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# to you under the Apache License, Version 2.0 (the
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# KIND, either express or implied. See the License for the
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"""This module contains a Google Dataflow Hook."""
from __future__ import annotations
import functools
import json
import re
import shlex
import subprocess
import time
import uuid
import warnings
from copy import deepcopy
from typing import Any, Callable, Generator, Sequence, TypeVar, cast
from google.cloud.dataflow_v1beta3 import GetJobRequest, Job, JobState, JobsV1Beta3AsyncClient, JobView
from googleapiclient.discovery import build
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
from airflow.providers.apache.beam.hooks.beam import BeamHook, BeamRunnerType, beam_options_to_args
from airflow.providers.google.common.hooks.base_google import (
PROVIDE_PROJECT_ID,
GoogleBaseAsyncHook,
GoogleBaseHook,
)
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.timeout import timeout
# This is the default location
# https://cloud.google.com/dataflow/pipelines/specifying-exec-params
[docs]DEFAULT_DATAFLOW_LOCATION = "us-central1"
[docs]JOB_ID_PATTERN = re.compile(
r"Submitted job: (?P<job_id_java>.*)|Created job with id: \[(?P<job_id_python>.*)\]"
)
[docs]T = TypeVar("T", bound=Callable)
[docs]def process_line_and_extract_dataflow_job_id_callback(
on_new_job_id_callback: Callable[[str], None] | None
) -> Callable[[str], None]:
"""Build callback that triggers the specified function.
The returned callback is intended to be used as ``process_line_callback`` in
:py:class:`~airflow.providers.apache.beam.hooks.beam.BeamCommandRunner`.
:param on_new_job_id_callback: Callback called when the job ID is known
"""
def _process_line_and_extract_job_id(line: str) -> None:
# Job id info: https://goo.gl/SE29y9.
if on_new_job_id_callback is None:
return
matched_job = JOB_ID_PATTERN.search(line)
if matched_job is None:
return
job_id = matched_job.group("job_id_java") or matched_job.group("job_id_python")
on_new_job_id_callback(job_id)
return _process_line_and_extract_job_id
def _fallback_variable_parameter(parameter_name: str, variable_key_name: str) -> Callable[[T], T]:
def _wrapper(func: T) -> T:
"""
Decorator that provides fallback for location from `region` key in `variables` parameters.
:param func: function to wrap
:return: result of the function call
"""
@functools.wraps(func)
def inner_wrapper(self: DataflowHook, *args, **kwargs):
if args:
raise AirflowException(
"You must use keyword arguments in this methods rather than positional"
)
parameter_location = kwargs.get(parameter_name)
variables_location = kwargs.get("variables", {}).get(variable_key_name)
if parameter_location and variables_location:
raise AirflowException(
f"The mutually exclusive parameter `{parameter_name}` and `{variable_key_name}` key "
f"in `variables` parameter are both present. Please remove one."
)
if parameter_location or variables_location:
kwargs[parameter_name] = parameter_location or variables_location
if variables_location:
copy_variables = deepcopy(kwargs["variables"])
del copy_variables[variable_key_name]
kwargs["variables"] = copy_variables
return func(self, *args, **kwargs)
return cast(T, inner_wrapper)
return _wrapper
_fallback_to_location_from_variables = _fallback_variable_parameter("location", "region")
_fallback_to_project_id_from_variables = _fallback_variable_parameter("project_id", "project")
[docs]class DataflowJobStatus:
"""
Helper class with Dataflow job statuses.
Reference: https://cloud.google.com/dataflow/docs/reference/rest/v1b3/projects.jobs#Job.JobState
"""
[docs] JOB_STATE_DONE = "JOB_STATE_DONE"
[docs] JOB_STATE_UNKNOWN = "JOB_STATE_UNKNOWN"
[docs] JOB_STATE_STOPPED = "JOB_STATE_STOPPED"
[docs] JOB_STATE_RUNNING = "JOB_STATE_RUNNING"
[docs] JOB_STATE_FAILED = "JOB_STATE_FAILED"
[docs] JOB_STATE_CANCELLED = "JOB_STATE_CANCELLED"
[docs] JOB_STATE_UPDATED = "JOB_STATE_UPDATED"
[docs] JOB_STATE_DRAINING = "JOB_STATE_DRAINING"
[docs] JOB_STATE_DRAINED = "JOB_STATE_DRAINED"
[docs] JOB_STATE_PENDING = "JOB_STATE_PENDING"
[docs] JOB_STATE_CANCELLING = "JOB_STATE_CANCELLING"
[docs] JOB_STATE_QUEUED = "JOB_STATE_QUEUED"
[docs] FAILED_END_STATES = {JOB_STATE_FAILED, JOB_STATE_CANCELLED}
[docs] SUCCEEDED_END_STATES = {JOB_STATE_DONE, JOB_STATE_UPDATED, JOB_STATE_DRAINED}
[docs] TERMINAL_STATES = SUCCEEDED_END_STATES | FAILED_END_STATES
[docs] AWAITING_STATES = {
JOB_STATE_RUNNING,
JOB_STATE_PENDING,
JOB_STATE_QUEUED,
JOB_STATE_CANCELLING,
JOB_STATE_DRAINING,
JOB_STATE_STOPPED,
}
[docs]class DataflowJobType:
"""Helper class with Dataflow job types."""
