`airflow.providers.google.cloud.operators.dataproc`¶

This module contains Google Dataproc operators.

Module Contents¶

Classes¶

`PreemptibilityType`	Contains possible Type values of Preemptibility applicable for every secondary worker of Cluster.
`InstanceSelection`	Defines machines types and a rank to which the machines types belong.
`InstanceFlexibilityPolicy`	Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
`ClusterGenerator`	Create a new Dataproc Cluster.
`DataprocCreateClusterOperator`	Create a new cluster on Google Cloud Dataproc.
`DataprocScaleClusterOperator`	Scale, up or down, a cluster on Google Cloud Dataproc.
`DataprocDeleteClusterOperator`	Delete a cluster in a project.
`DataprocStartClusterOperator`	Start a cluster in a project.
`DataprocStopClusterOperator`	Stop a cluster in a project.
`DataprocJobBaseOperator`	Base class for operators that launch job on DataProc.
`DataprocSubmitPigJobOperator`	Start a Pig query Job on a Cloud DataProc cluster.
`DataprocSubmitHiveJobOperator`	Start a Hive query Job on a Cloud DataProc cluster.
`DataprocSubmitSparkSqlJobOperator`	Start a Spark SQL query Job on a Cloud DataProc cluster.
`DataprocSubmitSparkJobOperator`	Start a Spark Job on a Cloud DataProc cluster.
`DataprocSubmitHadoopJobOperator`	Start a Hadoop Job on a Cloud DataProc cluster.
`DataprocSubmitPySparkJobOperator`	Start a PySpark Job on a Cloud DataProc cluster.
`DataprocCreateWorkflowTemplateOperator`	Creates new workflow template.
`DataprocInstantiateWorkflowTemplateOperator`	Instantiate a WorkflowTemplate on Google Cloud Dataproc.
`DataprocInstantiateInlineWorkflowTemplateOperator`	Instantiate a WorkflowTemplate Inline on Google Cloud Dataproc.
`DataprocSubmitJobOperator`	Submit a job to a cluster.
`DataprocUpdateClusterOperator`	Update a cluster in a project.
`DataprocDiagnoseClusterOperator`	Diagnose a cluster in a project.
`DataprocCreateBatchOperator`	Create a batch workload.
`DataprocDeleteBatchOperator`	Delete the batch workload resource.
`DataprocGetBatchOperator`	Get the batch workload resource representation.
`DataprocListBatchesOperator`	List batch workloads.
`DataprocCancelOperationOperator`	Cancel the batch workload resource.

class airflow.providers.google.cloud.operators.dataproc.PreemptibilityType[source]¶

Bases: enum.Enum

Contains possible Type values of Preemptibility applicable for every secondary worker of Cluster.

PREEMPTIBLE = 'PREEMPTIBLE'[source]¶

SPOT = 'SPOT'[source]¶

PREEMPTIBILITY_UNSPECIFIED = 'PREEMPTIBILITY_UNSPECIFIED'[source]¶

NON_PREEMPTIBLE = 'NON_PREEMPTIBLE'[source]¶

class airflow.providers.google.cloud.operators.dataproc.InstanceSelection[source]¶

Defines machines types and a rank to which the machines types belong.

Representation for google.cloud.dataproc.v1#google.cloud.dataproc.v1.InstanceFlexibilityPolicy.InstanceSelection.

Parameters

machine_types – Full machine-type names, e.g. “n1-standard-16”.
rank – 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.

machine_types: list[str][source]¶

rank: int = 0[source]¶

class airflow.providers.google.cloud.operators.dataproc.InstanceFlexibilityPolicy[source]¶

Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.

Representation for google.cloud.dataproc.v1#google.cloud.dataproc.v1.InstanceFlexibilityPolicy.

Parameters: instance_selection_list – List of instance selection options that the group will use when creating new VMs.

instance_selection_list: list[InstanceSelection][source]¶

class airflow.providers.google.cloud.operators.dataproc.ClusterGenerator(project_id, num_workers=None, min_num_workers=None, zone=None, network_uri=None, subnetwork_uri=None, internal_ip_only=None, tags=None, storage_bucket=None, init_actions_uris=None, init_action_timeout='10m', metadata=None, custom_image=None, custom_image_project_id=None, custom_image_family=None, image_version=None, autoscaling_policy=None, properties=None, optional_components=None, num_masters=1, master_machine_type='n1-standard-4', master_disk_type='pd-standard', master_disk_size=1024, master_accelerator_type=None, master_accelerator_count=None, worker_machine_type='n1-standard-4', worker_disk_type='pd-standard', worker_disk_size=1024, worker_accelerator_type=None, worker_accelerator_count=None, num_preemptible_workers=0, preemptibility=PreemptibilityType.PREEMPTIBLE.value, service_account=None, service_account_scopes=None, idle_delete_ttl=None, auto_delete_time=None, auto_delete_ttl=None, customer_managed_key=None, enable_component_gateway=False, driver_pool_size=0, driver_pool_id=None, secondary_worker_instance_flexibility_policy=None, secondary_worker_accelerator_type=None, secondary_worker_accelerator_count=None, **kwargs)[source]¶

Create a new Dataproc Cluster.

