi.String("string"),
			EnableEncryption: pulumi.Bool(false),
			EncryptionType:   pulumi.String("string"),
			Endpoint:         pulumi.String("string"),
			KmsKey:           pulumi.String("string"),
			Region:           pulumi.String("string"),
		},
		Volumes: &databricks.ClusterClusterLogConfVolumesArgs{
			Destination: pulumi.String("string"),
		},
	},
	AutoterminationMinutes: pulumi.Int(0),
	DockerImage: &databricks.ClusterDockerImageArgs{
		Url: pulumi.String("string"),
		BasicAuth: &databricks.ClusterDockerImageBasicAuthArgs{
			Password: pulumi.String("string"),
			Username: pulumi.String("string"),
		},
	},
	IsSingleNode: pulumi.Bool(false),
	Kind:         pulumi.String("string"),
	Libraries: databricks.ClusterLibraryArray{
		&databricks.ClusterLibraryArgs{
			Cran: &databricks.ClusterLibraryCranArgs{
				Package: pulumi.String("string"),
				Repo:    pulumi.String("string"),
			},
			Egg: pulumi.String("string"),
			Jar: pulumi.String("string"),
			Maven: &databricks.ClusterLibraryMavenArgs{
				Coordinates: pulumi.String("string"),
				Exclusions: pulumi.StringArray{
					pulumi.String("string"),
				},
				Repo: pulumi.String("string"),
			},
			Pypi: &databricks.ClusterLibraryPypiArgs{
				Package: pulumi.String("string"),
				Repo:    pulumi.String("string"),
			},
			Requirements: pulumi.String("string"),
			Whl:          pulumi.String("string"),
		},
	},
	NoWait:         pulumi.Bool(false),
	NodeTypeId:     pulumi.String("string"),
	NumWorkers:     pulumi.Int(0),
	PolicyId:       pulumi.String("string"),
	RuntimeEngine:  pulumi.String("string"),
	SingleUserName: pulumi.String("string"),
	SparkConf: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	SparkEnvVars: pulumi.StringMap{
		"string": pulumi.String("string"),
	},
	Autoscale: &databricks.ClusterAutoscaleArgs{
		MaxWorkers: pulumi.Int(0),
		MinWorkers: pulumi.Int(0),
	},
	SshPublicKeys: pulumi.StringArray{
		pulumi.String("string"),
	},
	UseMlRuntime: pulumi.Bool(false),
	WorkloadType: &databricks.ClusterWorkloadTypeArgs{
		Clients: &databricks.ClusterWorkloadTypeClientsArgs{
			Jobs:      pulumi.Bool(false),
			Notebooks: pulumi.Bool(false),
		},
	},
})

var clusterResource = new Cluster("clusterResource", ClusterArgs.builder()
    .sparkVersion("string")
    .idempotencyToken("string")
    .customTags(Map.of("string", "string"))
    .awsAttributes(ClusterAwsAttributesArgs.builder()
        .availability("string")
        .ebsVolumeCount(0)
        .ebsVolumeIops(0)
        .ebsVolumeSize(0)
        .ebsVolumeThroughput(0)
        .ebsVolumeType("string")
        .firstOnDemand(0)
        .instanceProfileArn("string")
        .spotBidPricePercent(0)
        .zoneId("string")
        .build())
    .azureAttributes(ClusterAzureAttributesArgs.builder()
        .availability("string")
        .firstOnDemand(0)
        .logAnalyticsInfo(ClusterAzureAttributesLogAnalyticsInfoArgs.builder()
            .logAnalyticsPrimaryKey("string")
            .logAnalyticsWorkspaceId("string")
            .build())
        .spotBidMaxPrice(0)
        .build())
    .initScripts(ClusterInitScriptArgs.builder()
        .abfss(ClusterInitScriptAbfssArgs.builder()
            .destination("string")
            .build())
        .file(ClusterInitScriptFileArgs.builder()
            .destination("string")
            .build())
        .gcs(ClusterInitScriptGcsArgs.builder()
            .destination("string")
            .build())
        .s3(ClusterInitScriptS3Args.builder()
            .destination("string")
            .cannedAcl("string")
            .enableEncryption(false)
            .encryptionType("string")
            .endpoint("string")
            .kmsKey("string")
            .region("string")
            .build())
        .volumes(ClusterInitScriptVolumesArgs.builder()
            .destination("string")
            .build())
        .workspace(ClusterInitScriptWorkspaceArgs.builder()
            .destination("string")
            .build())
        .build())
    .clusterMountInfos(ClusterClusterMountInfoArgs.builder()
        .localMountDirPath("string")
        .networkFilesystemInfo(ClusterClusterMountInfoNetworkFilesystemInfoArgs.builder()
            .serverAddress("string")
            .mountOptions("string")
            .build())
        .remoteMountDirPath("string")
        .build())
    .clusterName("string")
    .instancePoolId("string")
    .dataSecurityMode("string")
    .isPinned(false)
    .driverInstancePoolId("string")
    .driverNodeTypeId("string")
    .enableElasticDisk(false)
    .enableLocalDiskEncryption(false)
    .gcpAttributes(ClusterGcpAttributesArgs.builder()
        .availability("string")
        .bootDiskSize(0)
        .googleServiceAccount("string")
        .localSsdCount(0)
        .usePreemptibleExecutors(false)
        .zoneId("string")
        .build())
    .applyPolicyDefaultValues(false)
    .clusterLogConf(ClusterClusterLogConfArgs.builder()
        .dbfs(ClusterClusterLogConfDbfsArgs.builder()
            .destination("string")
            .build())
        .s3(ClusterClusterLogConfS3Args.builder()
            .destination("string")
            .cannedAcl("string")
            .enableEncryption(false)
            .encryptionType("string")
            .endpoint("string")
            .kmsKey("string")
            .region("string")
            .build())
        .volumes(ClusterClusterLogConfVolumesArgs.builder()
            .destination("string")
            .build())
        .build())
    .autoterminationMinutes(0)
    .dockerImage(ClusterDockerImageArgs.builder()
        .url("string")
        .basicAuth(ClusterDockerImageBasicAuthArgs.builder()
            .password("string")
            .username("string")
            .build())
        .build())
    .isSingleNode(false)
    .kind("string")
    .libraries(ClusterLibraryArgs.builder()
        .cran(ClusterLibraryCranArgs.builder()
            .package_("string")
            .repo("string")
            .build())
        .egg("string")
        .jar("string")
        .maven(ClusterLibraryMavenArgs.builder()
            .coordinates("string")
            .exclusions("string")
            .repo("string")
            .build())
        .pypi(ClusterLibraryPypiArgs.builder()
            .package_("string")
            .repo("string")
            .build())
        .requirements("string")
        .whl("string")
        .build())
    .noWait(false)
    .nodeTypeId("string")
    .numWorkers(0)
    .policyId("string")
    .runtimeEngine("string")
    .singleUserName("string")
    .sparkConf(Map.of("string", "string"))
    .sparkEnvVars(Map.of("string", "string"))
    .autoscale(ClusterAutoscaleArgs.builder()
        .maxWorkers(0)
        .minWorkers(0)
        .build())
    .sshPublicKeys("string")
    .useMlRuntime(false)
    .workloadType(ClusterWorkloadTypeArgs.builder()
        .clients(ClusterWorkloadTypeClientsArgs.builder()
            .jobs(false)
            .notebooks(false)
            .build())
        .build())
    .build());

cluster_resource = databricks.Cluster("clusterResource",
    spark_version="string",
    idempotency_token="string",
    custom_tags={
        "string": "string",
    },
    aws_attributes={
        "availability": "string",
        "ebs_volume_count": 0,
        "ebs_volume_iops": 0,
        "ebs_volume_size": 0,
        "ebs_volume_throughput": 0,
        "ebs_volume_type": "string",
        "first_on_demand": 0,
        "instance_profile_arn": "string",
        "spot_bid_price_percent": 0,
        "zone_id": "string",
    },
    azure_attributes={
        "availability": "string",
        "first_on_demand": 0,
        "log_analytics_info": {
            "log_analytics_primary_key": "string",
            "log_analytics_workspace_id": "string",
        },
        "spot_bid_max_price": 0,
    },
    init_scripts=[{
        "abfss": {
            "destination": "string",
        },
        "file": {
            "destination": "string",
        },
        "gcs": {
            "destination": "string",
        },
        "s3": {
            "destination": "string",
            "canned_acl": "string",
            "enable_encryption": False,
            "encryption_type": "string",
            "endpoint": "string",
            "kms_key": "string",
            "region": "string",
        },
        "volumes": {
            "destination": "string",
        },
        "workspace": {
            "destination": "string",
        },
    }],
    cluster_mount_infos=[{
        "local_mount_dir_path": "string",
        "network_filesystem_info": {
            "server_address": "string",
            "mount_options": "string",
        },
        "remote_mount_dir_path": "string",
    }],
    cluster_name="string",
    instance_pool_id="string",
    data_security_mode="string",
    is_pinned=False,
    driver_instance_pool_id="string",
    driver_node_type_id="string",
    enable_elastic_disk=False,
    enable_local_disk_encryption=False,
    gcp_attributes={
        "availability": "string",
        "boot_disk_size": 0,
        "google_service_account": "string",
        "local_ssd_count": 0,
        "use_preemptible_executors": False,
        "zone_id": "string",
    },
    apply_policy_default_values=False,
    cluster_log_conf={
        "dbfs": {
            "destination": "string",
        },
        "s3": {
            "destination": "string",
            "canned_acl": "string",
            "enable_encryption": False,
            "encryption_type": "string",
            "endpoint": "string",
            "kms_key": "string",
            "region": "string",
        },
        "volumes": {
            "destination": "string",
        },
    },
    autotermination_minutes=0,
    docker_image={
        "url": "string",
        "basic_auth": {
            "password": "string",
            "username": "string",
        },
    },
    is_single_node=False,
    kind="string",
    libraries=[{
        "cran": {
            "package": "string",
            "repo": "string",
        },
        "egg": "string",
        "jar": "string",
        "maven": {
            "coordinates": "string",
            "exclusions": ["string"],
            "repo": "string",
        },
        "pypi": {
            "package": "string",
            "repo": "string",
        },
        "requirements": "string",
        "whl": "string",
    }],
    no_wait=False,
    node_type_id="string",
    num_workers=0,
    policy_id="string",
    runtime_engine="string",
    single_user_name="string",
    spark_conf={
        "string": "string",
    },
    spark_env_vars={
        "string": "string",
    },
    autoscale={
        "max_workers": 0,
        "min_workers": 0,
    },
    ssh_public_keys=["string"],
    use_ml_runtime=False,
    workload_type={
        "clients": {
            "jobs": False,
            "notebooks": False,
        },
    })

const clusterResource = new databricks.Cluster("clusterResource", {
    sparkVersion: "string",
    idempotencyToken: "string",
    customTags: {
        string: "string",
    },
    awsAttributes: {
        availability: "string",
        ebsVolumeCount: 0,
        ebsVolumeIops: 0,
        ebsVolumeSize: 0,
        ebsVolumeThroughput: 0,
        ebsVolumeType: "string",
        firstOnDemand: 0,
        instanceProfileArn: "string",
        spotBidPricePercent: 0,
        zoneId: "string",
    },
    azureAttributes: {
        availability: "string",
        firstOnDemand: 0,
        logAnalyticsInfo: {
            logAnalyticsPrimaryKey: "string",
            logAnalyticsWorkspaceId: "string",
        },
        spotBidMaxPrice: 0,
    },
    initScripts: [{
        abfss: {
            destination: "string",
        },
        file: {
            destination: "string",
        },
        gcs: {
            destination: "string",
        },
        s3: {
            destination: "string",
            cannedAcl: "string",
            enableEncryption: false,
            encryptionType: "string",
            endpoint: "string",
            kmsKey: "string",
            region: "string",
        },
        volumes: {
            destination: "string",
        },
        workspace: {
            destination: "string",
        },
    }],
    clusterMountInfos: [{
        localMountDirPath: "string",
        networkFilesystemInfo: {
            serverAddress: "string",
            mountOptions: "string",
        },
        remoteMountDirPath: "string",
    }],
    clusterName: "string",
    instancePoolId: "string",
    dataSecurityMode: "string",
    isPinned: false,
    driverInstancePoolId: "string",
    driverNodeTypeId: "string",
    enableElasticDisk: false,
    enableLocalDiskEncryption: false,
    gcpAttributes: {
        availability: "string",
        bootDiskSize: 0,
        googleServiceAccount: "string",
        localSsdCount: 0,
        usePreemptibleExecutors: false,
        zoneId: "string",
    },
    applyPolicyDefaultValues: false,
    clusterLogConf: {
        dbfs: {
            destination: "string",
        },
        s3: {
            destination: "string",
            cannedAcl: "string",
            enableEncryption: false,
            encryptionType: "string",
            endpoint: "string",
            kmsKey: "string",
            region: "string",
        },
        volumes: {
            destination: "string",
        },
    },
    autoterminationMinutes: 0,
    dockerImage: {
        url: "string",
        basicAuth: {
            password: "string",
            username: "string",
        },
    },
    isSingleNode: false,
    kind: "string",
    libraries: [{
        cran: {
            "package": "string",
            repo: "string",
        },
        egg: "string",
        jar: "string",
        maven: {
            coordinates: "string",
            exclusions: ["string"],
            repo: "string",
        },
        pypi: {
            "package": "string",
            repo: "string",
        },
        requirements: "string",
        whl: "string",
    }],
    noWait: false,
    nodeTypeId: "string",
    numWorkers: 0,
    policyId: "string",
    runtimeEngine: "string",
    singleUserName: "string",
    sparkConf: {
        string: "string",
    },
    sparkEnvVars: {
        string: "string",
    },
    autoscale: {
        maxWorkers: 0,
        minWorkers: 0,
    },
    sshPublicKeys: ["string"],
    useMlRuntime: false,
    workloadType: {
        clients: {
            jobs: false,
            notebooks: false,
        },
    },
});

type: databricks:Cluster
properties:
    applyPolicyDefaultValues: false
    autoscale:
        maxWorkers: 0
        minWorkers: 0
    autoterminationMinutes: 0
    awsAttributes:
        availability: string
        ebsVolumeCount: 0
        ebsVolumeIops: 0
        ebsVolumeSize: 0
        ebsVolumeThroughput: 0
        ebsVolumeType: string
        firstOnDemand: 0
        instanceProfileArn: string
        spotBidPricePercent: 0
        zoneId: string
    azureAttributes:
        availability: string
        firstOnDemand: 0
        logAnalyticsInfo:
            logAnalyticsPrimaryKey: string
            logAnalyticsWorkspaceId: string
        spotBidMaxPrice: 0
    clusterLogConf:
        dbfs:
            destination: string
        s3:
            cannedAcl: string
            destination: string
            enableEncryption: false
            encryptionType: string
            endpoint: string
            kmsKey: string
            region: string
        volumes:
            destination: string
    clusterMountInfos:
        - localMountDirPath: string
          networkFilesystemInfo:
            mountOptions: string
            serverAddress: string
          remoteMountDirPath: string
    clusterName: string
    customTags:
        string: string
    dataSecurityMode: string
    dockerImage:
        basicAuth:
            password: string
            username: string
        url: string
    driverInstancePoolId: string
    driverNodeTypeId: string
    enableElasticDisk: false
    enableLocalDiskEncryption: false
    gcpAttributes:
        availability: string
        bootDiskSize: 0
        googleServiceAccount: string
        localSsdCount: 0
        usePreemptibleExecutors: false
        zoneId: string
    idempotencyToken: string
    initScripts:
        - abfss:
            destination: string
          file:
            destination: string
          gcs:
            destination: string
          s3:
            cannedAcl: string
            destination: string
            enableEncryption: false
            encryptionType: string
            endpoint: string
            kmsKey: string
            region: string
          volumes:
            destination: string
          workspace:
            destination: string
    instancePoolId: string
    isPinned: false
    isSingleNode: false
    kind: string
    libraries:
        - cran:
            package: string
            repo: string
          egg: string
          jar: string
          maven:
            coordinates: string
            exclusions:
                - string
            repo: string
          pypi:
            package: string
            repo: string
          requirements: string
          whl: string
    noWait: false
    nodeTypeId: string
    numWorkers: 0
    policyId: string
    runtimeEngine: string
    singleUserName: string
    sparkConf:
        string: string
    sparkEnvVars:
        string: string
    sparkVersion: string
    sshPublicKeys:
        - string
    useMlRuntime: false
    workloadType:
        clients:
            jobs: false
            notebooks: false

Cluster Resource Properties

To learn more about resource properties and how to use them, see Inputs and Outputs in the Architecture and Concepts docs.

Inputs

In Python, inputs that are objects can be passed either as argument classes or as dictionary literals.

The Cluster resource accepts the following input properties:

SparkVersion string

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

ApplyPolicyDefaultValues bool

Whether to use policy default values for missing cluster attributes.

Autoscale ClusterAutoscale

AutoterminationMinutes int

Automatically terminate the cluster after being inactive for this time in minutes. If specified, the threshold must be between 10 and 10000 minutes. You can also set this value to 0 to explicitly disable automatic termination. Defaults to 60. We highly recommend having this setting present for Interactive/BI clusters.

AwsAttributes ClusterAwsAttributes

AzureAttributes ClusterAzureAttributes

ClusterLogConf ClusterClusterLogConf

ClusterMountInfos List<ClusterClusterMountInfo>

ClusterName string

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

CustomTags Dictionary<string, string>

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

DataSecurityMode string

Select the security features of the cluster (see API docs for full list of values). Unity Catalog requires SINGLE_USER or USER_ISOLATION mode. LEGACY_PASSTHROUGH for passthrough cluster and LEGACY_TABLE_ACL for Table ACL cluster. If omitted, default security features are enabled. To disable security features use NONE or legacy mode NO_ISOLATION. If kind is specified, then the following options are available:

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

DockerImage ClusterDockerImage

DriverInstancePoolId string

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

DriverNodeTypeId string

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

EnableElasticDisk bool

If you don’t want to allocate a fixed number of EBS volumes at cluster creation time, use autoscaling local storage. With autoscaling local storage, Databricks monitors the amount of free disk space available on your cluster’s Spark workers. If a worker begins to run too low on disk, Databricks automatically attaches a new EBS volume to the worker before it runs out of disk space. EBS volumes are attached up to a limit of 5 TB of total disk space per instance (including the instance’s local storage). To scale down EBS usage, make sure you have autotermination_minutes and autoscale attributes set. More documentation available at cluster configuration page.

EnableLocalDiskEncryption bool

Some instance types you use to run clusters may have locally attached disks. Databricks may store shuffle data or temporary data on these locally attached disks. To ensure that all data at rest is encrypted for all storage types, including shuffle data stored temporarily on your cluster’s local disks, you can enable local disk encryption. When local disk encryption is enabled, Databricks generates an encryption key locally unique to each cluster node and uses it to encrypt all data stored on local disks. The scope of the key is local to each cluster node and is destroyed along with the cluster node itself. During its lifetime, the key resides in memory for encryption and decryption and is stored encrypted on the disk. Your workloads may run more slowly because of the performance impact of reading and writing encrypted data to and from local volumes. This feature is not available for all Azure Databricks subscriptions. Contact your Microsoft or Databricks account representative to request access.