[docs] JOB_TYPE_UNKNOWN = "JOB_TYPE_UNKNOWN"
[docs] JOB_TYPE_BATCH = "JOB_TYPE_BATCH"
[docs] JOB_TYPE_STREAMING = "JOB_TYPE_STREAMING"
class _DataflowJobsController(LoggingMixin):
"""
Interface for communication with Google API.
It's not use Apache Beam, but only Google Dataflow API.
:param dataflow: Discovery resource
:param project_number: The Google Cloud Project ID.
:param location: Job location.
:param poll_sleep: The status refresh rate for pending operations.
:param name: The Job ID prefix used when the multiple_jobs option is passed is set to True.
:param job_id: ID of a single job.
:param num_retries: Maximum number of retries in case of connection problems.
:param multiple_jobs: If set to true this task will be searched by name prefix (``name`` parameter),
not by specific job ID, then actions will be performed on all matching jobs.
:param drain_pipeline: Optional, set to True if we want to stop streaming job by draining it
instead of canceling.
:param cancel_timeout: wait time in seconds for successful job canceling
:param wait_until_finished: If True, wait for the end of pipeline execution before exiting. If False,
it only submits job and check once is job not in terminal state.
The default behavior depends on the type of pipeline:
* for the streaming pipeline, wait for jobs to be in JOB_STATE_RUNNING,
* for the batch pipeline, wait for the jobs to be in JOB_STATE_DONE.
"""
def __init__(
self,
dataflow: Any,
project_number: str,
location: str,
poll_sleep: int = 10,
name: str | None = None,
job_id: str | None = None,
num_retries: int = 0,
multiple_jobs: bool = False,
drain_pipeline: bool = False,
cancel_timeout: int | None = 5 * 60,
wait_until_finished: bool | None = None,
expected_terminal_state: str | None = None,
) -> None:
super().__init__()
self._dataflow = dataflow
self._project_number = project_number
self._job_name = name
self._job_location = location
self._multiple_jobs = multiple_jobs
self._job_id = job_id
self._num_retries = num_retries
self._poll_sleep = poll_sleep
self._cancel_timeout = cancel_timeout
self._jobs: list[dict] | None = None
self.drain_pipeline = drain_pipeline
self._wait_until_finished = wait_until_finished
self._expected_terminal_state = expected_terminal_state
def is_job_running(self) -> bool:
"""
Helper method to check if jos is still running in dataflow.
:return: True if job is running.
"""
self._refresh_jobs()
if not self._jobs:
return False
return any(job["currentState"] not in DataflowJobStatus.TERMINAL_STATES for job in self._jobs)
def _get_current_jobs(self) -> list[dict]:
"""
Helper method to get list of jobs that start with job name or id.
:return: list of jobs including id's
"""
if not self._multiple_jobs and self._job_id:
return [self.fetch_job_by_id(self._job_id)]
elif self._jobs:
return [self.fetch_job_by_id(job["id"]) for job in self._jobs]
elif self._job_name:
jobs = self._fetch_jobs_by_prefix_name(self._job_name.lower())
if len(jobs) == 1:
self._job_id = jobs[0]["id"]
return jobs
else:
raise Exception("Missing both dataflow job ID and name.")
def fetch_job_by_id(self, job_id: str) -> dict:
"""
Helper method to fetch the job with the specified Job ID.
:param job_id: Job ID to get.
:return: the Job
"""
return (
self._dataflow.projects()
.locations()
.jobs()
.get(
projectId=self._project_number,
location=self._job_location,
jobId=job_id,
)
.execute(num_retries=self._num_retries)
)
def fetch_job_metrics_by_id(self, job_id: str) -> dict:
"""
Helper method to fetch the job metrics with the specified Job ID.
:param job_id: Job ID to get.
:return: the JobMetrics. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/JobMetrics
"""
result = (
self._dataflow.projects()
.locations()
.jobs()
.getMetrics(projectId=self._project_number, location=self._job_location, jobId=job_id)
.execute(num_retries=self._num_retries)
)
self.log.debug("fetch_job_metrics_by_id %s:\n%s", job_id, result)
return result
def _fetch_list_job_messages_responses(self, job_id: str) -> Generator[dict, None, None]:
"""
Helper method to fetch ListJobMessagesResponse with the specified Job ID.
