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"""This module contains Google Dataflow operators."""
from __future__ import annotations
import copy
import re
import uuid
import warnings
from contextlib import ExitStack
from enum import Enum
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
from airflow.providers.apache.beam.hooks.beam import BeamHook, BeamRunnerType
from airflow.providers.google.cloud.hooks.dataflow import (
DEFAULT_DATAFLOW_LOCATION,
DataflowHook,
process_line_and_extract_dataflow_job_id_callback,
)
from airflow.providers.google.cloud.hooks.gcs import GCSHook
from airflow.providers.google.cloud.links.dataflow import DataflowJobLink
from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
from airflow.providers.google.cloud.triggers.dataflow import TemplateJobStartTrigger
from airflow.version import version
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class CheckJobRunning(Enum):
"""
Helper enum for choosing what to do if job is already running.
IgnoreJob - do not check if running
FinishIfRunning - finish current dag run with no action
WaitForRun - wait for job to finish and then continue with new job
"""
[docs]class DataflowConfiguration:
"""
Dataflow configuration for BeamRunJavaPipelineOperator and BeamRunPythonPipelineOperator.
.. seealso::
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`
and :class:`~airflow.providers.apache.beam.operators.beam.BeamRunPythonPipelineOperator`.
: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'`` or ``'job_name'``in ``options`` will be overwritten.
: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 location: Job location.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param poll_sleep: The time in seconds to sleep between polling Google
Cloud Platform for the dataflow job status while the job is in the
JOB_STATE_RUNNING state.
: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).
.. warning::
This option requires Apache Beam 2.39.0 or newer.
:param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
instead of canceling during killing task instance. See:
https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
:param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
successfully cancelled when task is being killed. (optional) default to 300s
:param wait_until_finished: (Optional)
If True, wait for the end of pipeline execution before exiting.
If False, only submits job.
If None, default behavior.
The default behavior depends on the type of pipeline:
* for the streaming pipeline, wait for jobs to start,
* for the batch pipeline, wait for the jobs to complete.
.. warning::
You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
always wait until finished. For more information, look at:
`Asynchronous execution
<https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__
The process of starting the Dataflow job in Airflow consists of two steps:
* running a subprocess and reading the stderr/stderr log for the job id.
* loop waiting for the end of the job ID from the previous step by checking its status.
Step two is started just after step one has finished, so if you have wait_until_finished in your
pipeline code, step two will not start until the process stops. When this process stops,
steps two will run, but it will only execute one iteration as the job will be in a terminal state.
If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
to the operator, the second loop will wait for the job's terminal state.
If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
to the operator, the second loop will check once is job not in terminal state and exit the loop.
:param multiple_jobs: If pipeline creates multiple jobs then monitor all jobs. Supported only by
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`.
:param check_if_running: Before running job, validate that a previous run is not in process.
Supported only by:
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`.
:param service_account: Run the job as a specific service account, instead of the default GCE robot.
"""
[docs] template_fields: Sequence[str] = ("job_name", "location")
def __init__(
self,
*,
job_name: str = "{{task.task_id}}",
append_job_name: bool = True,
project_id: str | None = None,
location: str | None = DEFAULT_DATAFLOW_LOCATION,
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,
multiple_jobs: bool | None = None,
check_if_running: CheckJobRunning = CheckJobRunning.WaitForRun,
service_account: str | None = None,
) -> None:
self.job_name = job_name
self.append_job_name = append_job_name
self.project_id = project_id
self.location = location
self.gcp_conn_id = gcp_conn_id
self.poll_sleep = poll_sleep
self.impersonation_chain = impersonation_chain
self.drain_pipeline = drain_pipeline
self.cancel_timeout = cancel_timeout
self.wait_until_finished = wait_until_finished
self.multiple_jobs = multiple_jobs
self.check_if_running = check_if_running
self.service_account = service_account
[docs]class DataflowCreateJavaJobOperator(GoogleCloudBaseOperator):
"""
Start a Java Cloud Dataflow batch job; the parameters of the operation will be passed to the job.
This class is deprecated.
Please use :class:`providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`.