Parameters

cluster_name – The name of the DataProc cluster to create. (templated)
project_id (str) – The ID of the google cloud project in which to create the cluster. (templated)
num_workers (int | None) – The # of workers to spin up. If set to zero will spin up cluster in a single node mode
min_num_workers (int | None) – The minimum number of primary worker instances to create. If more than min_num_workers VMs are created out of num_workers, the failed VMs will be deleted, cluster is resized to available VMs and set to RUNNING. If created VMs are less than min_num_workers, the cluster is placed in ERROR state. The failed VMs are not deleted.
storage_bucket (str | None) – The storage bucket to use, setting to None lets dataproc generate a custom one for you
init_actions_uris (list[str] | None) – List of GCS uri’s containing dataproc initialization scripts
init_action_timeout (str) – Amount of time executable scripts in init_actions_uris has to complete
metadata (dict | None) – dict of key-value google compute engine metadata entries to add to all instances
image_version (str | None) – the version of software inside the Dataproc cluster
custom_image (str | None) – custom Dataproc image for more info see https://cloud.google.com/dataproc/docs/guides/dataproc-images
custom_image_project_id (str | None) – project id for the custom Dataproc image, for more info see https://cloud.google.com/dataproc/docs/guides/dataproc-images
custom_image_family (str | None) – family for the custom Dataproc image, family name can be provide using –family flag while creating custom image, for more info see https://cloud.google.com/dataproc/docs/guides/dataproc-images
autoscaling_policy (str | None) – The autoscaling policy used by the cluster. Only resource names including projectid and location (region) are valid. Example: projects/[projectId]/locations/[dataproc_region]/autoscalingPolicies/[policy_id]
properties (dict | None) – dict of properties to set on config files (e.g. spark-defaults.conf), see https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.clusters#SoftwareConfig
optional_components (list[str] | None) – List of optional cluster components, for more info see https://cloud.google.com/dataproc/docs/reference/rest/v1/ClusterConfig#Component
num_masters (int) – The # of master nodes to spin up
master_machine_type (str) – Compute engine machine type to use for the primary node
master_disk_type (str) – Type of the boot disk for the primary node (default is pd-standard). Valid values: pd-ssd (Persistent Disk Solid State Drive) or pd-standard (Persistent Disk Hard Disk Drive).
master_disk_size (int) – Disk size for the primary node
master_accelerator_type (str | None) – Type of the accelerator card (GPU) to attach to the primary node, see https://cloud.google.com/dataproc/docs/reference/rest/v1/InstanceGroupConfig#acceleratorconfig
master_accelerator_count (int | None) – Number of accelerator cards (GPUs) to attach to the primary node
worker_machine_type (str) – Compute engine machine type to use for the worker nodes
worker_disk_type (str) – Type of the boot disk for the worker node (default is pd-standard). Valid values: pd-ssd (Persistent Disk Solid State Drive) or pd-standard (Persistent Disk Hard Disk Drive).
worker_disk_size (int) – Disk size for the worker nodes
worker_accelerator_type (str | None) – Type of the accelerator card (GPU) to attach to the worker nodes, see https://cloud.google.com/dataproc/docs/reference/rest/v1/InstanceGroupConfig#acceleratorconfig
worker_accelerator_count (int | None) – Number of accelerator cards (GPUs) to attach to the worker nodes
num_preemptible_workers (int) – The # of VM instances in the instance group as secondary workers inside the cluster with Preemptibility enabled by default. Note, that it is not possible to mix non-preemptible and preemptible secondary workers in one cluster.
preemptibility (str) – The type of Preemptibility applicable for every secondary worker, see https://cloud.google.com/dataproc/docs/reference/rpc/ google.cloud.dataproc.v1#google.cloud.dataproc.v1.InstanceGroupConfig.Preemptibility
zone (str | None) – The zone where the cluster will be located. Set to None to auto-zone. (templated)
network_uri (str | None) – The network uri to be used for machine communication, cannot be specified with subnetwork_uri
subnetwork_uri (str | None) – The subnetwork uri to be used for machine communication, cannot be specified with network_uri
internal_ip_only (bool | None) – If true, all instances in the cluster will only have internal IP addresses. This can only be enabled for subnetwork enabled networks
tags (list[str] | None) – The GCE tags to add to all instances
region – The specified region where the dataproc cluster is created.
gcp_conn_id – The connection ID to use connecting to Google Cloud.
service_account (str | None) – The service account of the dataproc instances.
service_account_scopes (list[str] | None) – The URIs of service account scopes to be included.
idle_delete_ttl (int | None) – The longest duration that cluster would keep alive while staying idle. Passing this threshold will cause cluster to be auto-deleted. A duration in seconds.
auto_delete_time (datetime.datetime | None) – The time when cluster will be auto-deleted.
auto_delete_ttl (int | None) – The life duration of cluster, the cluster will be auto-deleted at the end of this duration. A duration in seconds. (If auto_delete_time is set this parameter will be ignored)
customer_managed_key (str | None) – The customer-managed key used for disk encryption projects/[PROJECT_STORING_KEYS]/locations/[LOCATION]/keyRings/[KEY_RING_NAME]/cryptoKeys/[KEY_NAME] # noqa
enable_component_gateway (bool | None) – Provides access to the web interfaces of default and selected optional components on the cluster.
driver_pool_size (int) – The number of driver nodes in the node group.
driver_pool_id (str | None) – The ID for the driver pool. Must be unique within the cluster. Use this ID to identify the driver group in future operations, such as resizing the node group.
secondary_worker_instance_flexibility_policy (InstanceFlexibilityPolicy | None) – Instance flexibility Policy allowing a mixture of VM shapes and provisioning models.
secondary_worker_accelerator_type (str | None) – Type of the accelerator card (GPU) to attach to the secondary workers, see https://cloud.google.com/dataproc/docs/reference/rest/v1/InstanceGroupConfig#acceleratorconfig
secondary_worker_accelerator_count (int | None) – Number of accelerator cards (GPUs) to attach to the secondary workers

make()[source]¶

Act as a helper method for easier migration.