:param job_id: Job ID to get.
:return: yields the ListJobMessagesResponse. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/ListJobMessagesResponse
"""
request = (
self._dataflow.projects()
.locations()
.jobs()
.messages()
.list(projectId=self._project_number, location=self._job_location, jobId=job_id)
)
while request is not None:
response = request.execute(num_retries=self._num_retries)
yield response
request = (
self._dataflow.projects()
.locations()
.jobs()
.messages()
.list_next(previous_request=request, previous_response=response)
)
def fetch_job_messages_by_id(self, job_id: str) -> list[dict]:
"""
Helper method to fetch the job messages with the specified Job ID.
:param job_id: Job ID to get.
:return: the list of JobMessages. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/ListJobMessagesResponse#JobMessage
"""
messages: list[dict] = []
for response in self._fetch_list_job_messages_responses(job_id=job_id):
messages.extend(response.get("jobMessages", []))
return messages
def fetch_job_autoscaling_events_by_id(self, job_id: str) -> list[dict]:
"""
Helper method to fetch the job autoscaling events with the specified Job ID.
:param job_id: Job ID to get.
:return: the list of AutoscalingEvents. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/ListJobMessagesResponse#autoscalingevent
"""
autoscaling_events: list[dict] = []
for response in self._fetch_list_job_messages_responses(job_id=job_id):
autoscaling_events.extend(response.get("autoscalingEvents", []))
return autoscaling_events
def _fetch_all_jobs(self) -> list[dict]:
request = (
self._dataflow.projects()
.locations()
.jobs()
.list(projectId=self._project_number, location=self._job_location)
)
all_jobs: list[dict] = []
while request is not None:
response = request.execute(num_retries=self._num_retries)
jobs = response.get("jobs")
if jobs is None:
break
all_jobs.extend(jobs)
request = (
self._dataflow.projects()
.locations()
.jobs()
.list_next(previous_request=request, previous_response=response)
)
return all_jobs
def _fetch_jobs_by_prefix_name(self, prefix_name: str) -> list[dict]:
jobs = self._fetch_all_jobs()
jobs = [job for job in jobs if job["name"].startswith(prefix_name)]
return jobs
def _refresh_jobs(self) -> None:
"""
Helper method to get all jobs by name.
:return: jobs
"""
self._jobs = self._get_current_jobs()
if self._jobs:
for job in self._jobs:
self.log.info(
"Google Cloud DataFlow job %s is state: %s",
job["name"],
job["currentState"],
)
else:
self.log.info("Google Cloud DataFlow job not available yet..")
def _check_dataflow_job_state(self, job) -> bool:
"""
Helper method to check the state of one job in dataflow for this task if job failed raise exception.
:return: True if job is done.
:raise: Exception
"""
current_state = job["currentState"]
is_streaming = job.get("type") == DataflowJobType.JOB_TYPE_STREAMING
if self._expected_terminal_state is None:
if is_streaming:
self._expected_terminal_state = DataflowJobStatus.JOB_STATE_RUNNING
else:
self._expected_terminal_state = DataflowJobStatus.JOB_STATE_DONE
else:
terminal_states = DataflowJobStatus.TERMINAL_STATES | {DataflowJobStatus.JOB_STATE_RUNNING}
if self._expected_terminal_state not in terminal_states:
raise Exception(
f"Google Cloud Dataflow job's expected terminal state "
f"'{self._expected_terminal_state}' is invalid."
f" The value should be any of the following: {terminal_states}"
)
elif is_streaming and self._expected_terminal_state == DataflowJobStatus.JOB_STATE_DONE:
raise Exception(
"Google Cloud Dataflow job's expected terminal state cannot be "
"JOB_STATE_DONE while it is a streaming job"
)
elif not is_streaming and self._expected_terminal_state == DataflowJobStatus.JOB_STATE_DRAINED:
raise Exception(
"Google Cloud Dataflow job's expected terminal state cannot be "
"JOB_STATE_DRAINED while it is a batch job"
)
if not self._wait_until_finished and current_state == self._expected_terminal_state:
return True
if current_state in DataflowJobStatus.AWAITING_STATES:
return self._wait_until_finished is False
self.log.debug("Current job: %s", str(job))
raise Exception(
f"Google Cloud Dataflow job {job['name']} is in an unexpected terminal state: {current_state}, "
f"expected terminal state: {self._expected_terminal_state}"
)
def wait_for_done(self) -> None:
"""Helper method to wait for result of submitted job."""
self.log.info("Start waiting for done.")
self._refresh_jobs()
while self._jobs and not all(self._check_dataflow_job_state(job) for job in self._jobs):
self.log.info("Waiting for done. Sleep %s s", self._poll_sleep)
time.sleep(self._poll_sleep)
self._refresh_jobs()
def get_jobs(self, refresh: bool = False) -> list[dict]:
"""
Returns Dataflow jobs.