Example usage:
.. code-block:: python
default_args = {
"owner": "airflow",
"depends_on_past": False,
"start_date": (2016, 8, 1),
"email": ["alex@vanboxel.be"],
"email_on_failure": False,
"email_on_retry": False,
"retries": 1,
"retry_delay": timedelta(minutes=30),
"dataflow_default_options": {
"project": "my-gcp-project",
"zone": "us-central1-f",
"stagingLocation": "gs://bucket/tmp/dataflow/staging/",
},
}
dag = DAG("test-dag", default_args=default_args)
task = DataflowCreateJavaJobOperator(
gcp_conn_id="gcp_default",
task_id="normalize-cal",
jar="{{var.value.gcp_dataflow_base}}pipeline-ingress-cal-normalize-1.0.jar",
options={
"autoscalingAlgorithm": "BASIC",
"maxNumWorkers": "50",
"start": "{{ds}}",
"partitionType": "DAY",
},
dag=dag,
)
.. seealso::
For more detail on job submission have a look at the reference:
https://cloud.google.com/dataflow/pipelines/specifying-exec-params
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowCreateJavaJobOperator`
:param jar: The reference to a self executing Dataflow jar (templated).
: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 dataflow_default_options: Map of default job options.
:param options: Map of job specific options.The key must be a dictionary.
The value can contain different types:
* If the value is None, the single option - ``--key`` (without value) will be added.
* If the value is False, this option will be skipped
* If the value is True, the single option - ``--key`` (without value) will be added.
* If the value is list, the many options will be added for each key.
If the value is ``['A', 'B']`` and the key is ``key`` then the ``--key=A --key=B`` options
will be left
* Other value types will be replaced with the Python textual representation.
When defining labels (``labels`` option), you can also provide a dictionary.
: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 poll_sleep: The time in seconds to sleep between polling Google
Cloud Platform for the dataflow job status while the job is in the
JOB_STATE_RUNNING state.
:param job_class: The name of the dataflow job class to be executed, it
is often not the main class configured in the dataflow jar file.
:param multiple_jobs: If pipeline creates multiple jobs then monitor all jobs
:param check_if_running: before running job, validate that a previous run is not in process
if job is running finish with nothing, WaitForRun= wait until job finished and the run job)
``jar``, ``options``, and ``job_name`` are templated so you can use variables in them.
:param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
successfully cancelled when task is being killed.
:param wait_until_finished: (Optional)
If True, wait for the end of pipeline execution before exiting.
If False, only submits job.
If None, default behavior.
The default behavior depends on the type of pipeline:
* for the streaming pipeline, wait for jobs to start,
* for the batch pipeline, wait for the jobs to complete.
.. warning::
You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
always wait until finished. For more information, look at:
`Asynchronous execution
<https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__
The process of starting the Dataflow job in Airflow consists of two steps:
* running a subprocess and reading the stderr/stderr log for the job id.
* loop waiting for the end of the job ID from the previous step.
This loop checks the status of the job.
Step two is started just after step one has finished, so if you have wait_until_finished in your
pipeline code, step two will not start until the process stops. When this process stops,
steps two will run, but it will only execute one iteration as the job will be in a terminal state.
If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
to the operator, the second loop will wait for the job's terminal state.
If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
to the operator, the second loop will check once is job not in terminal state and exit the loop.
:param expected_terminal_state: The expected terminal state of the operator on which the corresponding
Airflow task succeeds. When not specified, it will be determined by the hook.
Note that both
``dataflow_default_options`` and ``options`` will be merged to specify pipeline
execution parameter, and ``dataflow_default_options`` is expected to save
high-level options, for instances, project and zone information, which
apply to all dataflow operators in the DAG.
It's a good practice to define dataflow_* parameters in the default_args of the dag
like the project, zone and staging location.
.. code-block:: python
default_args = {
"dataflow_default_options": {
"zone": "europe-west1-d",
"stagingLocation": "gs://my-staging-bucket/staging/",
}
}
You need to pass the path to your dataflow as a file reference with the ``jar``
parameter, the jar needs to be a self executing jar (see documentation here:
https://beam.apache.org/documentation/runners/dataflow/#self-executing-jar).
Use ``options`` to pass on options to your job.