GcpAttributes ClusterGcpAttributes

IdempotencyToken string

An optional token to guarantee the idempotency of cluster creation requests. If an active cluster with the provided token already exists, the request will not create a new cluster, but it will return the existing running cluster's ID instead. If you specify the idempotency token, upon failure, you can retry until the request succeeds. Databricks platform guarantees to launch exactly one cluster with that idempotency token. This token should have at most 64 characters.

InitScripts List<ClusterInitScript>

InstancePoolId string

To reduce cluster start time, you can attach a cluster to a predefined pool of idle instances. When attached to a pool, a cluster allocates its driver and worker nodes from the pool. If the pool does not have sufficient idle resources to accommodate the cluster’s request, it expands by allocating new instances from the instance provider. When an attached cluster changes its state to TERMINATED, the instances it used are returned to the pool and reused by a different cluster.

IsPinned bool

boolean value specifying if the cluster is pinned (not pinned by default). You must be a Databricks administrator to use this. The pinned clusters' maximum number is limited to 100, so apply may fail if you have more than that (this number may change over time, so check Databricks documentation for actual number).

IsSingleNode bool

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

Kind string

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

Libraries List<ClusterLibrary>

NoWait bool

If true, the provider will not wait for the cluster to reach RUNNING state when creating the cluster, allowing cluster creation and library installation to continue asynchronously. Defaults to false (the provider will wait for cluster creation and library installation to succeed).

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

NodeTypeId string

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

NumWorkers int

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

PolicyId string

Identifier of Cluster Policy to validate cluster and preset certain defaults. The primary use for cluster policies is to allow users to create policy-scoped clusters via UI rather than sharing configuration for API-created clusters. For example, when you specify policy_id of external metastore policy, you still have to fill in relevant keys for spark_conf. If relevant fields aren't filled in, then it will cause the configuration drift detected on each plan/apply, and Pulumi will try to apply the detected changes.

RuntimeEngine string

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

SingleUserName string

The optional user name of the user (or group name if kind if specified) to assign to an interactive cluster. This field is required when using data_security_mode set to SINGLE_USER or AAD Passthrough for Azure Data Lake Storage (ADLS) with a single-user cluster (i.e., not high-concurrency clusters).

SparkConf Dictionary<string, string>

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

SparkEnvVars Dictionary<string, string>

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

SshPublicKeys List<string>

SSH public key contents that will be added to each Spark node in this cluster. The corresponding private keys can be used to login with the user name ubuntu on port 2200. You can specify up to 10 keys.

UseMlRuntime bool

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

WorkloadType ClusterWorkloadType

SparkVersion string

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

ApplyPolicyDefaultValues bool

Whether to use policy default values for missing cluster attributes.

Autoscale ClusterAutoscaleArgs

AutoterminationMinutes int

AwsAttributes ClusterAwsAttributesArgs

AzureAttributes ClusterAzureAttributesArgs

ClusterLogConf ClusterClusterLogConfArgs

ClusterMountInfos []ClusterClusterMountInfoArgs

ClusterName string

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

CustomTags map[string]string

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

DataSecurityMode string

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

DockerImage ClusterDockerImageArgs

DriverInstancePoolId string

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

DriverNodeTypeId string

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

EnableElasticDisk bool

EnableLocalDiskEncryption bool

GcpAttributes ClusterGcpAttributesArgs

IdempotencyToken string

InitScripts []ClusterInitScriptArgs

InstancePoolId string

IsPinned bool

IsSingleNode bool

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

Kind string

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

Libraries []ClusterLibraryArgs

NoWait bool

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

NodeTypeId string

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

NumWorkers int

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

PolicyId string

RuntimeEngine string

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

SingleUserName string

SparkConf map[string]string

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

SparkEnvVars map[string]string

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

SshPublicKeys []string

UseMlRuntime bool

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

WorkloadType ClusterWorkloadTypeArgs

sparkVersion String

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

applyPolicyDefaultValues Boolean

Whether to use policy default values for missing cluster attributes.

autoscale ClusterAutoscale

autoterminationMinutes Integer

awsAttributes ClusterAwsAttributes

azureAttributes ClusterAzureAttributes

clusterLogConf ClusterClusterLogConf

clusterMountInfos List<ClusterClusterMountInfo>

clusterName String

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

customTags Map<String,String>

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

dataSecurityMode String

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

dockerImage ClusterDockerImage

driverInstancePoolId String

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driverNodeTypeId String

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enableElasticDisk Boolean

enableLocalDiskEncryption Boolean

gcpAttributes ClusterGcpAttributes

idempotencyToken String

initScripts List<ClusterInitScript>

instancePoolId String

isPinned Boolean

isSingleNode Boolean

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind String

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries List<ClusterLibrary>

noWait Boolean

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

nodeTypeId String

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

numWorkers Integer

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policyId String

runtimeEngine String

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

singleUserName String

sparkConf Map<String,String>

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

sparkEnvVars Map<String,String>

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

sshPublicKeys List<String>

useMlRuntime Boolean

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workloadType ClusterWorkloadType

sparkVersion string

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

applyPolicyDefaultValues boolean

Whether to use policy default values for missing cluster attributes.

autoscale ClusterAutoscale

autoterminationMinutes number

awsAttributes ClusterAwsAttributes

azureAttributes ClusterAzureAttributes

clusterLogConf ClusterClusterLogConf

clusterMountInfos ClusterClusterMountInfo[]

clusterName string

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

customTags {[key: string]: string}

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

dataSecurityMode string

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

dockerImage ClusterDockerImage

driverInstancePoolId string

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driverNodeTypeId string

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enableElasticDisk boolean

enableLocalDiskEncryption boolean

gcpAttributes ClusterGcpAttributes

idempotencyToken string

initScripts ClusterInitScript[]

instancePoolId string

isPinned boolean

isSingleNode boolean

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind string

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries ClusterLibrary[]

noWait boolean

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

nodeTypeId string

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

numWorkers number

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policyId string

runtimeEngine string

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

singleUserName string

sparkConf {[key: string]: string}

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

sparkEnvVars {[key: string]: string}

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

sshPublicKeys string[]

useMlRuntime boolean

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workloadType ClusterWorkloadType

spark_version str

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

apply_policy_default_values bool

Whether to use policy default values for missing cluster attributes.

autoscale ClusterAutoscaleArgs

autotermination_minutes int

aws_attributes ClusterAwsAttributesArgs

azure_attributes ClusterAzureAttributesArgs

cluster_log_conf ClusterClusterLogConfArgs

cluster_mount_infos Sequence[ClusterClusterMountInfoArgs]

cluster_name str

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

custom_tags Mapping[str, str]

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

data_security_mode str

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

docker_image ClusterDockerImageArgs

driver_instance_pool_id str

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driver_node_type_id str

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enable_elastic_disk bool

enable_local_disk_encryption bool

gcp_attributes ClusterGcpAttributesArgs

idempotency_token str

init_scripts Sequence[ClusterInitScriptArgs]

instance_pool_id str

is_pinned bool

is_single_node bool

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind str

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries Sequence[ClusterLibraryArgs]

no_wait bool

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

node_type_id str

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

num_workers int

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policy_id str

runtime_engine str

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

single_user_name str

spark_conf Mapping[str, str]

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

spark_env_vars Mapping[str, str]

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

ssh_public_keys Sequence[str]

use_ml_runtime bool

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workload_type ClusterWorkloadTypeArgs

sparkVersion String

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

applyPolicyDefaultValues Boolean

Whether to use policy default values for missing cluster attributes.

autoscale Property Map

autoterminationMinutes Number

awsAttributes Property Map

azureAttributes Property Map

clusterLogConf Property Map

clusterMountInfos List<Property Map>

clusterName String

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

customTags Map<String>

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

dataSecurityMode String

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

dockerImage Property Map

driverInstancePoolId String

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driverNodeTypeId String

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enableElasticDisk Boolean

enableLocalDiskEncryption Boolean

gcpAttributes Property Map

idempotencyToken String

initScripts List<Property Map>

instancePoolId String

isPinned Boolean

isSingleNode Boolean

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind String

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries List<Property Map>

noWait Boolean

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

nodeTypeId String

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

numWorkers Number

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policyId String

runtimeEngine String

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

singleUserName String

sparkConf Map<String>

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

sparkEnvVars Map<String>

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

sshPublicKeys List<String>

useMlRuntime Boolean

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workloadType Property Map

Outputs

All input properties are implicitly available as output properties. Additionally, the Cluster resource produces the following output properties:

ClusterId string
DefaultTags Dictionary<string, string>: (map) Tags that are added by Databricks by default, regardless of any custom_tags that may have been added. These include: Vendor: Databricks, Creator: <username_of_creator>, ClusterName: <name_of_cluster>, ClusterId: <id_of_cluster>, Name: , and any workspace and pool tags.
Id string: The provider-assigned unique ID for this managed resource.
State string: (string) State of the cluster.
Url string

ClusterId string
DefaultTags map[string]string: (map) Tags that are added by Databricks by default, regardless of any custom_tags that may have been added. These include: Vendor: Databricks, Creator: <username_of_creator>, ClusterName: <name_of_cluster>, ClusterId: <id_of_cluster>, Name: , and any workspace and pool tags.
Id string: The provider-assigned unique ID for this managed resource.
State string: (string) State of the cluster.
Url string

clusterId String
defaultTags Map<String,String>: (map) Tags that are added by Databricks by default, regardless of any custom_tags that may have been added. These include: Vendor: Databricks, Creator: <username_of_creator>, ClusterName: <name_of_cluster>, ClusterId: <id_of_cluster>, Name: , and any workspace and pool tags.
id String: The provider-assigned unique ID for this managed resource.
state String: (string) State of the cluster.
url String

clusterId string
defaultTags {[key: string]: string}: (map) Tags that are added by Databricks by default, regardless of any custom_tags that may have been added. These include: Vendor: Databricks, Creator: <username_of_creator>, ClusterName: <name_of_cluster>, ClusterId: <id_of_cluster>, Name: , and any workspace and pool tags.
id string: The provider-assigned unique ID for this managed resource.
state string: (string) State of the cluster.
url string

cluster_id str
default_tags Mapping[str, str]: (map) Tags that are added by Databricks by default, regardless of any custom_tags that may have been added. These include: Vendor: Databricks, Creator: <username_of_creator>, ClusterName: <name_of_cluster>, ClusterId: <id_of_cluster>, Name: , and any workspace and pool tags.
id str: The provider-assigned unique ID for this managed resource.
state str: (string) State of the cluster.
url str

clusterId String
defaultTags Map<String>: (map) Tags that are added by Databricks by default, regardless of any custom_tags that may have been added. These include: Vendor: Databricks, Creator: <username_of_creator>, ClusterName: <name_of_cluster>, ClusterId: <id_of_cluster>, Name: , and any workspace and pool tags.
id String: The provider-assigned unique ID for this managed resource.
state String: (string) State of the cluster.
url String

Look up Existing Cluster Resource

Get an existing Cluster resource’s state with the given name, ID, and optional extra properties used to qualify the lookup.

TypeScript
Python
Go
C#
Java
YAML

public static get(name: string, id: Input<ID>, state?: ClusterState, opts?: CustomResourceOptions): Cluster

@staticmethod
def get(resource_name: str,
        id: str,
        opts: Optional[ResourceOptions] = None,
        apply_policy_default_values: Optional[bool] = None,
        autoscale: Optional[ClusterAutoscaleArgs] = None,
        autotermination_minutes: Optional[int] = None,
        aws_attributes: Optional[ClusterAwsAttributesArgs] = None,
        azure_attributes: Optional[ClusterAzureAttributesArgs] = None,
        cluster_id: Optional[str] = None,
        cluster_log_conf: Optional[ClusterClusterLogConfArgs] = None,
        cluster_mount_infos: Optional[Sequence[ClusterClusterMountInfoArgs]] = None,
        cluster_name: Optional[str] = None,
        custom_tags: Optional[Mapping[str, str]] = None,
        data_security_mode: Optional[str] = None,
        default_tags: Optional[Mapping[str, str]] = None,
        docker_image: Optional[ClusterDockerImageArgs] = None,
        driver_instance_pool_id: Optional[str] = None,
        driver_node_type_id: Optional[str] = None,
        enable_elastic_disk: Optional[bool] = None,
        enable_local_disk_encryption: Optional[bool] = None,
        gcp_attributes: Optional[ClusterGcpAttributesArgs] = None,
        idempotency_token: Optional[str] = None,
        init_scripts: Optional[Sequence[ClusterInitScriptArgs]] = None,
        instance_pool_id: Optional[str] = None,
        is_pinned: Optional[bool] = None,
        is_single_node: Optional[bool] = None,
        kind: Optional[str] = None,
        libraries: Optional[Sequence[ClusterLibraryArgs]] = None,
        no_wait: Optional[bool] = None,
        node_type_id: Optional[str] = None,
        num_workers: Optional[int] = None,
        policy_id: Optional[str] = None,
        runtime_engine: Optional[str] = None,
        single_user_name: Optional[str] = None,
        spark_conf: Optional[Mapping[str, str]] = None,
        spark_env_vars: Optional[Mapping[str, str]] = None,
        spark_version: Optional[str] = None,
        ssh_public_keys: Optional[Sequence[str]] = None,
        state: Optional[str] = None,
        url: Optional[str] = None,
        use_ml_runtime: Optional[bool] = None,
        workload_type: Optional[ClusterWorkloadTypeArgs] = None) -> Cluster

func GetCluster(ctx *Context, name string, id IDInput, state *ClusterState, opts ...ResourceOption) (*Cluster, error)

public static Cluster Get(string name, Input<string> id, ClusterState? state, CustomResourceOptions? opts = null)

public static Cluster get(String name, Output<String> id, ClusterState state, CustomResourceOptions options)

resources:  _:    type: databricks:Cluster    get:      id: ${id}

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

resource_name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

name: The unique name of the resulting resource.
id: The unique provider ID of the resource to lookup.
state: Any extra arguments used during the lookup.
opts: A bag of options that control this resource's behavior.

The following state arguments are supported:

ApplyPolicyDefaultValues bool

Whether to use policy default values for missing cluster attributes.

Autoscale ClusterAutoscale

AutoterminationMinutes int

AwsAttributes ClusterAwsAttributes

AzureAttributes ClusterAzureAttributes

ClusterId string

ClusterLogConf ClusterClusterLogConf

ClusterMountInfos List<ClusterClusterMountInfo>

ClusterName string

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

CustomTags Dictionary<string, string>

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

DataSecurityMode string

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

DefaultTags Dictionary<string, string>

(map) Tags that are added by Databricks by default, regardless of any custom_tags that may have been added. These include: Vendor: Databricks, Creator: <username_of_creator>, ClusterName: <name_of_cluster>, ClusterId: <id_of_cluster>, Name: , and any workspace and pool tags.

DockerImage ClusterDockerImage

DriverInstancePoolId string

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

DriverNodeTypeId string

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

EnableElasticDisk bool

EnableLocalDiskEncryption bool

GcpAttributes ClusterGcpAttributes

IdempotencyToken string

InitScripts List<ClusterInitScript>

InstancePoolId string

IsPinned bool

IsSingleNode bool

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

Kind string

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

Libraries List<ClusterLibrary>

NoWait bool

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

NodeTypeId string

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

NumWorkers int

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

PolicyId string

RuntimeEngine string

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

SingleUserName string

SparkConf Dictionary<string, string>

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

SparkEnvVars Dictionary<string, string>

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

SparkVersion string

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

SshPublicKeys List<string>

State string

(string) State of the cluster.

Url string

UseMlRuntime bool

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

WorkloadType ClusterWorkloadType

ApplyPolicyDefaultValues bool

Whether to use policy default values for missing cluster attributes.

Autoscale ClusterAutoscaleArgs

AutoterminationMinutes int

AwsAttributes ClusterAwsAttributesArgs

AzureAttributes ClusterAzureAttributesArgs

ClusterId string

ClusterLogConf ClusterClusterLogConfArgs

ClusterMountInfos []ClusterClusterMountInfoArgs

ClusterName string

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

CustomTags map[string]string

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

DataSecurityMode string

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

DefaultTags map[string]string

DockerImage ClusterDockerImageArgs

DriverInstancePoolId string

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

DriverNodeTypeId string

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

EnableElasticDisk bool

EnableLocalDiskEncryption bool

GcpAttributes ClusterGcpAttributesArgs

IdempotencyToken string

InitScripts []ClusterInitScriptArgs

InstancePoolId string

IsPinned bool

IsSingleNode bool

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

Kind string

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

Libraries []ClusterLibraryArgs

NoWait bool

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

NodeTypeId string

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

NumWorkers int

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

PolicyId string

RuntimeEngine string

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

SingleUserName string

SparkConf map[string]string

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

SparkEnvVars map[string]string

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

SparkVersion string

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

SshPublicKeys []string

State string

(string) State of the cluster.