:param refresh: Forces the latest data to be fetched.
:return: list of jobs
"""
if not self._jobs or refresh:
self._refresh_jobs()
if not self._jobs:
raise ValueError("Could not read _jobs")
return self._jobs
def _wait_for_states(self, expected_states: set[str]):
"""Waiting for the jobs to reach a certain state."""
if not self._jobs:
raise ValueError("The _jobs should be set")
while True:
self._refresh_jobs()
job_states = {job["currentState"] for job in self._jobs}
if not job_states.difference(expected_states):
return
unexpected_failed_end_states = DataflowJobStatus.FAILED_END_STATES - expected_states
if unexpected_failed_end_states.intersection(job_states):
unexpected_failed_jobs = [
job for job in self._jobs if job["currentState"] in unexpected_failed_end_states
]
raise AirflowException(
"Jobs failed: "
+ ", ".join(
f"ID: {job['id']} name: {job['name']} state: {job['currentState']}"
for job in unexpected_failed_jobs
)
)
time.sleep(self._poll_sleep)
def cancel(self) -> None:
"""Cancels or drains current job."""
self._jobs = [
job for job in self.get_jobs() if job["currentState"] not in DataflowJobStatus.TERMINAL_STATES
]
job_ids = [job["id"] for job in self._jobs]
if job_ids:
self.log.info("Canceling jobs: %s", ", ".join(job_ids))
for job in self._jobs:
requested_state = (
DataflowJobStatus.JOB_STATE_DRAINED
if self.drain_pipeline and job["type"] == DataflowJobType.JOB_TYPE_STREAMING
else DataflowJobStatus.JOB_STATE_CANCELLED
)
request = (
self._dataflow.projects()
.locations()
.jobs()
.update(
projectId=self._project_number,
location=self._job_location,
jobId=job["id"],
body={"requestedState": requested_state},
)
)
request.execute(num_retries=self._num_retries)
if self._cancel_timeout and isinstance(self._cancel_timeout, int):
timeout_error_message = (
f"Canceling jobs failed due to timeout ({self._cancel_timeout}s): {', '.join(job_ids)}"
)
tm = timeout(seconds=self._cancel_timeout, error_message=timeout_error_message)
with tm:
self._wait_for_states(
{DataflowJobStatus.JOB_STATE_CANCELLED, DataflowJobStatus.JOB_STATE_DRAINED}
)
else:
self.log.info("No jobs to cancel")
[docs]class DataflowHook(GoogleBaseHook):
"""
Hook for Google Dataflow.
All the methods in the hook where project_id is used must be called with
keyword arguments rather than positional.
"""
def __init__(
self,
gcp_conn_id: str = "google_cloud_default",
poll_sleep: int = 10,
impersonation_chain: str | Sequence[str] | None = None,
drain_pipeline: bool = False,
cancel_timeout: int | None = 5 * 60,
wait_until_finished: bool | None = None,
expected_terminal_state: str | None = None,
**kwargs,
) -> None:
if kwargs.get("delegate_to") is not None:
raise RuntimeError(
"The `delegate_to` parameter has been deprecated before and finally removed in this version"
" of Google Provider. You MUST convert it to `impersonate_chain`"
)
self.poll_sleep = poll_sleep
self.drain_pipeline = drain_pipeline
self.cancel_timeout = cancel_timeout
self.wait_until_finished = wait_until_finished
self.job_id: str | None = None
self.beam_hook = BeamHook(BeamRunnerType.DataflowRunner)
self.expected_terminal_state = expected_terminal_state
super().__init__(
gcp_conn_id=gcp_conn_id,
impersonation_chain=impersonation_chain,
)
[docs] def get_conn(self) -> build:
"""Returns a Google Cloud Dataflow service object."""
http_authorized = self._authorize()
return build("dataflow", "v1b3", http=http_authorized, cache_discovery=False)
@_fallback_to_location_from_variables
@_fallback_to_project_id_from_variables
@GoogleBaseHook.fallback_to_default_project_id
[docs] def start_java_dataflow(
self,
job_name: str,
variables: dict,
jar: str,
project_id: str,
job_class: str | None = None,
append_job_name: bool = True,
multiple_jobs: bool = False,
on_new_job_id_callback: Callable[[str], None] | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> None:
"""
Starts Dataflow java job.
:param job_name: The name of the job.
:param variables: Variables passed to the job.
: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 jar: Name of the jar for the job
:param job_class: Name of the java class for the job.
:param append_job_name: True if unique suffix has to be appended to job name.
