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dataproc.py
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dataproc.py
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#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains Google Dataproc operators."""
from __future__ import annotations
import inspect
import ntpath
import os
import re
import time
import uuid
import warnings
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Any, Sequence
from google.api_core import operation # type: ignore
from google.api_core.exceptions import AlreadyExists, NotFound
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.retry import Retry, exponential_sleep_generator
from google.cloud.dataproc_v1 import Batch, Cluster, ClusterStatus, JobStatus
from google.protobuf.duration_pb2 import Duration
from google.protobuf.field_mask_pb2 import FieldMask
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
from airflow.providers.google.cloud.hooks.dataproc import DataprocHook, DataProcJobBuilder
from airflow.providers.google.cloud.hooks.gcs import GCSHook
from airflow.providers.google.cloud.links.dataproc import (
DATAPROC_BATCH_LINK,
DATAPROC_BATCHES_LINK,
DATAPROC_CLUSTER_LINK,
DATAPROC_JOB_LOG_LINK,
DATAPROC_WORKFLOW_LINK,
DATAPROC_WORKFLOW_TEMPLATE_LINK,
DataprocLink,
DataprocListLink,
)
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
from airflow.providers.google.cloud.triggers.dataproc import (
DataprocBatchTrigger,
DataprocClusterTrigger,
DataprocDeleteClusterTrigger,
DataprocSubmitTrigger,
DataprocWorkflowTrigger,
)
from airflow.utils import timezone
if TYPE_CHECKING:
from airflow.utils.context import Context
class ClusterGenerator:
"""Create a new Dataproc Cluster.
:param cluster_name: The name of the DataProc cluster to create. (templated)
:param project_id: The ID of the google cloud project in which
to create the cluster. (templated)
:param num_workers: The # of workers to spin up. If set to zero will
spin up cluster in a single node mode
:param storage_bucket: The storage bucket to use, setting to None lets dataproc
generate a custom one for you
:param init_actions_uris: List of GCS uri's containing
dataproc initialization scripts
:param init_action_timeout: Amount of time executable scripts in
init_actions_uris has to complete
:param metadata: dict of key-value google compute engine metadata entries
to add to all instances
:param image_version: the version of software inside the Dataproc cluster
:param custom_image: custom Dataproc image for more info see
https://cloud.google.com/dataproc/docs/guides/dataproc-images
:param custom_image_project_id: project id for the custom Dataproc image, for more info see
https://cloud.google.com/dataproc/docs/guides/dataproc-images
:param custom_image_family: 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
:param autoscaling_policy: 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]``
:param properties: 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
:param optional_components: List of optional cluster components, for more info see
https://cloud.google.com/dataproc/docs/reference/rest/v1/ClusterConfig#Component
:param num_masters: The # of master nodes to spin up
:param master_machine_type: Compute engine machine type to use for the primary node
:param master_disk_type: 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).
:param master_disk_size: Disk size for the primary node
:param worker_machine_type: Compute engine machine type to use for the worker nodes
:param worker_disk_type: 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).
:param worker_disk_size: Disk size for the worker nodes
:param num_preemptible_workers: The # of preemptible worker nodes to spin up
:param zone: The zone where the cluster will be located. Set to None to auto-zone. (templated)
:param network_uri: The network uri to be used for machine communication, cannot be
specified with subnetwork_uri
:param subnetwork_uri: The subnetwork uri to be used for machine communication,
cannot be specified with network_uri
:param internal_ip_only: If true, all instances in the cluster will only
have internal IP addresses. This can only be enabled for subnetwork
enabled networks
:param tags: The GCE tags to add to all instances
:param region: The specified region where the dataproc cluster is created.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param service_account: The service account of the dataproc instances.
:param service_account_scopes: The URIs of service account scopes to be included.
:param idle_delete_ttl: 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.
:param auto_delete_time: The time when cluster will be auto-deleted.
:param auto_delete_ttl: 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)
:param customer_managed_key: The customer-managed key used for disk encryption
``projects/[PROJECT_STORING_KEYS]/locations/[LOCATION]/keyRings/[KEY_RING_NAME]/cryptoKeys/[KEY_NAME]`` # noqa
:param enable_component_gateway: Provides access to the web interfaces of default and selected optional
components on the cluster.