Returns: Dict representing Dataproc cluster.

class airflow.providers.google.cloud.operators.dataproc.DataprocCreateClusterOperator(*, cluster_name, region, project_id=PROVIDE_PROJECT_ID, cluster_config=None, virtual_cluster_config=None, labels=None, request_id=None, delete_on_error=True, use_if_exists=True, num_retries_if_resource_is_not_ready=0, retry=DEFAULT, timeout=1 * 60 * 60, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Create a new cluster on Google Cloud Dataproc.

The operator will wait until the creation is successful or an error occurs in the creation process.

If the cluster already exists and use_if_exists is True, then the operator will: - if cluster state is ERROR then delete it if specified and raise error - if cluster state is CREATING wait for it and then check for ERROR state - if cluster state is DELETING wait for it and then create new cluster

Please refer to https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.clusters

for a detailed explanation on the different parameters. Most of the configuration parameters detailed in the link are available as a parameter to this operator.

See also

For more information on how to use this operator, take a look at the guide: Create a Cluster

Parameters

project_id (str) – The ID of the Google cloud project in which to create the cluster. (templated)
cluster_name (str) – Name of the cluster to create
labels (dict | None) – Labels that will be assigned to created cluster. Please, notice that adding labels to ClusterConfig object in cluster_config parameter will not lead to adding labels to the cluster. Labels for the clusters could be only set by passing values to parameter of DataprocCreateCluster operator.
cluster_config (dict | google.cloud.dataproc_v1.Cluster | None) – Required. The cluster config to create. If a dict is provided, it must be of the same form as the protobuf message ClusterConfig
virtual_cluster_config (dict | None) – Optional. The virtual cluster config, 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/concepts/jobs/dataproc-gke#create-a-dataproc-on-gke-cluster>
region (str) – The specified region where the dataproc cluster is created.
delete_on_error (bool) – If true the cluster will be deleted if created with ERROR state. Default value is true.
use_if_exists (bool) – If true use existing cluster
num_retries_if_resource_is_not_ready (int) – Optional. The number of retry for cluster creation request when resource is not ready error appears.
request_id (str | None) – Optional. A unique id used to identify the request. If the server receives two DeleteClusterRequest requests 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.
retry (google.api_core.retry_async.AsyncRetry | google.api_core.gapic_v1.method._MethodDefault | google.api_core.retry.Retry) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
deferrable (bool) – Run operator in the deferrable mode.
polling_interval_seconds (int) – Time (seconds) to wait between calls to check the run status.

template_fields: Sequence[str] = ('project_id', 'region', 'cluster_config', 'virtual_cluster_config', 'cluster_name', 'labels',...[source]¶

template_fields_renderers[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event)[source]¶

Act as a callback for when the trigger fires - returns immediately.

Relies on trigger to throw an exception, otherwise it assumes execution was successful.

class airflow.providers.google.cloud.operators.dataproc.DataprocScaleClusterOperator(*, cluster_name, project_id=PROVIDE_PROJECT_ID, region='global', num_workers=2, num_preemptible_workers=0, graceful_decommission_timeout=None, gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Scale, up or down, a cluster on Google Cloud Dataproc.

The operator will wait until the cluster is re-scaled.

Example usage:

t1 = DataprocClusterScaleOperator(
    task_id="dataproc_scale",
    project_id="my-project",
    cluster_name="cluster-1",
    num_workers=10,
    num_preemptible_workers=10,
    graceful_decommission_timeout="1h",
)

See also

For more detail on about scaling clusters have a look at the reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/scaling-clusters

Parameters

cluster_name (str) – The name of the cluster to scale. (templated)
project_id (str) – The ID of the google cloud project in which the cluster runs. (templated)
region (str) – The region for the dataproc cluster. (templated)
num_workers (int) – The new number of workers
num_preemptible_workers (int) – The new number of preemptible workers
graceful_decommission_timeout (str | None) – Timeout for graceful YARN decommissioning. Maximum value is 1d
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).

template_fields: Sequence[str] = ('cluster_name', 'project_id', 'region', 'impersonation_chain')[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Scale, up or down, a cluster on Google Cloud Dataproc.

class airflow.providers.google.cloud.operators.dataproc.DataprocDeleteClusterOperator(*, region, cluster_name, project_id=PROVIDE_PROJECT_ID, cluster_uuid=None, request_id=None, retry=DEFAULT, timeout=1 * 60 * 60, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Delete a cluster in a project.