:param multiple_jobs: True if to check for multiple job in dataflow
:param on_new_job_id_callback: Callback called when the job ID is known.
:param location: Job location.
"""
warnings.warn(
""""This method is deprecated.
Please use `airflow.providers.apache.beam.hooks.beam.start.start_java_pipeline`
to start pipeline and `providers.google.cloud.hooks.dataflow.DataflowHook.wait_for_done`
to wait for the required pipeline state.
""",
AirflowProviderDeprecationWarning,
stacklevel=3,
)
name = self.build_dataflow_job_name(job_name, append_job_name)
variables["jobName"] = name
variables["region"] = location
variables["project"] = project_id
if "labels" in variables:
variables["labels"] = json.dumps(variables["labels"], separators=(",", ":"))
self.beam_hook.start_java_pipeline(
variables=variables,
jar=jar,
job_class=job_class,
process_line_callback=process_line_and_extract_dataflow_job_id_callback(on_new_job_id_callback),
)
self.wait_for_done(
job_name=name,
location=location,
job_id=self.job_id,
multiple_jobs=multiple_jobs,
)
@_fallback_to_location_from_variables
@_fallback_to_project_id_from_variables
@GoogleBaseHook.fallback_to_default_project_id
[docs] def start_template_dataflow(
self,
job_name: str,
variables: dict,
parameters: dict,
dataflow_template: str,
project_id: str,
append_job_name: bool = True,
on_new_job_id_callback: Callable[[str], None] | None = None,
on_new_job_callback: Callable[[dict], None] | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
environment: dict | None = None,
) -> dict:
"""
Starts Dataflow template job.
:param job_name: The name of the job.
:param variables: Map of job runtime environment options.
It will update environment argument if passed.
.. seealso::
For more information on possible configurations, look at the API documentation
`https://cloud.google.com/dataflow/pipelines/specifying-exec-params
<https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment>`__
:param parameters: Parameters for the template
:param dataflow_template: GCS path to the template.
: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 append_job_name: True if unique suffix has to be appended to job name.
:param on_new_job_id_callback: (Deprecated) Callback called when the Job is known.
:param on_new_job_callback: Callback called when the Job is known.
:param location: Job location.
.. seealso::
For more information on possible configurations, look at the API documentation
`https://cloud.google.com/dataflow/pipelines/specifying-exec-params
<https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment>`__
"""
name = self.build_dataflow_job_name(job_name, append_job_name)
environment = self._update_environment(
variables=variables,
environment=environment,
)
service = self.get_conn()
request = (
service.projects()
.locations()
.templates()
.launch(
projectId=project_id,
location=location,
gcsPath=dataflow_template,
body={
"jobName": name,
"parameters": parameters,
"environment": environment,
},
)
)
response = request.execute(num_retries=self.num_retries)
job = response["job"]
if on_new_job_id_callback:
warnings.warn(
"on_new_job_id_callback is Deprecated. Please start using on_new_job_callback",
AirflowProviderDeprecationWarning,
stacklevel=3,
)
on_new_job_id_callback(job.get("id"))
if on_new_job_callback:
on_new_job_callback(job)
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
name=name,
job_id=job["id"],
location=location,
poll_sleep=self.poll_sleep,
num_retries=self.num_retries,
drain_pipeline=self.drain_pipeline,
cancel_timeout=self.cancel_timeout,
wait_until_finished=self.wait_until_finished,
expected_terminal_state=self.expected_terminal_state,
)
jobs_controller.wait_for_done()
return response["job"]
def _update_environment(self, variables: dict, environment: dict | None = None) -> dict:
environment = environment or {}
# available keys for runtime environment are listed here:
# https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment
environment_keys = {
"numWorkers",
"maxWorkers",
"zone",
"serviceAccountEmail",
"tempLocation",
"bypassTempDirValidation",
"machineType",
"additionalExperiments",
"network",
"subnetwork",
"additionalUserLabels",
"kmsKeyName",
"ipConfiguration",
"workerRegion",
"workerZone",
}
def _check_one(key, val):
if key in environment:
self.log.warning(
"%r parameter in 'variables' will override the same one passed in 'environment'!",
key,
)
return key, val
environment.update(_check_one(key, val) for key, val in variables.items() if key in environment_keys)
return environment
@GoogleBaseHook.fallback_to_default_project_id
[docs] def start_flex_template(
self,
body: dict,
location: str,
project_id: str,
on_new_job_id_callback: Callable[[str], None] | None = None,
on_new_job_callback: Callable[[dict], None] | None = None,
) -> dict:
"""
Starts flex templates with the Dataflow pipeline.
:param body: The request body. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/projects.locations.flexTemplates/launch#request-body
:param location: The location of the Dataflow job (for example europe-west1)
:param project_id: The ID of the GCP project that owns the job.