Url string

UseMlRuntime bool

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

WorkloadType ClusterWorkloadTypeArgs

applyPolicyDefaultValues Boolean

Whether to use policy default values for missing cluster attributes.

autoscale ClusterAutoscale

autoterminationMinutes Integer

awsAttributes ClusterAwsAttributes

azureAttributes ClusterAzureAttributes

clusterId String

clusterLogConf ClusterClusterLogConf

clusterMountInfos List<ClusterClusterMountInfo>

clusterName String

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

customTags Map<String,String>

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

dataSecurityMode String

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

defaultTags Map<String,String>

dockerImage ClusterDockerImage

driverInstancePoolId String

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driverNodeTypeId String

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enableElasticDisk Boolean

enableLocalDiskEncryption Boolean

gcpAttributes ClusterGcpAttributes

idempotencyToken String

initScripts List<ClusterInitScript>

instancePoolId String

isPinned Boolean

isSingleNode Boolean

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind String

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries List<ClusterLibrary>

noWait Boolean

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

nodeTypeId String

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

numWorkers Integer

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policyId String

runtimeEngine String

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

singleUserName String

sparkConf Map<String,String>

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

sparkEnvVars Map<String,String>

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

sparkVersion String

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

sshPublicKeys List<String>

state String

(string) State of the cluster.

url String

useMlRuntime Boolean

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workloadType ClusterWorkloadType

applyPolicyDefaultValues boolean

Whether to use policy default values for missing cluster attributes.

autoscale ClusterAutoscale

autoterminationMinutes number

awsAttributes ClusterAwsAttributes

azureAttributes ClusterAzureAttributes

clusterId string

clusterLogConf ClusterClusterLogConf

clusterMountInfos ClusterClusterMountInfo[]

clusterName string

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

customTags {[key: string]: string}

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

dataSecurityMode string

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

defaultTags {[key: string]: string}

dockerImage ClusterDockerImage

driverInstancePoolId string

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driverNodeTypeId string

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enableElasticDisk boolean

enableLocalDiskEncryption boolean

gcpAttributes ClusterGcpAttributes

idempotencyToken string

initScripts ClusterInitScript[]

instancePoolId string

isPinned boolean

isSingleNode boolean

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind string

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries ClusterLibrary[]

noWait boolean

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

nodeTypeId string

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

numWorkers number

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policyId string

runtimeEngine string

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

singleUserName string

sparkConf {[key: string]: string}

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

sparkEnvVars {[key: string]: string}

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

sparkVersion string

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

sshPublicKeys string[]

state string

(string) State of the cluster.

url string

useMlRuntime boolean

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workloadType ClusterWorkloadType

apply_policy_default_values bool

Whether to use policy default values for missing cluster attributes.

autoscale ClusterAutoscaleArgs

autotermination_minutes int

aws_attributes ClusterAwsAttributesArgs

azure_attributes ClusterAzureAttributesArgs

cluster_id str

cluster_log_conf ClusterClusterLogConfArgs

cluster_mount_infos Sequence[ClusterClusterMountInfoArgs]

cluster_name str

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

custom_tags Mapping[str, str]

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

data_security_mode str

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

default_tags Mapping[str, str]

docker_image ClusterDockerImageArgs

driver_instance_pool_id str

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driver_node_type_id str

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enable_elastic_disk bool

enable_local_disk_encryption bool

gcp_attributes ClusterGcpAttributesArgs

idempotency_token str

init_scripts Sequence[ClusterInitScriptArgs]

instance_pool_id str

is_pinned bool

is_single_node bool

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind str

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries Sequence[ClusterLibraryArgs]

no_wait bool

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

node_type_id str

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

num_workers int

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policy_id str

runtime_engine str

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

single_user_name str

spark_conf Mapping[str, str]

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

spark_env_vars Mapping[str, str]

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

spark_version str

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

ssh_public_keys Sequence[str]

state str

(string) State of the cluster.

url str

use_ml_runtime bool

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workload_type ClusterWorkloadTypeArgs

applyPolicyDefaultValues Boolean

Whether to use policy default values for missing cluster attributes.

autoscale Property Map

autoterminationMinutes Number

awsAttributes Property Map

azureAttributes Property Map

clusterId String

clusterLogConf Property Map

clusterMountInfos List<Property Map>

clusterName String

Cluster name, which doesn’t have to be unique. If not specified at creation, the cluster name will be an empty string.

customTags Map<String>

should have tag ResourceClass set to value Serverless

For example:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const clusterWithTableAccessControl = new databricks.Cluster("cluster_with_table_access_control", {
clusterName: "Shared High-Concurrency",
sparkVersion: latestLts.id,
nodeTypeId: smallest.id,
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.repl.allowedLanguages": "python,sql",
"spark.databricks.cluster.profile": "serverless",
},
customTags: {
ResourceClass: "Serverless",
},
});

import pulumi
import pulumi_databricks as databricks

cluster_with_table_access_control = databricks.Cluster("cluster_with_table_access_control",
    cluster_name="Shared High-Concurrency",
    spark_version=latest_lts["id"],
    node_type_id=smallest["id"],
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.repl.allowedLanguages": "python,sql",
        "spark.databricks.cluster.profile": "serverless",
    },
    custom_tags={
        "ResourceClass": "Serverless",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var clusterWithTableAccessControl = new Databricks.Cluster("cluster_with_table_access_control", new()
    {
        ClusterName = "Shared High-Concurrency",
        SparkVersion = latestLts.Id,
        NodeTypeId = smallest.Id,
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.repl.allowedLanguages", "python,sql" },
            { "spark.databricks.cluster.profile", "serverless" },
        },
        CustomTags = 
        {
            { "ResourceClass", "Serverless" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "cluster_with_table_access_control", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared High-Concurrency"),
			SparkVersion:           pulumi.Any(latestLts.Id),
			NodeTypeId:             pulumi.Any(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.repl.allowedLanguages": pulumi.String("python,sql"),
				"spark.databricks.cluster.profile":       pulumi.String("serverless"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("Serverless"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var clusterWithTableAccessControl = new Cluster("clusterWithTableAccessControl", ClusterArgs.builder()
            .clusterName("Shared High-Concurrency")
            .sparkVersion(latestLts.id())
            .nodeTypeId(smallest.id())
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.repl.allowedLanguages", "python,sql"),
                Map.entry("spark.databricks.cluster.profile", "serverless")
            ))
            .customTags(Map.of("ResourceClass", "Serverless"))
            .build());

    }
}

resources:
  clusterWithTableAccessControl:
    type: databricks:Cluster
    name: cluster_with_table_access_control
    properties:
      clusterName: Shared High-Concurrency
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.repl.allowedLanguages: python,sql
        spark.databricks.cluster.profile: serverless
      customTags:
        ResourceClass: Serverless

dataSecurityMode String

DATA_SECURITY_MODE_AUTO: Databricks will choose the most appropriate access mode depending on your compute configuration.
DATA_SECURITY_MODE_STANDARD: Alias for USER_ISOLATION.
DATA_SECURITY_MODE_DEDICATED: Alias for SINGLE_USER.

defaultTags Map<String>

dockerImage Property Map

driverInstancePoolId String

similar to instance_pool_id, but for driver node. If omitted, and instance_pool_id is specified, then the driver will be allocated from that pool.

driverNodeTypeId String

The node type of the Spark driver. This field is optional; if unset, API will set the driver node type to the same value as node_type_id defined above.

enableElasticDisk Boolean

enableLocalDiskEncryption Boolean

gcpAttributes Property Map

idempotencyToken String

initScripts List<Property Map>

instancePoolId String

isPinned Boolean

isSingleNode Boolean

When set to true, Databricks will automatically set single node related custom_tags, spark_conf, and num_workers.

kind String

The kind of compute described by this compute specification. Possible values (see API docs for full list): CLASSIC_PREVIEW (if corresponding public preview is enabled).

libraries List<Property Map>

noWait Boolean

The following example demonstrates how to create an autoscaling cluster with Delta Cache enabled:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const sharedAutoscaling = new databricks.Cluster("shared_autoscaling", {
clusterName: "Shared Autoscaling",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
autoscale: {
minWorkers: 1,
maxWorkers: 50,
},
sparkConf: {
"spark.databricks.io.cache.enabled": "true",
"spark.databricks.io.cache.maxDiskUsage": "50g",
"spark.databricks.io.cache.maxMetaDataCache": "1g",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
shared_autoscaling = databricks.Cluster("shared_autoscaling",
    cluster_name="Shared Autoscaling",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    autoscale={
        "min_workers": 1,
        "max_workers": 50,
    },
    spark_conf={
        "spark.databricks.io.cache.enabled": "true",
        "spark.databricks.io.cache.maxDiskUsage": "50g",
        "spark.databricks.io.cache.maxMetaDataCache": "1g",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var sharedAutoscaling = new Databricks.Cluster("shared_autoscaling", new()
    {
        ClusterName = "Shared Autoscaling",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        Autoscale = new Databricks.Inputs.ClusterAutoscaleArgs
        {
            MinWorkers = 1,
            MaxWorkers = 50,
        },
        SparkConf = 
        {
            { "spark.databricks.io.cache.enabled", "true" },
            { "spark.databricks.io.cache.maxDiskUsage", "50g" },
            { "spark.databricks.io.cache.maxMetaDataCache", "1g" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "shared_autoscaling", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Shared Autoscaling"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			Autoscale: &databricks.ClusterAutoscaleArgs{
				MinWorkers: pulumi.Int(1),
				MaxWorkers: pulumi.Int(50),
			},
			SparkConf: pulumi.StringMap{
				"spark.databricks.io.cache.enabled":          pulumi.String("true"),
				"spark.databricks.io.cache.maxDiskUsage":     pulumi.String("50g"),
				"spark.databricks.io.cache.maxMetaDataCache": pulumi.String("1g"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterAutoscaleArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var sharedAutoscaling = new Cluster("sharedAutoscaling", ClusterArgs.builder()
            .clusterName("Shared Autoscaling")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .autoscale(ClusterAutoscaleArgs.builder()
                .minWorkers(1)
                .maxWorkers(50)
                .build())
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.io.cache.enabled", true),
                Map.entry("spark.databricks.io.cache.maxDiskUsage", "50g"),
                Map.entry("spark.databricks.io.cache.maxMetaDataCache", "1g")
            ))
            .build());

    }
}

resources:
  sharedAutoscaling:
    type: databricks:Cluster
    name: shared_autoscaling
    properties:
      clusterName: Shared Autoscaling
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      autoscale:
        minWorkers: 1
        maxWorkers: 50
      sparkConf:
        spark.databricks.io.cache.enabled: true
        spark.databricks.io.cache.maxDiskUsage: 50g
        spark.databricks.io.cache.maxMetaDataCache: 1g
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

nodeTypeId String

Any supported databricks.getNodeType id. If instance_pool_id is specified, this field is not needed.

numWorkers Number

Number of worker nodes that this cluster should have. A cluster has one Spark driver and num_workers executors for a total of num_workers + 1 Spark nodes.

policyId String

runtimeEngine String

The type of runtime engine to use. If not specified, the runtime engine type is inferred based on the spark_version value. Allowed values include: PHOTON, STANDARD.

singleUserName String

sparkConf Map<String>

should have following items:

spark.databricks.repl.allowedLanguages set to a list of supported languages, for example: python,sql, or python,sql,r. Scala is not supported!
spark.databricks.cluster.profile set to serverless

sparkEnvVars Map<String>

Map with environment variable key-value pairs to fine-tune Spark clusters. Key-value pairs of the form (X,Y) are exported (i.e., X='Y') while launching the driver and workers.

sparkVersion String

Runtime version of the cluster. Any supported databricks.getSparkVersion id. We advise using Cluster Policies to restrict the list of versions for simplicity while maintaining enough control.

sshPublicKeys List<String>

state String

(string) State of the cluster.

url String

useMlRuntime Boolean

Whenever ML runtime should be selected or not. Actual runtime is determined by spark_version (DBR release), this field use_ml_runtime, and whether node_type_id is GPU node or not.

workloadType Property Map

Supporting Types

ClusterAutoscale
, ClusterAutoscaleArgs

MaxWorkers int

The maximum number of workers to which the cluster can scale up when overloaded. max_workers must be strictly greater than min_workers.

When using a Single Node cluster, num_workers needs to be 0. It can be set to 0 explicitly, or simply not specified, as it defaults to 0. When num_workers is 0, provider checks for presence of the required Spark configurations:

spark.master must have prefix local, like local[*]
spark.databricks.cluster.profile must have value singleNode

and also custom_tag entry:

"ResourceClass" = "SingleNode"

The following example demonstrates how to create an single node cluster:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const singleNode = new databricks.Cluster("single_node", {
clusterName: "Single Node",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "local[*]",
},
customTags: {
ResourceClass: "SingleNode",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
single_node = databricks.Cluster("single_node",
    cluster_name="Single Node",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.cluster.profile": "singleNode",
        "spark.master": "local[*]",
    },
    custom_tags={
        "ResourceClass": "SingleNode",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var singleNode = new Databricks.Cluster("single_node", new()
    {
        ClusterName = "Single Node",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.cluster.profile", "singleNode" },
            { "spark.master", "local[*]" },
        },
        CustomTags = 
        {
            { "ResourceClass", "SingleNode" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "single_node", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Single Node"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.cluster.profile": pulumi.String("singleNode"),
				"spark.master":                     pulumi.String("local[*]"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("SingleNode"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var singleNode = new Cluster("singleNode", ClusterArgs.builder()
            .clusterName("Single Node")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.cluster.profile", "singleNode"),
                Map.entry("spark.master", "local[*]")
            ))
            .customTags(Map.of("ResourceClass", "SingleNode"))
            .build());

    }
}

resources:
  singleNode:
    type: databricks:Cluster
    name: single_node
    properties:
      clusterName: Single Node
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.cluster.profile: singleNode
        spark.master: local[*]
      customTags:
        ResourceClass: SingleNode
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

MinWorkers int

The minimum number of workers to which the cluster can scale down when underutilized. It is also the initial number of workers the cluster will have after creation.

MaxWorkers int

The maximum number of workers to which the cluster can scale up when overloaded. max_workers must be strictly greater than min_workers.

spark.master must have prefix local, like local[*]
spark.databricks.cluster.profile must have value singleNode

and also custom_tag entry:

"ResourceClass" = "SingleNode"

The following example demonstrates how to create an single node cluster:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const singleNode = new databricks.Cluster("single_node", {
clusterName: "Single Node",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "local[*]",
},
customTags: {
ResourceClass: "SingleNode",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
single_node = databricks.Cluster("single_node",
    cluster_name="Single Node",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.cluster.profile": "singleNode",
        "spark.master": "local[*]",
    },
    custom_tags={
        "ResourceClass": "SingleNode",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var singleNode = new Databricks.Cluster("single_node", new()
    {
        ClusterName = "Single Node",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.cluster.profile", "singleNode" },
            { "spark.master", "local[*]" },
        },
        CustomTags = 
        {
            { "ResourceClass", "SingleNode" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "single_node", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Single Node"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.cluster.profile": pulumi.String("singleNode"),
				"spark.master":                     pulumi.String("local[*]"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("SingleNode"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var singleNode = new Cluster("singleNode", ClusterArgs.builder()
            .clusterName("Single Node")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.cluster.profile", "singleNode"),
                Map.entry("spark.master", "local[*]")
            ))
            .customTags(Map.of("ResourceClass", "SingleNode"))
            .build());

    }
}

resources:
  singleNode:
    type: databricks:Cluster
    name: single_node
    properties:
      clusterName: Single Node
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.cluster.profile: singleNode
        spark.master: local[*]
      customTags:
        ResourceClass: SingleNode
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

MinWorkers int

The minimum number of workers to which the cluster can scale down when underutilized. It is also the initial number of workers the cluster will have after creation.

maxWorkers Integer

The maximum number of workers to which the cluster can scale up when overloaded. max_workers must be strictly greater than min_workers.

spark.master must have prefix local, like local[*]
spark.databricks.cluster.profile must have value singleNode

and also custom_tag entry:

"ResourceClass" = "SingleNode"

The following example demonstrates how to create an single node cluster:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const singleNode = new databricks.Cluster("single_node", {
clusterName: "Single Node",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "local[*]",
},
customTags: {
ResourceClass: "SingleNode",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
single_node = databricks.Cluster("single_node",
    cluster_name="Single Node",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.cluster.profile": "singleNode",
        "spark.master": "local[*]",
    },
    custom_tags={
        "ResourceClass": "SingleNode",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var singleNode = new Databricks.Cluster("single_node", new()
    {
        ClusterName = "Single Node",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.cluster.profile", "singleNode" },
            { "spark.master", "local[*]" },
        },
        CustomTags = 
        {
            { "ResourceClass", "SingleNode" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "single_node", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Single Node"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.cluster.profile": pulumi.String("singleNode"),
				"spark.master":                     pulumi.String("local[*]"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("SingleNode"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var singleNode = new Cluster("singleNode", ClusterArgs.builder()
            .clusterName("Single Node")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.cluster.profile", "singleNode"),
                Map.entry("spark.master", "local[*]")
            ))
            .customTags(Map.of("ResourceClass", "SingleNode"))
            .build());

    }
}

resources:
  singleNode:
    type: databricks:Cluster
    name: single_node
    properties:
      clusterName: Single Node
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.cluster.profile: singleNode
        spark.master: local[*]
      customTags:
        ResourceClass: SingleNode
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

minWorkers Integer

The minimum number of workers to which the cluster can scale down when underutilized. It is also the initial number of workers the cluster will have after creation.

maxWorkers number

The maximum number of workers to which the cluster can scale up when overloaded. max_workers must be strictly greater than min_workers.

spark.master must have prefix local, like local[*]
spark.databricks.cluster.profile must have value singleNode

and also custom_tag entry:

"ResourceClass" = "SingleNode"

The following example demonstrates how to create an single node cluster:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const singleNode = new databricks.Cluster("single_node", {
clusterName: "Single Node",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "local[*]",
},
customTags: {
ResourceClass: "SingleNode",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
single_node = databricks.Cluster("single_node",
    cluster_name="Single Node",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.cluster.profile": "singleNode",
        "spark.master": "local[*]",
    },
    custom_tags={
        "ResourceClass": "SingleNode",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var singleNode = new Databricks.Cluster("single_node", new()
    {
        ClusterName = "Single Node",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.cluster.profile", "singleNode" },
            { "spark.master", "local[*]" },
        },
        CustomTags = 
        {
            { "ResourceClass", "SingleNode" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "single_node", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Single Node"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.cluster.profile": pulumi.String("singleNode"),
				"spark.master":                     pulumi.String("local[*]"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("SingleNode"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var singleNode = new Cluster("singleNode", ClusterArgs.builder()
            .clusterName("Single Node")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.cluster.profile", "singleNode"),
                Map.entry("spark.master", "local[*]")
            ))
            .customTags(Map.of("ResourceClass", "SingleNode"))
            .build());

    }
}

resources:
  singleNode:
    type: databricks:Cluster
    name: single_node
    properties:
      clusterName: Single Node
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.cluster.profile: singleNode
        spark.master: local[*]
      customTags:
        ResourceClass: SingleNode
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

minWorkers number

The minimum number of workers to which the cluster can scale down when underutilized. It is also the initial number of workers the cluster will have after creation.

max_workers int

The maximum number of workers to which the cluster can scale up when overloaded. max_workers must be strictly greater than min_workers.

spark.master must have prefix local, like local[*]
spark.databricks.cluster.profile must have value singleNode

and also custom_tag entry:

"ResourceClass" = "SingleNode"

The following example demonstrates how to create an single node cluster:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const singleNode = new databricks.Cluster("single_node", {
clusterName: "Single Node",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "local[*]",
},
customTags: {
ResourceClass: "SingleNode",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
single_node = databricks.Cluster("single_node",
    cluster_name="Single Node",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.cluster.profile": "singleNode",
        "spark.master": "local[*]",
    },
    custom_tags={
        "ResourceClass": "SingleNode",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var singleNode = new Databricks.Cluster("single_node", new()
    {
        ClusterName = "Single Node",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.cluster.profile", "singleNode" },
            { "spark.master", "local[*]" },
        },
        CustomTags = 
        {
            { "ResourceClass", "SingleNode" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "single_node", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Single Node"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.cluster.profile": pulumi.String("singleNode"),
				"spark.master":                     pulumi.String("local[*]"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("SingleNode"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var singleNode = new Cluster("singleNode", ClusterArgs.builder()
            .clusterName("Single Node")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.cluster.profile", "singleNode"),
                Map.entry("spark.master", "local[*]")
            ))
            .customTags(Map.of("ResourceClass", "SingleNode"))
            .build());

    }
}

resources:
  singleNode:
    type: databricks:Cluster
    name: single_node
    properties:
      clusterName: Single Node
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.cluster.profile: singleNode
        spark.master: local[*]
      customTags:
        ResourceClass: SingleNode
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

min_workers int

The minimum number of workers to which the cluster can scale down when underutilized. It is also the initial number of workers the cluster will have after creation.

maxWorkers Number

The maximum number of workers to which the cluster can scale up when overloaded. max_workers must be strictly greater than min_workers.

spark.master must have prefix local, like local[*]
spark.databricks.cluster.profile must have value singleNode

and also custom_tag entry:

"ResourceClass" = "SingleNode"

The following example demonstrates how to create an single node cluster:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const smallest = databricks.getNodeType({
localDisk: true,
});
const latestLts = databricks.getSparkVersion({
longTermSupport: true,
});
const singleNode = new databricks.Cluster("single_node", {
clusterName: "Single Node",
sparkVersion: latestLts.then(latestLts => latestLts.id),
nodeTypeId: smallest.then(smallest => smallest.id),
autoterminationMinutes: 20,
sparkConf: {
"spark.databricks.cluster.profile": "singleNode",
"spark.master": "local[*]",
},
customTags: {
ResourceClass: "SingleNode",
},
});

import pulumi
import pulumi_databricks as databricks

smallest = databricks.get_node_type(local_disk=True)
latest_lts = databricks.get_spark_version(long_term_support=True)
single_node = databricks.Cluster("single_node",
    cluster_name="Single Node",
    spark_version=latest_lts.id,
    node_type_id=smallest.id,
    autotermination_minutes=20,
    spark_conf={
        "spark.databricks.cluster.profile": "singleNode",
        "spark.master": "local[*]",
    },
    custom_tags={
        "ResourceClass": "SingleNode",
    })