.. code-block:: python
t1 = DataflowCreateJavaJobOperator(
task_id="dataflow_example",
jar="{{var.value.gcp_dataflow_base}}pipeline/build/libs/pipeline-example-1.0.jar",
options={
"autoscalingAlgorithm": "BASIC",
"maxNumWorkers": "50",
"start": "{{ds}}",
"partitionType": "DAY",
"labels": {"foo": "bar"},
},
gcp_conn_id="airflow-conn-id",
dag=my_dag,
)
"""
[docs] template_fields: Sequence[str] = ("options", "jar", "job_name")
def __init__(
self,
*,
jar: str,
job_name: str = "{{task.task_id}}",
dataflow_default_options: dict | None = None,
options: dict | None = None,
project_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
poll_sleep: int = 10,
job_class: str | None = None,
check_if_running: CheckJobRunning = CheckJobRunning.WaitForRun,
multiple_jobs: bool = False,
cancel_timeout: int | None = 10 * 60,
wait_until_finished: bool | None = None,
expected_terminal_state: str | None = None,
**kwargs,
) -> None:
# TODO: Remove one day
warnings.warn(
f"The `{self.__class__.__name__}` operator is deprecated, "
f"please use `providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator` instead.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
super().__init__(**kwargs)
dataflow_default_options = dataflow_default_options or {}
options = options or {}
options.setdefault("labels", {}).update(
{"airflow-version": "v" + version.replace(".", "-").replace("+", "-")}
)
self.project_id = project_id
self.location = location
self.gcp_conn_id = gcp_conn_id
self.jar = jar
self.multiple_jobs = multiple_jobs
self.job_name = job_name
self.dataflow_default_options = dataflow_default_options
self.options = options
self.poll_sleep = poll_sleep
self.job_class = job_class
self.check_if_running = check_if_running
self.cancel_timeout = cancel_timeout
self.wait_until_finished = wait_until_finished
self.expected_terminal_state = expected_terminal_state
self.job_id = None
self.beam_hook: BeamHook | None = None
self.dataflow_hook: DataflowHook | None = None
[docs] def execute(self, context: Context):
"""Execute the Apache Beam Pipeline."""
self.beam_hook = BeamHook(runner=BeamRunnerType.DataflowRunner)
self.dataflow_hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
poll_sleep=self.poll_sleep,
cancel_timeout=self.cancel_timeout,
wait_until_finished=self.wait_until_finished,
expected_terminal_state=self.expected_terminal_state,
)
job_name = self.dataflow_hook.build_dataflow_job_name(job_name=self.job_name)
pipeline_options = copy.deepcopy(self.dataflow_default_options)
pipeline_options["jobName"] = self.job_name
pipeline_options["project"] = self.project_id or self.dataflow_hook.project_id
pipeline_options["region"] = self.location
pipeline_options.update(self.options)
pipeline_options.setdefault("labels", {}).update(
{"airflow-version": "v" + version.replace(".", "-").replace("+", "-")}
)
pipeline_options.update(self.options)
def set_current_job_id(job_id):
self.job_id = job_id
process_line_callback = process_line_and_extract_dataflow_job_id_callback(
on_new_job_id_callback=set_current_job_id
)
with ExitStack() as exit_stack:
if self.jar.lower().startswith("gs://"):
gcs_hook = GCSHook(self.gcp_conn_id)
tmp_gcs_file = exit_stack.enter_context(gcs_hook.provide_file(object_url=self.jar))
self.jar = tmp_gcs_file.name
is_running = False
if self.check_if_running != CheckJobRunning.IgnoreJob:
is_running = self.dataflow_hook.is_job_dataflow_running(
name=self.job_name,
variables=pipeline_options,
)
while is_running and self.check_if_running == CheckJobRunning.WaitForRun:
is_running = self.dataflow_hook.is_job_dataflow_running(
name=self.job_name,
variables=pipeline_options,
)
if not is_running:
pipeline_options["jobName"] = job_name
with self.dataflow_hook.provide_authorized_gcloud():
self.beam_hook.start_java_pipeline(
variables=pipeline_options,
jar=self.jar,
job_class=self.job_class,
process_line_callback=process_line_callback,
)
self.dataflow_hook.wait_for_done(
job_name=job_name,
location=self.location,
job_id=self.job_id,
multiple_jobs=self.multiple_jobs,
)
return {"job_id": self.job_id}
[docs] def on_kill(self) -> None:
self.log.info("On kill.")