""" # noqa: E501
def __init__(
self,
project_id: str,
num_workers: int | None = None,
zone: str | None = None,
network_uri: str | None = None,
subnetwork_uri: str | None = None,
internal_ip_only: bool | None = None,
tags: list[str] | None = None,
storage_bucket: str | None = None,
init_actions_uris: list[str] | None = None,
init_action_timeout: str = "10m",
metadata: dict | None = None,
custom_image: str | None = None,
custom_image_project_id: str | None = None,
custom_image_family: str | None = None,
image_version: str | None = None,
autoscaling_policy: str | None = None,
properties: dict | None = None,
optional_components: list[str] | None = None,
num_masters: int = 1,
master_machine_type: str = "n1-standard-4",
master_disk_type: str = "pd-standard",
master_disk_size: int = 1024,
worker_machine_type: str = "n1-standard-4",
worker_disk_type: str = "pd-standard",
worker_disk_size: int = 1024,
num_preemptible_workers: int = 0,
service_account: str | None = None,
service_account_scopes: list[str] | None = None,
idle_delete_ttl: int | None = None,
auto_delete_time: datetime | None = None,
auto_delete_ttl: int | None = None,
customer_managed_key: str | None = None,
enable_component_gateway: bool | None = False,
**kwargs,
) -> None:
self.project_id = project_id
self.num_masters = num_masters
self.num_workers = num_workers
self.num_preemptible_workers = num_preemptible_workers
self.storage_bucket = storage_bucket
self.init_actions_uris = init_actions_uris
self.init_action_timeout = init_action_timeout
self.metadata = metadata
self.custom_image = custom_image
self.custom_image_project_id = custom_image_project_id
self.custom_image_family = custom_image_family
self.image_version = image_version
self.properties = properties or {}
self.optional_components = optional_components
self.master_machine_type = master_machine_type
self.master_disk_type = master_disk_type
self.master_disk_size = master_disk_size
self.autoscaling_policy = autoscaling_policy
self.worker_machine_type = worker_machine_type
self.worker_disk_type = worker_disk_type
self.worker_disk_size = worker_disk_size
self.zone = zone
self.network_uri = network_uri
self.subnetwork_uri = subnetwork_uri
self.internal_ip_only = internal_ip_only
self.tags = tags
self.service_account = service_account
self.service_account_scopes = service_account_scopes
self.idle_delete_ttl = idle_delete_ttl
self.auto_delete_time = auto_delete_time
self.auto_delete_ttl = auto_delete_ttl
self.customer_managed_key = customer_managed_key
self.enable_component_gateway = enable_component_gateway
self.single_node = num_workers == 0
if self.custom_image and self.image_version:
raise ValueError("The custom_image and image_version can't be both set")
if self.custom_image_family and self.image_version:
raise ValueError("The image_version and custom_image_family can't be both set")
if self.custom_image_family and self.custom_image:
raise ValueError("The custom_image and custom_image_family can't be both set")
if self.single_node and self.num_preemptible_workers > 0:
raise ValueError("Single node cannot have preemptible workers.")
def _get_init_action_timeout(self) -> dict:
match = re.match(r"^(\d+)([sm])$", self.init_action_timeout)
if match:
val = float(match.group(1))
if match.group(2) == "s":
return {"seconds": int(val)}
elif match.group(2) == "m":
return {"seconds": int(timedelta(minutes=val).total_seconds())}
raise AirflowException(
"DataprocClusterCreateOperator init_action_timeout"
" should be expressed in minutes or seconds. i.e. 10m, 30s"
)
def _build_gce_cluster_config(self, cluster_data):
# This variable is created since same string was being used multiple times
config = "gce_cluster_config"
if self.zone:
zone_uri = f"https://www.googleapis.com/compute/v1/projects/{self.project_id}/zones/{self.zone}"
cluster_data[config]["zone_uri"] = zone_uri
if self.metadata:
cluster_data[config]["metadata"] = self.metadata
if self.network_uri:
cluster_data[config]["network_uri"] = self.network_uri
if self.subnetwork_uri:
cluster_data[config]["subnetwork_uri"] = self.subnetwork_uri
if self.internal_ip_only:
if not self.subnetwork_uri:
raise AirflowException("Set internal_ip_only to true only when you pass a subnetwork_uri.")