Parameters

region (str) – Required. The Cloud Dataproc region in which to handle the request (templated).
cluster_name (str) – Required. The cluster name (templated).
project_id (str) – Optional. The ID of the Google Cloud project that the cluster belongs to (templated).
cluster_uuid (str | None) – Optional. Specifying the cluster_uuid means the RPC should fail if cluster with specified UUID does not exist.
request_id (str | None) – Optional. A unique id used to identify the request. If the server receives two DeleteClusterRequest requests 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.
retry (google.api_core.retry_async.AsyncRetry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
deferrable (bool) – Run operator in the deferrable mode.
polling_interval_seconds (int) – Time (seconds) to wait between calls to check the cluster status.

template_fields: Sequence[str] = ('project_id', 'region', 'cluster_name', 'impersonation_chain')[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires - returns immediately.

Relies on trigger to throw an exception, otherwise it assumes execution was successful.

class airflow.providers.google.cloud.operators.dataproc.DataprocStartClusterOperator(*, cluster_name, region, project_id=PROVIDE_PROJECT_ID, cluster_uuid=None, request_id=None, retry=DEFAULT, timeout=1 * 60 * 60, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]¶

Bases: _DataprocStartStopClusterBaseOperator

Start a cluster in a project.

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocStopClusterOperator(*, cluster_name, region, project_id=PROVIDE_PROJECT_ID, cluster_uuid=None, request_id=None, retry=DEFAULT, timeout=1 * 60 * 60, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]¶

Bases: _DataprocStartStopClusterBaseOperator

Stop a cluster in a project.

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocJobBaseOperator(*, region, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', project_id=PROVIDE_PROJECT_ID, dataproc_properties=None, dataproc_jars=None, gcp_conn_id='google_cloud_default', labels=None, job_error_states=None, impersonation_chain=None, asynchronous=False, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Base class for operators that launch job on DataProc.

Parameters

region (str) – The specified region where the dataproc cluster is created.
job_name (str) – The job name used in the DataProc cluster. This name by default is the task_id appended with the execution data, but can be templated. The name will always be appended with a random number to avoid name clashes.
cluster_name (str) – The name of the DataProc cluster.
project_id (str) – The ID of the Google Cloud project the cluster belongs to, if not specified the project will be inferred from the provided GCP connection.
dataproc_properties (dict | None) – Map for the Hive properties. Ideal to put in default arguments (templated)
dataproc_jars (list[str] | None) – HCFS URIs of jar files to add to the CLASSPATH of the Hive server and Hadoop MapReduce (MR) tasks. Can contain Hive SerDes and UDFs. (templated)
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
labels (dict | None) – The labels to associate with this job. Label keys must contain 1 to 63 characters, and must conform to RFC 1035. Label values may be empty, but, if present, must contain 1 to 63 characters, and must conform to RFC 1035. No more than 32 labels can be associated with a job.
job_error_states (set[str] | None) – Job states that should be considered error states. Any states in this set will result in an error being raised and failure of the task. Eg, if the CANCELLED state should also be considered a task failure, pass in {'ERROR', 'CANCELLED'}. Possible values are currently only 'ERROR' and 'CANCELLED', but could change in the future. Defaults to {'ERROR'}.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
asynchronous (bool) – Flag to return after submitting the job to the Dataproc API. This is useful for submitting long running jobs and waiting on them asynchronously using the DataprocJobSensor
deferrable (bool) – Run operator in the deferrable mode
polling_interval_seconds (int) – time in seconds between polling for job completion. The value is considered only when running in deferrable mode. Must be greater than 0.

Variables

dataproc_job_id (str) – The actual “jobId” as submitted to the Dataproc API. This is useful for identifying or linking to the job in the Google Cloud Console Dataproc UI, as the actual “jobId” submitted to the Dataproc API is appended with an 8 character random string.

job_type = ''[source]¶

operator_extra_links = ()[source]¶

create_job_template()[source]¶

Initialize self.job_template with default values.

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires - returns immediately.

Relies on trigger to throw an exception, otherwise it assumes execution was successful.

on_kill()[source]¶

Act as a callback called when the operator is killed; cancel any running job.

class airflow.providers.google.cloud.operators.dataproc.DataprocSubmitPigJobOperator(*, query=None, query_uri=None, variables=None, impersonation_chain=None, region, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', dataproc_properties=None, dataproc_jars=None, **kwargs)[source]¶

Bases: DataprocJobBaseOperator

Start a Pig query Job on a Cloud DataProc cluster.

See also

This operator is deprecated, please use DataprocSubmitJobOperator:

The parameters of the operation will be passed to the cluster.

It’s a good practice to define dataproc_* parameters in the default_args of the dag like the cluster name and UDFs.

default_args = {
    "cluster_name": "cluster-1",
    "dataproc_pig_jars": [
        "gs://example/udf/jar/datafu/1.2.0/datafu.jar",
        "gs://example/udf/jar/gpig/1.2/gpig.jar",
    ],
}

You can pass a pig script as string or file reference. Use variables to pass on variables for the pig script to be resolved on the cluster or use the parameters to be resolved in the script as template parameters.

t1 = DataProcPigOperator(
    task_id="dataproc_pig",
    query="a_pig_script.pig",
    variables={"out": "gs://example/output/{{ds}}"},
)

See also

For more detail on about job submission have a look at the reference: https://cloud.google.com/dataproc/reference/rest/v1/projects.regions.jobs

Parameters

query (str | None) – The query or reference to the query file (pg or pig extension). (templated)
query_uri (str | None) – The HCFS URI of the script that contains the Pig queries.
variables (dict | None) – Map of named parameters for the query. (templated)

template_fields: Sequence[str] = ('query', 'variables', 'job_name', 'cluster_name', 'region', 'dataproc_jars',...[source]¶

template_ext = ('.pg', '.pig')[source]¶

ui_color = '#0273d4'[source]¶

job_type = 'pig_job'[source]¶

operator_extra_links = ()[source]¶

generate_job()[source]¶

Act as a helper method for easier migration to DataprocSubmitJobOperator.

