Source code for airflow.providers.google.cloud.operators.workflows

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import hashlib
import json
import re
import uuid
from datetime import datetime, timedelta
from typing import TYPE_CHECKING, Dict, Optional, Sequence, Tuple, Union

import pytz
from google.api_core.exceptions import AlreadyExists
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.retry import Retry
from google.cloud.workflows.executions_v1beta import Execution
from google.cloud.workflows_v1beta import Workflow
from google.protobuf.field_mask_pb2 import FieldMask

from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.workflows import WorkflowsHook
from airflow.providers.google.cloud.links.workflows import (
    WorkflowsExecutionLink,
    WorkflowsListOfWorkflowsLink,
    WorkflowsWorkflowDetailsLink,
)

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class WorkflowsCreateWorkflowOperator(BaseOperator): """ Creates a new workflow. If a workflow with the specified name already exists in the specified project and location, the long running operation will return [ALREADY_EXISTS][google.rpc.Code.ALREADY_EXISTS] error. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsCreateWorkflowOperator` :param workflow: Required. Workflow to be created. :param workflow_id: Required. The ID of the workflow to be created. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "workflow", "workflow_id")
[docs] template_fields_renderers = {"workflow": "json"}
def __init__( self, *, workflow: Dict, workflow_id: str, location: str, project_id: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", force_rerun: bool = False, impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow = workflow self.workflow_id = workflow_id self.location = location self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain self.force_rerun = force_rerun def _workflow_id(self, context): if self.workflow_id and not self.force_rerun: # If users provide workflow id then assuring the idempotency # is on their side return self.workflow_id if self.force_rerun: hash_base = str(uuid.uuid4()) else: hash_base = json.dumps(self.workflow, sort_keys=True) # We are limited by allowed length of workflow_id so # we use hash of whole information exec_date = context['execution_date'].isoformat() base = f"airflow_{self.dag_id}_{self.task_id}_{exec_date}_{hash_base}" workflow_id = hashlib.md5(base.encode()).hexdigest() return re.sub(r"[:\-+.]", "_", workflow_id)
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) workflow_id = self._workflow_id(context) self.log.info("Creating workflow") try: operation = hook.create_workflow( workflow=self.workflow, workflow_id=workflow_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) workflow = operation.result() except AlreadyExists: workflow = hook.get_workflow( workflow_id=workflow_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) WorkflowsWorkflowDetailsLink.persist( context=context, task_instance=self, location_id=self.location, workflow_id=self.workflow_id, project_id=self.project_id or hook.project_id, ) return Workflow.to_dict(workflow)
[docs]class WorkflowsUpdateWorkflowOperator(BaseOperator): """ Updates an existing workflow. Running this method has no impact on already running executions of the workflow. A new revision of the workflow may be created as a result of a successful update operation. In that case, such revision will be used in new workflow executions. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsUpdateWorkflowOperator` :param workflow_id: Required. The ID of the workflow to be updated. :param location: Required. The GCP region in which to handle the request. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param update_mask: List of fields to be updated. If not present, the entire workflow will be updated. :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. """
[docs] template_fields: Sequence[str] = ("workflow_id", "update_mask")
[docs] template_fields_renderers = {"update_mask": "json"}
def __init__( self, *, workflow_id: str, location: str, project_id: Optional[str] = None, update_mask: Optional[FieldMask] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow_id = workflow_id self.location = location self.project_id = project_id self.update_mask = update_mask self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) workflow = hook.get_workflow( workflow_id=self.workflow_id, project_id=self.project_id, location=self.location, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) self.log.info("Updating workflow") operation = hook.update_workflow( workflow=workflow, update_mask=self.update_mask, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) workflow = operation.result() WorkflowsWorkflowDetailsLink.persist( context=context, task_instance=self, location_id=self.location, workflow_id=self.workflow_id, project_id=self.project_id or hook.project_id, ) return Workflow.to_dict(workflow)
[docs]class WorkflowsDeleteWorkflowOperator(BaseOperator): """ Deletes a workflow with the specified name. This method also cancels and deletes all running executions of the workflow. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsDeleteWorkflowOperator` :param workflow_id: Required. The ID of the workflow to be created. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "workflow_id")
def __init__( self, *, workflow_id: str, location: str, project_id: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow_id = workflow_id self.location = location self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) self.log.info("Deleting workflow %s", self.workflow_id) operation = hook.delete_workflow( workflow_id=self.workflow_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) operation.result()
[docs]class WorkflowsListWorkflowsOperator(BaseOperator): """ Lists Workflows in a given project and location. The default order is not specified. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsListWorkflowsOperator` :param filter_: Filter to restrict results to specific workflows. :param order_by: Comma-separated list of fields that specifies the order of the results. Default sorting order for a field is ascending. To specify descending order for a field, append a "desc" suffix. If not specified, the results will be returned in an unspecified order. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "order_by", "filter_")
def __init__( self, *, location: str, project_id: Optional[str] = None, filter_: Optional[str] = None, order_by: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.filter_ = filter_ self.order_by = order_by self.location = location self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) self.log.info("Retrieving workflows") workflows_iter = hook.list_workflows( filter_=self.filter_, order_by=self.order_by, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) WorkflowsListOfWorkflowsLink.persist( context=context, task_instance=self, project_id=self.project_id or hook.project_id, ) return [Workflow.to_dict(w) for w in workflows_iter]