cluster_data[config]["internal_ip_only"] = True
if self.tags:
cluster_data[config]["tags"] = self.tags
if self.service_account:
cluster_data[config]["service_account"] = self.service_account
if self.service_account_scopes:
cluster_data[config]["service_account_scopes"] = self.service_account_scopes
return cluster_data
def _build_lifecycle_config(self, cluster_data):
# This variable is created since same string was being used multiple times
lifecycle_config = "lifecycle_config"
if self.idle_delete_ttl:
cluster_data[lifecycle_config]["idle_delete_ttl"] = {"seconds": self.idle_delete_ttl}
if self.auto_delete_time:
utc_auto_delete_time = timezone.convert_to_utc(self.auto_delete_time)
cluster_data[lifecycle_config]["auto_delete_time"] = utc_auto_delete_time.strftime(
"%Y-%m-%dT%H:%M:%S.%fZ"
)
elif self.auto_delete_ttl:
cluster_data[lifecycle_config]["auto_delete_ttl"] = {"seconds": int(self.auto_delete_ttl)}
return cluster_data
def _build_cluster_data(self):
if self.zone:
master_type_uri = (
f"projects/{self.project_id}/zones/{self.zone}/machineTypes/{self.master_machine_type}"
)
worker_type_uri = (
f"projects/{self.project_id}/zones/{self.zone}/machineTypes/{self.worker_machine_type}"
)
else:
master_type_uri = self.master_machine_type
worker_type_uri = self.worker_machine_type
cluster_data = {
"gce_cluster_config": {},
"master_config": {
"num_instances": self.num_masters,
"machine_type_uri": master_type_uri,
"disk_config": {
"boot_disk_type": self.master_disk_type,
"boot_disk_size_gb": self.master_disk_size,
},
},
"worker_config": {
"num_instances": self.num_workers,
"machine_type_uri": worker_type_uri,
"disk_config": {
"boot_disk_type": self.worker_disk_type,
"boot_disk_size_gb": self.worker_disk_size,
},
},
"secondary_worker_config": {},
"software_config": {},
"lifecycle_config": {},
"encryption_config": {},
"autoscaling_config": {},
"endpoint_config": {},
}
if self.num_preemptible_workers > 0:
cluster_data["secondary_worker_config"] = {
"num_instances": self.num_preemptible_workers,
"machine_type_uri": worker_type_uri,
"disk_config": {
"boot_disk_type": self.worker_disk_type,
"boot_disk_size_gb": self.worker_disk_size,
},
"is_preemptible": True,
}
if self.storage_bucket:
cluster_data["config_bucket"] = self.storage_bucket
if self.image_version:
cluster_data["software_config"]["image_version"] = self.image_version
elif self.custom_image:
project_id = self.custom_image_project_id or self.project_id
custom_image_url = (
f"https://www.googleapis.com/compute/beta/projects/{project_id}"
f"/global/images/{self.custom_image}"
)
cluster_data["master_config"]["image_uri"] = custom_image_url
if not self.single_node:
cluster_data["worker_config"]["image_uri"] = custom_image_url
elif self.custom_image_family:
project_id = self.custom_image_project_id or self.project_id
custom_image_url = (
"https://www.googleapis.com/compute/beta/projects/"
f"{project_id}/global/images/family/{self.custom_image_family}"
)
cluster_data["master_config"]["image_uri"] = custom_image_url
if not self.single_node:
cluster_data["worker_config"]["image_uri"] = custom_image_url
cluster_data = self._build_gce_cluster_config(cluster_data)
if self.single_node:
self.properties["dataproc:dataproc.allow.zero.workers"] = "true"
if self.properties:
cluster_data["software_config"]["properties"] = self.properties
if self.optional_components:
cluster_data["software_config"]["optional_components"] = self.optional_components
cluster_data = self._build_lifecycle_config(cluster_data)
if self.init_actions_uris:
init_actions_dict = [
{"executable_file": uri, "execution_timeout": self._get_init_action_timeout()}
for uri in self.init_actions_uris
]
cluster_data["initialization_actions"] = init_actions_dict
if self.customer_managed_key:
cluster_data["encryption_config"] = {"gce_pd_kms_key_name": self.customer_managed_key}
if self.autoscaling_policy:
cluster_data["autoscaling_config"] = {"policy_uri": self.autoscaling_policy}
if self.enable_component_gateway:
cluster_data["endpoint_config"] = {"enable_http_port_access": self.enable_component_gateway}
return cluster_data
def make(self):
"""
Helper method for easier migration.