If set to ``None`` or missing, the default project_id from the GCP connection is used.
:param on_new_job_id_callback: (Deprecated) A callback that is called when a Job ID is detected.
:param on_new_job_callback: A callback that is called when a Job is detected.
:return: the Job
"""
service = self.get_conn()
request = (
service.projects()
.locations()
.flexTemplates()
.launch(projectId=project_id, body=body, location=location)
)
response = request.execute(num_retries=self.num_retries)
job = response["job"]
if on_new_job_id_callback:
warnings.warn(
"on_new_job_id_callback is Deprecated. Please start using on_new_job_callback",
AirflowProviderDeprecationWarning,
stacklevel=3,
)
on_new_job_id_callback(job.get("id"))
if on_new_job_callback:
on_new_job_callback(job)
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
job_id=job.get("id"),
location=location,
poll_sleep=self.poll_sleep,
num_retries=self.num_retries,
cancel_timeout=self.cancel_timeout,
wait_until_finished=self.wait_until_finished,
)
jobs_controller.wait_for_done()
return jobs_controller.get_jobs(refresh=True)[0]
@_fallback_to_location_from_variables
@_fallback_to_project_id_from_variables
@GoogleBaseHook.fallback_to_default_project_id
[docs] def start_python_dataflow(
self,
job_name: str,
variables: dict,
dataflow: str,
py_options: list[str],
project_id: str,
py_interpreter: str = "python3",
py_requirements: list[str] | None = None,
py_system_site_packages: bool = False,
append_job_name: bool = True,
on_new_job_id_callback: Callable[[str], None] | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
):
"""
Starts Dataflow job.
:param job_name: The name of the job.
:param variables: Variables passed to the job.
:param dataflow: Name of the Dataflow process.
:param py_options: Additional options.
:param project_id: The ID of the GCP project that owns the job.
If set to ``None`` or missing, the default project_id from the GCP connection is used.
:param py_interpreter: Python version of the beam pipeline.
If None, this defaults to the python3.
To track python versions supported by beam and related
issues check: https://issues.apache.org/jira/browse/BEAM-1251
:param py_requirements: Additional python package(s) to install.
If a value is passed to this parameter, a new virtual environment has been created with
additional packages installed.
You could also install the apache-beam package if it is not installed on your system or you want
to use a different version.
:param py_system_site_packages: Whether to include system_site_packages in your virtualenv.
See virtualenv documentation for more information.
This option is only relevant if the ``py_requirements`` parameter is not None.
:param append_job_name: True if unique suffix has to be appended to job name.
: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 on_new_job_id_callback: Callback called when the job ID is known.
:param location: Job location.
"""
warnings.warn(
"""This method is deprecated.
Please use `airflow.providers.apache.beam.hooks.beam.start.start_python_pipeline`
to start pipeline and `providers.google.cloud.hooks.dataflow.DataflowHook.wait_for_done`
to wait for the required pipeline state.
""",
AirflowProviderDeprecationWarning,
stacklevel=3,
)
name = self.build_dataflow_job_name(job_name, append_job_name)
variables["job_name"] = name
variables["region"] = location
variables["project"] = project_id
self.beam_hook.start_python_pipeline(
variables=variables,
py_file=dataflow,
py_options=py_options,
py_interpreter=py_interpreter,
py_requirements=py_requirements,
py_system_site_packages=py_system_site_packages,
process_line_callback=process_line_and_extract_dataflow_job_id_callback(on_new_job_id_callback),
)
self.wait_for_done(
job_name=name,
location=location,
job_id=self.job_id,
)
@staticmethod
[docs] def build_dataflow_job_name(job_name: str, append_job_name: bool = True) -> str:
"""Builds Dataflow job name."""
base_job_name = str(job_name).replace("_", "-")
if not re.fullmatch(r"[a-z]([-a-z0-9]*[a-z0-9])?", base_job_name):
raise ValueError(
f"Invalid job_name ({base_job_name}); the name must consist of only the characters "
f"[-a-z0-9], starting with a letter and ending with a letter or number "
)
if append_job_name:
safe_job_name = base_job_name + "-" + str(uuid.uuid4())[:8]
else:
safe_job_name = base_job_name
return safe_job_name
@_fallback_to_location_from_variables
@_fallback_to_project_id_from_variables
@GoogleBaseHook.fallback_to_default_project_id
[docs] def is_job_dataflow_running(
self,
name: str,
project_id: str,
location: str = DEFAULT_DATAFLOW_LOCATION,
variables: dict | None = None,
) -> bool:
"""
Helper method to check if jos is still running in dataflow.
:param name: The name of the job.
: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.
:return: True if job is running.