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var smallest = Databricks.GetNodeType.Invoke(new()
    {
        LocalDisk = true,
    });

    var latestLts = Databricks.GetSparkVersion.Invoke(new()
    {
        LongTermSupport = true,
    });

    var singleNode = new Databricks.Cluster("single_node", new()
    {
        ClusterName = "Single Node",
        SparkVersion = latestLts.Apply(getSparkVersionResult => getSparkVersionResult.Id),
        NodeTypeId = smallest.Apply(getNodeTypeResult => getNodeTypeResult.Id),
        AutoterminationMinutes = 20,
        SparkConf = 
        {
            { "spark.databricks.cluster.profile", "singleNode" },
            { "spark.master", "local[*]" },
        },
        CustomTags = 
        {
            { "ResourceClass", "SingleNode" },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		smallest, err := databricks.GetNodeType(ctx, &databricks.GetNodeTypeArgs{
			LocalDisk: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		latestLts, err := databricks.GetSparkVersion(ctx, &databricks.GetSparkVersionArgs{
			LongTermSupport: pulumi.BoolRef(true),
		}, nil)
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "single_node", &databricks.ClusterArgs{
			ClusterName:            pulumi.String("Single Node"),
			SparkVersion:           pulumi.String(latestLts.Id),
			NodeTypeId:             pulumi.String(smallest.Id),
			AutoterminationMinutes: pulumi.Int(20),
			SparkConf: pulumi.StringMap{
				"spark.databricks.cluster.profile": pulumi.String("singleNode"),
				"spark.master":                     pulumi.String("local[*]"),
			},
			CustomTags: pulumi.StringMap{
				"ResourceClass": pulumi.String("SingleNode"),
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.DatabricksFunctions;
import com.pulumi.databricks.inputs.GetNodeTypeArgs;
import com.pulumi.databricks.inputs.GetSparkVersionArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var smallest = DatabricksFunctions.getNodeType(GetNodeTypeArgs.builder()
            .localDisk(true)
            .build());

        final var latestLts = DatabricksFunctions.getSparkVersion(GetSparkVersionArgs.builder()
            .longTermSupport(true)
            .build());

        var singleNode = new Cluster("singleNode", ClusterArgs.builder()
            .clusterName("Single Node")
            .sparkVersion(latestLts.applyValue(getSparkVersionResult -> getSparkVersionResult.id()))
            .nodeTypeId(smallest.applyValue(getNodeTypeResult -> getNodeTypeResult.id()))
            .autoterminationMinutes(20)
            .sparkConf(Map.ofEntries(
                Map.entry("spark.databricks.cluster.profile", "singleNode"),
                Map.entry("spark.master", "local[*]")
            ))
            .customTags(Map.of("ResourceClass", "SingleNode"))
            .build());

    }
}

resources:
  singleNode:
    type: databricks:Cluster
    name: single_node
    properties:
      clusterName: Single Node
      sparkVersion: ${latestLts.id}
      nodeTypeId: ${smallest.id}
      autoterminationMinutes: 20
      sparkConf:
        spark.databricks.cluster.profile: singleNode
        spark.master: local[*]
      customTags:
        ResourceClass: SingleNode
variables:
  smallest:
    fn::invoke:
      function: databricks:getNodeType
      arguments:
        localDisk: true
  latestLts:
    fn::invoke:
      function: databricks:getSparkVersion
      arguments:
        longTermSupport: true

minWorkers Number

The minimum number of workers to which the cluster can scale down when underutilized. It is also the initial number of workers the cluster will have after creation.

ClusterAwsAttributes
, ClusterAwsAttributesArgs

Availability string: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT, SPOT_WITH_FALLBACK and ON_DEMAND. Note: If first_on_demand is zero, this availability type will be used for the entire cluster. Backend default value is SPOT_WITH_FALLBACK and could change in the future
EbsVolumeCount int: The number of volumes launched for each instance. You can choose up to 10 volumes. This feature is only enabled for supported node types. Legacy node types cannot specify custom EBS volumes. For node types with no instance store, at least one EBS volume needs to be specified; otherwise, cluster creation will fail. These EBS volumes will be mounted at /ebs0, /ebs1, and etc. Instance store volumes will be mounted at /local_disk0, /local_disk1, and etc. If EBS volumes are attached, Databricks will configure Spark to use only the EBS volumes for scratch storage because heterogeneously sized scratch devices can lead to inefficient disk utilization. If no EBS volumes are attached, Databricks will configure Spark to use instance store volumes. If EBS volumes are specified, then the Spark configuration spark.local.dir will be overridden.
EbsVolumeIops int
EbsVolumeSize int: The size of each EBS volume (in GiB) launched for each instance. For general purpose SSD, this value must be within the range 100 - 4096. For throughput optimized HDD, this value must be within the range 500 - 4096. Custom EBS volumes cannot be specified for the legacy node types (memory-optimized and compute-optimized).
EbsVolumeThroughput int
EbsVolumeType string: The type of EBS volumes that will be launched with this cluster. Valid values are GENERAL_PURPOSE_SSD or THROUGHPUT_OPTIMIZED_HDD. Use this option only if you're not picking Delta Optimized i3.* node types.
FirstOnDemand int: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster. Backend default value is 1 and could change in the future
InstanceProfileArn string: Nodes for this cluster will only be placed on AWS instances with this instance profile. Please see databricks.InstanceProfile resource documentation for extended examples on adding a valid instance profile using Pulumi.
SpotBidPricePercent int: The max price for AWS spot instances, as a percentage of the corresponding instance type’s on-demand price. For example, if this field is set to 50, and the cluster needs a new i3.xlarge spot instance, then the max price is half of the price of on-demand i3.xlarge instances. Similarly, if this field is set to 200, the max price is twice the price of on-demand i3.xlarge instances. If not specified, the default value is 100. When spot instances are requested for this cluster, only spot instances whose max price percentage matches this field will be considered. For safety, we enforce this field to be no more than 10000.
ZoneId string: Identifier for the availability zone/datacenter in which the cluster resides. This string will be of a form like us-west-2a. The provided availability zone must be in the same region as the Databricks deployment. For example, us-west-2a is not a valid zone ID if the Databricks deployment resides in the us-east-1 region. Enable automatic availability zone selection ("Auto-AZ"), by setting the value auto. Databricks selects the AZ based on available IPs in the workspace subnets and retries in other availability zones if AWS returns insufficient capacity errors.

Availability string: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT, SPOT_WITH_FALLBACK and ON_DEMAND. Note: If first_on_demand is zero, this availability type will be used for the entire cluster. Backend default value is SPOT_WITH_FALLBACK and could change in the future
EbsVolumeCount int: The number of volumes launched for each instance. You can choose up to 10 volumes. This feature is only enabled for supported node types. Legacy node types cannot specify custom EBS volumes. For node types with no instance store, at least one EBS volume needs to be specified; otherwise, cluster creation will fail. These EBS volumes will be mounted at /ebs0, /ebs1, and etc. Instance store volumes will be mounted at /local_disk0, /local_disk1, and etc. If EBS volumes are attached, Databricks will configure Spark to use only the EBS volumes for scratch storage because heterogeneously sized scratch devices can lead to inefficient disk utilization. If no EBS volumes are attached, Databricks will configure Spark to use instance store volumes. If EBS volumes are specified, then the Spark configuration spark.local.dir will be overridden.
EbsVolumeIops int
EbsVolumeSize int: The size of each EBS volume (in GiB) launched for each instance. For general purpose SSD, this value must be within the range 100 - 4096. For throughput optimized HDD, this value must be within the range 500 - 4096. Custom EBS volumes cannot be specified for the legacy node types (memory-optimized and compute-optimized).
EbsVolumeThroughput int
EbsVolumeType string: The type of EBS volumes that will be launched with this cluster. Valid values are GENERAL_PURPOSE_SSD or THROUGHPUT_OPTIMIZED_HDD. Use this option only if you're not picking Delta Optimized i3.* node types.
FirstOnDemand int: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster. Backend default value is 1 and could change in the future
InstanceProfileArn string: Nodes for this cluster will only be placed on AWS instances with this instance profile. Please see databricks.InstanceProfile resource documentation for extended examples on adding a valid instance profile using Pulumi.
SpotBidPricePercent int: The max price for AWS spot instances, as a percentage of the corresponding instance type’s on-demand price. For example, if this field is set to 50, and the cluster needs a new i3.xlarge spot instance, then the max price is half of the price of on-demand i3.xlarge instances. Similarly, if this field is set to 200, the max price is twice the price of on-demand i3.xlarge instances. If not specified, the default value is 100. When spot instances are requested for this cluster, only spot instances whose max price percentage matches this field will be considered. For safety, we enforce this field to be no more than 10000.
ZoneId string: Identifier for the availability zone/datacenter in which the cluster resides. This string will be of a form like us-west-2a. The provided availability zone must be in the same region as the Databricks deployment. For example, us-west-2a is not a valid zone ID if the Databricks deployment resides in the us-east-1 region. Enable automatic availability zone selection ("Auto-AZ"), by setting the value auto. Databricks selects the AZ based on available IPs in the workspace subnets and retries in other availability zones if AWS returns insufficient capacity errors.

availability String: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT, SPOT_WITH_FALLBACK and ON_DEMAND. Note: If first_on_demand is zero, this availability type will be used for the entire cluster. Backend default value is SPOT_WITH_FALLBACK and could change in the future
ebsVolumeCount Integer: The number of volumes launched for each instance. You can choose up to 10 volumes. This feature is only enabled for supported node types. Legacy node types cannot specify custom EBS volumes. For node types with no instance store, at least one EBS volume needs to be specified; otherwise, cluster creation will fail. These EBS volumes will be mounted at /ebs0, /ebs1, and etc. Instance store volumes will be mounted at /local_disk0, /local_disk1, and etc. If EBS volumes are attached, Databricks will configure Spark to use only the EBS volumes for scratch storage because heterogeneously sized scratch devices can lead to inefficient disk utilization. If no EBS volumes are attached, Databricks will configure Spark to use instance store volumes. If EBS volumes are specified, then the Spark configuration spark.local.dir will be overridden.
ebsVolumeIops Integer
ebsVolumeSize Integer: The size of each EBS volume (in GiB) launched for each instance. For general purpose SSD, this value must be within the range 100 - 4096. For throughput optimized HDD, this value must be within the range 500 - 4096. Custom EBS volumes cannot be specified for the legacy node types (memory-optimized and compute-optimized).
ebsVolumeThroughput Integer
ebsVolumeType String: The type of EBS volumes that will be launched with this cluster. Valid values are GENERAL_PURPOSE_SSD or THROUGHPUT_OPTIMIZED_HDD. Use this option only if you're not picking Delta Optimized i3.* node types.
firstOnDemand Integer: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster. Backend default value is 1 and could change in the future
instanceProfileArn String: Nodes for this cluster will only be placed on AWS instances with this instance profile. Please see databricks.InstanceProfile resource documentation for extended examples on adding a valid instance profile using Pulumi.
spotBidPricePercent Integer: The max price for AWS spot instances, as a percentage of the corresponding instance type’s on-demand price. For example, if this field is set to 50, and the cluster needs a new i3.xlarge spot instance, then the max price is half of the price of on-demand i3.xlarge instances. Similarly, if this field is set to 200, the max price is twice the price of on-demand i3.xlarge instances. If not specified, the default value is 100. When spot instances are requested for this cluster, only spot instances whose max price percentage matches this field will be considered. For safety, we enforce this field to be no more than 10000.
zoneId String: Identifier for the availability zone/datacenter in which the cluster resides. This string will be of a form like us-west-2a. The provided availability zone must be in the same region as the Databricks deployment. For example, us-west-2a is not a valid zone ID if the Databricks deployment resides in the us-east-1 region. Enable automatic availability zone selection ("Auto-AZ"), by setting the value auto. Databricks selects the AZ based on available IPs in the workspace subnets and retries in other availability zones if AWS returns insufficient capacity errors.

availability string: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT, SPOT_WITH_FALLBACK and ON_DEMAND. Note: If first_on_demand is zero, this availability type will be used for the entire cluster. Backend default value is SPOT_WITH_FALLBACK and could change in the future
ebsVolumeCount number: The number of volumes launched for each instance. You can choose up to 10 volumes. This feature is only enabled for supported node types. Legacy node types cannot specify custom EBS volumes. For node types with no instance store, at least one EBS volume needs to be specified; otherwise, cluster creation will fail. These EBS volumes will be mounted at /ebs0, /ebs1, and etc. Instance store volumes will be mounted at /local_disk0, /local_disk1, and etc. If EBS volumes are attached, Databricks will configure Spark to use only the EBS volumes for scratch storage because heterogeneously sized scratch devices can lead to inefficient disk utilization. If no EBS volumes are attached, Databricks will configure Spark to use instance store volumes. If EBS volumes are specified, then the Spark configuration spark.local.dir will be overridden.
ebsVolumeIops number
ebsVolumeSize number: The size of each EBS volume (in GiB) launched for each instance. For general purpose SSD, this value must be within the range 100 - 4096. For throughput optimized HDD, this value must be within the range 500 - 4096. Custom EBS volumes cannot be specified for the legacy node types (memory-optimized and compute-optimized).
ebsVolumeThroughput number
ebsVolumeType string: The type of EBS volumes that will be launched with this cluster. Valid values are GENERAL_PURPOSE_SSD or THROUGHPUT_OPTIMIZED_HDD. Use this option only if you're not picking Delta Optimized i3.* node types.
firstOnDemand number: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster. Backend default value is 1 and could change in the future
instanceProfileArn string: Nodes for this cluster will only be placed on AWS instances with this instance profile. Please see databricks.InstanceProfile resource documentation for extended examples on adding a valid instance profile using Pulumi.
spotBidPricePercent number: The max price for AWS spot instances, as a percentage of the corresponding instance type’s on-demand price. For example, if this field is set to 50, and the cluster needs a new i3.xlarge spot instance, then the max price is half of the price of on-demand i3.xlarge instances. Similarly, if this field is set to 200, the max price is twice the price of on-demand i3.xlarge instances. If not specified, the default value is 100. When spot instances are requested for this cluster, only spot instances whose max price percentage matches this field will be considered. For safety, we enforce this field to be no more than 10000.
zoneId string: Identifier for the availability zone/datacenter in which the cluster resides. This string will be of a form like us-west-2a. The provided availability zone must be in the same region as the Databricks deployment. For example, us-west-2a is not a valid zone ID if the Databricks deployment resides in the us-east-1 region. Enable automatic availability zone selection ("Auto-AZ"), by setting the value auto. Databricks selects the AZ based on available IPs in the workspace subnets and retries in other availability zones if AWS returns insufficient capacity errors.

availability str: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT, SPOT_WITH_FALLBACK and ON_DEMAND. Note: If first_on_demand is zero, this availability type will be used for the entire cluster. Backend default value is SPOT_WITH_FALLBACK and could change in the future
ebs_volume_count int: The number of volumes launched for each instance. You can choose up to 10 volumes. This feature is only enabled for supported node types. Legacy node types cannot specify custom EBS volumes. For node types with no instance store, at least one EBS volume needs to be specified; otherwise, cluster creation will fail. These EBS volumes will be mounted at /ebs0, /ebs1, and etc. Instance store volumes will be mounted at /local_disk0, /local_disk1, and etc. If EBS volumes are attached, Databricks will configure Spark to use only the EBS volumes for scratch storage because heterogeneously sized scratch devices can lead to inefficient disk utilization. If no EBS volumes are attached, Databricks will configure Spark to use instance store volumes. If EBS volumes are specified, then the Spark configuration spark.local.dir will be overridden.
ebs_volume_iops int
ebs_volume_size int: The size of each EBS volume (in GiB) launched for each instance. For general purpose SSD, this value must be within the range 100 - 4096. For throughput optimized HDD, this value must be within the range 500 - 4096. Custom EBS volumes cannot be specified for the legacy node types (memory-optimized and compute-optimized).
ebs_volume_throughput int
ebs_volume_type str: The type of EBS volumes that will be launched with this cluster. Valid values are GENERAL_PURPOSE_SSD or THROUGHPUT_OPTIMIZED_HDD. Use this option only if you're not picking Delta Optimized i3.* node types.
first_on_demand int: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster. Backend default value is 1 and could change in the future
instance_profile_arn str: Nodes for this cluster will only be placed on AWS instances with this instance profile. Please see databricks.InstanceProfile resource documentation for extended examples on adding a valid instance profile using Pulumi.
spot_bid_price_percent int: The max price for AWS spot instances, as a percentage of the corresponding instance type’s on-demand price. For example, if this field is set to 50, and the cluster needs a new i3.xlarge spot instance, then the max price is half of the price of on-demand i3.xlarge instances. Similarly, if this field is set to 200, the max price is twice the price of on-demand i3.xlarge instances. If not specified, the default value is 100. When spot instances are requested for this cluster, only spot instances whose max price percentage matches this field will be considered. For safety, we enforce this field to be no more than 10000.
zone_id str: Identifier for the availability zone/datacenter in which the cluster resides. This string will be of a form like us-west-2a. The provided availability zone must be in the same region as the Databricks deployment. For example, us-west-2a is not a valid zone ID if the Databricks deployment resides in the us-east-1 region. Enable automatic availability zone selection ("Auto-AZ"), by setting the value auto. Databricks selects the AZ based on available IPs in the workspace subnets and retries in other availability zones if AWS returns insufficient capacity errors.

availability String: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT, SPOT_WITH_FALLBACK and ON_DEMAND. Note: If first_on_demand is zero, this availability type will be used for the entire cluster. Backend default value is SPOT_WITH_FALLBACK and could change in the future
ebsVolumeCount Number: The number of volumes launched for each instance. You can choose up to 10 volumes. This feature is only enabled for supported node types. Legacy node types cannot specify custom EBS volumes. For node types with no instance store, at least one EBS volume needs to be specified; otherwise, cluster creation will fail. These EBS volumes will be mounted at /ebs0, /ebs1, and etc. Instance store volumes will be mounted at /local_disk0, /local_disk1, and etc. If EBS volumes are attached, Databricks will configure Spark to use only the EBS volumes for scratch storage because heterogeneously sized scratch devices can lead to inefficient disk utilization. If no EBS volumes are attached, Databricks will configure Spark to use instance store volumes. If EBS volumes are specified, then the Spark configuration spark.local.dir will be overridden.
ebsVolumeIops Number
ebsVolumeSize Number: The size of each EBS volume (in GiB) launched for each instance. For general purpose SSD, this value must be within the range 100 - 4096. For throughput optimized HDD, this value must be within the range 500 - 4096. Custom EBS volumes cannot be specified for the legacy node types (memory-optimized and compute-optimized).
ebsVolumeThroughput Number
ebsVolumeType String: The type of EBS volumes that will be launched with this cluster. Valid values are GENERAL_PURPOSE_SSD or THROUGHPUT_OPTIMIZED_HDD. Use this option only if you're not picking Delta Optimized i3.* node types.
firstOnDemand Number: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster. Backend default value is 1 and could change in the future
instanceProfileArn String: Nodes for this cluster will only be placed on AWS instances with this instance profile. Please see databricks.InstanceProfile resource documentation for extended examples on adding a valid instance profile using Pulumi.
spotBidPricePercent Number: The max price for AWS spot instances, as a percentage of the corresponding instance type’s on-demand price. For example, if this field is set to 50, and the cluster needs a new i3.xlarge spot instance, then the max price is half of the price of on-demand i3.xlarge instances. Similarly, if this field is set to 200, the max price is twice the price of on-demand i3.xlarge instances. If not specified, the default value is 100. When spot instances are requested for this cluster, only spot instances whose max price percentage matches this field will be considered. For safety, we enforce this field to be no more than 10000.
zoneId String: Identifier for the availability zone/datacenter in which the cluster resides. This string will be of a form like us-west-2a. The provided availability zone must be in the same region as the Databricks deployment. For example, us-west-2a is not a valid zone ID if the Databricks deployment resides in the us-east-1 region. Enable automatic availability zone selection ("Auto-AZ"), by setting the value auto. Databricks selects the AZ based on available IPs in the workspace subnets and retries in other availability zones if AWS returns insufficient capacity errors.