:return: Dict representing Dataproc cluster.
"""
return self._build_cluster_data()
class DataprocCreateClusterOperator(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, the operator will:
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.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataprocCreateClusterOperator`
:param project_id: The ID of the Google cloud project in which
to create the cluster. (templated)
:param cluster_name: Name of the cluster to create
:param labels: 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.
:param cluster_config: Required. The cluster config to create.
If a dict is provided, it must be of the same form as the protobuf message
:class:`~google.cloud.dataproc_v1.types.ClusterConfig`
:param virtual_cluster_config: 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>`
:param region: The specified region where the dataproc cluster is created.
:param delete_on_error: If true the cluster will be deleted if created with ERROR state. Default
value is true.
:param use_if_exists: If true use existing cluster
:param request_id: 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.
:param retry: A retry object used to retry requests. If ``None`` is specified, requests will not be
retried.
:param timeout: 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.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param impersonation_chain: 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).
:param deferrable: Run operator in the deferrable mode.
:param polling_interval_seconds: 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",
"impersonation_chain",
)
template_fields_renderers = {"cluster_config": "json", "virtual_cluster_config": "json"}
operator_extra_links = (DataprocLink(),)
def __init__(
self,
*,
cluster_name: str,
region: str,
project_id: str | None = None,
cluster_config: dict | Cluster | None = None,
virtual_cluster_config: dict | None = None,
labels: dict | None = None,
request_id: str | None = None,
delete_on_error: bool = True,
use_if_exists: bool = True,
retry: Retry | _MethodDefault = DEFAULT,
timeout: float = 1 * 60 * 60,
metadata: Sequence[tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
polling_interval_seconds: int = 10,
**kwargs,
) -> None:
# TODO: remove one day
if cluster_config is None and virtual_cluster_config is None:
warnings.warn(
f"Passing cluster parameters by keywords to `{type(self).__name__}` will be deprecated. "
"Please provide cluster_config object using `cluster_config` parameter. "
"You can use `airflow.dataproc.ClusterGenerator.generate_cluster` "
"method to obtain cluster object.",
AirflowProviderDeprecationWarning,
stacklevel=1,
)
# Remove result of apply defaults
if "params" in kwargs:
del kwargs["params"]
# Create cluster object from kwargs
if project_id is None:
raise AirflowException(
"project_id argument is required when building cluster from keywords parameters"
)
kwargs["project_id"] = project_id
cluster_config = ClusterGenerator(**kwargs).make()
# Remove from kwargs cluster params passed for backward compatibility
cluster_params = inspect.signature(ClusterGenerator.__init__).parameters
for arg in cluster_params:
if arg in kwargs:
del kwargs[arg]
super().__init__(**kwargs)
if deferrable and polling_interval_seconds <= 0:
raise ValueError("Invalid value for polling_interval_seconds. Expected value greater than 0")
self.cluster_config = cluster_config
self.cluster_name = cluster_name
self.labels = labels
self.project_id = project_id
self.region = region
self.request_id = request_id
self.retry = retry
self.timeout = timeout
self.metadata = metadata
self.gcp_conn_id = gcp_conn_id
self.delete_on_error = delete_on_error
self.use_if_exists = use_if_exists
self.impersonation_chain = impersonation_chain
self.virtual_cluster_config = virtual_cluster_config
self.deferrable = deferrable
self.polling_interval_seconds = polling_interval_seconds
def _create_cluster(self, hook: DataprocHook):
return hook.create_cluster(
project_id=self.project_id,
region=self.region,
cluster_name=self.cluster_name,
labels=self.labels,
cluster_config=self.cluster_config,
virtual_cluster_config=self.virtual_cluster_config,
request_id=self.request_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
def _delete_cluster(self, hook):
self.log.info("Deleting the cluster")
hook.delete_cluster(region=self.region, cluster_name=self.cluster_name, project_id=self.project_id)
def _get_cluster(self, hook: DataprocHook) -> Cluster:
return hook.get_cluster(
project_id=self.project_id,
region=self.region,
cluster_name=self.cluster_name,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
def _handle_error_state(self, hook: DataprocHook, cluster: Cluster) -> None:
if cluster.status.state != cluster.status.State.ERROR:
return
self.log.info("Cluster is in ERROR state")
gcs_uri = hook.diagnose_cluster(
region=self.region, cluster_name=self.cluster_name, project_id=self.project_id
)
self.log.info("Diagnostic information for cluster %s available at: %s", self.cluster_name, gcs_uri)
if self.delete_on_error:
self._delete_cluster(hook)
raise AirflowException("Cluster was created but was in ERROR state.")