Returns: Dict representing Dataproc job

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocSubmitHiveJobOperator(*, query=None, query_uri=None, variables=None, impersonation_chain=None, region, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', dataproc_properties=None, dataproc_jars=None, **kwargs)[source]¶

Bases: DataprocJobBaseOperator

Start a Hive query Job on a Cloud DataProc cluster.

See also

This operator is deprecated, please use DataprocSubmitJobOperator:

Parameters

query (str | None) – The query or reference to the query file (q extension).
query_uri (str | None) – The HCFS URI of the script that contains the Hive queries.
variables (dict | None) – Map of named parameters for the query.

template_fields: Sequence[str] = ('query', 'variables', 'job_name', 'cluster_name', 'region', 'dataproc_jars',...[source]¶

template_ext = ('.q', '.hql')[source]¶

ui_color = '#0273d4'[source]¶

job_type = 'hive_job'[source]¶

generate_job()[source]¶

Act as a helper method for easier migration to DataprocSubmitJobOperator.

Returns: Dict representing Dataproc job

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocSubmitSparkSqlJobOperator(*, query=None, query_uri=None, variables=None, impersonation_chain=None, region, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', dataproc_properties=None, dataproc_jars=None, **kwargs)[source]¶

Bases: DataprocJobBaseOperator

Start a Spark SQL query Job on a Cloud DataProc cluster.

See also

This operator is deprecated, please use DataprocSubmitJobOperator:

Parameters

query (str | None) – The query or reference to the query file (q extension). (templated)
query_uri (str | None) – The HCFS URI of the script that contains the SQL queries.
variables (dict | None) – Map of named parameters for the query. (templated)

template_fields: Sequence[str] = ('query', 'variables', 'job_name', 'cluster_name', 'region', 'dataproc_jars',...[source]¶

template_ext = ('.q',)[source]¶

template_fields_renderers[source]¶

ui_color = '#0273d4'[source]¶

job_type = 'spark_sql_job'[source]¶

generate_job()[source]¶

Act as a helper method for easier migration to DataprocSubmitJobOperator.

Returns: Dict representing Dataproc job

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocSubmitSparkJobOperator(*, main_jar=None, main_class=None, arguments=None, archives=None, files=None, impersonation_chain=None, region, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', dataproc_properties=None, dataproc_jars=None, **kwargs)[source]¶

Bases: DataprocJobBaseOperator

Start a Spark Job on a Cloud DataProc cluster.

See also

This operator is deprecated, please use DataprocSubmitJobOperator:

Parameters

main_jar (str | None) – The HCFS URI of the jar file that contains the main class (use this or the main_class, not both together).
main_class (str | None) – Name of the job class. (use this or the main_jar, not both together).
arguments (list | None) – Arguments for the job. (templated)
archives (list | None) – List of archived files that will be unpacked in the work directory. Should be stored in Cloud Storage.
files (list | None) – List of files to be copied to the working directory

template_fields: Sequence[str] = ('arguments', 'job_name', 'cluster_name', 'region', 'dataproc_jars', 'dataproc_properties',...[source]¶

ui_color = '#0273d4'[source]¶

job_type = 'spark_job'[source]¶

generate_job()[source]¶

Act as a helper method for easier migration to DataprocSubmitJobOperator.

Returns: Dict representing Dataproc job

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocSubmitHadoopJobOperator(*, main_jar=None, main_class=None, arguments=None, archives=None, files=None, impersonation_chain=None, region, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', dataproc_properties=None, dataproc_jars=None, **kwargs)[source]¶

Bases: DataprocJobBaseOperator

Start a Hadoop Job on a Cloud DataProc cluster.

See also

This operator is deprecated, please use DataprocSubmitJobOperator:

Parameters

main_jar (str | None) – The HCFS URI of the jar file containing the main class (use this or the main_class, not both together).
main_class (str | None) – Name of the job class. (use this or the main_jar, not both together).
arguments (list | None) – Arguments for the job. (templated)
archives (list | None) – List of archived files that will be unpacked in the work directory. Should be stored in Cloud Storage.
files (list | None) – List of files to be copied to the working directory

template_fields: Sequence[str] = ('arguments', 'job_name', 'cluster_name', 'region', 'dataproc_jars', 'dataproc_properties',...[source]¶

ui_color = '#0273d4'[source]¶

job_type = 'hadoop_job'[source]¶

generate_job()[source]¶

Act as a helper method for easier migration to DataprocSubmitJobOperator.

Returns: Dict representing Dataproc job

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocSubmitPySparkJobOperator(*, main, arguments=None, archives=None, pyfiles=None, files=None, impersonation_chain=None, region, job_name='{{task.task_id}}_{{ds_nodash}}', cluster_name='cluster-1', dataproc_properties=None, dataproc_jars=None, **kwargs)[source]¶

Bases: DataprocJobBaseOperator

Start a PySpark Job on a Cloud DataProc cluster.