ClusterAzureAttributes
, ClusterAzureAttributesArgs

Availability string: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT_AZURE, SPOT_WITH_FALLBACK_AZURE, and ON_DEMAND_AZURE. Note: If first_on_demand is zero, this availability type will be used for the entire cluster.
FirstOnDemand int: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster.
LogAnalyticsInfo ClusterAzureAttributesLogAnalyticsInfo
SpotBidMaxPrice double: The max bid price used for Azure spot instances. You can set this to greater than or equal to the current spot price. You can also set this to -1, which specifies that the instance cannot be evicted on the basis of price. The price for the instance will be the current price for spot instances or the price for a standard instance.

Availability string: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT_AZURE, SPOT_WITH_FALLBACK_AZURE, and ON_DEMAND_AZURE. Note: If first_on_demand is zero, this availability type will be used for the entire cluster.
FirstOnDemand int: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster.
LogAnalyticsInfo ClusterAzureAttributesLogAnalyticsInfo
SpotBidMaxPrice float64: The max bid price used for Azure spot instances. You can set this to greater than or equal to the current spot price. You can also set this to -1, which specifies that the instance cannot be evicted on the basis of price. The price for the instance will be the current price for spot instances or the price for a standard instance.

availability String: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT_AZURE, SPOT_WITH_FALLBACK_AZURE, and ON_DEMAND_AZURE. Note: If first_on_demand is zero, this availability type will be used for the entire cluster.
firstOnDemand Integer: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster.
logAnalyticsInfo ClusterAzureAttributesLogAnalyticsInfo
spotBidMaxPrice Double: The max bid price used for Azure spot instances. You can set this to greater than or equal to the current spot price. You can also set this to -1, which specifies that the instance cannot be evicted on the basis of price. The price for the instance will be the current price for spot instances or the price for a standard instance.

availability string: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT_AZURE, SPOT_WITH_FALLBACK_AZURE, and ON_DEMAND_AZURE. Note: If first_on_demand is zero, this availability type will be used for the entire cluster.
firstOnDemand number: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster.
logAnalyticsInfo ClusterAzureAttributesLogAnalyticsInfo
spotBidMaxPrice number: The max bid price used for Azure spot instances. You can set this to greater than or equal to the current spot price. You can also set this to -1, which specifies that the instance cannot be evicted on the basis of price. The price for the instance will be the current price for spot instances or the price for a standard instance.

availability str: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT_AZURE, SPOT_WITH_FALLBACK_AZURE, and ON_DEMAND_AZURE. Note: If first_on_demand is zero, this availability type will be used for the entire cluster.
first_on_demand int: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster.
log_analytics_info ClusterAzureAttributesLogAnalyticsInfo
spot_bid_max_price float: The max bid price used for Azure spot instances. You can set this to greater than or equal to the current spot price. You can also set this to -1, which specifies that the instance cannot be evicted on the basis of price. The price for the instance will be the current price for spot instances or the price for a standard instance.

availability String: Availability type used for all subsequent nodes past the first_on_demand ones. Valid values are SPOT_AZURE, SPOT_WITH_FALLBACK_AZURE, and ON_DEMAND_AZURE. Note: If first_on_demand is zero, this availability type will be used for the entire cluster.
firstOnDemand Number: The first first_on_demand nodes of the cluster will be placed on on-demand instances. If this value is greater than 0, the cluster driver node will be placed on an on-demand instance. If this value is greater than or equal to the current cluster size, all nodes will be placed on on-demand instances. If this value is less than the current cluster size, first_on_demand nodes will be placed on on-demand instances, and the remainder will be placed on availability instances. This value does not affect cluster size and cannot be mutated over the lifetime of a cluster.
logAnalyticsInfo Property Map
spotBidMaxPrice Number: The max bid price used for Azure spot instances. You can set this to greater than or equal to the current spot price. You can also set this to -1, which specifies that the instance cannot be evicted on the basis of price. The price for the instance will be the current price for spot instances or the price for a standard instance.

ClusterAzureAttributesLogAnalyticsInfo
, ClusterAzureAttributesLogAnalyticsInfoArgs

LogAnalyticsPrimaryKey string
LogAnalyticsWorkspaceId string

LogAnalyticsPrimaryKey string
LogAnalyticsWorkspaceId string

logAnalyticsPrimaryKey String
logAnalyticsWorkspaceId String

logAnalyticsPrimaryKey string
logAnalyticsWorkspaceId string

log_analytics_primary_key str
log_analytics_workspace_id str

logAnalyticsPrimaryKey String
logAnalyticsWorkspaceId String

ClusterClusterLogConf
, ClusterClusterLogConfArgs

Dbfs ClusterClusterLogConfDbfs
S3 ClusterClusterLogConfS3
Volumes ClusterClusterLogConfVolumes

Dbfs ClusterClusterLogConfDbfs
S3 ClusterClusterLogConfS3
Volumes ClusterClusterLogConfVolumes

dbfs ClusterClusterLogConfDbfs
s3 ClusterClusterLogConfS3
volumes ClusterClusterLogConfVolumes

dbfs ClusterClusterLogConfDbfs
s3 ClusterClusterLogConfS3
volumes ClusterClusterLogConfVolumes

dbfs ClusterClusterLogConfDbfs
s3 ClusterClusterLogConfS3
volumes ClusterClusterLogConfVolumes

dbfs Property Map
s3 Property Map
volumes Property Map

ClusterClusterLogConfDbfs
, ClusterClusterLogConfDbfsArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterClusterLogConfS3
, ClusterClusterLogConfS3Args

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
CannedAcl string: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
EnableEncryption bool: Enable server-side encryption, false by default.
EncryptionType string: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
Endpoint string: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
KmsKey string: KMS key used if encryption is enabled and encryption type is set to sse-kms.
Region string: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
CannedAcl string: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
EnableEncryption bool: Enable server-side encryption, false by default.
EncryptionType string: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
Endpoint string: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
KmsKey string: KMS key used if encryption is enabled and encryption type is set to sse-kms.
Region string: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
cannedAcl String: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enableEncryption Boolean: Enable server-side encryption, false by default.
encryptionType String: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint String: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kmsKey String: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region String: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
cannedAcl string: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enableEncryption boolean: Enable server-side encryption, false by default.
encryptionType string: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint string: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kmsKey string: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region string: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
canned_acl str: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enable_encryption bool: Enable server-side encryption, false by default.
encryption_type str: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint str: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kms_key str: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region str: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
cannedAcl String: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enableEncryption Boolean: Enable server-side encryption, false by default.
encryptionType String: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint String: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kmsKey String: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region String: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

ClusterClusterLogConfVolumes
, ClusterClusterLogConfVolumesArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterClusterMountInfo
, ClusterClusterMountInfoArgs

LocalMountDirPath string

path inside the Spark container.

For example, you can mount Azure Data Lake Storage container using the following code:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const storageAccount = "ewfw3ggwegwg";
const storageContainer = "test";
const withNfs = new databricks.Cluster("with_nfs", {clusterMountInfos: [{
networkFilesystemInfo: {
serverAddress: ${storageAccount}.blob.core.windows.net,
mountOptions: "sec=sys,vers=3,nolock,proto=tcp",
},
remoteMountDirPath: ${storageAccount}/${storageContainer},
localMountDirPath: "/mnt/nfs-test",
}]});

import pulumi
import pulumi_databricks as databricks

storage_account = "ewfw3ggwegwg"
storage_container = "test"
with_nfs = databricks.Cluster("with_nfs", cluster_mount_infos=[{
    "network_filesystem_info": {
        "server_address": f"{storage_account}.blob.core.windows.net",
        "mount_options": "sec=sys,vers=3,nolock,proto=tcp",
    },
    "remote_mount_dir_path": f"{storage_account}/{storage_container}",
    "local_mount_dir_path": "/mnt/nfs-test",
}])

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var storageAccount = "ewfw3ggwegwg";

    var storageContainer = "test";

    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        ClusterMountInfos = new[]
        {
            new Databricks.Inputs.ClusterClusterMountInfoArgs
            {
                NetworkFilesystemInfo = new Databricks.Inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs
                {
                    ServerAddress = $"{storageAccount}.blob.core.windows.net",
                    MountOptions = "sec=sys,vers=3,nolock,proto=tcp",
                },
                RemoteMountDirPath = $"{storageAccount}/{storageContainer}",
                LocalMountDirPath = "/mnt/nfs-test",
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		storageAccount := "ewfw3ggwegwg"
		storageContainer := "test"
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			ClusterMountInfos: databricks.ClusterClusterMountInfoArray{
				&databricks.ClusterClusterMountInfoArgs{
					NetworkFilesystemInfo: &databricks.ClusterClusterMountInfoNetworkFilesystemInfoArgs{
						ServerAddress: pulumi.Sprintf("%v.blob.core.windows.net", storageAccount),
						MountOptions:  pulumi.String("sec=sys,vers=3,nolock,proto=tcp"),
					},
					RemoteMountDirPath: pulumi.Sprintf("%v/%v", storageAccount, storageContainer),
					LocalMountDirPath:  pulumi.String("/mnt/nfs-test"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var storageAccount = "ewfw3ggwegwg";

        final var storageContainer = "test";

        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .clusterMountInfos(ClusterClusterMountInfoArgs.builder()
                .networkFilesystemInfo(ClusterClusterMountInfoNetworkFilesystemInfoArgs.builder()
                    .serverAddress(String.format("%s.blob.core.windows.net", storageAccount))
                    .mountOptions("sec=sys,vers=3,nolock,proto=tcp")
                    .build())
                .remoteMountDirPath(String.format("%s/%s", storageAccount,storageContainer))
                .localMountDirPath("/mnt/nfs-test")
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      clusterMountInfos:
        - networkFilesystemInfo:
            serverAddress: ${storageAccount}.blob.core.windows.net
            mountOptions: sec=sys,vers=3,nolock,proto=tcp
          remoteMountDirPath: ${storageAccount}/${storageContainer}
          localMountDirPath: /mnt/nfs-test
variables:
  storageAccount: ewfw3ggwegwg
  storageContainer: test

NetworkFilesystemInfo ClusterClusterMountInfoNetworkFilesystemInfo

block specifying connection. It consists of:

RemoteMountDirPath string

string specifying path to mount on the remote service.

LocalMountDirPath string

path inside the Spark container.

For example, you can mount Azure Data Lake Storage container using the following code:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const storageAccount = "ewfw3ggwegwg";
const storageContainer = "test";
const withNfs = new databricks.Cluster("with_nfs", {clusterMountInfos: [{
networkFilesystemInfo: {
serverAddress: ${storageAccount}.blob.core.windows.net,
mountOptions: "sec=sys,vers=3,nolock,proto=tcp",
},
remoteMountDirPath: ${storageAccount}/${storageContainer},
localMountDirPath: "/mnt/nfs-test",
}]});

import pulumi
import pulumi_databricks as databricks

storage_account = "ewfw3ggwegwg"
storage_container = "test"
with_nfs = databricks.Cluster("with_nfs", cluster_mount_infos=[{
    "network_filesystem_info": {
        "server_address": f"{storage_account}.blob.core.windows.net",
        "mount_options": "sec=sys,vers=3,nolock,proto=tcp",
    },
    "remote_mount_dir_path": f"{storage_account}/{storage_container}",
    "local_mount_dir_path": "/mnt/nfs-test",
}])

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var storageAccount = "ewfw3ggwegwg";

    var storageContainer = "test";

    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        ClusterMountInfos = new[]
        {
            new Databricks.Inputs.ClusterClusterMountInfoArgs
            {
                NetworkFilesystemInfo = new Databricks.Inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs
                {
                    ServerAddress = $"{storageAccount}.blob.core.windows.net",
                    MountOptions = "sec=sys,vers=3,nolock,proto=tcp",
                },
                RemoteMountDirPath = $"{storageAccount}/{storageContainer}",
                LocalMountDirPath = "/mnt/nfs-test",
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		storageAccount := "ewfw3ggwegwg"
		storageContainer := "test"
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			ClusterMountInfos: databricks.ClusterClusterMountInfoArray{
				&databricks.ClusterClusterMountInfoArgs{
					NetworkFilesystemInfo: &databricks.ClusterClusterMountInfoNetworkFilesystemInfoArgs{
						ServerAddress: pulumi.Sprintf("%v.blob.core.windows.net", storageAccount),
						MountOptions:  pulumi.String("sec=sys,vers=3,nolock,proto=tcp"),
					},
					RemoteMountDirPath: pulumi.Sprintf("%v/%v", storageAccount, storageContainer),
					LocalMountDirPath:  pulumi.String("/mnt/nfs-test"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var storageAccount = "ewfw3ggwegwg";

        final var storageContainer = "test";

        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .clusterMountInfos(ClusterClusterMountInfoArgs.builder()
                .networkFilesystemInfo(ClusterClusterMountInfoNetworkFilesystemInfoArgs.builder()
                    .serverAddress(String.format("%s.blob.core.windows.net", storageAccount))
                    .mountOptions("sec=sys,vers=3,nolock,proto=tcp")
                    .build())
                .remoteMountDirPath(String.format("%s/%s", storageAccount,storageContainer))
                .localMountDirPath("/mnt/nfs-test")
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      clusterMountInfos:
        - networkFilesystemInfo:
            serverAddress: ${storageAccount}.blob.core.windows.net
            mountOptions: sec=sys,vers=3,nolock,proto=tcp
          remoteMountDirPath: ${storageAccount}/${storageContainer}
          localMountDirPath: /mnt/nfs-test
variables:
  storageAccount: ewfw3ggwegwg
  storageContainer: test

NetworkFilesystemInfo ClusterClusterMountInfoNetworkFilesystemInfo

block specifying connection. It consists of:

RemoteMountDirPath string

string specifying path to mount on the remote service.

localMountDirPath String

path inside the Spark container.

For example, you can mount Azure Data Lake Storage container using the following code:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const storageAccount = "ewfw3ggwegwg";
const storageContainer = "test";
const withNfs = new databricks.Cluster("with_nfs", {clusterMountInfos: [{
networkFilesystemInfo: {
serverAddress: ${storageAccount}.blob.core.windows.net,
mountOptions: "sec=sys,vers=3,nolock,proto=tcp",
},
remoteMountDirPath: ${storageAccount}/${storageContainer},
localMountDirPath: "/mnt/nfs-test",
}]});

import pulumi
import pulumi_databricks as databricks

storage_account = "ewfw3ggwegwg"
storage_container = "test"
with_nfs = databricks.Cluster("with_nfs", cluster_mount_infos=[{
    "network_filesystem_info": {
        "server_address": f"{storage_account}.blob.core.windows.net",
        "mount_options": "sec=sys,vers=3,nolock,proto=tcp",
    },
    "remote_mount_dir_path": f"{storage_account}/{storage_container}",
    "local_mount_dir_path": "/mnt/nfs-test",
}])

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var storageAccount = "ewfw3ggwegwg";

    var storageContainer = "test";

    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        ClusterMountInfos = new[]
        {
            new Databricks.Inputs.ClusterClusterMountInfoArgs
            {
                NetworkFilesystemInfo = new Databricks.Inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs
                {
                    ServerAddress = $"{storageAccount}.blob.core.windows.net",
                    MountOptions = "sec=sys,vers=3,nolock,proto=tcp",
                },
                RemoteMountDirPath = $"{storageAccount}/{storageContainer}",
                LocalMountDirPath = "/mnt/nfs-test",
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		storageAccount := "ewfw3ggwegwg"
		storageContainer := "test"
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			ClusterMountInfos: databricks.ClusterClusterMountInfoArray{
				&databricks.ClusterClusterMountInfoArgs{
					NetworkFilesystemInfo: &databricks.ClusterClusterMountInfoNetworkFilesystemInfoArgs{
						ServerAddress: pulumi.Sprintf("%v.blob.core.windows.net", storageAccount),
						MountOptions:  pulumi.String("sec=sys,vers=3,nolock,proto=tcp"),
					},
					RemoteMountDirPath: pulumi.Sprintf("%v/%v", storageAccount, storageContainer),
					LocalMountDirPath:  pulumi.String("/mnt/nfs-test"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var storageAccount = "ewfw3ggwegwg";

        final var storageContainer = "test";

        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .clusterMountInfos(ClusterClusterMountInfoArgs.builder()
                .networkFilesystemInfo(ClusterClusterMountInfoNetworkFilesystemInfoArgs.builder()
                    .serverAddress(String.format("%s.blob.core.windows.net", storageAccount))
                    .mountOptions("sec=sys,vers=3,nolock,proto=tcp")
                    .build())
                .remoteMountDirPath(String.format("%s/%s", storageAccount,storageContainer))
                .localMountDirPath("/mnt/nfs-test")
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      clusterMountInfos:
        - networkFilesystemInfo:
            serverAddress: ${storageAccount}.blob.core.windows.net
            mountOptions: sec=sys,vers=3,nolock,proto=tcp
          remoteMountDirPath: ${storageAccount}/${storageContainer}
          localMountDirPath: /mnt/nfs-test
variables:
  storageAccount: ewfw3ggwegwg
  storageContainer: test

networkFilesystemInfo ClusterClusterMountInfoNetworkFilesystemInfo

block specifying connection. It consists of:

remoteMountDirPath String

string specifying path to mount on the remote service.

localMountDirPath string

path inside the Spark container.