raise AirflowException("Cluster was created but is in ERROR state")
def _wait_for_cluster_in_deleting_state(self, hook: DataprocHook) -> None:
time_left = self.timeout
for time_to_sleep in exponential_sleep_generator(initial=10, maximum=120):
if time_left < 0:
raise AirflowException(f"Cluster {self.cluster_name} is still DELETING state, aborting")
time.sleep(time_to_sleep)
time_left = time_left - time_to_sleep
try:
self._get_cluster(hook)
except NotFound:
break
def _wait_for_cluster_in_creating_state(self, hook: DataprocHook) -> Cluster:
time_left = self.timeout
cluster = self._get_cluster(hook)
for time_to_sleep in exponential_sleep_generator(initial=10, maximum=120):
if cluster.status.state != cluster.status.State.CREATING:
break
if time_left < 0:
raise AirflowException(f"Cluster {self.cluster_name} is still CREATING state, aborting")
time.sleep(time_to_sleep)
time_left = time_left - time_to_sleep
cluster = self._get_cluster(hook)
return cluster
def execute(self, context: Context) -> dict:
self.log.info("Creating cluster: %s", self.cluster_name)
hook = DataprocHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
# Save data required to display extra link no matter what the cluster status will be
DataprocLink.persist(
context=context, task_instance=self, url=DATAPROC_CLUSTER_LINK, resource=self.cluster_name
)
try:
# First try to create a new cluster
operation = self._create_cluster(hook)
if not self.deferrable:
cluster = hook.wait_for_operation(
timeout=self.timeout, result_retry=self.retry, operation=operation
)
self.log.info("Cluster created.")
return Cluster.to_dict(cluster)
else:
self.defer(
trigger=DataprocClusterTrigger(
cluster_name=self.cluster_name,
project_id=self.project_id,
region=self.region,
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
polling_interval_seconds=self.polling_interval_seconds,
),
method_name="execute_complete",
)
except AlreadyExists:
if not self.use_if_exists:
raise
self.log.info("Cluster already exists.")
cluster = self._get_cluster(hook)
# Check if cluster is not in ERROR state
self._handle_error_state(hook, cluster)
if cluster.status.state == cluster.status.State.CREATING:
# Wait for cluster to be created
cluster = self._wait_for_cluster_in_creating_state(hook)
self._handle_error_state(hook, cluster)
elif cluster.status.state == cluster.status.State.DELETING:
# Wait for cluster to be deleted
self._wait_for_cluster_in_deleting_state(hook)
# Create new cluster
cluster = self._create_cluster(hook)
self._handle_error_state(hook, cluster)
return Cluster.to_dict(cluster)
def execute_complete(self, context: Context, event: dict[str, Any]) -> Any:
"""
Callback for when the trigger fires - returns immediately.
Relies on trigger to throw an exception, otherwise it assumes execution was successful.
"""
cluster_state = event["cluster_state"]
cluster_name = event["cluster_name"]
if cluster_state == ClusterStatus.State.ERROR:
raise AirflowException(f"Cluster is in ERROR state:\n{cluster_name}")
self.log.info("%s completed successfully.", self.task_id)
return event["cluster"]
class DataprocScaleClusterOperator(GoogleCloudBaseOperator):
"""Scale, up or down, a cluster on Google Cloud Dataproc.
