close()

Close httplib2 connections.

create(projectId, region, actionOnFailedPrimaryWorkers=None, body=None, requestId=None, x__xgafv=None)

Creates a cluster in a project. The returned Operation.metadata will be ClusterOperationMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#clusteroperationmetadata).

Args:
projectId: string, Required. The ID of the Google Cloud Platform project that the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
body: object, The request body.
The object takes the form of:

{ # Describes the identifying information, config, and status of a Dataproc cluster
"clusterName": "A String", # Required. The cluster name, which must be unique within a project. The name must start with a lowercase letter, and can contain up to 51 lowercase letters, numbers, and hyphens. It cannot end with a hyphen. The name of a deleted cluster can be reused.
"clusterUuid": "A String", # Output only. A cluster UUID (Unique Universal Identifier). Dataproc generates this value when it creates the cluster.
"config": { # The cluster config. # Optional. The cluster config for a cluster of Compute Engine Instances. Note that Dataproc may set default values, and values may change when clusters are updated.Exactly one of ClusterConfig or VirtualClusterConfig must be specified.
"autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
"policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
},
"auxiliaryNodeGroups": [ # Optional. The node group settings.
{ # Node group identification and configuration information.
"nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
"labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
"a_key": "A String",
},
"name": "A String", # The Node group resource name (https://aip.dev/122).
"nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"roles": [ # Required. Node group roles.
"A String",
],
},
"nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
},
],
"configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
"metrics": [ # Required. Metrics sources to enable.
{ # A Dataproc custom metric.
"metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK andd YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
"A String",
],
"metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
},
],
},
"encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
"gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
"kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
},
"endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
"enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
"httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
"a_key": "A String",
},
},
"gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
"confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
"enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
},
"internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
"metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
"a_key": "A String",
},
"networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
"nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
"nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
},
"privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
"reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
"consumeReservationType": "A String", # Optional. Type of reservation to consume
"key": "A String", # Optional. Corresponds to the label key of reservation resource.
"values": [ # Optional. Corresponds to the label values of reservation resource.
"A String",
],
},
"serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
"serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
"A String",
],
"shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
"enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
"enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
"enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
},
"subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
"tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
"A String",
],
"zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
},
"gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
{ # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
"executableFile": "A String", # Required. Cloud Storage URI of executable file.
"executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
},
],
"lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
"autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
},
"masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
"identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
"userServiceAccountMapping": { # Required. Map of user to service account.
"a_key": "A String",
},
},
"kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
"crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
"crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
"enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
"kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
"keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
"keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
"keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
"kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
"realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
"rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
"tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
"truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
"truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
},
},
"softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
"imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
"optionalComponents": [ # Optional. The set of components to activate on the cluster.
"A String",
],
"properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
"tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
},
"labels": { # Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster.
"a_key": "A String",
},
"metrics": { # Contains cluster daemon metrics, such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. # Output only. Contains cluster daemon metrics such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release.
"hdfsMetrics": { # The HDFS metrics.
"a_key": "A String",
},
"yarnMetrics": { # YARN metrics.
"a_key": "A String",
},
},
"projectId": "A String", # Required. The Google Cloud Platform project ID that the cluster belongs to.
"status": { # The status of a cluster and its instances. # Output only. Cluster status.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
"statusHistory": [ # Output only. The previous cluster status.
{ # The status of a cluster and its instances.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
],
"virtualClusterConfig": { # The Dataproc cluster config for a cluster that does not directly control the underlying compute resources, such as a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). # Optional. The virtual cluster config is used when creating a Dataproc cluster that does not directly control the underlying compute resources, for example, when creating a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). Dataproc may set default values, and values may change when clusters are updated. Exactly one of config or virtual_cluster_config must be specified.
"auxiliaryServicesConfig": { # Auxiliary services configuration for a Cluster. # Optional. Configuration of auxiliary services used by this cluster.
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. The Hive Metastore configuration for this workload.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"sparkHistoryServerConfig": { # Spark History Server configuration for the workload. # Optional. The Spark History Server configuration for the workload.
"dataprocCluster": "A String", # Optional. Resource name of an existing Dataproc Cluster to act as a Spark History Server for the workload.Example: projects/[project_id]/regions/[region]/clusters/[cluster_name]
},
},
"kubernetesClusterConfig": { # The configuration for running the Dataproc cluster on Kubernetes. # Required. The configuration for running the Dataproc cluster on Kubernetes.
"gkeClusterConfig": { # The cluster's GKE config. # Required. The configuration for running the Dataproc cluster on GKE.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"kubernetesNamespace": "A String", # Optional. A namespace within the Kubernetes cluster to deploy into. If this namespace does not exist, it is created. If it exists, Dataproc verifies that another Dataproc VirtualCluster is not installed into it. If not specified, the name of the Dataproc Cluster is used.
"kubernetesSoftwareConfig": { # The software configuration for this Dataproc cluster running on Kubernetes. # Optional. The software configuration for this Dataproc cluster running on Kubernetes.
"componentVersion": { # The components that should be installed in this Dataproc cluster. The key must be a string from the KubernetesComponent enumeration. The value is the version of the software to be installed. At least one entry must be specified.
"a_key": "A String",
},
"properties": { # The properties to set on daemon config files.Property keys are specified in prefix:property format, for example spark:spark.kubernetes.container.image. The following are supported prefixes and their mappings: spark: spark-defaults.confFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
},
"stagingBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
},
}

actionOnFailedPrimaryWorkers: string, Optional. Failure action when primary worker creation fails.
Allowed values
FAILURE_ACTION_UNSPECIFIED - When FailureAction is unspecified, failure action defaults to NO_ACTION.
NO_ACTION - Take no action on failure to create a cluster resource. NO_ACTION is the default.
DELETE - Delete the failed cluster resource.
requestId: string, Optional. A unique ID used to identify the request. If the server receives two CreateClusterRequest (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#google.cloud.dataproc.v1.CreateClusterRequest)s with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

delete(projectId, region, clusterName, clusterUuid=None, gracefulTerminationTimeout=None, requestId=None, x__xgafv=None)

Deletes a cluster in a project. The returned Operation.metadata will be ClusterOperationMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#clusteroperationmetadata).

Args:
projectId: string, Required. The ID of the Google Cloud Platform project that the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
clusterName: string, Required. The cluster name. (required)
clusterUuid: string, Optional. Specifying the cluster_uuid means the RPC should fail (with error NOT_FOUND) if cluster with specified UUID does not exist.
gracefulTerminationTimeout: string, Optional. The graceful termination timeout for the deletion of the cluster. Indicate the time the request will wait to complete the running jobs on the cluster before its forceful deletion. Default value is 0 indicating that the user has not enabled the graceful termination. Value can be between 60 second and 6 Hours, in case the graceful termination is enabled. (There is no separate flag to check the enabling or disabling of graceful termination, it can be checked by the values in the field).
requestId: string, Optional. A unique ID used to identify the request. If the server receives two DeleteClusterRequest (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#google.cloud.dataproc.v1.DeleteClusterRequest)s with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

diagnose(projectId, region, clusterName, body=None, x__xgafv=None)

Gets cluster diagnostic information. The returned Operation.metadata will be ClusterOperationMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#clusteroperationmetadata). After the operation completes, Operation.response contains DiagnoseClusterResults (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#diagnoseclusterresults).

Args:
  projectId: string, Required. The ID of the Google Cloud Platform project that the cluster belongs to. (required)
  region: string, Required. The Dataproc region in which to handle the request. (required)
  clusterName: string, Required. The cluster name. (required)
  body: object, The request body.
    The object takes the form of:

{ # A request to collect cluster diagnostic information.
  "diagnosisInterval": { # Represents a time interval, encoded as a Timestamp start (inclusive) and a Timestamp end (exclusive).The start must be less than or equal to the end. When the start equals the end, the interval is empty (matches no time). When both start and end are unspecified, the interval matches any time. # Optional. Time interval in which diagnosis should be carried out on the cluster.
    "endTime": "A String", # Optional. Exclusive end of the interval.If specified, a Timestamp matching this interval will have to be before the end.
    "startTime": "A String", # Optional. Inclusive start of the interval.If specified, a Timestamp matching this interval will have to be the same or after the start.
  },
  "job": "A String", # Optional. DEPRECATED Specifies the job on which diagnosis is to be performed. Format: projects/{project}/regions/{region}/jobs/{job}
  "jobs": [ # Optional. Specifies a list of jobs on which diagnosis is to be performed. Format: projects/{project}/regions/{region}/jobs/{job}
    "A String",
  ],
  "tarballAccess": "A String", # Optional. (Optional) The access type to the diagnostic tarball. If not specified, falls back to default access of the bucket
  "tarballGcsDir": "A String", # Optional. (Optional) The output Cloud Storage directory for the diagnostic tarball. If not specified, a task-specific directory in the cluster's staging bucket will be used.
  "yarnApplicationId": "A String", # Optional. DEPRECATED Specifies the yarn application on which diagnosis is to be performed.
  "yarnApplicationIds": [ # Optional. Specifies a list of yarn applications on which diagnosis is to be performed.
    "A String",
  ],
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
  "error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

get(projectId, region, clusterName, x__xgafv=None)

Gets the resource representation for a cluster in a project.