"""
if variables:
warnings.warn(
"The variables parameter has been deprecated. You should pass location using "
"the location parameter.",
AirflowProviderDeprecationWarning,
stacklevel=4,
)
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
name=name,
location=location,
poll_sleep=self.poll_sleep,
drain_pipeline=self.drain_pipeline,
num_retries=self.num_retries,
cancel_timeout=self.cancel_timeout,
)
return jobs_controller.is_job_running()
@GoogleBaseHook.fallback_to_default_project_id
[docs] def cancel_job(
self,
project_id: str,
job_name: str | None = None,
job_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> None:
"""
Cancels the job with the specified name prefix or Job ID.
Parameter ``name`` and ``job_id`` are mutually exclusive.
:param job_name: Name prefix specifying which jobs are to be canceled.
:param job_id: Job ID specifying which jobs are to be canceled.
:param location: Job location.
: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.
"""
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
name=job_name,
job_id=job_id,
location=location,
poll_sleep=self.poll_sleep,
drain_pipeline=self.drain_pipeline,
num_retries=self.num_retries,
cancel_timeout=self.cancel_timeout,
)
jobs_controller.cancel()
@GoogleBaseHook.fallback_to_default_project_id
[docs] def start_sql_job(
self,
job_name: str,
query: str,
options: dict[str, Any],
project_id: str,
location: str = DEFAULT_DATAFLOW_LOCATION,
on_new_job_id_callback: Callable[[str], None] | None = None,
on_new_job_callback: Callable[[dict], None] | None = None,
):
"""
Starts Dataflow SQL query.
:param job_name: The unique name to assign to the Cloud Dataflow job.
:param query: The SQL query to execute.
:param options: Job parameters to be executed.
For more information, look at:
`https://cloud.google.com/sdk/gcloud/reference/beta/dataflow/sql/query
<gcloud beta dataflow sql query>`__
command reference
:param location: The location of the Dataflow job (for example europe-west1)
:param project_id: The ID of the GCP project that owns the job.
If set to ``None`` or missing, the default project_id from the GCP connection is used.
:param on_new_job_id_callback: (Deprecated) Callback called when the job ID is known.
:param on_new_job_callback: Callback called when the job is known.
:return: the new job object
"""
gcp_options = [
f"--project={project_id}",
"--format=value(job.id)",
f"--job-name={job_name}",
f"--region={location}",
]
if self.impersonation_chain:
if isinstance(self.impersonation_chain, str):
impersonation_account = self.impersonation_chain
elif len(self.impersonation_chain) == 1:
impersonation_account = self.impersonation_chain[0]
else:
raise AirflowException(
"Chained list of accounts is not supported, please specify only one service account"
)
gcp_options.append(f"--impersonate-service-account={impersonation_account}")
cmd = [
"gcloud",
"dataflow",
"sql",
"query",
query,
*gcp_options,
*(beam_options_to_args(options)),
]
self.log.info("Executing command: %s", " ".join(shlex.quote(c) for c in cmd))
with self.provide_authorized_gcloud():
proc = subprocess.run(cmd, capture_output=True)
self.log.info("Output: %s", proc.stdout.decode())
self.log.warning("Stderr: %s", proc.stderr.decode())
self.log.info("Exit code %d", proc.returncode)
stderr_last_20_lines = "\n".join(proc.stderr.decode().strip().splitlines()[-20:])
if proc.returncode != 0:
raise AirflowException(
f"Process exit with non-zero exit code. Exit code: {proc.returncode} Error Details : "
f"{stderr_last_20_lines}"
)
job_id = proc.stdout.decode().strip()
self.log.info("Created job ID: %s", job_id)
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
job_id=job_id,
location=location,
poll_sleep=self.poll_sleep,
num_retries=self.num_retries,
drain_pipeline=self.drain_pipeline,
wait_until_finished=self.wait_until_finished,
)
job = jobs_controller.get_jobs(refresh=True)[0]
if on_new_job_id_callback:
warnings.warn(
"on_new_job_id_callback is Deprecated. Please start using on_new_job_callback",
AirflowProviderDeprecationWarning,
stacklevel=3,
)
on_new_job_id_callback(cast(str, job.get("id")))
if on_new_job_callback:
on_new_job_callback(job)
jobs_controller.wait_for_done()
return jobs_controller.get_jobs(refresh=True)[0]
@GoogleBaseHook.fallback_to_default_project_id
[docs] def get_job(
self,
job_id: str,
project_id: str = PROVIDE_PROJECT_ID,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> dict:
"""
Gets the job with the specified Job ID.
:param job_id: Job ID to get.
: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
:return: the Job
"""
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
location=location,
)
return jobs_controller.fetch_job_by_id(job_id)
@GoogleBaseHook.fallback_to_default_project_id
[docs] def fetch_job_metrics_by_id(
self,
job_id: str,
project_id: str,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> dict:
"""
Gets the job metrics with the specified Job ID.