For example, you can mount Azure Data Lake Storage container using the following code:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const storageAccount = "ewfw3ggwegwg";
const storageContainer = "test";
const withNfs = new databricks.Cluster("with_nfs", {clusterMountInfos: [{
networkFilesystemInfo: {
serverAddress: ${storageAccount}.blob.core.windows.net,
mountOptions: "sec=sys,vers=3,nolock,proto=tcp",
},
remoteMountDirPath: ${storageAccount}/${storageContainer},
localMountDirPath: "/mnt/nfs-test",
}]});

import pulumi
import pulumi_databricks as databricks

storage_account = "ewfw3ggwegwg"
storage_container = "test"
with_nfs = databricks.Cluster("with_nfs", cluster_mount_infos=[{
    "network_filesystem_info": {
        "server_address": f"{storage_account}.blob.core.windows.net",
        "mount_options": "sec=sys,vers=3,nolock,proto=tcp",
    },
    "remote_mount_dir_path": f"{storage_account}/{storage_container}",
    "local_mount_dir_path": "/mnt/nfs-test",
}])

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var storageAccount = "ewfw3ggwegwg";

    var storageContainer = "test";

    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        ClusterMountInfos = new[]
        {
            new Databricks.Inputs.ClusterClusterMountInfoArgs
            {
                NetworkFilesystemInfo = new Databricks.Inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs
                {
                    ServerAddress = $"{storageAccount}.blob.core.windows.net",
                    MountOptions = "sec=sys,vers=3,nolock,proto=tcp",
                },
                RemoteMountDirPath = $"{storageAccount}/{storageContainer}",
                LocalMountDirPath = "/mnt/nfs-test",
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		storageAccount := "ewfw3ggwegwg"
		storageContainer := "test"
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			ClusterMountInfos: databricks.ClusterClusterMountInfoArray{
				&databricks.ClusterClusterMountInfoArgs{
					NetworkFilesystemInfo: &databricks.ClusterClusterMountInfoNetworkFilesystemInfoArgs{
						ServerAddress: pulumi.Sprintf("%v.blob.core.windows.net", storageAccount),
						MountOptions:  pulumi.String("sec=sys,vers=3,nolock,proto=tcp"),
					},
					RemoteMountDirPath: pulumi.Sprintf("%v/%v", storageAccount, storageContainer),
					LocalMountDirPath:  pulumi.String("/mnt/nfs-test"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var storageAccount = "ewfw3ggwegwg";

        final var storageContainer = "test";

        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .clusterMountInfos(ClusterClusterMountInfoArgs.builder()
                .networkFilesystemInfo(ClusterClusterMountInfoNetworkFilesystemInfoArgs.builder()
                    .serverAddress(String.format("%s.blob.core.windows.net", storageAccount))
                    .mountOptions("sec=sys,vers=3,nolock,proto=tcp")
                    .build())
                .remoteMountDirPath(String.format("%s/%s", storageAccount,storageContainer))
                .localMountDirPath("/mnt/nfs-test")
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      clusterMountInfos:
        - networkFilesystemInfo:
            serverAddress: ${storageAccount}.blob.core.windows.net
            mountOptions: sec=sys,vers=3,nolock,proto=tcp
          remoteMountDirPath: ${storageAccount}/${storageContainer}
          localMountDirPath: /mnt/nfs-test
variables:
  storageAccount: ewfw3ggwegwg
  storageContainer: test

networkFilesystemInfo ClusterClusterMountInfoNetworkFilesystemInfo

block specifying connection. It consists of:

remoteMountDirPath string

string specifying path to mount on the remote service.

local_mount_dir_path str

path inside the Spark container.

For example, you can mount Azure Data Lake Storage container using the following code:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const storageAccount = "ewfw3ggwegwg";
const storageContainer = "test";
const withNfs = new databricks.Cluster("with_nfs", {clusterMountInfos: [{
networkFilesystemInfo: {
serverAddress: ${storageAccount}.blob.core.windows.net,
mountOptions: "sec=sys,vers=3,nolock,proto=tcp",
},
remoteMountDirPath: ${storageAccount}/${storageContainer},
localMountDirPath: "/mnt/nfs-test",
}]});

import pulumi
import pulumi_databricks as databricks

storage_account = "ewfw3ggwegwg"
storage_container = "test"
with_nfs = databricks.Cluster("with_nfs", cluster_mount_infos=[{
    "network_filesystem_info": {
        "server_address": f"{storage_account}.blob.core.windows.net",
        "mount_options": "sec=sys,vers=3,nolock,proto=tcp",
    },
    "remote_mount_dir_path": f"{storage_account}/{storage_container}",
    "local_mount_dir_path": "/mnt/nfs-test",
}])

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var storageAccount = "ewfw3ggwegwg";

    var storageContainer = "test";

    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        ClusterMountInfos = new[]
        {
            new Databricks.Inputs.ClusterClusterMountInfoArgs
            {
                NetworkFilesystemInfo = new Databricks.Inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs
                {
                    ServerAddress = $"{storageAccount}.blob.core.windows.net",
                    MountOptions = "sec=sys,vers=3,nolock,proto=tcp",
                },
                RemoteMountDirPath = $"{storageAccount}/{storageContainer}",
                LocalMountDirPath = "/mnt/nfs-test",
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		storageAccount := "ewfw3ggwegwg"
		storageContainer := "test"
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			ClusterMountInfos: databricks.ClusterClusterMountInfoArray{
				&databricks.ClusterClusterMountInfoArgs{
					NetworkFilesystemInfo: &databricks.ClusterClusterMountInfoNetworkFilesystemInfoArgs{
						ServerAddress: pulumi.Sprintf("%v.blob.core.windows.net", storageAccount),
						MountOptions:  pulumi.String("sec=sys,vers=3,nolock,proto=tcp"),
					},
					RemoteMountDirPath: pulumi.Sprintf("%v/%v", storageAccount, storageContainer),
					LocalMountDirPath:  pulumi.String("/mnt/nfs-test"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var storageAccount = "ewfw3ggwegwg";

        final var storageContainer = "test";

        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .clusterMountInfos(ClusterClusterMountInfoArgs.builder()
                .networkFilesystemInfo(ClusterClusterMountInfoNetworkFilesystemInfoArgs.builder()
                    .serverAddress(String.format("%s.blob.core.windows.net", storageAccount))
                    .mountOptions("sec=sys,vers=3,nolock,proto=tcp")
                    .build())
                .remoteMountDirPath(String.format("%s/%s", storageAccount,storageContainer))
                .localMountDirPath("/mnt/nfs-test")
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      clusterMountInfos:
        - networkFilesystemInfo:
            serverAddress: ${storageAccount}.blob.core.windows.net
            mountOptions: sec=sys,vers=3,nolock,proto=tcp
          remoteMountDirPath: ${storageAccount}/${storageContainer}
          localMountDirPath: /mnt/nfs-test
variables:
  storageAccount: ewfw3ggwegwg
  storageContainer: test

network_filesystem_info ClusterClusterMountInfoNetworkFilesystemInfo

block specifying connection. It consists of:

remote_mount_dir_path str

string specifying path to mount on the remote service.

localMountDirPath String

path inside the Spark container.

For example, you can mount Azure Data Lake Storage container using the following code:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const storageAccount = "ewfw3ggwegwg";
const storageContainer = "test";
const withNfs = new databricks.Cluster("with_nfs", {clusterMountInfos: [{
networkFilesystemInfo: {
serverAddress: ${storageAccount}.blob.core.windows.net,
mountOptions: "sec=sys,vers=3,nolock,proto=tcp",
},
remoteMountDirPath: ${storageAccount}/${storageContainer},
localMountDirPath: "/mnt/nfs-test",
}]});

import pulumi
import pulumi_databricks as databricks

storage_account = "ewfw3ggwegwg"
storage_container = "test"
with_nfs = databricks.Cluster("with_nfs", cluster_mount_infos=[{
    "network_filesystem_info": {
        "server_address": f"{storage_account}.blob.core.windows.net",
        "mount_options": "sec=sys,vers=3,nolock,proto=tcp",
    },
    "remote_mount_dir_path": f"{storage_account}/{storage_container}",
    "local_mount_dir_path": "/mnt/nfs-test",
}])

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var storageAccount = "ewfw3ggwegwg";

    var storageContainer = "test";

    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        ClusterMountInfos = new[]
        {
            new Databricks.Inputs.ClusterClusterMountInfoArgs
            {
                NetworkFilesystemInfo = new Databricks.Inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs
                {
                    ServerAddress = $"{storageAccount}.blob.core.windows.net",
                    MountOptions = "sec=sys,vers=3,nolock,proto=tcp",
                },
                RemoteMountDirPath = $"{storageAccount}/{storageContainer}",
                LocalMountDirPath = "/mnt/nfs-test",
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		storageAccount := "ewfw3ggwegwg"
		storageContainer := "test"
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			ClusterMountInfos: databricks.ClusterClusterMountInfoArray{
				&databricks.ClusterClusterMountInfoArgs{
					NetworkFilesystemInfo: &databricks.ClusterClusterMountInfoNetworkFilesystemInfoArgs{
						ServerAddress: pulumi.Sprintf("%v.blob.core.windows.net", storageAccount),
						MountOptions:  pulumi.String("sec=sys,vers=3,nolock,proto=tcp"),
					},
					RemoteMountDirPath: pulumi.Sprintf("%v/%v", storageAccount, storageContainer),
					LocalMountDirPath:  pulumi.String("/mnt/nfs-test"),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoArgs;
import com.pulumi.databricks.inputs.ClusterClusterMountInfoNetworkFilesystemInfoArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        final var storageAccount = "ewfw3ggwegwg";

        final var storageContainer = "test";

        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .clusterMountInfos(ClusterClusterMountInfoArgs.builder()
                .networkFilesystemInfo(ClusterClusterMountInfoNetworkFilesystemInfoArgs.builder()
                    .serverAddress(String.format("%s.blob.core.windows.net", storageAccount))
                    .mountOptions("sec=sys,vers=3,nolock,proto=tcp")
                    .build())
                .remoteMountDirPath(String.format("%s/%s", storageAccount,storageContainer))
                .localMountDirPath("/mnt/nfs-test")
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      clusterMountInfos:
        - networkFilesystemInfo:
            serverAddress: ${storageAccount}.blob.core.windows.net
            mountOptions: sec=sys,vers=3,nolock,proto=tcp
          remoteMountDirPath: ${storageAccount}/${storageContainer}
          localMountDirPath: /mnt/nfs-test
variables:
  storageAccount: ewfw3ggwegwg
  storageContainer: test

networkFilesystemInfo Property Map

block specifying connection. It consists of:

remoteMountDirPath String

string specifying path to mount on the remote service.

ClusterClusterMountInfoNetworkFilesystemInfo
, ClusterClusterMountInfoNetworkFilesystemInfoArgs

ServerAddress string: host name.
MountOptions string: string that will be passed as options passed to the mount command.

ServerAddress string: host name.
MountOptions string: string that will be passed as options passed to the mount command.

serverAddress String: host name.
mountOptions String: string that will be passed as options passed to the mount command.

serverAddress string: host name.
mountOptions string: string that will be passed as options passed to the mount command.

server_address str: host name.
mount_options str: string that will be passed as options passed to the mount command.

serverAddress String: host name.
mountOptions String: string that will be passed as options passed to the mount command.

ClusterDockerImage
, ClusterDockerImageArgs

Url string

URL for the Docker image

BasicAuth ClusterDockerImageBasicAuth

basic_auth.username and basic_auth.password for Docker repository. Docker registry credentials are encrypted when they are stored in Databricks internal storage and when they are passed to a registry upon fetching Docker images at cluster launch. However, other authenticated and authorized API users of this workspace can access the username and password.

Example usage with azurerm_container_registry and docker_registry_image, that you can adapt to your specific use-case:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
import * as docker from "@pulumi/docker";
const _this = new docker.index.RegistryImage("this", {
build: [{}],
name: ${thisAzurermContainerRegistry.loginServer}/sample:latest,
});
const thisCluster = new databricks.Cluster("this", {dockerImage: {
url: _this.name,
basicAuth: {
username: thisAzurermContainerRegistry.adminUsername,
password: thisAzurermContainerRegistry.adminPassword,
},
}});

import pulumi
import pulumi_databricks as databricks
import pulumi_docker as docker

this = docker.index.RegistryImage("this",
    build=[{}],
    name=f{this_azurerm_container_registry.login_server}/sample:latest)
this_cluster = databricks.Cluster("this", docker_image={
    "url": this["name"],
    "basic_auth": {
        "username": this_azurerm_container_registry["adminUsername"],
        "password": this_azurerm_container_registry["adminPassword"],
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;
using Docker = Pulumi.Docker;

return await Deployment.RunAsync(() => 
{
    var @this = new Docker.Index.RegistryImage("this", new()
    {
        Build = new[]
        {
            null,
        },
        Name = $"{thisAzurermContainerRegistry.LoginServer}/sample:latest",
    });

    var thisCluster = new Databricks.Cluster("this", new()
    {
        DockerImage = new Databricks.Inputs.ClusterDockerImageArgs
        {
            Url = @this.Name,
            BasicAuth = new Databricks.Inputs.ClusterDockerImageBasicAuthArgs
            {
                Username = thisAzurermContainerRegistry.AdminUsername,
                Password = thisAzurermContainerRegistry.AdminPassword,
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi-docker/sdk/v4/go/docker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		this, err := docker.NewRegistryImage(ctx, "this", &docker.RegistryImageArgs{
			Build: []map[string]interface{}{
				map[string]interface{}{},
			},
			Name: fmt.Sprintf("%v/sample:latest", thisAzurermContainerRegistry.LoginServer),
		})
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "this", &databricks.ClusterArgs{
			DockerImage: &databricks.ClusterDockerImageArgs{
				Url: this.Name,
				BasicAuth: &databricks.ClusterDockerImageBasicAuthArgs{
					Username: pulumi.Any(thisAzurermContainerRegistry.AdminUsername),
					Password: pulumi.Any(thisAzurermContainerRegistry.AdminPassword),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.docker.registryImage;
import com.pulumi.docker.RegistryImageArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageBasicAuthArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var this_ = new RegistryImage("this", RegistryImageArgs.builder()
            .build()
            .name(String.format("%s/sample:latest", thisAzurermContainerRegistry.loginServer()))
            .build());

        var thisCluster = new Cluster("thisCluster", ClusterArgs.builder()
            .dockerImage(ClusterDockerImageArgs.builder()
                .url(this_.name())
                .basicAuth(ClusterDockerImageBasicAuthArgs.builder()
                    .username(thisAzurermContainerRegistry.adminUsername())
                    .password(thisAzurermContainerRegistry.adminPassword())
                    .build())
                .build())
            .build());

    }
}

resources:
  this:
    type: docker:registryImage
    properties:
      build:
        - {}
      name: ${thisAzurermContainerRegistry.loginServer}/sample:latest
  thisCluster:
    type: databricks:Cluster
    name: this
    properties:
      dockerImage:
        url: ${this.name}
        basicAuth:
          username: ${thisAzurermContainerRegistry.adminUsername}
          password: ${thisAzurermContainerRegistry.adminPassword}

Url string

URL for the Docker image

BasicAuth ClusterDockerImageBasicAuth

Example usage with azurerm_container_registry and docker_registry_image, that you can adapt to your specific use-case:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
import * as docker from "@pulumi/docker";
const _this = new docker.index.RegistryImage("this", {
build: [{}],
name: ${thisAzurermContainerRegistry.loginServer}/sample:latest,
});
const thisCluster = new databricks.Cluster("this", {dockerImage: {
url: _this.name,
basicAuth: {
username: thisAzurermContainerRegistry.adminUsername,
password: thisAzurermContainerRegistry.adminPassword,
},
}});

import pulumi
import pulumi_databricks as databricks
import pulumi_docker as docker

this = docker.index.RegistryImage("this",
    build=[{}],
    name=f{this_azurerm_container_registry.login_server}/sample:latest)
this_cluster = databricks.Cluster("this", docker_image={
    "url": this["name"],
    "basic_auth": {
        "username": this_azurerm_container_registry["adminUsername"],
        "password": this_azurerm_container_registry["adminPassword"],
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;
using Docker = Pulumi.Docker;

return await Deployment.RunAsync(() => 
{
    var @this = new Docker.Index.RegistryImage("this", new()
    {
        Build = new[]
        {
            null,
        },
        Name = $"{thisAzurermContainerRegistry.LoginServer}/sample:latest",
    });

    var thisCluster = new Databricks.Cluster("this", new()
    {
        DockerImage = new Databricks.Inputs.ClusterDockerImageArgs
        {
            Url = @this.Name,
            BasicAuth = new Databricks.Inputs.ClusterDockerImageBasicAuthArgs
            {
                Username = thisAzurermContainerRegistry.AdminUsername,
                Password = thisAzurermContainerRegistry.AdminPassword,
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi-docker/sdk/v4/go/docker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		this, err := docker.NewRegistryImage(ctx, "this", &docker.RegistryImageArgs{
			Build: []map[string]interface{}{
				map[string]interface{}{},
			},
			Name: fmt.Sprintf("%v/sample:latest", thisAzurermContainerRegistry.LoginServer),
		})
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "this", &databricks.ClusterArgs{
			DockerImage: &databricks.ClusterDockerImageArgs{
				Url: this.Name,
				BasicAuth: &databricks.ClusterDockerImageBasicAuthArgs{
					Username: pulumi.Any(thisAzurermContainerRegistry.AdminUsername),
					Password: pulumi.Any(thisAzurermContainerRegistry.AdminPassword),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.docker.registryImage;
import com.pulumi.docker.RegistryImageArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageBasicAuthArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var this_ = new RegistryImage("this", RegistryImageArgs.builder()
            .build()
            .name(String.format("%s/sample:latest", thisAzurermContainerRegistry.loginServer()))
            .build());

        var thisCluster = new Cluster("thisCluster", ClusterArgs.builder()
            .dockerImage(ClusterDockerImageArgs.builder()
                .url(this_.name())
                .basicAuth(ClusterDockerImageBasicAuthArgs.builder()
                    .username(thisAzurermContainerRegistry.adminUsername())
                    .password(thisAzurermContainerRegistry.adminPassword())
                    .build())
                .build())
            .build());

    }
}

resources:
  this:
    type: docker:registryImage
    properties:
      build:
        - {}
      name: ${thisAzurermContainerRegistry.loginServer}/sample:latest
  thisCluster:
    type: databricks:Cluster
    name: this
    properties:
      dockerImage:
        url: ${this.name}
        basicAuth:
          username: ${thisAzurermContainerRegistry.adminUsername}
          password: ${thisAzurermContainerRegistry.adminPassword}

url String

URL for the Docker image

basicAuth ClusterDockerImageBasicAuth

Example usage with azurerm_container_registry and docker_registry_image, that you can adapt to your specific use-case:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
import * as docker from "@pulumi/docker";
const _this = new docker.index.RegistryImage("this", {
build: [{}],
name: ${thisAzurermContainerRegistry.loginServer}/sample:latest,
});
const thisCluster = new databricks.Cluster("this", {dockerImage: {
url: _this.name,
basicAuth: {
username: thisAzurermContainerRegistry.adminUsername,
password: thisAzurermContainerRegistry.adminPassword,
},
}});

import pulumi
import pulumi_databricks as databricks
import pulumi_docker as docker

this = docker.index.RegistryImage("this",
    build=[{}],
    name=f{this_azurerm_container_registry.login_server}/sample:latest)
this_cluster = databricks.Cluster("this", docker_image={
    "url": this["name"],
    "basic_auth": {
        "username": this_azurerm_container_registry["adminUsername"],
        "password": this_azurerm_container_registry["adminPassword"],
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;
using Docker = Pulumi.Docker;

return await Deployment.RunAsync(() => 
{
    var @this = new Docker.Index.RegistryImage("this", new()
    {
        Build = new[]
        {
            null,
        },
        Name = $"{thisAzurermContainerRegistry.LoginServer}/sample:latest",
    });

    var thisCluster = new Databricks.Cluster("this", new()
    {
        DockerImage = new Databricks.Inputs.ClusterDockerImageArgs
        {
            Url = @this.Name,
            BasicAuth = new Databricks.Inputs.ClusterDockerImageBasicAuthArgs
            {
                Username = thisAzurermContainerRegistry.AdminUsername,
                Password = thisAzurermContainerRegistry.AdminPassword,
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi-docker/sdk/v4/go/docker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		this, err := docker.NewRegistryImage(ctx, "this", &docker.RegistryImageArgs{
			Build: []map[string]interface{}{
				map[string]interface{}{},
			},
			Name: fmt.Sprintf("%v/sample:latest", thisAzurermContainerRegistry.LoginServer),
		})
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "this", &databricks.ClusterArgs{
			DockerImage: &databricks.ClusterDockerImageArgs{
				Url: this.Name,
				BasicAuth: &databricks.ClusterDockerImageBasicAuthArgs{
					Username: pulumi.Any(thisAzurermContainerRegistry.AdminUsername),
					Password: pulumi.Any(thisAzurermContainerRegistry.AdminPassword),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.docker.registryImage;
import com.pulumi.docker.RegistryImageArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageBasicAuthArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var this_ = new RegistryImage("this", RegistryImageArgs.builder()
            .build()
            .name(String.format("%s/sample:latest", thisAzurermContainerRegistry.loginServer()))
            .build());

        var thisCluster = new Cluster("thisCluster", ClusterArgs.builder()
            .dockerImage(ClusterDockerImageArgs.builder()
                .url(this_.name())
                .basicAuth(ClusterDockerImageBasicAuthArgs.builder()
                    .username(thisAzurermContainerRegistry.adminUsername())
                    .password(thisAzurermContainerRegistry.adminPassword())
                    .build())
                .build())
            .build());