Args:
projectId: string, Required. The ID of the Google Cloud Platform project that the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
clusterName: string, Required. The cluster name. (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Describes the identifying information, config, and status of a Dataproc cluster
"clusterName": "A String", # Required. The cluster name, which must be unique within a project. The name must start with a lowercase letter, and can contain up to 51 lowercase letters, numbers, and hyphens. It cannot end with a hyphen. The name of a deleted cluster can be reused.
"clusterUuid": "A String", # Output only. A cluster UUID (Unique Universal Identifier). Dataproc generates this value when it creates the cluster.
"config": { # The cluster config. # Optional. The cluster config for a cluster of Compute Engine Instances. Note that Dataproc may set default values, and values may change when clusters are updated.Exactly one of ClusterConfig or VirtualClusterConfig must be specified.
"autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
"policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
},
"auxiliaryNodeGroups": [ # Optional. The node group settings.
{ # Node group identification and configuration information.
"nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
"labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
"a_key": "A String",
},
"name": "A String", # The Node group resource name (https://aip.dev/122).
"nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"roles": [ # Required. Node group roles.
"A String",
],
},
"nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
},
],
"configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
"metrics": [ # Required. Metrics sources to enable.
{ # A Dataproc custom metric.
"metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK andd YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
"A String",
],
"metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
},
],
},
"encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
"gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
"kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
},
"endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
"enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
"httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
"a_key": "A String",
},
},
"gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
"confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
"enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
},
"internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
"metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
"a_key": "A String",
},
"networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
"nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
"nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
},
"privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
"reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
"consumeReservationType": "A String", # Optional. Type of reservation to consume
"key": "A String", # Optional. Corresponds to the label key of reservation resource.
"values": [ # Optional. Corresponds to the label values of reservation resource.
"A String",
],
},
"serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
"serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
"A String",
],
"shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
"enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
"enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
"enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
},
"subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
"tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
"A String",
],
"zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
},
"gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
{ # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
"executableFile": "A String", # Required. Cloud Storage URI of executable file.
"executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
},
],
"lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
"autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
},
"masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
"identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
"userServiceAccountMapping": { # Required. Map of user to service account.
"a_key": "A String",
},
},
"kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
"crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
"crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
"enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
"kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
"keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
"keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
"keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
"kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
"realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
"rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
"tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
"truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
"truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
},
},
"softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
"imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
"optionalComponents": [ # Optional. The set of components to activate on the cluster.
"A String",
],
"properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
"tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
},
"labels": { # Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster.
"a_key": "A String",
},
"metrics": { # Contains cluster daemon metrics, such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. # Output only. Contains cluster daemon metrics such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release.
"hdfsMetrics": { # The HDFS metrics.
"a_key": "A String",
},
"yarnMetrics": { # YARN metrics.
"a_key": "A String",
},
},
"projectId": "A String", # Required. The Google Cloud Platform project ID that the cluster belongs to.
"status": { # The status of a cluster and its instances. # Output only. Cluster status.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
"statusHistory": [ # Output only. The previous cluster status.
{ # The status of a cluster and its instances.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
],
"virtualClusterConfig": { # The Dataproc cluster config for a cluster that does not directly control the underlying compute resources, such as a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). # Optional. The virtual cluster config is used when creating a Dataproc cluster that does not directly control the underlying compute resources, for example, when creating a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). Dataproc may set default values, and values may change when clusters are updated. Exactly one of config or virtual_cluster_config must be specified.
"auxiliaryServicesConfig": { # Auxiliary services configuration for a Cluster. # Optional. Configuration of auxiliary services used by this cluster.
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. The Hive Metastore configuration for this workload.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"sparkHistoryServerConfig": { # Spark History Server configuration for the workload. # Optional. The Spark History Server configuration for the workload.
"dataprocCluster": "A String", # Optional. Resource name of an existing Dataproc Cluster to act as a Spark History Server for the workload.Example: projects/[project_id]/regions/[region]/clusters/[cluster_name]
},
},
"kubernetesClusterConfig": { # The configuration for running the Dataproc cluster on Kubernetes. # Required. The configuration for running the Dataproc cluster on Kubernetes.
"gkeClusterConfig": { # The cluster's GKE config. # Required. The configuration for running the Dataproc cluster on GKE.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"kubernetesNamespace": "A String", # Optional. A namespace within the Kubernetes cluster to deploy into. If this namespace does not exist, it is created. If it exists, Dataproc verifies that another Dataproc VirtualCluster is not installed into it. If not specified, the name of the Dataproc Cluster is used.
"kubernetesSoftwareConfig": { # The software configuration for this Dataproc cluster running on Kubernetes. # Optional. The software configuration for this Dataproc cluster running on Kubernetes.
"componentVersion": { # The components that should be installed in this Dataproc cluster. The key must be a string from the KubernetesComponent enumeration. The value is the version of the software to be installed. At least one entry must be specified.
"a_key": "A String",
},
"properties": { # The properties to set on daemon config files.Property keys are specified in prefix:property format, for example spark:spark.kubernetes.container.image. The following are supported prefixes and their mappings: spark: spark-defaults.confFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
},
"stagingBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
},
}

getIamPolicy(resource, body=None, x__xgafv=None)

Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

Args:
resource: string, REQUIRED: The resource for which the policy is being requested. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
body: object, The request body.
The object takes the form of:

{ # Request message for GetIamPolicy method.
"options": { # Encapsulates settings provided to GetIamPolicy. # OPTIONAL: A GetPolicyOptions object for specifying options to GetIamPolicy.
"requestedPolicyVersion": 42, # Optional. The maximum policy version that will be used to format the policy.Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected.Requests for policies with any conditional role bindings must specify version 3. Policies with no conditional role bindings may specify any valid value or leave the field unset.The policy in the response might use the policy version that you specified, or it might use a lower policy version. For example, if you specify version 3, but the policy has no conditional role bindings, the response uses version 1.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
},
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/).
"bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
{ # Associates members, or principals, with a role.
"condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
"description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
"expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
"location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
"title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
},
"members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
"A String",
],
"role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
},
],
"etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
"version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
}

injectCredentials(project, region, cluster, body=None, x__xgafv=None)

Inject encrypted credentials into all of the VMs in a cluster.The target cluster must be a personal auth cluster assigned to the user who is issuing the RPC.

Args:
project: string, Required. The ID of the Google Cloud Platform project the cluster belongs to, of the form projects/. (required)
region: string, Required. The region containing the cluster, of the form regions/. (required)
cluster: string, Required. The cluster, in the form clusters/. (required)
body: object, The request body.
The object takes the form of:

{ # A request to inject credentials into a cluster.
"clusterUuid": "A String", # Required. The cluster UUID.
"credentialsCiphertext": "A String", # Required. The encrypted credentials being injected in to the cluster.The client is responsible for encrypting the credentials in a way that is supported by the cluster.A wrapped value is used here so that the actual contents of the encrypted credentials are not written to audit logs.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

list(projectId, region, filter=None, pageSize=None, pageToken=None, x__xgafv=None)

Lists all regions/{region}/clusters in a project alphabetically.