See also

This operator is deprecated, please use DataprocSubmitJobOperator:

Parameters

main (str) – [Required] The Hadoop Compatible Filesystem (HCFS) URI of the main Python file to use as the driver. Must be a .py file. (templated)
arguments (list | None) – Arguments for the job. (templated)
archives (list | None) – List of archived files that will be unpacked in the work directory. Should be stored in Cloud Storage.
files (list | None) – List of files to be copied to the working directory
pyfiles (list | None) – List of Python files to pass to the PySpark framework. Supported file types: .py, .egg, and .zip

template_fields: Sequence[str] = ('main', 'arguments', 'job_name', 'cluster_name', 'region', 'dataproc_jars',...[source]¶

ui_color = '#0273d4'[source]¶

job_type = 'pyspark_job'[source]¶

generate_job()[source]¶

Act as a helper method for easier migration to DataprocSubmitJobOperator.

Returns: Dict representing Dataproc job

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocCreateWorkflowTemplateOperator(*, template, region, project_id=PROVIDE_PROJECT_ID, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Creates new workflow template.

Parameters

project_id (str) – Optional. The ID of the Google Cloud project the cluster belongs to.
region (str) – Required. The Cloud Dataproc region in which to handle the request.
template (dict) – The Dataproc workflow template to create. If a dict is provided, it must be of the same form as the protobuf message WorkflowTemplate.
retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.

template_fields: Sequence[str] = ('region', 'template')[source]¶

template_fields_renderers[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocInstantiateWorkflowTemplateOperator(*, template_id, region, project_id=PROVIDE_PROJECT_ID, version=None, request_id=None, parameters=None, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, cancel_on_kill=True, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Instantiate a WorkflowTemplate on Google Cloud Dataproc.

The operator will wait until the WorkflowTemplate is finished executing.

Parameters

template_id (str) – The id of the template. (templated)
project_id (str) – The ID of the google cloud project in which the template runs
region (str) – The specified region where the dataproc cluster is created.
parameters (dict[str, str] | None) – a map of parameters for Dataproc Template in key-value format: map (key: string, value: string) Example: { “date_from”: “2019-08-01”, “date_to”: “2019-08-02”}. Values may not exceed 100 characters. Please refer to: https://cloud.google.com/dataproc/docs/concepts/workflows/workflow-parameters
request_id (str | None) – Optional. A unique id used to identify the request. If the server receives two SubmitJobRequest requests with the same id, then the second request will be ignored and the first Job created and stored in the backend is returned. It is recommended to always set this value to a UUID.
retry (google.api_core.retry_async.AsyncRetry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
deferrable (bool) – Run operator in the deferrable mode.
polling_interval_seconds (int) – Time (seconds) to wait between calls to check the run status.
cancel_on_kill (bool) – Flag which indicates whether cancel the workflow, when on_kill is called

template_fields: Sequence[str] = ('template_id', 'impersonation_chain', 'request_id', 'parameters')[source]¶

template_fields_renderers[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires.

This returns immediately. It relies on trigger to throw an exception, otherwise it assumes execution was successful.

on_kill()[source]¶

Override this method to clean up subprocesses when a task instance gets killed.

Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up, or it will leave ghost processes behind.

class airflow.providers.google.cloud.operators.dataproc.DataprocInstantiateInlineWorkflowTemplateOperator(*, template, region, project_id=PROVIDE_PROJECT_ID, request_id=None, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, cancel_on_kill=True, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Instantiate a WorkflowTemplate Inline on Google Cloud Dataproc.

The operator will wait until the WorkflowTemplate is finished executing.

See also

For more information on how to use this operator, take a look at the guide: Create a Cluster

For more detail on about instantiate inline have a look at the reference: https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.workflowTemplates/instantiateInline

Parameters

template (dict) – The template contents. (templated)
project_id (str) – The ID of the google cloud project in which the template runs
region (str) – The specified region where the dataproc cluster is created.
parameters – a map of parameters for Dataproc Template in key-value format: map (key: string, value: string) Example: { “date_from”: “2019-08-01”, “date_to”: “2019-08-02”}. Values may not exceed 100 characters. Please refer to: https://cloud.google.com/dataproc/docs/concepts/workflows/workflow-parameters
request_id (str | None) – Optional. A unique id used to identify the request. If the server receives two SubmitJobRequest requests with the same id, then the second request will be ignored and the first Job created and stored in the backend is returned. It is recommended to always set this value to a UUID.
retry (google.api_core.retry_async.AsyncRetry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
deferrable (bool) – Run operator in the deferrable mode.
polling_interval_seconds (int) – Time (seconds) to wait between calls to check the run status.
cancel_on_kill (bool) – Flag which indicates whether cancel the workflow, when on_kill is called

template_fields: Sequence[str] = ('template', 'impersonation_chain')[source]¶

template_fields_renderers[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires.

This returns immediately. It relies on trigger to throw an exception, otherwise it assumes execution was successful.

on_kill()[source]¶

Override this method to clean up subprocesses when a task instance gets killed.

Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up, or it will leave ghost processes behind.

class airflow.providers.google.cloud.operators.dataproc.DataprocSubmitJobOperator(*, job, region, project_id=PROVIDE_PROJECT_ID, request_id=None, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, asynchronous=False, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, cancel_on_kill=True, wait_timeout=None, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Submit a job to a cluster.