    }
}

resources:
  this:
    type: docker:registryImage
    properties:
      build:
        - {}
      name: ${thisAzurermContainerRegistry.loginServer}/sample:latest
  thisCluster:
    type: databricks:Cluster
    name: this
    properties:
      dockerImage:
        url: ${this.name}
        basicAuth:
          username: ${thisAzurermContainerRegistry.adminUsername}
          password: ${thisAzurermContainerRegistry.adminPassword}

url string

URL for the Docker image

basicAuth ClusterDockerImageBasicAuth

Example usage with azurerm_container_registry and docker_registry_image, that you can adapt to your specific use-case:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
import * as docker from "@pulumi/docker";
const _this = new docker.index.RegistryImage("this", {
build: [{}],
name: ${thisAzurermContainerRegistry.loginServer}/sample:latest,
});
const thisCluster = new databricks.Cluster("this", {dockerImage: {
url: _this.name,
basicAuth: {
username: thisAzurermContainerRegistry.adminUsername,
password: thisAzurermContainerRegistry.adminPassword,
},
}});

import pulumi
import pulumi_databricks as databricks
import pulumi_docker as docker

this = docker.index.RegistryImage("this",
    build=[{}],
    name=f{this_azurerm_container_registry.login_server}/sample:latest)
this_cluster = databricks.Cluster("this", docker_image={
    "url": this["name"],
    "basic_auth": {
        "username": this_azurerm_container_registry["adminUsername"],
        "password": this_azurerm_container_registry["adminPassword"],
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;
using Docker = Pulumi.Docker;

return await Deployment.RunAsync(() => 
{
    var @this = new Docker.Index.RegistryImage("this", new()
    {
        Build = new[]
        {
            null,
        },
        Name = $"{thisAzurermContainerRegistry.LoginServer}/sample:latest",
    });

    var thisCluster = new Databricks.Cluster("this", new()
    {
        DockerImage = new Databricks.Inputs.ClusterDockerImageArgs
        {
            Url = @this.Name,
            BasicAuth = new Databricks.Inputs.ClusterDockerImageBasicAuthArgs
            {
                Username = thisAzurermContainerRegistry.AdminUsername,
                Password = thisAzurermContainerRegistry.AdminPassword,
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi-docker/sdk/v4/go/docker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		this, err := docker.NewRegistryImage(ctx, "this", &docker.RegistryImageArgs{
			Build: []map[string]interface{}{
				map[string]interface{}{},
			},
			Name: fmt.Sprintf("%v/sample:latest", thisAzurermContainerRegistry.LoginServer),
		})
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "this", &databricks.ClusterArgs{
			DockerImage: &databricks.ClusterDockerImageArgs{
				Url: this.Name,
				BasicAuth: &databricks.ClusterDockerImageBasicAuthArgs{
					Username: pulumi.Any(thisAzurermContainerRegistry.AdminUsername),
					Password: pulumi.Any(thisAzurermContainerRegistry.AdminPassword),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.docker.registryImage;
import com.pulumi.docker.RegistryImageArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageBasicAuthArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var this_ = new RegistryImage("this", RegistryImageArgs.builder()
            .build()
            .name(String.format("%s/sample:latest", thisAzurermContainerRegistry.loginServer()))
            .build());

        var thisCluster = new Cluster("thisCluster", ClusterArgs.builder()
            .dockerImage(ClusterDockerImageArgs.builder()
                .url(this_.name())
                .basicAuth(ClusterDockerImageBasicAuthArgs.builder()
                    .username(thisAzurermContainerRegistry.adminUsername())
                    .password(thisAzurermContainerRegistry.adminPassword())
                    .build())
                .build())
            .build());

    }
}

resources:
  this:
    type: docker:registryImage
    properties:
      build:
        - {}
      name: ${thisAzurermContainerRegistry.loginServer}/sample:latest
  thisCluster:
    type: databricks:Cluster
    name: this
    properties:
      dockerImage:
        url: ${this.name}
        basicAuth:
          username: ${thisAzurermContainerRegistry.adminUsername}
          password: ${thisAzurermContainerRegistry.adminPassword}

url str

URL for the Docker image

basic_auth ClusterDockerImageBasicAuth

Example usage with azurerm_container_registry and docker_registry_image, that you can adapt to your specific use-case:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
import * as docker from "@pulumi/docker";
const _this = new docker.index.RegistryImage("this", {
build: [{}],
name: ${thisAzurermContainerRegistry.loginServer}/sample:latest,
});
const thisCluster = new databricks.Cluster("this", {dockerImage: {
url: _this.name,
basicAuth: {
username: thisAzurermContainerRegistry.adminUsername,
password: thisAzurermContainerRegistry.adminPassword,
},
}});

import pulumi
import pulumi_databricks as databricks
import pulumi_docker as docker

this = docker.index.RegistryImage("this",
    build=[{}],
    name=f{this_azurerm_container_registry.login_server}/sample:latest)
this_cluster = databricks.Cluster("this", docker_image={
    "url": this["name"],
    "basic_auth": {
        "username": this_azurerm_container_registry["adminUsername"],
        "password": this_azurerm_container_registry["adminPassword"],
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;
using Docker = Pulumi.Docker;

return await Deployment.RunAsync(() => 
{
    var @this = new Docker.Index.RegistryImage("this", new()
    {
        Build = new[]
        {
            null,
        },
        Name = $"{thisAzurermContainerRegistry.LoginServer}/sample:latest",
    });

    var thisCluster = new Databricks.Cluster("this", new()
    {
        DockerImage = new Databricks.Inputs.ClusterDockerImageArgs
        {
            Url = @this.Name,
            BasicAuth = new Databricks.Inputs.ClusterDockerImageBasicAuthArgs
            {
                Username = thisAzurermContainerRegistry.AdminUsername,
                Password = thisAzurermContainerRegistry.AdminPassword,
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi-docker/sdk/v4/go/docker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		this, err := docker.NewRegistryImage(ctx, "this", &docker.RegistryImageArgs{
			Build: []map[string]interface{}{
				map[string]interface{}{},
			},
			Name: fmt.Sprintf("%v/sample:latest", thisAzurermContainerRegistry.LoginServer),
		})
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "this", &databricks.ClusterArgs{
			DockerImage: &databricks.ClusterDockerImageArgs{
				Url: this.Name,
				BasicAuth: &databricks.ClusterDockerImageBasicAuthArgs{
					Username: pulumi.Any(thisAzurermContainerRegistry.AdminUsername),
					Password: pulumi.Any(thisAzurermContainerRegistry.AdminPassword),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.docker.registryImage;
import com.pulumi.docker.RegistryImageArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageBasicAuthArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var this_ = new RegistryImage("this", RegistryImageArgs.builder()
            .build()
            .name(String.format("%s/sample:latest", thisAzurermContainerRegistry.loginServer()))
            .build());

        var thisCluster = new Cluster("thisCluster", ClusterArgs.builder()
            .dockerImage(ClusterDockerImageArgs.builder()
                .url(this_.name())
                .basicAuth(ClusterDockerImageBasicAuthArgs.builder()
                    .username(thisAzurermContainerRegistry.adminUsername())
                    .password(thisAzurermContainerRegistry.adminPassword())
                    .build())
                .build())
            .build());

    }
}

resources:
  this:
    type: docker:registryImage
    properties:
      build:
        - {}
      name: ${thisAzurermContainerRegistry.loginServer}/sample:latest
  thisCluster:
    type: databricks:Cluster
    name: this
    properties:
      dockerImage:
        url: ${this.name}
        basicAuth:
          username: ${thisAzurermContainerRegistry.adminUsername}
          password: ${thisAzurermContainerRegistry.adminPassword}

url String

URL for the Docker image

basicAuth Property Map

Example usage with azurerm_container_registry and docker_registry_image, that you can adapt to your specific use-case:

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
import * as docker from "@pulumi/docker";
const _this = new docker.index.RegistryImage("this", {
build: [{}],
name: ${thisAzurermContainerRegistry.loginServer}/sample:latest,
});
const thisCluster = new databricks.Cluster("this", {dockerImage: {
url: _this.name,
basicAuth: {
username: thisAzurermContainerRegistry.adminUsername,
password: thisAzurermContainerRegistry.adminPassword,
},
}});

import pulumi
import pulumi_databricks as databricks
import pulumi_docker as docker

this = docker.index.RegistryImage("this",
    build=[{}],
    name=f{this_azurerm_container_registry.login_server}/sample:latest)
this_cluster = databricks.Cluster("this", docker_image={
    "url": this["name"],
    "basic_auth": {
        "username": this_azurerm_container_registry["adminUsername"],
        "password": this_azurerm_container_registry["adminPassword"],
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;
using Docker = Pulumi.Docker;

return await Deployment.RunAsync(() => 
{
    var @this = new Docker.Index.RegistryImage("this", new()
    {
        Build = new[]
        {
            null,
        },
        Name = $"{thisAzurermContainerRegistry.LoginServer}/sample:latest",
    });

    var thisCluster = new Databricks.Cluster("this", new()
    {
        DockerImage = new Databricks.Inputs.ClusterDockerImageArgs
        {
            Url = @this.Name,
            BasicAuth = new Databricks.Inputs.ClusterDockerImageBasicAuthArgs
            {
                Username = thisAzurermContainerRegistry.AdminUsername,
                Password = thisAzurermContainerRegistry.AdminPassword,
            },
        },
    });

});

package main

import (
	"fmt"

	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi-docker/sdk/v4/go/docker"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		this, err := docker.NewRegistryImage(ctx, "this", &docker.RegistryImageArgs{
			Build: []map[string]interface{}{
				map[string]interface{}{},
			},
			Name: fmt.Sprintf("%v/sample:latest", thisAzurermContainerRegistry.LoginServer),
		})
		if err != nil {
			return err
		}
		_, err = databricks.NewCluster(ctx, "this", &databricks.ClusterArgs{
			DockerImage: &databricks.ClusterDockerImageArgs{
				Url: this.Name,
				BasicAuth: &databricks.ClusterDockerImageBasicAuthArgs{
					Username: pulumi.Any(thisAzurermContainerRegistry.AdminUsername),
					Password: pulumi.Any(thisAzurermContainerRegistry.AdminPassword),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.docker.registryImage;
import com.pulumi.docker.RegistryImageArgs;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageArgs;
import com.pulumi.databricks.inputs.ClusterDockerImageBasicAuthArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var this_ = new RegistryImage("this", RegistryImageArgs.builder()
            .build()
            .name(String.format("%s/sample:latest", thisAzurermContainerRegistry.loginServer()))
            .build());

        var thisCluster = new Cluster("thisCluster", ClusterArgs.builder()
            .dockerImage(ClusterDockerImageArgs.builder()
                .url(this_.name())
                .basicAuth(ClusterDockerImageBasicAuthArgs.builder()
                    .username(thisAzurermContainerRegistry.adminUsername())
                    .password(thisAzurermContainerRegistry.adminPassword())
                    .build())
                .build())
            .build());

    }
}

resources:
  this:
    type: docker:registryImage
    properties:
      build:
        - {}
      name: ${thisAzurermContainerRegistry.loginServer}/sample:latest
  thisCluster:
    type: databricks:Cluster
    name: this
    properties:
      dockerImage:
        url: ${this.name}
        basicAuth:
          username: ${thisAzurermContainerRegistry.adminUsername}
          password: ${thisAzurermContainerRegistry.adminPassword}

ClusterDockerImageBasicAuth
, ClusterDockerImageBasicAuthArgs

Password string
Username string

Password string
Username string

password String
username String

password string
username string

password str
username str

password String
username String

ClusterGcpAttributes
, ClusterGcpAttributesArgs

Availability string

Availability type used for all nodes. Valid values are PREEMPTIBLE_GCP, PREEMPTIBLE_WITH_FALLBACK_GCP and ON_DEMAND_GCP, default: ON_DEMAND_GCP.

BootDiskSize int

Boot disk size in GB

GoogleServiceAccount string

Google Service Account email address that the cluster uses to authenticate with Google Identity. This field is used for authentication with the GCS and BigQuery data sources.

LocalSsdCount int

Number of local SSD disks (each is 375GB in size) that will be attached to each node of the cluster.

UsePreemptibleExecutors bool

if we should use preemptible executors (GCP documentation). Warning: this field is deprecated in favor of availability, and will be removed soon.

ZoneId string

Identifier for the availability zone in which the cluster resides. This can be one of the following:

HA (default): High availability, spread nodes across availability zones for a Databricks deployment region.
AUTO: Databricks picks an availability zone to schedule the cluster on.
name of a GCP availability zone: pick one of the available zones from the list of available availability zones.

Availability string

Availability type used for all nodes. Valid values are PREEMPTIBLE_GCP, PREEMPTIBLE_WITH_FALLBACK_GCP and ON_DEMAND_GCP, default: ON_DEMAND_GCP.

BootDiskSize int

Boot disk size in GB

GoogleServiceAccount string

Google Service Account email address that the cluster uses to authenticate with Google Identity. This field is used for authentication with the GCS and BigQuery data sources.

LocalSsdCount int

Number of local SSD disks (each is 375GB in size) that will be attached to each node of the cluster.

UsePreemptibleExecutors bool

if we should use preemptible executors (GCP documentation). Warning: this field is deprecated in favor of availability, and will be removed soon.

ZoneId string

Identifier for the availability zone in which the cluster resides. This can be one of the following:

HA (default): High availability, spread nodes across availability zones for a Databricks deployment region.
AUTO: Databricks picks an availability zone to schedule the cluster on.
name of a GCP availability zone: pick one of the available zones from the list of available availability zones.

availability String

Availability type used for all nodes. Valid values are PREEMPTIBLE_GCP, PREEMPTIBLE_WITH_FALLBACK_GCP and ON_DEMAND_GCP, default: ON_DEMAND_GCP.

bootDiskSize Integer

Boot disk size in GB

googleServiceAccount String

Google Service Account email address that the cluster uses to authenticate with Google Identity. This field is used for authentication with the GCS and BigQuery data sources.

localSsdCount Integer

Number of local SSD disks (each is 375GB in size) that will be attached to each node of the cluster.

usePreemptibleExecutors Boolean

if we should use preemptible executors (GCP documentation). Warning: this field is deprecated in favor of availability, and will be removed soon.

zoneId String

Identifier for the availability zone in which the cluster resides. This can be one of the following:

HA (default): High availability, spread nodes across availability zones for a Databricks deployment region.
AUTO: Databricks picks an availability zone to schedule the cluster on.
name of a GCP availability zone: pick one of the available zones from the list of available availability zones.

availability string

Availability type used for all nodes. Valid values are PREEMPTIBLE_GCP, PREEMPTIBLE_WITH_FALLBACK_GCP and ON_DEMAND_GCP, default: ON_DEMAND_GCP.

bootDiskSize number

Boot disk size in GB

googleServiceAccount string

Google Service Account email address that the cluster uses to authenticate with Google Identity. This field is used for authentication with the GCS and BigQuery data sources.

localSsdCount number

Number of local SSD disks (each is 375GB in size) that will be attached to each node of the cluster.

usePreemptibleExecutors boolean

if we should use preemptible executors (GCP documentation). Warning: this field is deprecated in favor of availability, and will be removed soon.

zoneId string

Identifier for the availability zone in which the cluster resides. This can be one of the following:

HA (default): High availability, spread nodes across availability zones for a Databricks deployment region.
AUTO: Databricks picks an availability zone to schedule the cluster on.
name of a GCP availability zone: pick one of the available zones from the list of available availability zones.

availability str

Availability type used for all nodes. Valid values are PREEMPTIBLE_GCP, PREEMPTIBLE_WITH_FALLBACK_GCP and ON_DEMAND_GCP, default: ON_DEMAND_GCP.

boot_disk_size int

Boot disk size in GB

google_service_account str

Google Service Account email address that the cluster uses to authenticate with Google Identity. This field is used for authentication with the GCS and BigQuery data sources.

local_ssd_count int

Number of local SSD disks (each is 375GB in size) that will be attached to each node of the cluster.

use_preemptible_executors bool

if we should use preemptible executors (GCP documentation). Warning: this field is deprecated in favor of availability, and will be removed soon.

zone_id str

Identifier for the availability zone in which the cluster resides. This can be one of the following:

HA (default): High availability, spread nodes across availability zones for a Databricks deployment region.
AUTO: Databricks picks an availability zone to schedule the cluster on.
name of a GCP availability zone: pick one of the available zones from the list of available availability zones.

availability String

Availability type used for all nodes. Valid values are PREEMPTIBLE_GCP, PREEMPTIBLE_WITH_FALLBACK_GCP and ON_DEMAND_GCP, default: ON_DEMAND_GCP.

bootDiskSize Number

Boot disk size in GB

googleServiceAccount String

Google Service Account email address that the cluster uses to authenticate with Google Identity. This field is used for authentication with the GCS and BigQuery data sources.

localSsdCount Number

Number of local SSD disks (each is 375GB in size) that will be attached to each node of the cluster.

usePreemptibleExecutors Boolean

if we should use preemptible executors (GCP documentation). Warning: this field is deprecated in favor of availability, and will be removed soon.

zoneId String

Identifier for the availability zone in which the cluster resides. This can be one of the following:

HA (default): High availability, spread nodes across availability zones for a Databricks deployment region.
AUTO: Databricks picks an availability zone to schedule the cluster on.
name of a GCP availability zone: pick one of the available zones from the list of available availability zones.