if self.job_id:
self.dataflow_hook.cancel_job(
job_id=self.job_id, project_id=self.project_id or self.dataflow_hook.project_id
)
[docs]class DataflowTemplatedJobStartOperator(GoogleCloudBaseOperator):
"""
Start a Templated Cloud Dataflow job; the parameters of the operation will be passed to the job.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowTemplatedJobStartOperator`
:param template: The reference to the Dataflow template.
:param job_name: The 'jobName' to use when executing the Dataflow template
(templated).
:param options: 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 dataflow_default_options: Map of default job environment options.
:param parameters: Map of job specific parameters for 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 location: Job location.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param poll_sleep: The time in seconds to sleep between polling Google
Cloud Platform for the dataflow job status while the job is in the
JOB_STATE_RUNNING state.
: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 environment: Optional, Map of job runtime environment options.
.. 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 cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
successfully cancelled when task is being killed.
:param append_job_name: True if unique suffix has to be appended to job name.
:param wait_until_finished: (Optional)
If True, wait for the end of pipeline execution before exiting.
If False, only submits job.
If None, default behavior.
The default behavior depends on the type of pipeline:
* for the streaming pipeline, wait for jobs to start,
* for the batch pipeline, wait for the jobs to complete.
.. warning::
You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
always wait until finished. For more information, look at:
`Asynchronous execution
<https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__
The process of starting the Dataflow job in Airflow consists of two steps:
* running a subprocess and reading the stderr/stderr log for the job id.
* loop waiting for the end of the job ID from the previous step.
This loop checks the status of the job.
Step two is started just after step one has finished, so if you have wait_until_finished in your
pipeline code, step two will not start until the process stops. When this process stops,
steps two will run, but it will only execute one iteration as the job will be in a terminal state.
If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
to the operator, the second loop will wait for the job's terminal state.
If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
to the operator, the second loop will check once is job not in terminal state and exit the loop.
:param expected_terminal_state: The expected terminal state of the operator on which the corresponding
Airflow task succeeds. When not specified, it will be determined by the hook.
It's a good practice to define dataflow_* parameters in the default_args of the dag
like the project, zone and staging location.
.. seealso::
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/LaunchTemplateParameters
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment
.. code-block:: python
default_args = {
"dataflow_default_options": {
"zone": "europe-west1-d",
"tempLocation": "gs://my-staging-bucket/staging/",
}
}
You need to pass the path to your dataflow template as a file reference with the
``template`` parameter. Use ``parameters`` to pass on parameters to your job.
Use ``environment`` to pass on runtime environment variables to your job.
.. code-block:: python
t1 = DataflowTemplatedJobStartOperator(
task_id="dataflow_example",
template="{{var.value.gcp_dataflow_base}}",
parameters={
"inputFile": "gs://bucket/input/my_input.txt",
"outputFile": "gs://bucket/output/my_output.txt",
},
gcp_conn_id="airflow-conn-id",
dag=my_dag,
)
``template``, ``dataflow_default_options``, ``parameters``, and ``job_name`` are
templated, so you can use variables in them.
Note that ``dataflow_default_options`` is expected to save high-level options
for project information, which apply to all dataflow operators in the DAG.
.. seealso::
https://cloud.google.com/dataflow/docs/reference/rest/v1b3
/LaunchTemplateParameters
https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment
For more detail on job template execution have a look at the reference:
https://cloud.google.com/dataflow/docs/templates/executing-templates
:param deferrable: Run operator in the deferrable mode.