Parameters

project_id (str) – Optional. The ID of the Google Cloud project that the job belongs to.
region (str) – Required. The Cloud Dataproc region in which to handle the request.
job (dict) – Required. The job resource. If a dict is provided, it must be of the same form as the protobuf message Job. For the complete list of supported job types and their configurations please take a look here https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs
request_id (str | None) – Optional. A unique id used to identify the request. If the server receives two SubmitJobRequest requests with the same id, then the second request will be ignored and the first Job created and stored in the backend is returned. It is recommended to always set this value to a UUID.
retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) –
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
asynchronous (bool) – Flag to return after submitting the job to the Dataproc API. This is useful for submitting long-running jobs and waiting on them asynchronously using the DataprocJobSensor
deferrable (bool) – Run operator in the deferrable mode
polling_interval_seconds (int) – time in seconds between polling for job completion. The value is considered only when running in deferrable mode. Must be greater than 0.
cancel_on_kill (bool) – Flag which indicates whether cancel the hook’s job or not, when on_kill is called
wait_timeout (int | None) – How many seconds wait for job to be ready. Used only if asynchronous is False

template_fields: Sequence[str] = ('project_id', 'region', 'job', 'impersonation_chain', 'request_id')[source]¶

template_fields_renderers[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires.

This returns immediately. It relies on trigger to throw an exception, otherwise it assumes execution was successful.

on_kill()[source]¶

Override this method to clean up subprocesses when a task instance gets killed.

Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up, or it will leave ghost processes behind.

class airflow.providers.google.cloud.operators.dataproc.DataprocUpdateClusterOperator(*, cluster_name, cluster, update_mask, graceful_decommission_timeout, region, request_id=None, project_id=PROVIDE_PROJECT_ID, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Update a cluster in a project.

Parameters

region (str) – Required. The Cloud Dataproc region in which to handle the request.
project_id (str) – Optional. The ID of the Google Cloud project the cluster belongs to.
cluster_name (str) – Required. The cluster name.
cluster (dict | google.cloud.dataproc_v1.Cluster) –
Required. The changes to the cluster.

If a dict is provided, it must be of the same form as the protobuf message Cluster
update_mask (dict | google.protobuf.field_mask_pb2.FieldMask) – 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. If a dict is provided, it must be of the same form as the protobuf message FieldMask
graceful_decommission_timeout (dict | google.protobuf.duration_pb2.Duration) – 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.
request_id (str | None) – Optional. A unique id used to identify the request. If the server receives two UpdateClusterRequest requests with the same id, then the second request will be ignored and the first google.long-running.Operation created and stored in the backend is returned.
retry (google.api_core.retry_async.AsyncRetry | google.api_core.gapic_v1.method._MethodDefault | google.api_core.retry.Retry) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
deferrable (bool) – Run operator in the deferrable mode.
polling_interval_seconds (int) – Time (seconds) to wait between calls to check the run status.

template_fields: Sequence[str] = ('cluster_name', 'cluster', 'region', 'request_id', 'project_id', 'impersonation_chain')[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event)[source]¶

Act as a callback for when the trigger fires - returns immediately.

Relies on trigger to throw an exception, otherwise it assumes execution was successful.

class airflow.providers.google.cloud.operators.dataproc.DataprocDiagnoseClusterOperator(*, region, cluster_name, project_id=PROVIDE_PROJECT_ID, tarball_gcs_dir=None, diagnosis_interval=None, jobs=None, yarn_application_ids=None, retry=DEFAULT, timeout=1 * 60 * 60, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=10, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Diagnose a cluster in a project.

After the operation completes, the response contains the Cloud Storage URI of the diagnostic output report containing a summary of collected diagnostics.

Parameters

region (str) – Required. The Cloud Dataproc region in which to handle the request (templated).
project_id (str) – Optional. The ID of the Google Cloud project that the cluster belongs to (templated).
cluster_name (str) – Required. The cluster name (templated).
tarball_gcs_dir (str | None) – 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.
diagnosis_interval (dict | google.type.interval_pb2.Interval | None) – Time interval in which diagnosis should be carried out on the cluster.
jobs (collections.abc.MutableSequence[str] | None) – Specifies a list of jobs on which diagnosis is to be performed. Format: projects/{project}/regions/{region}/jobs/{job}
yarn_application_ids (collections.abc.MutableSequence[str] | None) – Specifies a list of yarn applications on which diagnosis is to be performed.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
retry (google.api_core.retry_async.AsyncRetry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
deferrable (bool) – Run operator in the deferrable mode.
polling_interval_seconds (int) – Time (seconds) to wait between calls to check the cluster status.

template_fields: Sequence[str] = ('project_id', 'region', 'cluster_name', 'impersonation_chain', 'tarball_gcs_dir',...[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires.

This returns immediately. It relies on trigger to throw an exception, otherwise it assumes execution was successful.

class airflow.providers.google.cloud.operators.dataproc.DataprocCreateBatchOperator(*, region, project_id=PROVIDE_PROJECT_ID, batch, batch_id=None, request_id=None, num_retries_if_resource_is_not_ready=0, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, result_retry=DEFAULT, asynchronous=False, deferrable=conf.getboolean('operators', 'default_deferrable', fallback=False), polling_interval_seconds=5, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Create a batch workload.