The operator will wait until the cluster is re-scaled.
Example usage:
.. code-block:: python
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",
)
.. seealso::
For more detail on about scaling clusters have a look at the reference:
https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/scaling-clusters
:param cluster_name: The name of the cluster to scale. (templated)
:param project_id: The ID of the google cloud project in which
the cluster runs. (templated)
:param region: The region for the dataproc cluster. (templated)
:param num_workers: The new number of workers
:param num_preemptible_workers: The new number of preemptible workers
:param graceful_decommission_timeout: Timeout for graceful YARN decommissioning.
Maximum value is 1d
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param impersonation_chain: 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")
operator_extra_links = (DataprocLink(),)
def __init__(
self,
*,
cluster_name: str,
project_id: str | None = None,
region: str = "global",
num_workers: int = 2,
num_preemptible_workers: int = 0,
graceful_decommission_timeout: str | None = None,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.project_id = project_id
self.region = region
self.cluster_name = cluster_name
self.num_workers = num_workers
self.num_preemptible_workers = num_preemptible_workers
self.graceful_decommission_timeout = graceful_decommission_timeout
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
# TODO: Remove one day
warnings.warn(
f"The `{type(self).__name__}` operator is deprecated, "
"please use `DataprocUpdateClusterOperator` instead.",
AirflowProviderDeprecationWarning,
stacklevel=1,
)
def _build_scale_cluster_data(self) -> dict:
scale_data = {
"config": {
"worker_config": {"num_instances": self.num_workers},
"secondary_worker_config": {"num_instances": self.num_preemptible_workers},
}
}
return scale_data
@property
def _graceful_decommission_timeout_object(self) -> dict[str, int] | None:
if not self.graceful_decommission_timeout:
return None
timeout = None
match = re.match(r"^(\d+)([smdh])$", self.graceful_decommission_timeout)
if match:
if match.group(2) == "s":
timeout = int(match.group(1))
elif match.group(2) == "m":
val = float(match.group(1))
timeout = int(timedelta(minutes=val).total_seconds())
elif match.group(2) == "h":
val = float(match.group(1))
timeout = int(timedelta(hours=val).total_seconds())
elif match.group(2) == "d":
val = float(match.group(1))
timeout = int(timedelta(days=val).total_seconds())
if not timeout:
raise AirflowException(
"DataprocClusterScaleOperator "
" should be expressed in day, hours, minutes or seconds. "
" i.e. 1d, 4h, 10m, 30s"
)
return {"seconds": timeout}
def execute(self, context: Context) -> None:
"""Scale, up or down, a cluster on Google Cloud Dataproc."""
self.log.info("Scaling cluster: %s", self.cluster_name)
scaling_cluster_data = self._build_scale_cluster_data()
update_mask = ["config.worker_config.num_instances", "config.secondary_worker_config.num_instances"]
hook = DataprocHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
# Save data required to display extra link no matter what the cluster status will be
DataprocLink.persist(
context=context, task_instance=self, url=DATAPROC_CLUSTER_LINK, resource=self.cluster_name
)
operation = hook.update_cluster(
project_id=self.project_id,
region=self.region,
cluster_name=self.cluster_name,
cluster=scaling_cluster_data,
graceful_decommission_timeout=self._graceful_decommission_timeout_object,
update_mask={"paths": update_mask},
)
operation.result()
self.log.info("Cluster scaling finished")
class DataprocDeleteClusterOperator(GoogleCloudBaseOperator):
"""Delete a cluster in a project.
:param region: Required. The Cloud Dataproc region in which to handle the request (templated).
:param cluster_name: Required. The cluster name (templated).
:param project_id: Optional. The ID of the Google Cloud project that the cluster belongs to (templated).
:param cluster_uuid: Optional. Specifying the ``cluster_uuid`` means the RPC should fail
if cluster with specified UUID does not exist.
:param request_id: 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.
:param retry: A retry object used to retry requests. If ``None`` is specified, requests will not be
retried.
:param timeout: 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.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param impersonation_chain: 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).
:param deferrable: Run operator in the deferrable mode.
:param polling_interval_seconds: Time (seconds) to wait between calls to check the cluster status.