"""
[docs] template_fields: Sequence[str] = (
"template",
"job_name",
"options",
"parameters",
"project_id",
"location",
"gcp_conn_id",
"impersonation_chain",
"environment",
"dataflow_default_options",
)
def __init__(
self,
*,
template: str,
project_id: str | None = None,
job_name: str = "{{task.task_id}}",
options: dict[str, Any] | None = None,
dataflow_default_options: dict[str, Any] | None = None,
parameters: dict[str, str] | None = None,
location: str | None = None,
gcp_conn_id: str = "google_cloud_default",
poll_sleep: int = 10,
impersonation_chain: str | Sequence[str] | None = None,
environment: dict | None = None,
cancel_timeout: int | None = 10 * 60,
wait_until_finished: bool | None = None,
append_job_name: bool = True,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
expected_terminal_state: str | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.template = template
self.job_name = job_name
self.options = options or {}
self.dataflow_default_options = dataflow_default_options or {}
self.parameters = parameters or {}
self.project_id = project_id
self.location = location
self.gcp_conn_id = gcp_conn_id
self.poll_sleep = poll_sleep
self.impersonation_chain = impersonation_chain
self.environment = environment
self.cancel_timeout = cancel_timeout
self.wait_until_finished = wait_until_finished
self.append_job_name = append_job_name
self.deferrable = deferrable
self.expected_terminal_state = expected_terminal_state
self.job: dict | None = None
self._validate_deferrable_params()
def _validate_deferrable_params(self):
if self.deferrable and self.wait_until_finished:
raise ValueError(
"Conflict between deferrable and wait_until_finished parameters "
"because it makes operator as blocking when it requires to be deferred. "
"It should be True as deferrable parameter or True as wait_until_finished."
)
if self.deferrable and self.wait_until_finished is None:
self.wait_until_finished = False
@cached_property
[docs] def hook(self) -> DataflowHook:
hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
poll_sleep=self.poll_sleep,
impersonation_chain=self.impersonation_chain,
cancel_timeout=self.cancel_timeout,
wait_until_finished=self.wait_until_finished,
expected_terminal_state=self.expected_terminal_state,
)
return hook
[docs] def execute(self, context: Context):
def set_current_job(current_job):
self.job = current_job
DataflowJobLink.persist(self, context, self.project_id, self.location, self.job.get("id"))
options = self.dataflow_default_options
options.update(self.options)
self.job = self.hook.start_template_dataflow(
job_name=self.job_name,
variables=options,
parameters=self.parameters,
dataflow_template=self.template,
on_new_job_callback=set_current_job,
project_id=self.project_id,
location=self.location,
environment=self.environment,
append_job_name=self.append_job_name,
)
job_id = self.job.get("id")
if job_id is None:
raise AirflowException(
"While reading job object after template execution error occurred. Job object has no id."
)
if not self.deferrable:
return job_id
context["ti"].xcom_push(key="job_id", value=job_id)
self.defer(
trigger=TemplateJobStartTrigger(
project_id=self.project_id,
job_id=job_id,
location=self.location if self.location else DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id=self.gcp_conn_id,
poll_sleep=self.poll_sleep,
impersonation_chain=self.impersonation_chain,
cancel_timeout=self.cancel_timeout,
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: Context, event: dict[str, Any]):
"""Method which executes after trigger finishes its work."""
if event["status"] in ("error", "stopped"):
self.log.info("status: %s, msg: %s", event["status"], event["message"])
raise AirflowException(event["message"])
job_id = event["job_id"]
self.log.info("Task %s completed with response %s", self.task_id, event["message"])
return job_id
[docs] def on_kill(self) -> None:
self.log.info("On kill.")
if self.job is not None:
self.log.info("Cancelling job %s", self.job_name)
self.hook.cancel_job(
job_name=self.job_name,
job_id=self.job.get("id"),
project_id=self.job.get("projectId"),
location=self.job.get("location"),
)
[docs]class DataflowStartFlexTemplateOperator(GoogleCloudBaseOperator):
"""
Starts flex templates with the Dataflow pipeline.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowStartFlexTemplateOperator`
: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.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud
Platform.
:param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
instead of canceling during killing task instance. See:
https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
:param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
successfully cancelled when task is being killed.
:param wait_until_finished: (Optional)
If True, wait for the end of pipeline execution before exiting.
If False, only submits job.
If None, default behavior.
The default behavior depends on the type of pipeline:
* for the streaming pipeline, wait for jobs to start,
* for the batch pipeline, wait for the jobs to complete.