Args:
projectId: string, Required. The ID of the Google Cloud Platform project that the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
filter: string, Optional. A filter constraining the clusters to list. Filters are case-sensitive and have the following syntax:field = value AND field = value ...where field is one of status.state, clusterName, or labels.[KEY], and [KEY] is a label key. value can be * to match all values. status.state can be one of the following: ACTIVE, INACTIVE, CREATING, RUNNING, ERROR, DELETING, UPDATING, STOPPING, or STOPPED. ACTIVE contains the CREATING, UPDATING, and RUNNING states. INACTIVE contains the DELETING, ERROR, STOPPING, and STOPPED states. clusterName is the name of the cluster provided at creation time. Only the logical AND operator is supported; space-separated items are treated as having an implicit AND operator.Example filter:status.state = ACTIVE AND clusterName = mycluster AND labels.env = staging AND labels.starred = *
pageSize: integer, Optional. The standard List page size.
pageToken: string, Optional. The standard List page token.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # The list of all clusters in a project.
"clusters": [ # Output only. The clusters in the project.
{ # Describes the identifying information, config, and status of a Dataproc cluster
"clusterName": "A String", # Required. The cluster name, which must be unique within a project. The name must start with a lowercase letter, and can contain up to 51 lowercase letters, numbers, and hyphens. It cannot end with a hyphen. The name of a deleted cluster can be reused.
"clusterUuid": "A String", # Output only. A cluster UUID (Unique Universal Identifier). Dataproc generates this value when it creates the cluster.
"config": { # The cluster config. # Optional. The cluster config for a cluster of Compute Engine Instances. Note that Dataproc may set default values, and values may change when clusters are updated.Exactly one of ClusterConfig or VirtualClusterConfig must be specified.
"autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
"policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
},
"auxiliaryNodeGroups": [ # Optional. The node group settings.
{ # Node group identification and configuration information.
"nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
"labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
"a_key": "A String",
},
"name": "A String", # The Node group resource name (https://aip.dev/122).
"nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"roles": [ # Required. Node group roles.
"A String",
],
},
"nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
},
],
"configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
"metrics": [ # Required. Metrics sources to enable.
{ # A Dataproc custom metric.
"metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK andd YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
"A String",
],
"metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
},
],
},
"encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
"gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
"kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
},
"endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
"enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
"httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
"a_key": "A String",
},
},
"gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
"confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
"enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
},
"internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
"metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
"a_key": "A String",
},
"networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
"nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
"nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
},
"privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
"reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
"consumeReservationType": "A String", # Optional. Type of reservation to consume
"key": "A String", # Optional. Corresponds to the label key of reservation resource.
"values": [ # Optional. Corresponds to the label values of reservation resource.
"A String",
],
},
"serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
"serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
"A String",
],
"shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
"enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
"enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
"enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
},
"subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
"tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
"A String",
],
"zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
},
"gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
{ # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
"executableFile": "A String", # Required. Cloud Storage URI of executable file.
"executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
},
],
"lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
"autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
},
"masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
"identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
"userServiceAccountMapping": { # Required. Map of user to service account.
"a_key": "A String",
},
},
"kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
"crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
"crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
"enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
"kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
"keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
"keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
"keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
"kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
"realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
"rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
"tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
"truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
"truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
},
},
"softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
"imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
"optionalComponents": [ # Optional. The set of components to activate on the cluster.
"A String",
],
"properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
"tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
},
"labels": { # Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster.
"a_key": "A String",
},
"metrics": { # Contains cluster daemon metrics, such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. # Output only. Contains cluster daemon metrics such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release.
"hdfsMetrics": { # The HDFS metrics.
"a_key": "A String",
},
"yarnMetrics": { # YARN metrics.
"a_key": "A String",
},
},
"projectId": "A String", # Required. The Google Cloud Platform project ID that the cluster belongs to.
"status": { # The status of a cluster and its instances. # Output only. Cluster status.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
"statusHistory": [ # Output only. The previous cluster status.
{ # The status of a cluster and its instances.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
],
"virtualClusterConfig": { # The Dataproc cluster config for a cluster that does not directly control the underlying compute resources, such as a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). # Optional. The virtual cluster config is used when creating a Dataproc cluster that does not directly control the underlying compute resources, for example, when creating a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). Dataproc may set default values, and values may change when clusters are updated. Exactly one of config or virtual_cluster_config must be specified.
"auxiliaryServicesConfig": { # Auxiliary services configuration for a Cluster. # Optional. Configuration of auxiliary services used by this cluster.
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. The Hive Metastore configuration for this workload.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"sparkHistoryServerConfig": { # Spark History Server configuration for the workload. # Optional. The Spark History Server configuration for the workload.
"dataprocCluster": "A String", # Optional. Resource name of an existing Dataproc Cluster to act as a Spark History Server for the workload.Example: projects/[project_id]/regions/[region]/clusters/[cluster_name]
},
},
"kubernetesClusterConfig": { # The configuration for running the Dataproc cluster on Kubernetes. # Required. The configuration for running the Dataproc cluster on Kubernetes.
"gkeClusterConfig": { # The cluster's GKE config. # Required. The configuration for running the Dataproc cluster on GKE.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"kubernetesNamespace": "A String", # Optional. A namespace within the Kubernetes cluster to deploy into. If this namespace does not exist, it is created. If it exists, Dataproc verifies that another Dataproc VirtualCluster is not installed into it. If not specified, the name of the Dataproc Cluster is used.
"kubernetesSoftwareConfig": { # The software configuration for this Dataproc cluster running on Kubernetes. # Optional. The software configuration for this Dataproc cluster running on Kubernetes.
"componentVersion": { # The components that should be installed in this Dataproc cluster. The key must be a string from the KubernetesComponent enumeration. The value is the version of the software to be installed. At least one entry must be specified.
"a_key": "A String",
},
"properties": { # The properties to set on daemon config files.Property keys are specified in prefix:property format, for example spark:spark.kubernetes.container.image. The following are supported prefixes and their mappings: spark: spark-defaults.confFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
},
"stagingBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
},
},
],
"nextPageToken": "A String", # Output only. This token is included in the response if there are more results to fetch. To fetch additional results, provide this value as the page_token in a subsequent ListClustersRequest.
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.

patch(projectId, region, clusterName, body=None, gracefulDecommissionTimeout=None, requestId=None, updateMask=None, x__xgafv=None)

Updates a cluster in a project. The returned Operation.metadata will be ClusterOperationMetadata (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#clusteroperationmetadata). The cluster must be in a RUNNING state or an error is returned.

Args:
projectId: string, Required. The ID of the Google Cloud Platform project the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
clusterName: string, Required. The cluster name. (required)
body: object, The request body.
The object takes the form of:

{ # Describes the identifying information, config, and status of a Dataproc cluster
"clusterName": "A String", # Required. The cluster name, which must be unique within a project. The name must start with a lowercase letter, and can contain up to 51 lowercase letters, numbers, and hyphens. It cannot end with a hyphen. The name of a deleted cluster can be reused.
"clusterUuid": "A String", # Output only. A cluster UUID (Unique Universal Identifier). Dataproc generates this value when it creates the cluster.
"config": { # The cluster config. # Optional. The cluster config for a cluster of Compute Engine Instances. Note that Dataproc may set default values, and values may change when clusters are updated.Exactly one of ClusterConfig or VirtualClusterConfig must be specified.
"autoscalingConfig": { # Autoscaling Policy config associated with the cluster. # Optional. Autoscaling config for the policy associated with the cluster. Cluster does not autoscale if this field is unset.
"policyUri": "A String", # Optional. The autoscaling policy used by the cluster.Only resource names including projectid and location (region) are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id] projects/[project_id]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]Note that the policy must be in the same project and Dataproc region.
},
"auxiliaryNodeGroups": [ # Optional. The node group settings.
{ # Node group identification and configuration information.
"nodeGroup": { # Dataproc Node Group. The Dataproc NodeGroup resource is not related to the Dataproc NodeGroupAffinity resource. # Required. Node group configuration.
"labels": { # Optional. Node group labels. Label keys must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values can be empty. If specified, they must consist of from 1 to 63 characters and conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). The node group must have no more than 32 labels.
"a_key": "A String",
},
"name": "A String", # The Node group resource name (https://aip.dev/122).
"nodeGroupConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The node group instance group configuration.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"roles": [ # Required. Node group roles.
"A String",
],
},
"nodeGroupId": "A String", # Optional. A node group ID. Generated if not specified.The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). Cannot begin or end with underscore or hyphen. Must consist of from 3 to 33 characters.
},
],
"configBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"dataprocMetricConfig": { # Dataproc metric config. # Optional. The config for Dataproc metrics.
"metrics": [ # Required. Metrics sources to enable.
{ # A Dataproc custom metric.
"metricOverrides": [ # Optional. Specify one or more Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) to collect for the metric course (for the SPARK metric source (any Spark metric (https://spark.apache.org/docs/latest/monitoring.html#metrics) can be specified).Provide metrics in the following format: METRIC_SOURCE: INSTANCE:GROUP:METRIC Use camelcase as appropriate.Examples: yarn:ResourceManager:QueueMetrics:AppsCompleted spark:driver:DAGScheduler:job.allJobs sparkHistoryServer:JVM:Memory:NonHeapMemoryUsage.committed hiveserver2:JVM:Memory:NonHeapMemoryUsage.used Notes: Only the specified overridden metrics are collected for the metric source. For example, if one or more spark:executive metrics are listed as metric overrides, other SPARK metrics are not collected. The collection of the metrics for other enabled custom metric sources is unaffected. For example, if both SPARK andd YARN metric sources are enabled, and overrides are provided for Spark metrics only, all YARN metrics are collected.
"A String",
],
"metricSource": "A String", # Required. A standard set of metrics is collected unless metricOverrides are specified for the metric source (see Custom metrics (https://cloud.google.com/dataproc/docs/guides/dataproc-metrics#custom_metrics) for more information).
},
],
},
"encryptionConfig": { # Encryption settings for the cluster. # Optional. Encryption settings for the cluster.
"gcePdKmsKeyName": "A String", # Optional. The Cloud KMS key resource name to use for persistent disk encryption for all instances in the cluster. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.
"kmsKey": "A String", # Optional. The Cloud KMS key resource name to use for cluster persistent disk and job argument encryption. See Use CMEK with cluster data (https://cloud.google.com//dataproc/docs/concepts/configuring-clusters/customer-managed-encryption#use_cmek_with_cluster_data) for more information.When this key resource name is provided, the following job arguments of the following job types submitted to the cluster are encrypted using CMEK: FlinkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/FlinkJob) HadoopJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/HadoopJob) SparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkJob) SparkRJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkRJob) PySparkJob args (https://cloud.google.com/dataproc/docs/reference/rest/v1/PySparkJob) SparkSqlJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/SparkSqlJob) scriptVariables and queryList.queries HiveJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/HiveJob) scriptVariables and queryList.queries PigJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob) scriptVariables and queryList.queries PrestoJob (https://cloud.google.com/dataproc/docs/reference/rest/v1/PrestoJob) scriptVariables and queryList.queries
},
"endpointConfig": { # Endpoint config for this cluster # Optional. Port/endpoint configuration for this cluster
"enableHttpPortAccess": True or False, # Optional. If true, enable http access to specific ports on the cluster from external sources. Defaults to false.
"httpPorts": { # Output only. The map of port descriptions to URLs. Will only be populated if enable_http_port_access is true.
"a_key": "A String",
},
},
"gceClusterConfig": { # Common config settings for resources of Compute Engine cluster instances, applicable to all instances in the cluster. # Optional. The shared Compute Engine config settings for all instances in a cluster.
"confidentialInstanceConfig": { # Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs) # Optional. Confidential Instance Config for clusters using Confidential VMs (https://cloud.google.com/compute/confidential-vm/docs).
"enableConfidentialCompute": True or False, # Optional. Defines whether the instance should have confidential compute enabled.
},
"internalIpOnly": True or False, # Optional. This setting applies to subnetwork-enabled networks. It is set to true by default in clusters created with image versions 2.2.x.When set to true: All cluster VMs have internal IP addresses. Google Private Access (https://cloud.google.com/vpc/docs/private-google-access) must be enabled to access Dataproc and other Google Cloud APIs. Off-cluster dependencies must be configured to be accessible without external IP addresses.When set to false: Cluster VMs are not restricted to internal IP addresses. Ephemeral external IP addresses are assigned to each cluster VM.
"metadata": { # Optional. The Compute Engine metadata entries to add to all instances (see Project and instance metadata (https://cloud.google.com/compute/docs/storing-retrieving-metadata#project_and_instance_metadata)).
"a_key": "A String",
},
"networkUri": "A String", # Optional. The Compute Engine network to be used for machine communications. Cannot be specified with subnetwork_uri. If neither network_uri nor subnetwork_uri is specified, the "default" network of the project is used, if it exists. Cannot be a "Custom Subnet Network" (see Using Subnetworks (https://cloud.google.com/compute/docs/subnetworks) for more information).A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/networks/default projects/[project_id]/global/networks/default default
"nodeGroupAffinity": { # Node Group Affinity for clusters using sole-tenant node groups. The Dataproc NodeGroupAffinity resource is not related to the Dataproc NodeGroup resource. # Optional. Node Group Affinity for sole-tenant clusters.
"nodeGroupUri": "A String", # Required. The URI of a sole-tenant node group resource (https://cloud.google.com/compute/docs/reference/rest/v1/nodeGroups) that the cluster will be created on.A full URL, partial URI, or node group name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 projects/[project_id]/zones/[zone]/nodeGroups/node-group-1 node-group-1
},
"privateIpv6GoogleAccess": "A String", # Optional. The type of IPv6 access for a cluster.
"reservationAffinity": { # Reservation Affinity for consuming Zonal reservation. # Optional. Reservation Affinity for consuming Zonal reservation.
"consumeReservationType": "A String", # Optional. Type of reservation to consume
"key": "A String", # Optional. Corresponds to the label key of reservation resource.
"values": [ # Optional. Corresponds to the label values of reservation resource.
"A String",
],
},
"serviceAccount": "A String", # Optional. The Dataproc service account (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/service-accounts#service_accounts_in_dataproc) (also see VM Data Plane identity (https://cloud.google.com/dataproc/docs/concepts/iam/dataproc-principals#vm_service_account_data_plane_identity)) used by Dataproc cluster VM instances to access Google Cloud Platform services.If not specified, the Compute Engine default service account (https://cloud.google.com/compute/docs/access/service-accounts#default_service_account) is used.
"serviceAccountScopes": [ # Optional. The URIs of service account scopes to be included in Compute Engine instances. The following base set of scopes is always included: https://www.googleapis.com/auth/cloud.useraccounts.readonly https://www.googleapis.com/auth/devstorage.read_write https://www.googleapis.com/auth/logging.writeIf no scopes are specified, the following defaults are also provided: https://www.googleapis.com/auth/bigquery https://www.googleapis.com/auth/bigtable.admin.table https://www.googleapis.com/auth/bigtable.data https://www.googleapis.com/auth/devstorage.full_control
"A String",
],
"shieldedInstanceConfig": { # Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm). # Optional. Shielded Instance Config for clusters using Compute Engine Shielded VMs (https://cloud.google.com/security/shielded-cloud/shielded-vm).
"enableIntegrityMonitoring": True or False, # Optional. Defines whether instances have integrity monitoring enabled.
"enableSecureBoot": True or False, # Optional. Defines whether instances have Secure Boot enabled.
"enableVtpm": True or False, # Optional. Defines whether instances have the vTPM enabled.
},
"subnetworkUri": "A String", # Optional. The Compute Engine subnetwork to be used for machine communications. Cannot be specified with network_uri.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/regions/[region]/subnetworks/sub0 projects/[project_id]/regions/[region]/subnetworks/sub0 sub0
"tags": [ # The Compute Engine network tags to add to all instances (see Tagging instances (https://cloud.google.com/vpc/docs/add-remove-network-tags)).
"A String",
],
"zoneUri": "A String", # Optional. The Compute Engine zone where the Dataproc cluster will be located. If omitted, the service will pick a zone in the cluster's Compute Engine region. On a get request, zone will always be present.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone] projects/[project_id]/zones/[zone] [zone]
},
"gkeClusterConfig": { # The cluster's GKE config. # Optional. BETA. The Kubernetes Engine config for Dataproc clusters deployed to The Kubernetes Engine config for Dataproc clusters deployed to Kubernetes. These config settings are mutually exclusive with Compute Engine-based options, such as gce_cluster_config, master_config, worker_config, secondary_worker_config, and autoscaling_config.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"initializationActions": [ # Optional. Commands to execute on each node after config is completed. By default, executables are run on master and all worker nodes. You can test a node's role metadata to run an executable on a master or worker node, as shown below using curl (you can also use wget): ROLE=$(curl -H Metadata-Flavor:Google http://metadata/computeMetadata/v1/instance/attributes/dataproc-role) if [[ "${ROLE}" == 'Master' ]]; then ... master specific actions ... else ... worker specific actions ... fi
{ # Specifies an executable to run on a fully configured node and a timeout period for executable completion.
"executableFile": "A String", # Required. Cloud Storage URI of executable file.
"executionTimeout": "A String", # Optional. Amount of time executable has to complete. Default is 10 minutes (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Cluster creation fails with an explanatory error message (the name of the executable that caused the error and the exceeded timeout period) if the executable is not completed at end of the timeout period.
},
],
"lifecycleConfig": { # Specifies the cluster auto-delete schedule configuration. # Optional. Lifecycle setting for the cluster.
"autoDeleteTime": "A String", # Optional. The time when cluster will be auto-deleted (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"autoDeleteTtl": "A String", # Optional. The lifetime duration of cluster. The cluster will be auto-deleted at the end of this period. Minimum value is 10 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleDeleteTtl": "A String", # Optional. The duration to keep the cluster alive while idling (when no jobs are running). Passing this threshold will cause the cluster to be deleted. Minimum value is 5 minutes; maximum value is 14 days (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"idleStartTime": "A String", # Output only. The time when cluster became idle (most recent job finished) and became eligible for deletion due to idleness (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
},
"masterConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's master instance.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. Metastore configuration.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"secondaryWorkerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for a cluster's secondary worker instances
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
"securityConfig": { # Security related configuration, including encryption, Kerberos, etc. # Optional. Security settings for the cluster.
"identityConfig": { # Identity related configuration, including service account based secure multi-tenancy user mappings. # Optional. Identity related configuration, including service account based secure multi-tenancy user mappings.
"userServiceAccountMapping": { # Required. Map of user to service account.
"a_key": "A String",
},
},
"kerberosConfig": { # Specifies Kerberos related configuration. # Optional. Kerberos related configuration.
"crossRealmTrustAdminServer": "A String", # Optional. The admin server (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustKdc": "A String", # Optional. The KDC (IP or hostname) for the remote trusted realm in a cross realm trust relationship.
"crossRealmTrustRealm": "A String", # Optional. The remote realm the Dataproc on-cluster KDC will trust, should the user enable cross realm trust.
"crossRealmTrustSharedPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the shared password between the on-cluster Kerberos realm and the remote trusted realm, in a cross realm trust relationship.
"enableKerberos": True or False, # Optional. Flag to indicate whether to Kerberize the cluster (default: false). Set this field to true to enable Kerberos on a cluster.
"kdcDbKeyUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the master key of the KDC database.
"keyPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided key. For the self-signed certificate, this password is generated by Dataproc.
"keystorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided keystore. For the self-signed certificate, this password is generated by Dataproc.
"keystoreUri": "A String", # Optional. The Cloud Storage URI of the keystore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
"kmsKeyUri": "A String", # Optional. The URI of the KMS key used to encrypt sensitive files.
"realm": "A String", # Optional. The name of the on-cluster Kerberos realm. If not specified, the uppercased domain of hostnames will be the realm.
"rootPrincipalPasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the root principal password.
"tgtLifetimeHours": 42, # Optional. The lifetime of the ticket granting ticket, in hours. If not specified, or user specifies 0, then default value 10 will be used.
"truststorePasswordUri": "A String", # Optional. The Cloud Storage URI of a KMS encrypted file containing the password to the user provided truststore. For the self-signed certificate, this password is generated by Dataproc.
"truststoreUri": "A String", # Optional. The Cloud Storage URI of the truststore file used for SSL encryption. If not provided, Dataproc will provide a self-signed certificate.
},
},
"softwareConfig": { # Specifies the selection and config of software inside the cluster. # Optional. The config settings for cluster software.
"imageVersion": "A String", # Optional. The version of software inside the cluster. It must be one of the supported Dataproc Versions (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#supported-dataproc-image-versions), such as "1.2" (including a subminor version, such as "1.2.29"), or the "preview" version (https://cloud.google.com/dataproc/docs/concepts/versioning/dataproc-versions#other_versions). If unspecified, it defaults to the latest Debian version.
"optionalComponents": [ # Optional. The set of components to activate on the cluster.
"A String",
],
"properties": { # Optional. The properties to set on daemon config files.Property keys are specified in prefix:property format, for example core:hadoop.tmp.dir. The following are supported prefixes and their mappings: capacity-scheduler: capacity-scheduler.xml core: core-site.xml distcp: distcp-default.xml hdfs: hdfs-site.xml hive: hive-site.xml mapred: mapred-site.xml pig: pig.properties spark: spark-defaults.conf yarn: yarn-site.xmlFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
"tempBucket": "A String", # Optional. A Cloud Storage bucket used to store ephemeral cluster and jobs data, such as Spark and MapReduce history files. If you do not specify a temp bucket, Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's temp bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket. The default bucket has a TTL of 90 days, but you can use any TTL (or none) if you specify a bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
"workerConfig": { # The config settings for Compute Engine resources in an instance group, such as a master or worker group. # Optional. The Compute Engine config settings for the cluster's worker instances.
"accelerators": [ # Optional. The Compute Engine accelerator configuration for these instances.
{ # Specifies the type and number of accelerator cards attached to the instances of an instance. See GPUs on Compute Engine (https://cloud.google.com/compute/docs/gpus/).
"acceleratorCount": 42, # The number of the accelerator cards of this type exposed to this instance.