:param job_id: Job ID to get.
: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
:return: the JobMetrics. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/JobMetrics
"""
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
location=location,
)
return jobs_controller.fetch_job_metrics_by_id(job_id)
@GoogleBaseHook.fallback_to_default_project_id
[docs] def fetch_job_messages_by_id(
self,
job_id: str,
project_id: str,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> list[dict]:
"""
Gets the job messages with the specified Job ID.
:param job_id: Job ID to get.
: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.
:return: the list of JobMessages. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/ListJobMessagesResponse#JobMessage
"""
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
location=location,
)
return jobs_controller.fetch_job_messages_by_id(job_id)
@GoogleBaseHook.fallback_to_default_project_id
[docs] def fetch_job_autoscaling_events_by_id(
self,
job_id: str,
project_id: str,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> list[dict]:
"""
Gets the job autoscaling events with the specified Job ID.
:param job_id: Job ID to get.
: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.
:return: the list of AutoscalingEvents. See:
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/ListJobMessagesResponse#autoscalingevent
"""
jobs_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
location=location,
)
return jobs_controller.fetch_job_autoscaling_events_by_id(job_id)
@GoogleBaseHook.fallback_to_default_project_id
[docs] def wait_for_done(
self,
job_name: str,
location: str,
project_id: str,
job_id: str | None = None,
multiple_jobs: bool = False,
) -> None:
"""
Wait for Dataflow job.
:param job_name: The 'jobName' to use when executing the DataFlow job
(templated). This ends up being set in the pipeline options, so any entry
with key ``'jobName'`` in ``options`` will be overwritten.
:param location: location the job is running
: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 job_id: a Dataflow job ID
:param multiple_jobs: If pipeline creates multiple jobs then monitor all jobs
"""
job_controller = _DataflowJobsController(
dataflow=self.get_conn(),
project_number=project_id,
name=job_name,
location=location,
poll_sleep=self.poll_sleep,
job_id=job_id or self.job_id,
num_retries=self.num_retries,
multiple_jobs=multiple_jobs,
drain_pipeline=self.drain_pipeline,
cancel_timeout=self.cancel_timeout,
wait_until_finished=self.wait_until_finished,
)
job_controller.wait_for_done()
[docs]class AsyncDataflowHook(GoogleBaseAsyncHook):
"""Async hook class for dataflow service."""
[docs] sync_hook_class = DataflowHook
def __init__(self, **kwargs):
if kwargs.get("delegate_to") is not None:
raise RuntimeError(
"The `delegate_to` parameter has been deprecated before and finally removed in this version"
" of Google Provider. You MUST convert it to `impersonate_chain`"
)
super().__init__(**kwargs)
[docs] async def initialize_client(self, client_class):
"""
Initialize object of the given class.
Method is used to initialize asynchronous client. Because of the big amount of the classes which are
used for Dataflow service it was decided to initialize them the same way with credentials which are
received from the method of the GoogleBaseHook class.
:param client_class: Class of the Google cloud SDK
"""
credentials = (await self.get_sync_hook()).get_credentials()
return client_class(
credentials=credentials,
)
[docs] async def get_project_id(self) -> str:
project_id = (await self.get_sync_hook()).project_id
return project_id
[docs] async def get_job(
self,
job_id: str,
project_id: str = PROVIDE_PROJECT_ID,
job_view: int = JobView.JOB_VIEW_SUMMARY,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> Job:
"""
Gets the job with the specified Job ID.
:param job_id: Job ID to get.
:param project_id: 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 job_view: Optional. JobView object which determines representation of the returned data
:param location: Optional. The location of the Dataflow job (for example europe-west1). See:
https://cloud.google.com/dataflow/docs/concepts/regional-endpoints
"""
project_id = project_id or (await self.get_project_id())
client = await self.initialize_client(JobsV1Beta3AsyncClient)
request = GetJobRequest(
{
"project_id": project_id,
"job_id": job_id,
"view": job_view,
"location": location,
}
)
job = await client.get_job(
request=request,
)
return job
[docs] async def get_job_status(
self,
job_id: str,
project_id: str = PROVIDE_PROJECT_ID,
job_view: int = JobView.JOB_VIEW_SUMMARY,
location: str = DEFAULT_DATAFLOW_LOCATION,
) -> JobState:
"""
Gets the job status with the specified Job ID.
:param job_id: Job ID to get.
:param project_id: 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 job_view: Optional. JobView object which determines representation of the returned data
:param location: Optional. The location of the Dataflow job (for example europe-west1). See:
https://cloud.google.com/dataflow/docs/concepts/regional-endpoints
"""
job = await self.get_job(
project_id=project_id,
job_id=job_id,
job_view=job_view,
location=location,
)
state = job.current_state
return state
