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"""This module contains a Google Cloud Dataflow sensor."""
from __future__ import annotations
from typing import TYPE_CHECKING, Callable, Sequence
from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.dataflow import (
DEFAULT_DATAFLOW_LOCATION,
DataflowHook,
DataflowJobStatus,
)
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class DataflowJobStatusSensor(BaseSensorOperator):
"""
Checks for the status of a job in Google Cloud Dataflow.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowJobStatusSensor`
:param job_id: ID of the job to be checked.
:param expected_statuses: The expected state of the operation.
See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/projects.jobs#Job.JobState
:param project_id: Optional, the Google Cloud project ID in which to start a job.
If set to None or missing, the default project_id from the Google Cloud connection is used.
:param location: The location of the Dataflow job (for example europe-west1). See:
https://cloud.google.com/dataflow/docs/concepts/regional-endpoints
: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).
"""
[docs] template_fields: Sequence[str] = ("job_id",)
def __init__(
self,
*,
job_id: str,
expected_statuses: set[str] | str,
project_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.job_id = job_id
self.expected_statuses = (
{expected_statuses} if isinstance(expected_statuses, str) else expected_statuses
)
self.project_id = project_id
self.location = location
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.hook: DataflowHook | None = None
[docs] def poke(self, context: Context) -> bool:
self.log.info(
"Waiting for job %s to be in one of the states: %s.",
self.job_id,
", ".join(self.expected_statuses),
)
self.hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
job = self.hook.get_job(
job_id=self.job_id,
project_id=self.project_id,
location=self.location,
)
job_status = job["currentState"]
self.log.debug("Current job status for job %s: %s.", self.job_id, job_status)
if job_status in self.expected_statuses:
return True
elif job_status in DataflowJobStatus.TERMINAL_STATES:
raise AirflowException(f"Job with id '{self.job_id}' is already in terminal state: {job_status}")
return False
[docs]class DataflowJobMetricsSensor(BaseSensorOperator):
"""
Checks the metrics of a job in Google Cloud Dataflow.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowJobMetricsSensor`
:param job_id: ID of the job to be checked.
:param callback: callback which is called with list of read job metrics
See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/MetricUpdate
:param fail_on_terminal_state: If set to true sensor will raise Exception when
job is in terminal state
:param project_id: Optional, the Google Cloud project ID in which to start a job.
If set to None or missing, the default project_id from the Google Cloud connection is used.
:param location: The location of the Dataflow job (for example europe-west1). See:
https://cloud.google.com/dataflow/docs/concepts/regional-endpoints
: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).
"""
[docs] template_fields: Sequence[str] = ("job_id",)
def __init__(
self,
*,
job_id: str,
callback: Callable[[dict], bool],
fail_on_terminal_state: bool = True,
project_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.job_id = job_id
self.project_id = project_id
self.callback = callback
self.fail_on_terminal_state = fail_on_terminal_state
self.location = location
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.hook: DataflowHook | None = None
[docs] def poke(self, context: Context) -> bool:
self.hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
if self.fail_on_terminal_state:
job = self.hook.get_job(
job_id=self.job_id,
project_id=self.project_id,
location=self.location,
)
job_status = job["currentState"]
if job_status in DataflowJobStatus.TERMINAL_STATES:
raise AirflowException(
f"Job with id '{self.job_id}' is already in terminal state: {job_status}"
)
result = self.hook.fetch_job_metrics_by_id(
job_id=self.job_id,
project_id=self.project_id,
location=self.location,
)
return self.callback(result["metrics"])
[docs]class DataflowJobMessagesSensor(BaseSensorOperator):
"""
Checks for the job message in Google Cloud Dataflow.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowJobMessagesSensor`
:param job_id: ID of the job to be checked.
:param callback: callback which is called with list of read job metrics
See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/MetricUpdate
:param fail_on_terminal_state: If set to true sensor will raise Exception when
job is in terminal state
:param project_id: Optional, the Google Cloud project ID in which to start a job.
If set to None or missing, the default project_id from the Google Cloud connection is used.
:param location: Job location.
: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).
"""
[docs] template_fields: Sequence[str] = ("job_id",)
def __init__(
self,
*,
job_id: str,
callback: Callable,
fail_on_terminal_state: bool = True,
project_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.job_id = job_id
self.project_id = project_id
self.callback = callback
self.fail_on_terminal_state = fail_on_terminal_state
self.location = location
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.hook: DataflowHook | None = None
[docs] def poke(self, context: Context) -> bool:
self.hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
if self.fail_on_terminal_state:
job = self.hook.get_job(
job_id=self.job_id,
project_id=self.project_id,
location=self.location,
)
job_status = job["currentState"]
if job_status in DataflowJobStatus.TERMINAL_STATES:
raise AirflowException(
f"Job with id '{self.job_id}' is already in terminal state: {job_status}"
)
result = self.hook.fetch_job_messages_by_id(
job_id=self.job_id,
project_id=self.project_id,
location=self.location,
)
return self.callback(result)
[docs]class DataflowJobAutoScalingEventsSensor(BaseSensorOperator):
"""
Checks for the job autoscaling event in Google Cloud Dataflow.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowJobAutoScalingEventsSensor`
:param job_id: ID of the job to be checked.
:param callback: callback which is called with list of read job metrics
See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/MetricUpdate
:param fail_on_terminal_state: If set to true sensor will raise Exception when
job is in terminal state
:param project_id: Optional, the Google Cloud project ID in which to start a job.
If set to None or missing, the default project_id from the Google Cloud connection is used.
:param location: Job location.
: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).
"""
[docs] template_fields: Sequence[str] = ("job_id",)
def __init__(
self,
*,
job_id: str,
callback: Callable,
fail_on_terminal_state: bool = True,
project_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: str | Sequence[str] | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.job_id = job_id
self.project_id = project_id
self.callback = callback
self.fail_on_terminal_state = fail_on_terminal_state
self.location = location
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.hook: DataflowHook | None = None
[docs] def poke(self, context: Context) -> bool:
self.hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
if self.fail_on_terminal_state:
job = self.hook.get_job(
job_id=self.job_id,
project_id=self.project_id,
location=self.location,
)
job_status = job["currentState"]
if job_status in DataflowJobStatus.TERMINAL_STATES:
raise AirflowException(
f"Job with id '{self.job_id}' is already in terminal state: {job_status}"
)
result = self.hook.fetch_job_autoscaling_events_by_id(
job_id=self.job_id,
project_id=self.project_id,
location=self.location,
)
return self.callback(result)