[docs]class WorkflowsGetWorkflowOperator(BaseOperator): """ Gets details of a single Workflow. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsGetWorkflowOperator` :param workflow_id: Required. The ID of the workflow to be created. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "workflow_id")
def __init__( self, *, workflow_id: str, location: str, project_id: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow_id = workflow_id self.location = location self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) self.log.info("Retrieving workflow") workflow = hook.get_workflow( workflow_id=self.workflow_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) WorkflowsWorkflowDetailsLink.persist( context=context, task_instance=self, location_id=self.location, workflow_id=self.workflow_id, project_id=self.project_id or hook.project_id, ) return Workflow.to_dict(workflow)
[docs]class WorkflowsCreateExecutionOperator(BaseOperator): """ Creates a new execution using the latest revision of the given workflow. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsCreateExecutionOperator` :param execution: Required. Execution to be created. :param workflow_id: Required. The ID of the workflow. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "workflow_id", "execution")
[docs] template_fields_renderers = {"execution": "json"}
def __init__( self, *, workflow_id: str, execution: Dict, location: str, project_id: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow_id = workflow_id self.execution = execution self.location = location self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) self.log.info("Creating execution") execution = hook.create_execution( workflow_id=self.workflow_id, execution=self.execution, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) execution_id = execution.name.split("/")[-1] self.xcom_push(context, key="execution_id", value=execution_id) WorkflowsExecutionLink.persist( context=context, task_instance=self, location_id=self.location, workflow_id=self.workflow_id, execution_id=execution_id, project_id=self.project_id or hook.project_id, ) return Execution.to_dict(execution)
[docs]class WorkflowsCancelExecutionOperator(BaseOperator): """ Cancels an execution using the given ``workflow_id`` and ``execution_id``. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsCancelExecutionOperator` :param workflow_id: Required. The ID of the workflow. :param execution_id: Required. The ID of the execution. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "workflow_id", "execution_id")
def __init__( self, *, workflow_id: str, execution_id: str, location: str, project_id: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow_id = workflow_id self.execution_id = execution_id self.location = location self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) self.log.info("Canceling execution %s", self.execution_id) execution = hook.cancel_execution( workflow_id=self.workflow_id, execution_id=self.execution_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) WorkflowsExecutionLink.persist( context=context, task_instance=self, location_id=self.location, workflow_id=self.workflow_id, execution_id=self.execution_id, project_id=self.project_id or hook.project_id, ) return Execution.to_dict(execution)
[docs]class WorkflowsListExecutionsOperator(BaseOperator): """ Returns a list of executions which belong to the workflow with the given name. The method returns executions of all workflow revisions. Returned executions are ordered by their start time (newest first). .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsListExecutionsOperator` :param workflow_id: Required. The ID of the workflow to be created. :param start_date_filter: If passed only executions older that this date will be returned. By default operators return executions from last 60 minutes :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "workflow_id")
def __init__( self, *, workflow_id: str, location: str, start_date_filter: Optional[datetime] = None, project_id: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow_id = workflow_id self.location = location self.start_date_filter = start_date_filter or datetime.now(tz=pytz.UTC) - timedelta(minutes=60) self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) self.log.info("Retrieving executions for workflow %s", self.workflow_id) execution_iter = hook.list_executions( workflow_id=self.workflow_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) WorkflowsWorkflowDetailsLink.persist( context=context, task_instance=self, location_id=self.location, workflow_id=self.workflow_id, project_id=self.project_id or hook.project_id, ) return [Execution.to_dict(e) for e in execution_iter if e.start_time > self.start_date_filter]
[docs]class WorkflowsGetExecutionOperator(BaseOperator): """ Returns an execution for the given ``workflow_id`` and ``execution_id``. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:WorkflowsGetExecutionOperator` :param workflow_id: Required. The ID of the workflow. :param execution_id: Required. The ID of the execution. :param project_id: Required. The ID of the Google Cloud project the cluster belongs to. :param location: Required. The GCP region in which to handle the request. :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. """
[docs] template_fields: Sequence[str] = ("location", "workflow_id", "execution_id")
def __init__( self, *, workflow_id: str, execution_id: str, location: str, project_id: Optional[str] = None, retry: Union[Retry, _MethodDefault] = DEFAULT, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", impersonation_chain: Optional[Union[str, Sequence[str]]] = None, **kwargs, ): super().__init__(**kwargs) self.workflow_id = workflow_id self.execution_id = execution_id self.location = location self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'): hook = WorkflowsHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain) self.log.info("Retrieving execution %s for workflow %s", self.execution_id, self.workflow_id) execution = hook.get_execution( workflow_id=self.workflow_id, execution_id=self.execution_id, location=self.location, project_id=self.project_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) WorkflowsExecutionLink.persist( context=context, task_instance=self, location_id=self.location, workflow_id=self.workflow_id, execution_id=self.execution_id, project_id=self.project_id or hook.project_id, ) return Execution.to_dict(execution)

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