"acceleratorTypeUri": "A String", # Full URL, partial URI, or short name of the accelerator type resource to expose to this instance. See Compute Engine AcceleratorTypes (https://cloud.google.com/compute/docs/reference/v1/acceleratorTypes).Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 projects/[project_id]/zones/[zone]/acceleratorTypes/nvidia-tesla-t4 nvidia-tesla-t4Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the accelerator type resource, for example, nvidia-tesla-t4.
},
],
"diskConfig": { # Specifies the config of disk options for a group of VM instances. # Optional. Disk option config settings.
"bootDiskProvisionedIops": "A String", # Optional. Indicates how many IOPS to provision for the disk. This sets the number of I/O operations per second that the disk can handle. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskProvisionedThroughput": "A String", # Optional. Indicates how much throughput to provision for the disk. This sets the number of throughput mb per second that the disk can handle. Values must be greater than or equal to 1. Note: This field is only supported if boot_disk_type is hyperdisk-balanced.
"bootDiskSizeGb": 42, # Optional. Size in GB of the boot disk (default is 500GB).
"bootDiskType": "A String", # Optional. Type of the boot disk (default is "pd-standard"). Valid values: "pd-balanced" (Persistent Disk Balanced Solid State Drive), "pd-ssd" (Persistent Disk Solid State Drive), or "pd-standard" (Persistent Disk Hard Disk Drive). See Disk types (https://cloud.google.com/compute/docs/disks#disk-types).
"localSsdInterface": "A String", # Optional. Interface type of local SSDs (default is "scsi"). Valid values: "scsi" (Small Computer System Interface), "nvme" (Non-Volatile Memory Express). See local SSD performance (https://cloud.google.com/compute/docs/disks/local-ssd#performance).
"numLocalSsds": 42, # Optional. Number of attached SSDs, from 0 to 8 (default is 0). If SSDs are not attached, the boot disk is used to store runtime logs and HDFS (https://hadoop.apache.org/docs/r1.2.1/hdfs_user_guide.html) data. If one or more SSDs are attached, this runtime bulk data is spread across them, and the boot disk contains only basic config and installed binaries.Note: Local SSD options may vary by machine type and number of vCPUs selected.
},
"imageUri": "A String", # Optional. The Compute Engine image resource used for cluster instances.The URI can represent an image or image family.Image examples: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/[image-id] projects/[project_id]/global/images/[image-id] image-idImage family examples. Dataproc will use the most recent image from the family: https://www.googleapis.com/compute/v1/projects/[project_id]/global/images/family/[custom-image-family-name] projects/[project_id]/global/images/family/[custom-image-family-name]If the URI is unspecified, it will be inferred from SoftwareConfig.image_version or the system default.
"instanceFlexibilityPolicy": { # Instance flexibility Policy allowing a mixture of VM shapes and provisioning models. # Optional. Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
"instanceSelectionList": [ # Optional. List of instance selection options that the group will use when creating new VMs.
{ # Defines machines types and a rank to which the machines types belong.
"machineTypes": [ # Optional. Full machine-type names, e.g. "n1-standard-16".
"A String",
],
"rank": 42, # Optional. Preference of this instance selection. Lower number means higher preference. Dataproc will first try to create a VM based on the machine-type with priority rank and fallback to next rank based on availability. Machine types and instance selections with the same priority have the same preference.
},
],
"instanceSelectionResults": [ # Output only. A list of instance selection results in the group.
{ # Defines a mapping from machine types to the number of VMs that are created with each machine type.
"machineType": "A String", # Output only. Full machine-type names, e.g. "n1-standard-16".
"vmCount": 42, # Output only. Number of VM provisioned with the machine_type.
},
],
"provisioningModelMix": { # Defines how Dataproc should create VMs with a mixture of provisioning models. # Optional. Defines how the Group selects the provisioning model to ensure required reliability.
"standardCapacityBase": 42, # Optional. The base capacity that will always use Standard VMs to avoid risk of more preemption than the minimum capacity you need. Dataproc will create only standard VMs until it reaches standard_capacity_base, then it will start using standard_capacity_percent_above_base to mix Spot with Standard VMs. eg. If 15 instances are requested and standard_capacity_base is 5, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances.
"standardCapacityPercentAboveBase": 42, # Optional. The percentage of target capacity that should use Standard VM. The remaining percentage will use Spot VMs. The percentage applies only to the capacity above standard_capacity_base. eg. If 15 instances are requested and standard_capacity_base is 5 and standard_capacity_percent_above_base is 30, Dataproc will create 5 standard VMs and then start mixing spot and standard VMs for remaining 10 instances. The mix will be 30% standard and 70% spot.
},
},
"instanceNames": [ # Output only. The list of instance names. Dataproc derives the names from cluster_name, num_instances, and the instance group.
"A String",
],
"instanceReferences": [ # Output only. List of references to Compute Engine instances.
{ # A reference to a Compute Engine instance.
"instanceId": "A String", # The unique identifier of the Compute Engine instance.
"instanceName": "A String", # The user-friendly name of the Compute Engine instance.
"publicEciesKey": "A String", # The public ECIES key used for sharing data with this instance.
"publicKey": "A String", # The public RSA key used for sharing data with this instance.
},
],
"isPreemptible": True or False, # Output only. Specifies that this instance group contains preemptible instances.
"machineTypeUri": "A String", # Optional. The Compute Engine machine type used for cluster instances.A full URL, partial URI, or short name are valid. Examples: https://www.googleapis.com/compute/v1/projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 projects/[project_id]/zones/[zone]/machineTypes/n1-standard-2 n1-standard-2Auto Zone Exception: If you are using the Dataproc Auto Zone Placement (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/auto-zone#using_auto_zone_placement) feature, you must use the short name of the machine type resource, for example, n1-standard-2.
"managedGroupConfig": { # Specifies the resources used to actively manage an instance group. # Output only. The config for Compute Engine Instance Group Manager that manages this group. This is only used for preemptible instance groups.
"instanceGroupManagerName": "A String", # Output only. The name of the Instance Group Manager for this group.
"instanceGroupManagerUri": "A String", # Output only. The partial URI to the instance group manager for this group. E.g. projects/my-project/regions/us-central1/instanceGroupManagers/my-igm.
"instanceTemplateName": "A String", # Output only. The name of the Instance Template used for the Managed Instance Group.
},
"minCpuPlatform": "A String", # Optional. Specifies the minimum cpu platform for the Instance Group. See Dataproc -> Minimum CPU Platform (https://cloud.google.com/dataproc/docs/concepts/compute/dataproc-min-cpu).
"minNumInstances": 42, # Optional. The minimum number of primary worker instances to create. If min_num_instances is set, cluster creation will succeed if the number of primary workers created is at least equal to the min_num_instances number.Example: Cluster creation request with num_instances = 5 and min_num_instances = 3: If 4 VMs are created and 1 instance fails, the failed VM is deleted. The cluster is resized to 4 instances and placed in a RUNNING state. If 2 instances are created and 3 instances fail, the cluster in placed in an ERROR state. The failed VMs are not deleted.
"numInstances": 42, # Optional. The number of VM instances in the instance group. For HA cluster master_config groups, must be set to 3. For standard cluster master_config groups, must be set to 1.
"preemptibility": "A String", # Optional. Specifies the preemptibility of the instance group.The default value for master and worker groups is NON_PREEMPTIBLE. This default cannot be changed.The default value for secondary instances is PREEMPTIBLE.
"startupConfig": { # Configuration to handle the startup of instances during cluster create and update process. # Optional. Configuration to handle the startup of instances during cluster create and update process.
"requiredRegistrationFraction": 3.14, # Optional. The config setting to enable cluster creation/ updation to be successful only after required_registration_fraction of instances are up and running. This configuration is applicable to only secondary workers for now. The cluster will fail if required_registration_fraction of instances are not available. This will include instance creation, agent registration, and service registration (if enabled).
},
},
},
"labels": { # Optional. The labels to associate with this cluster. Label keys must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035 (https://www.ietf.org/rfc/rfc1035.txt). No more than 32 labels can be associated with a cluster.
"a_key": "A String",
},
"metrics": { # Contains cluster daemon metrics, such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release. # Output only. Contains cluster daemon metrics such as HDFS and YARN stats.Beta Feature: This report is available for testing purposes only. It may be changed before final release.
"hdfsMetrics": { # The HDFS metrics.
"a_key": "A String",
},
"yarnMetrics": { # YARN metrics.
"a_key": "A String",
},
},
"projectId": "A String", # Required. The Google Cloud Platform project ID that the cluster belongs to.
"status": { # The status of a cluster and its instances. # Output only. Cluster status.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
"statusHistory": [ # Output only. The previous cluster status.
{ # The status of a cluster and its instances.
"detail": "A String", # Optional. Output only. Details of cluster's state.
"state": "A String", # Output only. The cluster's state.
"stateStartTime": "A String", # Output only. Time when this state was entered (see JSON representation of Timestamp (https://developers.google.com/protocol-buffers/docs/proto3#json)).
"substate": "A String", # Output only. Additional state information that includes status reported by the agent.
},
],
"virtualClusterConfig": { # The Dataproc cluster config for a cluster that does not directly control the underlying compute resources, such as a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). # Optional. The virtual cluster config is used when creating a Dataproc cluster that does not directly control the underlying compute resources, for example, when creating a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/guides/dpgke/dataproc-gke-overview). Dataproc may set default values, and values may change when clusters are updated. Exactly one of config or virtual_cluster_config must be specified.
"auxiliaryServicesConfig": { # Auxiliary services configuration for a Cluster. # Optional. Configuration of auxiliary services used by this cluster.
"metastoreConfig": { # Specifies a Metastore configuration. # Optional. The Hive Metastore configuration for this workload.
"dataprocMetastoreService": "A String", # Required. Resource name of an existing Dataproc Metastore service.Example: projects/[project_id]/locations/[dataproc_region]/services/[service-name]
},
"sparkHistoryServerConfig": { # Spark History Server configuration for the workload. # Optional. The Spark History Server configuration for the workload.
"dataprocCluster": "A String", # Optional. Resource name of an existing Dataproc Cluster to act as a Spark History Server for the workload.Example: projects/[project_id]/regions/[region]/clusters/[cluster_name]
},
},
"kubernetesClusterConfig": { # The configuration for running the Dataproc cluster on Kubernetes. # Required. The configuration for running the Dataproc cluster on Kubernetes.
"gkeClusterConfig": { # The cluster's GKE config. # Required. The configuration for running the Dataproc cluster on GKE.
"gkeClusterTarget": "A String", # Optional. A target GKE cluster to deploy to. It must be in the same project and region as the Dataproc cluster (the GKE cluster can be zonal or regional). Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
"namespacedGkeDeploymentTarget": { # Deprecated. Used only for the deprecated beta. A full, namespace-isolated deployment target for an existing GKE cluster. # Optional. Deprecated. Use gkeClusterTarget. Used only for the deprecated beta. A target for the deployment.
"clusterNamespace": "A String", # Optional. A namespace within the GKE cluster to deploy into.
"targetGkeCluster": "A String", # Optional. The target GKE cluster to deploy to. Format: 'projects/{project}/locations/{location}/clusters/{cluster_id}'
},
"nodePoolTarget": [ # Optional. GKE node pools where workloads will be scheduled. At least one node pool must be assigned the DEFAULT GkeNodePoolTarget.Role. If a GkeNodePoolTarget is not specified, Dataproc constructs a DEFAULT GkeNodePoolTarget. Each role can be given to only one GkeNodePoolTarget. All node pools must have the same location settings.
{ # GKE node pools that Dataproc workloads run on.
"nodePool": "A String", # Required. The target GKE node pool. Format: 'projects/{project}/locations/{location}/clusters/{cluster}/nodePools/{node_pool}'
"nodePoolConfig": { # The configuration of a GKE node pool used by a Dataproc-on-GKE cluster (https://cloud.google.com/dataproc/docs/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster). # Input only. The configuration for the GKE node pool.If specified, Dataproc attempts to create a node pool with the specified shape. If one with the same name already exists, it is verified against all specified fields. If a field differs, the virtual cluster creation will fail.If omitted, any node pool with the specified name is used. If a node pool with the specified name does not exist, Dataproc create a node pool with default values.This is an input only field. It will not be returned by the API.
"autoscaling": { # GkeNodePoolAutoscaling contains information the cluster autoscaler needs to adjust the size of the node pool to the current cluster usage. # Optional. The autoscaler configuration for this node pool. The autoscaler is enabled only when a valid configuration is present.
"maxNodeCount": 42, # The maximum number of nodes in the node pool. Must be >= min_node_count, and must be > 0. Note: Quota must be sufficient to scale up the cluster.
"minNodeCount": 42, # The minimum number of nodes in the node pool. Must be >= 0 and <= max_node_count.
},
"config": { # Parameters that describe cluster nodes. # Optional. The node pool configuration.
"accelerators": [ # Optional. A list of hardware accelerators (https://cloud.google.com/compute/docs/gpus) to attach to each node.
{ # A GkeNodeConfigAcceleratorConfig represents a Hardware Accelerator request for a node pool.
"acceleratorCount": "A String", # The number of accelerator cards exposed to an instance.
"acceleratorType": "A String", # The accelerator type resource namename (see GPUs on Compute Engine).
"gpuPartitionSize": "A String", # Size of partitions to create on the GPU. Valid values are described in the NVIDIA mig user guide (https://docs.nvidia.com/datacenter/tesla/mig-user-guide/#partitioning).
},
],
"bootDiskKmsKey": "A String", # Optional. The Customer Managed Encryption Key (CMEK) (https://cloud.google.com/kubernetes-engine/docs/how-to/using-cmek) used to encrypt the boot disk attached to each node in the node pool. Specify the key using the following format: projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}
"localSsdCount": 42, # Optional. The number of local SSD disks to attach to the node, which is limited by the maximum number of disks allowable per zone (see Adding Local SSDs (https://cloud.google.com/compute/docs/disks/local-ssd)).
"machineType": "A String", # Optional. The name of a Compute Engine machine type (https://cloud.google.com/compute/docs/machine-types).
"minCpuPlatform": "A String", # Optional. Minimum CPU platform (https://cloud.google.com/compute/docs/instances/specify-min-cpu-platform) to be used by this instance. The instance may be scheduled on the specified or a newer CPU platform. Specify the friendly names of CPU platforms, such as "Intel Haswell"` or Intel Sandy Bridge".
"preemptible": True or False, # Optional. Whether the nodes are created as legacy preemptible VM instances (https://cloud.google.com/compute/docs/instances/preemptible). Also see Spot VMs, preemptible VM instances without a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
"spot": True or False, # Optional. Whether the nodes are created as Spot VM instances (https://cloud.google.com/compute/docs/instances/spot). Spot VMs are the latest update to legacy preemptible VMs. Spot VMs do not have a maximum lifetime. Legacy and Spot preemptible nodes cannot be used in a node pool with the CONTROLLER role or in the DEFAULT node pool if the CONTROLLER role is not assigned (the DEFAULT node pool will assume the CONTROLLER role).
},
"locations": [ # Optional. The list of Compute Engine zones (https://cloud.google.com/compute/docs/zones#available) where node pool nodes associated with a Dataproc on GKE virtual cluster will be located.Note: All node pools associated with a virtual cluster must be located in the same region as the virtual cluster, and they must be located in the same zone within that region.If a location is not specified during node pool creation, Dataproc on GKE will choose the zone.
"A String",
],
},
"roles": [ # Required. The roles associated with the GKE node pool.
"A String",
],
},
],
},
"kubernetesNamespace": "A String", # Optional. A namespace within the Kubernetes cluster to deploy into. If this namespace does not exist, it is created. If it exists, Dataproc verifies that another Dataproc VirtualCluster is not installed into it. If not specified, the name of the Dataproc Cluster is used.
"kubernetesSoftwareConfig": { # The software configuration for this Dataproc cluster running on Kubernetes. # Optional. The software configuration for this Dataproc cluster running on Kubernetes.
"componentVersion": { # The components that should be installed in this Dataproc cluster. The key must be a string from the KubernetesComponent enumeration. The value is the version of the software to be installed. At least one entry must be specified.
"a_key": "A String",
},
"properties": { # The properties to set on daemon config files.Property keys are specified in prefix:property format, for example spark:spark.kubernetes.container.image. The following are supported prefixes and their mappings: spark: spark-defaults.confFor more information, see Cluster properties (https://cloud.google.com/dataproc/docs/concepts/cluster-properties).
"a_key": "A String",
},
},
},
"stagingBucket": "A String", # Optional. A Cloud Storage bucket used to stage job dependencies, config files, and job driver console output. If you do not specify a staging bucket, Cloud Dataproc will determine a Cloud Storage location (US, ASIA, or EU) for your cluster's staging bucket according to the Compute Engine zone where your cluster is deployed, and then create and manage this project-level, per-location bucket (see Dataproc staging and temp buckets (https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/staging-bucket)). This field requires a Cloud Storage bucket name, not a gs://... URI to a Cloud Storage bucket.
},
}