ClusterInitScript
, ClusterInitScriptArgs

Abfss ClusterInitScriptAbfss
Dbfs ClusterInitScriptDbfs: Deprecated: For init scripts use 'volumes', 'workspace' or cloud storage location instead of 'dbfs'.
File ClusterInitScriptFile
Gcs ClusterInitScriptGcs
S3 ClusterInitScriptS3
Volumes ClusterInitScriptVolumes
Workspace ClusterInitScriptWorkspace

Abfss ClusterInitScriptAbfss
Dbfs ClusterInitScriptDbfs: Deprecated: For init scripts use 'volumes', 'workspace' or cloud storage location instead of 'dbfs'.
File ClusterInitScriptFile
Gcs ClusterInitScriptGcs
S3 ClusterInitScriptS3
Volumes ClusterInitScriptVolumes
Workspace ClusterInitScriptWorkspace

abfss ClusterInitScriptAbfss
dbfs ClusterInitScriptDbfs: Deprecated: For init scripts use 'volumes', 'workspace' or cloud storage location instead of 'dbfs'.
file ClusterInitScriptFile
gcs ClusterInitScriptGcs
s3 ClusterInitScriptS3
volumes ClusterInitScriptVolumes
workspace ClusterInitScriptWorkspace

abfss ClusterInitScriptAbfss
dbfs ClusterInitScriptDbfs: Deprecated: For init scripts use 'volumes', 'workspace' or cloud storage location instead of 'dbfs'.
file ClusterInitScriptFile
gcs ClusterInitScriptGcs
s3 ClusterInitScriptS3
volumes ClusterInitScriptVolumes
workspace ClusterInitScriptWorkspace

abfss ClusterInitScriptAbfss
dbfs ClusterInitScriptDbfs: Deprecated: For init scripts use 'volumes', 'workspace' or cloud storage location instead of 'dbfs'.
file ClusterInitScriptFile
gcs ClusterInitScriptGcs
s3 ClusterInitScriptS3
volumes ClusterInitScriptVolumes
workspace ClusterInitScriptWorkspace

abfss Property Map
dbfs Property Map: Deprecated: For init scripts use 'volumes', 'workspace' or cloud storage location instead of 'dbfs'.
file Property Map
gcs Property Map
s3 Property Map
volumes Property Map
workspace Property Map

ClusterInitScriptAbfss
, ClusterInitScriptAbfssArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterInitScriptDbfs
, ClusterInitScriptDbfsArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterInitScriptFile
, ClusterInitScriptFileArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterInitScriptGcs
, ClusterInitScriptGcsArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterInitScriptS3
, ClusterInitScriptS3Args

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
CannedAcl string: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
EnableEncryption bool: Enable server-side encryption, false by default.
EncryptionType string: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
Endpoint string: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
KmsKey string: KMS key used if encryption is enabled and encryption type is set to sse-kms.
Region string: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
CannedAcl string: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
EnableEncryption bool: Enable server-side encryption, false by default.
EncryptionType string: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
Endpoint string: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
KmsKey string: KMS key used if encryption is enabled and encryption type is set to sse-kms.
Region string: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
cannedAcl String: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enableEncryption Boolean: Enable server-side encryption, false by default.
encryptionType String: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint String: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kmsKey String: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region String: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
cannedAcl string: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enableEncryption boolean: Enable server-side encryption, false by default.
encryptionType string: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint string: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kmsKey string: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region string: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
canned_acl str: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enable_encryption bool: Enable server-side encryption, false by default.
encryption_type str: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint str: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kms_key str: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region str: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.
cannedAcl String: Set canned access control list, e.g. bucket-owner-full-control. If canned_cal is set, the cluster instance profile must have s3:PutObjectAcl permission on the destination bucket and prefix. The full list of possible canned ACLs can be found here. By default, only the object owner gets full control. If you are using a cross-account role for writing data, you may want to set bucket-owner-full-control to make bucket owners able to read the logs.
enableEncryption Boolean: Enable server-side encryption, false by default.
encryptionType String: The encryption type, it could be sse-s3 or sse-kms. It is used only when encryption is enabled, and the default type is sse-s3.
endpoint String: S3 endpoint, e.g. https://s3-us-west-2.amazonaws.com. Either region or endpoint needs to be set. If both are set, the endpoint is used.
kmsKey String: KMS key used if encryption is enabled and encryption type is set to sse-kms.
region String: S3 region, e.g. us-west-2. Either region or endpoint must be set. If both are set, the endpoint is used.

ClusterInitScriptVolumes
, ClusterInitScriptVolumesArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterInitScriptWorkspace
, ClusterInitScriptWorkspaceArgs

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

Destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination string: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination str: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

destination String: S3 destination, e.g., s3://my-bucket/some-prefix You must configure the cluster with an instance profile, and the instance profile must have write access to the destination. You cannot use AWS keys.

ClusterLibrary
, ClusterLibraryArgs

Cran ClusterLibraryCran
Egg string
Jar string
Maven ClusterLibraryMaven
Pypi ClusterLibraryPypi
Requirements string
Whl string

Cran ClusterLibraryCran
Egg string
Jar string
Maven ClusterLibraryMaven
Pypi ClusterLibraryPypi
Requirements string
Whl string

cran ClusterLibraryCran
egg String
jar String
maven ClusterLibraryMaven
pypi ClusterLibraryPypi
requirements String
whl String

cran ClusterLibraryCran
egg string
jar string
maven ClusterLibraryMaven
pypi ClusterLibraryPypi
requirements string
whl string

cran ClusterLibraryCran
egg str
jar str
maven ClusterLibraryMaven
pypi ClusterLibraryPypi
requirements str
whl str

cran Property Map
egg String
jar String
maven Property Map
pypi Property Map
requirements String
whl String

ClusterLibraryCran
, ClusterLibraryCranArgs

Package string
Repo string

Package string
Repo string

package_ String
repo String

package string
repo string

package str
repo str

package String
repo String

ClusterLibraryMaven
, ClusterLibraryMavenArgs

Coordinates string
Exclusions List<string>
Repo string

Coordinates string
Exclusions []string
Repo string

coordinates String
exclusions List<String>
repo String

coordinates string
exclusions string[]
repo string

coordinates str
exclusions Sequence[str]
repo str

coordinates String
exclusions List<String>
repo String

ClusterLibraryPypi
, ClusterLibraryPypiArgs

Package string
Repo string

Package string
Repo string

package_ String
repo String

package string
repo string

package str
repo str

package String
repo String

ClusterWorkloadType
, ClusterWorkloadTypeArgs

Clients ClusterWorkloadTypeClients

Clients ClusterWorkloadTypeClients

clients ClusterWorkloadTypeClients

clients ClusterWorkloadTypeClients

clients ClusterWorkloadTypeClients

clients Property Map

ClusterWorkloadTypeClients
, ClusterWorkloadTypeClientsArgs

Jobs bool

boolean flag defining if it's possible to run Databricks Jobs on this cluster. Default: true.

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const withNfs = new databricks.Cluster("with_nfs", {workloadType: {
clients: {
jobs: false,
notebooks: true,
},
}});

import pulumi
import pulumi_databricks as databricks

with_nfs = databricks.Cluster("with_nfs", workload_type={
    "clients": {
        "jobs": False,
        "notebooks": True,
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        WorkloadType = new Databricks.Inputs.ClusterWorkloadTypeArgs
        {
            Clients = new Databricks.Inputs.ClusterWorkloadTypeClientsArgs
            {
                Jobs = false,
                Notebooks = true,
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			WorkloadType: &databricks.ClusterWorkloadTypeArgs{
				Clients: &databricks.ClusterWorkloadTypeClientsArgs{
					Jobs:      pulumi.Bool(false),
					Notebooks: pulumi.Bool(true),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeClientsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .workloadType(ClusterWorkloadTypeArgs.builder()
                .clients(ClusterWorkloadTypeClientsArgs.builder()
                    .jobs(false)
                    .notebooks(true)
                    .build())
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      workloadType:
        clients:
          jobs: false
          notebooks: true

title="Optional">  Notebooks bool

boolean flag defining if it’s possible to run notebooks on this cluster. Default: true.

Jobs bool

boolean flag defining if it's possible to run Databricks Jobs on this cluster. Default: true.

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const withNfs = new databricks.Cluster("with_nfs", {workloadType: {
clients: {
jobs: false,
notebooks: true,
},
}});

import pulumi
import pulumi_databricks as databricks

with_nfs = databricks.Cluster("with_nfs", workload_type={
    "clients": {
        "jobs": False,
        "notebooks": True,
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        WorkloadType = new Databricks.Inputs.ClusterWorkloadTypeArgs
        {
            Clients = new Databricks.Inputs.ClusterWorkloadTypeClientsArgs
            {
                Jobs = false,
                Notebooks = true,
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			WorkloadType: &databricks.ClusterWorkloadTypeArgs{
				Clients: &databricks.ClusterWorkloadTypeClientsArgs{
					Jobs:      pulumi.Bool(false),
					Notebooks: pulumi.Bool(true),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeClientsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .workloadType(ClusterWorkloadTypeArgs.builder()
                .clients(ClusterWorkloadTypeClientsArgs.builder()
                    .jobs(false)
                    .notebooks(true)
                    .build())
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      workloadType:
        clients:
          jobs: false
          notebooks: true

title="Optional">  Notebooks bool

boolean flag defining if it’s possible to run notebooks on this cluster. Default: true.

jobs Boolean

boolean flag defining if it's possible to run Databricks Jobs on this cluster. Default: true.

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const withNfs = new databricks.Cluster("with_nfs", {workloadType: {
clients: {
jobs: false,
notebooks: true,
},
}});

import pulumi
import pulumi_databricks as databricks

with_nfs = databricks.Cluster("with_nfs", workload_type={
    "clients": {
        "jobs": False,
        "notebooks": True,
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        WorkloadType = new Databricks.Inputs.ClusterWorkloadTypeArgs
        {
            Clients = new Databricks.Inputs.ClusterWorkloadTypeClientsArgs
            {
                Jobs = false,
                Notebooks = true,
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			WorkloadType: &databricks.ClusterWorkloadTypeArgs{
				Clients: &databricks.ClusterWorkloadTypeClientsArgs{
					Jobs:      pulumi.Bool(false),
					Notebooks: pulumi.Bool(true),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeClientsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .workloadType(ClusterWorkloadTypeArgs.builder()
                .clients(ClusterWorkloadTypeClientsArgs.builder()
                    .jobs(false)
                    .notebooks(true)
                    .build())
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      workloadType:
        clients:
          jobs: false
          notebooks: true

title="Optional">  notebooks Boolean

boolean flag defining if it’s possible to run notebooks on this cluster. Default: true.

jobs boolean

boolean flag defining if it's possible to run Databricks Jobs on this cluster. Default: true.

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const withNfs = new databricks.Cluster("with_nfs", {workloadType: {
clients: {
jobs: false,
notebooks: true,
},
}});

import pulumi
import pulumi_databricks as databricks

with_nfs = databricks.Cluster("with_nfs", workload_type={
    "clients": {
        "jobs": False,
        "notebooks": True,
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        WorkloadType = new Databricks.Inputs.ClusterWorkloadTypeArgs
        {
            Clients = new Databricks.Inputs.ClusterWorkloadTypeClientsArgs
            {
                Jobs = false,
                Notebooks = true,
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			WorkloadType: &databricks.ClusterWorkloadTypeArgs{
				Clients: &databricks.ClusterWorkloadTypeClientsArgs{
					Jobs:      pulumi.Bool(false),
					Notebooks: pulumi.Bool(true),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeClientsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .workloadType(ClusterWorkloadTypeArgs.builder()
                .clients(ClusterWorkloadTypeClientsArgs.builder()
                    .jobs(false)
                    .notebooks(true)
                    .build())
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      workloadType:
        clients:
          jobs: false
          notebooks: true

title="Optional">  notebooks boolean

boolean flag defining if it’s possible to run notebooks on this cluster. Default: true.

jobs bool

boolean flag defining if it's possible to run Databricks Jobs on this cluster. Default: true.

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const withNfs = new databricks.Cluster("with_nfs", {workloadType: {
clients: {
jobs: false,
notebooks: true,
},
}});

import pulumi
import pulumi_databricks as databricks

with_nfs = databricks.Cluster("with_nfs", workload_type={
    "clients": {
        "jobs": False,
        "notebooks": True,
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        WorkloadType = new Databricks.Inputs.ClusterWorkloadTypeArgs
        {
            Clients = new Databricks.Inputs.ClusterWorkloadTypeClientsArgs
            {
                Jobs = false,
                Notebooks = true,
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			WorkloadType: &databricks.ClusterWorkloadTypeArgs{
				Clients: &databricks.ClusterWorkloadTypeClientsArgs{
					Jobs:      pulumi.Bool(false),
					Notebooks: pulumi.Bool(true),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeClientsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .workloadType(ClusterWorkloadTypeArgs.builder()
                .clients(ClusterWorkloadTypeClientsArgs.builder()
                    .jobs(false)
                    .notebooks(true)
                    .build())
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      workloadType:
        clients:
          jobs: false
          notebooks: true

title="Optional">  notebooks bool

boolean flag defining if it’s possible to run notebooks on this cluster. Default: true.

jobs Boolean

boolean flag defining if it's possible to run Databricks Jobs on this cluster. Default: true.

import * as pulumi from "@pulumi/pulumi";
import * as databricks from "@pulumi/databricks";
const withNfs = new databricks.Cluster("with_nfs", {workloadType: {
clients: {
jobs: false,
notebooks: true,
},
}});

import pulumi
import pulumi_databricks as databricks

with_nfs = databricks.Cluster("with_nfs", workload_type={
    "clients": {
        "jobs": False,
        "notebooks": True,
    },
})

using System.Collections.Generic;
using System.Linq;
using Pulumi;
using Databricks = Pulumi.Databricks;

return await Deployment.RunAsync(() => 
{
    var withNfs = new Databricks.Cluster("with_nfs", new()
    {
        WorkloadType = new Databricks.Inputs.ClusterWorkloadTypeArgs
        {
            Clients = new Databricks.Inputs.ClusterWorkloadTypeClientsArgs
            {
                Jobs = false,
                Notebooks = true,
            },
        },
    });

});

package main

import (
	"github.com/pulumi/pulumi-databricks/sdk/go/databricks"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

func main() {
	pulumi.Run(func(ctx *pulumi.Context) error {
		_, err := databricks.NewCluster(ctx, "with_nfs", &databricks.ClusterArgs{
			WorkloadType: &databricks.ClusterWorkloadTypeArgs{
				Clients: &databricks.ClusterWorkloadTypeClientsArgs{
					Jobs:      pulumi.Bool(false),
					Notebooks: pulumi.Bool(true),
				},
			},
		})
		if err != nil {
			return err
		}
		return nil
	})
}

package generated_program;

import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.databricks.Cluster;
import com.pulumi.databricks.ClusterArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeArgs;
import com.pulumi.databricks.inputs.ClusterWorkloadTypeClientsArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;

public class App {
    public static void main(String[] args) {
        Pulumi.run(App::stack);
    }

    public static void stack(Context ctx) {
        var withNfs = new Cluster("withNfs", ClusterArgs.builder()
            .workloadType(ClusterWorkloadTypeArgs.builder()
                .clients(ClusterWorkloadTypeClientsArgs.builder()
                    .jobs(false)
                    .notebooks(true)
                    .build())
                .build())
            .build());

    }
}

resources:
  withNfs:
    type: databricks:Cluster
    name: with_nfs
    properties:
      workloadType:
        clients:
          jobs: false
          notebooks: true

title="Optional">  notebooks Boolean

boolean flag defining if it’s possible to run notebooks on this cluster. Default: true.

Package Details

Repository: databricks pulumi/pulumi-databricks
License: Apache-2.0
Notes: This Pulumi package is based on the databricks Terraform Provider.

Databricks v1.63.0 published on Thursday, Mar 13, 2025 by Pulumi

pulumi/pulumi-databricks

databricks.Cluster

On this page

On this page

Import

Create Cluster Resource

Constructor syntax

Parameters

Constructor example

Cluster Resource Properties

Inputs

Outputs

Look up Existing Cluster Resource

Supporting Types

ClusterAutoscale, ClusterAutoscaleArgs

ClusterAwsAttributes, ClusterAwsAttributesArgs

ClusterAzureAttributes, ClusterAzureAttributesArgs

ClusterAzureAttributesLogAnalyticsInfo, ClusterAzureAttributesLogAnalyticsInfoArgs

ClusterClusterLogConf, ClusterClusterLogConfArgs

ClusterClusterLogConfDbfs, ClusterClusterLogConfDbfsArgs

ClusterClusterLogConfS3, ClusterClusterLogConfS3Args

ClusterClusterLogConfVolumes, ClusterClusterLogConfVolumesArgs

ClusterClusterMountInfo, ClusterClusterMountInfoArgs

ClusterClusterMountInfoNetworkFilesystemInfo, ClusterClusterMountInfoNetworkFilesystemInfoArgs

ClusterDockerImage, ClusterDockerImageArgs

ClusterDockerImageBasicAuth, ClusterDockerImageBasicAuthArgs

ClusterGcpAttributes, ClusterGcpAttributesArgs

ClusterInitScript, ClusterInitScriptArgs

ClusterInitScriptAbfss, ClusterInitScriptAbfssArgs

ClusterInitScriptDbfs, ClusterInitScriptDbfsArgs

ClusterInitScriptFile, ClusterInitScriptFileArgs

ClusterInitScriptGcs, ClusterInitScriptGcsArgs

ClusterInitScriptS3, ClusterInitScriptS3Args

ClusterInitScriptVolumes, ClusterInitScriptVolumesArgs

ClusterInitScriptWorkspace, ClusterInitScriptWorkspaceArgs

ClusterLibrary, ClusterLibraryArgs

ClusterLibraryCran, ClusterLibraryCranArgs

ClusterLibraryMaven, ClusterLibraryMavenArgs

ClusterLibraryPypi, ClusterLibraryPypiArgs

ClusterWorkloadType, ClusterWorkloadTypeArgs

ClusterWorkloadTypeClients, ClusterWorkloadTypeClientsArgs

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ClusterAutoscale
, ClusterAutoscaleArgs

ClusterAwsAttributes
, ClusterAwsAttributesArgs

ClusterAzureAttributes
, ClusterAzureAttributesArgs

ClusterAzureAttributesLogAnalyticsInfo
, ClusterAzureAttributesLogAnalyticsInfoArgs

ClusterClusterLogConf
, ClusterClusterLogConfArgs

ClusterClusterLogConfDbfs
, ClusterClusterLogConfDbfsArgs

ClusterClusterLogConfS3
, ClusterClusterLogConfS3Args

ClusterClusterLogConfVolumes
, ClusterClusterLogConfVolumesArgs

ClusterClusterMountInfo
, ClusterClusterMountInfoArgs

ClusterClusterMountInfoNetworkFilesystemInfo
, ClusterClusterMountInfoNetworkFilesystemInfoArgs

ClusterDockerImage
, ClusterDockerImageArgs

ClusterDockerImageBasicAuth
, ClusterDockerImageBasicAuthArgs

ClusterGcpAttributes
, ClusterGcpAttributesArgs

ClusterInitScript
, ClusterInitScriptArgs

ClusterInitScriptAbfss
, ClusterInitScriptAbfssArgs

ClusterInitScriptDbfs
, ClusterInitScriptDbfsArgs

ClusterInitScriptFile
, ClusterInitScriptFileArgs

ClusterInitScriptGcs
, ClusterInitScriptGcsArgs

ClusterInitScriptS3
, ClusterInitScriptS3Args

ClusterInitScriptVolumes
, ClusterInitScriptVolumesArgs

ClusterInitScriptWorkspace
, ClusterInitScriptWorkspaceArgs

ClusterLibrary
, ClusterLibraryArgs

ClusterLibraryCran
, ClusterLibraryCranArgs

ClusterLibraryMaven
, ClusterLibraryMavenArgs

ClusterLibraryPypi
, ClusterLibraryPypiArgs

ClusterWorkloadType
, ClusterWorkloadTypeArgs

ClusterWorkloadTypeClients
, ClusterWorkloadTypeClientsArgs