.. warning::
You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
always wait until finished. For more information, look at:
`Asynchronous execution
<https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__
The process of starting the Dataflow job in Airflow consists of two steps:
* running a subprocess and reading the stderr/stderr log for the job id.
* loop waiting for the end of the job ID from the previous step.
This loop checks the status of the job.
Step two is started just after step one has finished, so if you have wait_until_finished in your
pipeline code, step two will not start until the process stops. When this process stops,
steps two will run, but it will only execute one iteration as the job will be in a terminal state.
If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
to the operator, the second loop will wait for the job's terminal state.
If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
to the operator, the second loop will check once is job not in terminal state and exit the loop.
: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 expected_terminal_state: The expected final status of the operator on which the corresponding
Airflow task succeeds. When not specified, it will be determined by the hook.
:param append_job_name: True if unique suffix has to be appended to job name.
"""
[docs] template_fields: Sequence[str] = ("body", "location", "project_id", "gcp_conn_id")
def __init__(
self,
body: dict,
location: str,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
drain_pipeline: bool = False,
cancel_timeout: int | None = 10 * 60,
wait_until_finished: bool | None = None,
impersonation_chain: str | Sequence[str] | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
append_job_name: bool = True,
expected_terminal_state: str | None = None,
*args,
**kwargs,
) -> None:
super().__init__(*args, **kwargs)
self.body = body
self.location = location
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.drain_pipeline = drain_pipeline
self.cancel_timeout = cancel_timeout
self.wait_until_finished = wait_until_finished
self.job: dict | None = None
self.impersonation_chain = impersonation_chain
self.deferrable = deferrable
self.expected_terminal_state = expected_terminal_state
self.append_job_name = append_job_name
self._validate_deferrable_params()
def _validate_deferrable_params(self):
if self.deferrable and self.wait_until_finished:
raise ValueError(
"Conflict between deferrable and wait_until_finished parameters "
"because it makes operator as blocking when it requires to be deferred. "
"It should be True as deferrable parameter or True as wait_until_finished."
)
if self.deferrable and self.wait_until_finished is None:
self.wait_until_finished = False
@cached_property
[docs] def hook(self) -> DataflowHook:
hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
drain_pipeline=self.drain_pipeline,
cancel_timeout=self.cancel_timeout,
wait_until_finished=self.wait_until_finished,
impersonation_chain=self.impersonation_chain,
expected_terminal_state=self.expected_terminal_state,
)
return hook
[docs] def execute(self, context: Context):
if self.append_job_name:
self._append_uuid_to_job_name()
def set_current_job(current_job):
self.job = current_job
DataflowJobLink.persist(self, context, self.project_id, self.location, self.job.get("id"))
self.job = self.hook.start_flex_template(
body=self.body,
location=self.location,
project_id=self.project_id,
on_new_job_callback=set_current_job,
)
job_id = self.job.get("id")
if job_id is None:
raise AirflowException(
"While reading job object after template execution error occurred. Job object has no id."
)
if not self.deferrable:
return self.job
self.defer(
trigger=TemplateJobStartTrigger(
project_id=self.project_id,
job_id=job_id,
location=self.location,
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
cancel_timeout=self.cancel_timeout,
),
method_name="execute_complete",
)
def _append_uuid_to_job_name(self):
job_body = self.body.get("launch_parameter") or self.body.get("launchParameter")
job_name = job_body.get("jobName")
if job_name:
job_name += f"-{uuid.uuid4()!s:.8}"
job_body["jobName"] = job_name
self.log.info("Job name was changed to %s", job_name)
[docs] def execute_complete(self, context: Context, event: dict):
"""Method which executes after trigger finishes its work."""
if event["status"] in ("error", "stopped"):
self.log.info("status: %s, msg: %s", event["status"], event["message"])
raise AirflowException(event["message"])
job_id = event["job_id"]
self.log.info("Task %s completed with response %s", job_id, event["message"])
job = self.hook.get_job(job_id=job_id, project_id=self.project_id, location=self.location)
return job
[docs] def on_kill(self) -> None:
self.log.info("On kill.")
if self.job is not None:
self.hook.cancel_job(
job_id=self.job.get("id"),
project_id=self.job.get("projectId"),
location=self.job.get("location"),
)
[docs]class DataflowStartSqlJobOperator(GoogleCloudBaseOperator):
"""
Starts Dataflow SQL query.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowStartSqlJobOperator`
.. warning::
This operator requires ``gcloud`` command (Google Cloud SDK) must be installed on the Airflow worker
<https://cloud.google.com/sdk/docs/install>`__
: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. It can be a dictionary with the following keys.