gracefulDecommissionTimeout: string, Optional. Timeout for graceful YARN decommissioning. Graceful decommissioning allows removing nodes from the cluster without interrupting jobs in progress. Timeout specifies how long to wait for jobs in progress to finish before forcefully removing nodes (and potentially interrupting jobs). Default timeout is 0 (for forceful decommission), and the maximum allowed timeout is 1 day. (see JSON representation of Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).Only supported on Dataproc image versions 1.2 and higher.
requestId: string, Optional. A unique ID used to identify the request. If the server receives two UpdateClusterRequest (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#google.cloud.dataproc.v1.UpdateClusterRequest)s with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.It is recommended to always set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
updateMask: string, Required. Specifies the path, relative to Cluster, of the field to update. For example, to change the number of workers in a cluster to 5, the update_mask parameter would be specified as config.worker_config.num_instances, and the PATCH request body would specify the new value, as follows: { "config":{ "workerConfig":{ "numInstances":"5" } } } Similarly, to change the number of preemptible workers in a cluster to 5, the update_mask parameter would be config.secondary_worker_config.num_instances, and the PATCH request body would be set as follows: { "config":{ "secondaryWorkerConfig":{ "numInstances":"5" } } } *Note:* Currently, only the following fields can be updated: *Mask* *Purpose* *labels* Update labels *config.worker_config.num_instances* Resize primary worker group *config.secondary_worker_config.num_instances* Resize secondary worker group config.autoscaling_config.policy_uri Use, stop using, or change autoscaling policies
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