Parameters

project_id (str) – Optional. The ID of the Google Cloud project that the cluster belongs to. (templated)
region (str) – Required. The Cloud Dataproc region in which to handle the request. (templated)
batch (dict | google.cloud.dataproc_v1.Batch) – Required. The batch to create. (templated)
batch_id (str | None) – Required. The ID to use for the batch, which will become the final component of the batch’s resource name. This value must be 4-63 characters. Valid characters are /[a-z][0-9]-/. (templated)
request_id (str | None) – Optional. A unique id used to identify the request. If the server receives two CreateBatchRequest requests 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.
num_retries_if_resource_is_not_ready (int) – Optional. The number of retry for cluster creation request when resource is not ready error appears.
retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
result_retry (google.api_core.retry_async.AsyncRetry | google.api_core.gapic_v1.method._MethodDefault | google.api_core.retry.Retry) – Result retry object used to retry requests. Is used to decrease delay between executing chained tasks in a DAG by specifying exact amount of seconds for executing.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
asynchronous (bool) – Flag to return after creating batch to the Dataproc API. This is useful for creating long-running batch and waiting on them asynchronously using the DataprocBatchSensor
deferrable (bool) – Run operator in the deferrable mode.
polling_interval_seconds (int) – Time (seconds) to wait between calls to check the run status.

template_fields: Sequence[str] = ('project_id', 'batch', 'batch_id', 'region', 'impersonation_chain')[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

hook()[source]¶

execute_complete(context, event=None)[source]¶

Act as a callback for when the trigger fires.

This returns immediately. It relies on trigger to throw an exception, otherwise it assumes execution was successful.

on_kill()[source]¶

Override this method to clean up subprocesses when a task instance gets killed.

Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up, or it will leave ghost processes behind.

handle_batch_status(context, state, batch_id, state_message=None)[source]¶

retry_batch_creation(previous_batch_id)[source]¶

class airflow.providers.google.cloud.operators.dataproc.DataprocDeleteBatchOperator(*, batch_id, region, project_id=PROVIDE_PROJECT_ID, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Delete the batch workload resource.

Parameters

batch_id (str) – Required. The ID to use for the batch, which will become the final component of the batch’s resource name. This value must be 4-63 characters. Valid characters are /[a-z][0-9]-/.
region (str) – Required. The Cloud Dataproc region in which to handle the request.
project_id (str) – Optional. The ID of the Google Cloud project that the cluster belongs to.
retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).

template_fields: Sequence[str] = ('batch_id', 'region', 'project_id', 'impersonation_chain')[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocGetBatchOperator(*, batch_id, region, project_id=PROVIDE_PROJECT_ID, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Get the batch workload resource representation.

Parameters

batch_id (str) – Required. The ID to use for the batch, which will become the final component of the batch’s resource name. This value must be 4-63 characters. Valid characters are /[a-z][0-9]-/.
region (str) – Required. The Cloud Dataproc region in which to handle the request.
project_id (str) – Optional. The ID of the Google Cloud project that the cluster belongs to.
retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).

template_fields: Sequence[str] = ('batch_id', 'region', 'project_id', 'impersonation_chain')[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocListBatchesOperator(*, region, project_id=PROVIDE_PROJECT_ID, page_size=None, page_token=None, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, filter=None, order_by=None, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

List batch workloads.

Parameters

region (str) – Required. The Cloud Dataproc region in which to handle the request.
project_id (str) – Optional. The ID of the Google Cloud project that the cluster belongs to.
page_size (int | None) – Optional. The maximum number of batches to return in each response. The service may return fewer than this value. The default page size is 20; the maximum page size is 1000.
page_token (str | None) – Optional. A page token received from a previous ListBatches call. Provide this token to retrieve the subsequent page.
retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – Optional, a retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – Optional, the amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Optional, additional metadata that is provided to the method.
gcp_conn_id (str) – Optional, the connection ID used to connect to Google Cloud Platform.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).
filter (str | None) – Result filters as specified in ListBatchesRequest
order_by (str | None) – How to order results as specified in ListBatchesRequest

template_fields: Sequence[str] = ('region', 'project_id', 'impersonation_chain')[source]¶

operator_extra_links = ()[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.dataproc.DataprocCancelOperationOperator(*, operation_name, region, project_id=PROVIDE_PROJECT_ID, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]¶

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Cancel the batch workload resource.

Parameters

operation_name (str) – Required. The name of the operation resource to be cancelled.
region (str) – Required. The Cloud Dataproc region in which to handle the request.
project_id (str) – Optional. The ID of the Google Cloud project that the cluster belongs to.
retry (google.api_core.retry.Retry | google.api_core.gapic_v1.method._MethodDefault) – A retry object used to retry requests. If None is specified, requests will not be retried.
timeout (float | None) – The amount of time, in seconds, to wait for the request to complete. Note that if retry is specified, the timeout applies to each individual attempt.
metadata (Sequence[tuple[str, str]]) – Additional metadata that is provided to the method.
gcp_conn_id (str) – The connection ID to use connecting to Google Cloud.
impersonation_chain (str | Sequence[str] | None) – Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated).

template_fields: Sequence[str] = ('operation_name', 'region', 'project_id', 'impersonation_chain')[source]¶

execute(context)[source]¶

Derive when creating an operator.

Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

airflow.providers.google.cloud.operators.dataproc¶

Module Contents¶

Classes¶

`airflow.providers.google.cloud.operators.dataproc`¶