"""
template_fields: Sequence[str] = ("project_id", "region", "cluster_name", "impersonation_chain")
def __init__(
self,
*,
region: str,
cluster_name: str,
project_id: str | None = None,
cluster_uuid: str | None = None,
request_id: str | None = None,
retry: Retry | _MethodDefault = DEFAULT,
timeout: float = 1 * 60 * 60,
metadata: Sequence[tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
polling_interval_seconds: int = 10,
**kwargs,
):
super().__init__(**kwargs)
if deferrable and polling_interval_seconds <= 0:
raise ValueError("Invalid value for polling_interval_seconds. Expected value greater than 0")
self.project_id = project_id
self.region = region
self.cluster_name = cluster_name
self.cluster_uuid = cluster_uuid
self.request_id = request_id
self.retry = retry
self.timeout = timeout
self.metadata = metadata
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.deferrable = deferrable
self.polling_interval_seconds = polling_interval_seconds
def execute(self, context: Context) -> None:
hook = DataprocHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
operation = self._delete_cluster(hook)
if not self.deferrable:
hook.wait_for_operation(timeout=self.timeout, result_retry=self.retry, operation=operation)
self.log.info("Cluster deleted.")
else:
end_time: float = time.time() + self.timeout
self.defer(
trigger=DataprocDeleteClusterTrigger(
gcp_conn_id=self.gcp_conn_id,
project_id=self.project_id,
region=self.region,
cluster_name=self.cluster_name,
end_time=end_time,
metadata=self.metadata,
impersonation_chain=self.impersonation_chain,
polling_interval_seconds=self.polling_interval_seconds,
),
method_name="execute_complete",
)
def execute_complete(self, context: Context, event: dict[str, Any] | None = None) -> Any:
"""
Callback for when the trigger fires - returns immediately.
Relies on trigger to throw an exception, otherwise it assumes execution was successful.
"""
if event and event["status"] == "error":
raise AirflowException(event["message"])
elif event is None:
raise AirflowException("No event received in trigger callback")
self.log.info("Cluster deleted.")
def _delete_cluster(self, hook: DataprocHook):
self.log.info("Deleting cluster: %s", self.cluster_name)
return hook.delete_cluster(
project_id=self.project_id,
region=self.region,
cluster_name=self.cluster_name,
cluster_uuid=self.cluster_uuid,
request_id=self.request_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
class DataprocJobBaseOperator(GoogleCloudBaseOperator):
"""Base class for operators that launch job on DataProc.
:param region: The specified region where the dataproc cluster is created.
:param job_name: 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.
:param cluster_name: The name of the DataProc cluster.
:param project_id: The ID of the Google Cloud project the cluster belongs to,
if not specified the project will be inferred from the provided GCP connection.
:param dataproc_properties: Map for the Hive properties. Ideal to put in
default arguments (templated)
:param dataproc_jars: 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)
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param labels: 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.
:param job_error_states: 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'}``.
:param impersonation_chain: 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).
:param asynchronous: 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
:param deferrable: Run operator in the deferrable mode
:param polling_interval_seconds: time in seconds between polling for job completion.
The value is considered only when running in deferrable mode. Must be greater than 0.
:var dataproc_job_id: 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.
:vartype dataproc_job_id: str
"""
job_type = ""
operator_extra_links = (DataprocLink(),)
def __init__(
self,
*,
region: str,
job_name: str = "{{task.task_id}}_{{ds_nodash}}",
cluster_name: str = "cluster-1",
project_id: str | None = None,
dataproc_properties: dict | None = None,
dataproc_jars: list[str] | None = None,
gcp_conn_id: str = "google_cloud_default",
labels: dict | None = None,
job_error_states: set[str] | None = None,
impersonation_chain: str | Sequence[str] | None = None,
asynchronous: bool = False,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
polling_interval_seconds: int = 10,
**kwargs,
) -> None:
super().__init__(**kwargs)
if deferrable and polling_interval_seconds <= 0:
raise ValueError("Invalid value for polling_interval_seconds. Expected value greater than 0")
self.gcp_conn_id = gcp_conn_id
self.labels = labels
self.job_name = job_name
self.cluster_name = cluster_name
self.dataproc_properties = dataproc_properties
self.dataproc_jars = dataproc_jars
self.region = region