repair(projectId, region, clusterName, body=None, x__xgafv=None)

Repairs a cluster.

Args:
projectId: string, Required. The ID of the Google Cloud Platform project the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
clusterName: string, Required. The cluster name. (required)
body: object, The request body.
The object takes the form of:

{ # A request to repair a cluster.
"cluster": { # Cluster to be repaired # Optional. Cluster to be repaired
"clusterRepairAction": "A String", # Required. Repair action to take on the cluster resource.
},
"clusterUuid": "A String", # Optional. Specifying the cluster_uuid means the RPC will fail (with error NOT_FOUND) if a cluster with the specified UUID does not exist.
"gracefulDecommissionTimeout": "A String", # Optional. Timeout for graceful YARN decommissioning. Graceful decommissioning facilitates the removal of cluster nodes without interrupting jobs in progress. The timeout specifies the amount of time to wait for jobs finish before forcefully removing nodes. The default timeout is 0 for forceful decommissioning, and the maximum timeout period is 1 day. (see JSON Mapping—Duration (https://developers.google.com/protocol-buffers/docs/proto3#json)).graceful_decommission_timeout is supported in Dataproc image versions 1.2+.
"nodePools": [ # Optional. Node pools and corresponding repair action to be taken. All node pools should be unique in this request. i.e. Multiple entries for the same node pool id are not allowed.
{ # indicating a list of workers of same type
"id": "A String", # Required. A unique id of the node pool. Primary and Secondary workers can be specified using special reserved ids PRIMARY_WORKER_POOL and SECONDARY_WORKER_POOL respectively. Aux node pools can be referenced using corresponding pool id.
"instanceNames": [ # Name of instances to be repaired. These instances must belong to specified node pool.
"A String",
],
"repairAction": "A String", # Required. Repair action to take on specified resources of the node pool.
},
],
"parentOperationId": "A String", # Optional. operation id of the parent operation sending the repair request
"requestId": "A String", # Optional. A unique ID used to identify the request. If the server receives two RepairClusterRequests with the same ID, the second request is ignored, and the first google.longrunning.Operation created and stored in the backend is returned.Recommendation: Set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

setIamPolicy(resource, body=None, x__xgafv=None)

Sets the access control policy on the specified resource. Replaces any existing policy.Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.

Args:
  resource: string, REQUIRED: The resource for which the policy is being specified. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for SetIamPolicy method.
  "policy": { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/). # REQUIRED: The complete policy to be applied to the resource. The size of the policy is limited to a few 10s of KB. An empty policy is a valid policy but certain Google Cloud services (such as Projects) might reject them.
    "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
      { # Associates members, or principals, with a role.
        "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
          "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
          "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
          "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
          "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
        },
        "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
          "A String",
        ],
        "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
      },
    ],
    "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
    "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # An Identity and Access Management (IAM) policy, which specifies access controls for Google Cloud resources.A Policy is a collection of bindings. A binding binds one or more members, or principals, to a single role. Principals can be user accounts, service accounts, Google groups, and domains (such as G Suite). A role is a named list of permissions; each role can be an IAM predefined role or a user-created custom role.For some types of Google Cloud resources, a binding can also specify a condition, which is a logical expression that allows access to a resource only if the expression evaluates to true. A condition can add constraints based on attributes of the request, the resource, or both. To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).JSON example: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": [ "user:eve@example.com" ], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ], "etag": "BwWWja0YfJA=", "version": 3 } YAML example: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') etag: BwWWja0YfJA= version: 3 For a description of IAM and its features, see the IAM documentation (https://cloud.google.com/iam/docs/).
  "bindings": [ # Associates a list of members, or principals, with a role. Optionally, may specify a condition that determines how and when the bindings are applied. Each of the bindings must contain at least one principal.The bindings in a Policy can refer to up to 1,500 principals; up to 250 of these principals can be Google groups. Each occurrence of a principal counts towards these limits. For example, if the bindings grant 50 different roles to user:alice@example.com, and not to any other principal, then you can add another 1,450 principals to the bindings in the Policy.
    { # Associates members, or principals, with a role.
      "condition": { # Represents a textual expression in the Common Expression Language (CEL) syntax. CEL is a C-like expression language. The syntax and semantics of CEL are documented at https://github.com/google/cel-spec.Example (Comparison): title: "Summary size limit" description: "Determines if a summary is less than 100 chars" expression: "document.summary.size() < 100" Example (Equality): title: "Requestor is owner" description: "Determines if requestor is the document owner" expression: "document.owner == request.auth.claims.email" Example (Logic): title: "Public documents" description: "Determine whether the document should be publicly visible" expression: "document.type != 'private' && document.type != 'internal'" Example (Data Manipulation): title: "Notification string" description: "Create a notification string with a timestamp." expression: "'New message received at ' + string(document.create_time)" The exact variables and functions that may be referenced within an expression are determined by the service that evaluates it. See the service documentation for additional information. # The condition that is associated with this binding.If the condition evaluates to true, then this binding applies to the current request.If the condition evaluates to false, then this binding does not apply to the current request. However, a different role binding might grant the same role to one or more of the principals in this binding.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
        "description": "A String", # Optional. Description of the expression. This is a longer text which describes the expression, e.g. when hovered over it in a UI.
        "expression": "A String", # Textual representation of an expression in Common Expression Language syntax.
        "location": "A String", # Optional. String indicating the location of the expression for error reporting, e.g. a file name and a position in the file.
        "title": "A String", # Optional. Title for the expression, i.e. a short string describing its purpose. This can be used e.g. in UIs which allow to enter the expression.
      },
      "members": [ # Specifies the principals requesting access for a Google Cloud resource. members can have the following values: allUsers: A special identifier that represents anyone who is on the internet; with or without a Google account. allAuthenticatedUsers: A special identifier that represents anyone who is authenticated with a Google account or a service account. Does not include identities that come from external identity providers (IdPs) through identity federation. user:{emailid}: An email address that represents a specific Google account. For example, alice@example.com . serviceAccount:{emailid}: An email address that represents a Google service account. For example, my-other-app@appspot.gserviceaccount.com. serviceAccount:{projectid}.svc.id.goog[{namespace}/{kubernetes-sa}]: An identifier for a Kubernetes service account (https://cloud.google.com/kubernetes-engine/docs/how-to/kubernetes-service-accounts). For example, my-project.svc.id.goog[my-namespace/my-kubernetes-sa]. group:{emailid}: An email address that represents a Google group. For example, admins@example.com. domain:{domain}: The G Suite domain (primary) that represents all the users of that domain. For example, google.com or example.com. principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workforce identity pool. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/group/{group_id}: All workforce identities in a group. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All workforce identities with a specific attribute value. principalSet://iam.googleapis.com/locations/global/workforcePools/{pool_id}/*: All identities in a workforce identity pool. principal://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/subject/{subject_attribute_value}: A single identity in a workload identity pool. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/group/{group_id}: A workload identity pool group. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/attribute.{attribute_name}/{attribute_value}: All identities in a workload identity pool with a certain attribute. principalSet://iam.googleapis.com/projects/{project_number}/locations/global/workloadIdentityPools/{pool_id}/*: All identities in a workload identity pool. deleted:user:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a user that has been recently deleted. For example, alice@example.com?uid=123456789012345678901. If the user is recovered, this value reverts to user:{emailid} and the recovered user retains the role in the binding. deleted:serviceAccount:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a service account that has been recently deleted. For example, my-other-app@appspot.gserviceaccount.com?uid=123456789012345678901. If the service account is undeleted, this value reverts to serviceAccount:{emailid} and the undeleted service account retains the role in the binding. deleted:group:{emailid}?uid={uniqueid}: An email address (plus unique identifier) representing a Google group that has been recently deleted. For example, admins@example.com?uid=123456789012345678901. If the group is recovered, this value reverts to group:{emailid} and the recovered group retains the role in the binding. deleted:principal://iam.googleapis.com/locations/global/workforcePools/{pool_id}/subject/{subject_attribute_value}: Deleted single identity in a workforce identity pool. For example, deleted:principal://iam.googleapis.com/locations/global/workforcePools/my-pool-id/subject/my-subject-attribute-value.
        "A String",
      ],
      "role": "A String", # Role that is assigned to the list of members, or principals. For example, roles/viewer, roles/editor, or roles/owner.For an overview of the IAM roles and permissions, see the IAM documentation (https://cloud.google.com/iam/docs/roles-overview). For a list of the available pre-defined roles, see here (https://cloud.google.com/iam/docs/understanding-roles).
    },
  ],
  "etag": "A String", # etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a policy from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform policy updates in order to avoid race conditions: An etag is returned in the response to getIamPolicy, and systems are expected to put that etag in the request to setIamPolicy to ensure that their change will be applied to the same version of the policy.Important: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.
  "version": 42, # Specifies the format of the policy.Valid values are 0, 1, and 3. Requests that specify an invalid value are rejected.Any operation that affects conditional role bindings must specify version 3. This requirement applies to the following operations: Getting a policy that includes a conditional role binding Adding a conditional role binding to a policy Changing a conditional role binding in a policy Removing any role binding, with or without a condition, from a policy that includes conditionsImportant: If you use IAM Conditions, you must include the etag field whenever you call setIamPolicy. If you omit this field, then IAM allows you to overwrite a version 3 policy with a version 1 policy, and all of the conditions in the version 3 policy are lost.If a policy does not include any conditions, operations on that policy may specify any valid version or leave the field unset.To learn which resources support conditions in their IAM policies, see the IAM documentation (https://cloud.google.com/iam/help/conditions/resource-policies).
}

start(projectId, region, clusterName, body=None, x__xgafv=None)

Starts a cluster in a project.

Args:
projectId: string, Required. The ID of the Google Cloud Platform project the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
clusterName: string, Required. The cluster name. (required)
body: object, The request body.
The object takes the form of:

{ # A request to start a cluster.
"clusterUuid": "A String", # Optional. Specifying the cluster_uuid means the RPC will fail (with error NOT_FOUND) if a cluster with the specified UUID does not exist.
"requestId": "A String", # Optional. A unique ID used to identify the request. If the server receives two StartClusterRequest (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#google.cloud.dataproc.v1.StartClusterRequest)s with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.Recommendation: Set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

stop(projectId, region, clusterName, body=None, x__xgafv=None)

Stops a cluster in a project.

Args:
projectId: string, Required. The ID of the Google Cloud Platform project the cluster belongs to. (required)
region: string, Required. The Dataproc region in which to handle the request. (required)
clusterName: string, Required. The cluster name. (required)
body: object, The request body.
The object takes the form of:

{ # A request to stop a cluster.
"clusterUuid": "A String", # Optional. Specifying the cluster_uuid means the RPC will fail (with error NOT_FOUND) if a cluster with the specified UUID does not exist.
"requestId": "A String", # Optional. A unique ID used to identify the request. If the server receives two StopClusterRequest (https://cloud.google.com/dataproc/docs/reference/rpc/google.cloud.dataproc.v1#google.cloud.dataproc.v1.StopClusterRequest)s with the same id, then the second request will be ignored and the first google.longrunning.Operation created and stored in the backend is returned.Recommendation: Set this value to a UUID (https://en.wikipedia.org/wiki/Universally_unique_identifier).The ID must contain only letters (a-z, A-Z), numbers (0-9), underscores (_), and hyphens (-). The maximum length is 40 characters.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is false, it means the operation is still in progress. If true, the operation is completed, and either error or response is available.
"error": { # The Status type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by gRPC (https://github.com/grpc). Each Status message contains three pieces of data: error code, error message, and error details.You can find out more about this error model and how to work with it in the API Design Guide (https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the name should be a resource name ending with operations/{unique_id}.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as Delete, the response is google.protobuf.Empty. If the original method is standard Get/Create/Update, the response should be the resource. For other methods, the response should have the type XxxResponse, where Xxx is the original method name. For example, if the original method name is TakeSnapshot(), the inferred response type is TakeSnapshotResponse.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

testIamPermissions(resource, body=None, x__xgafv=None)

Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a NOT_FOUND error.Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.

Args:
  resource: string, REQUIRED: The resource for which the policy detail is being requested. See Resource names (https://cloud.google.com/apis/design/resource_names) for the appropriate value for this field. (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for TestIamPermissions method.
  "permissions": [ # The set of permissions to check for the resource. Permissions with wildcards (such as * or storage.*) are not allowed. For more information see IAM Overview (https://cloud.google.com/iam/docs/overview#permissions).
    "A String",
  ],
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for TestIamPermissions method.
  "permissions": [ # A subset of TestPermissionsRequest.permissions that the caller is allowed.
    "A String",
  ],
}

Cloud Dataproc API . projects . regions . clusters

Instance Methods

Method Details