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 gcp_conn_id: The connection ID to use connecting to Google Cloud
Platform.
:param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
instead of canceling during killing task instance. See:
https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
: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_name",
"query",
"options",
"location",
"project_id",
"gcp_conn_id",
)
[docs] template_fields_renderers = {"query": "sql"}
def __init__(
self,
job_name: str,
query: str,
options: dict[str, Any],
location: str = DEFAULT_DATAFLOW_LOCATION,
project_id: str | None = None,
gcp_conn_id: str = "google_cloud_default",
drain_pipeline: bool = False,
impersonation_chain: str | Sequence[str] | None = None,
*args,
**kwargs,
) -> None:
super().__init__(*args, **kwargs)
self.job_name = job_name
self.query = query
self.options = options
self.location = location
self.project_id = project_id
self.gcp_conn_id = gcp_conn_id
self.drain_pipeline = drain_pipeline
self.impersonation_chain = impersonation_chain
self.job = None
self.hook: DataflowHook | None = None
[docs] def execute(self, context: Context):
self.hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
drain_pipeline=self.drain_pipeline,
impersonation_chain=self.impersonation_chain,
)
def set_current_job(current_job):
self.job = current_job
job = self.hook.start_sql_job(
job_name=self.job_name,
query=self.query,
options=self.options,
location=self.location,
project_id=self.project_id,
on_new_job_callback=set_current_job,
)
return job
[docs] def on_kill(self) -> None:
self.log.info("On kill.")
if self.job:
self.hook.cancel_job(
job_id=self.job.get("id"),
project_id=self.job.get("projectId"),
location=self.job.get("location"),
)
[docs]class DataflowCreatePythonJobOperator(GoogleCloudBaseOperator):
"""
Launching Cloud Dataflow jobs written in python.
Note that both dataflow_default_options and options will be merged to specify pipeline
execution parameter, and dataflow_default_options is expected to save high-level options,
for instances, project and zone information, which apply to all dataflow operators in the DAG.
This class is deprecated.
Please use :class:`providers.apache.beam.operators.beam.BeamRunPythonPipelineOperator`.
.. seealso::
For more detail on job submission have a look at the reference:
https://cloud.google.com/dataflow/pipelines/specifying-exec-params
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowCreatePythonJobOperator`
:param py_file: Reference to the python dataflow pipeline file.py, e.g.,
/some/local/file/path/to/your/python/pipeline/file. (templated)
:param job_name: The 'job_name' to use when executing the Dataflow job
(templated). This ends up being set in the pipeline options, so any entry
with key ``'jobName'`` or ``'job_name'`` in ``options`` will be overwritten.
:param py_options: Additional python options, e.g., ["-m", "-v"].
:param dataflow_default_options: Map of default job options.
:param options: Map of job specific options.The key must be a dictionary.
The value can contain different types:
* If the value is None, the single option - ``--key`` (without value) will be added.
* If the value is False, this option will be skipped
* If the value is True, the single option - ``--key`` (without value) will be added.
* If the value is list, the many options will be added for each key.
If the value is ``['A', 'B']`` and the key is ``key`` then the ``--key=A --key=B`` options
will be left
* Other value types will be replaced with the Python textual representation.
When defining labels (``labels`` option), you can also provide a dictionary.
: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 gcp_conn_id: The connection ID to use connecting to Google Cloud.
: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 poll_sleep: The time in seconds to sleep between polling Google
Cloud Platform for the dataflow job status while the job is in the
JOB_STATE_RUNNING state.
:param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
instead of canceling during killing task instance. See:
https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
:param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
successfully cancelled when task is being killed.
:param wait_until_finished: (Optional)
If True, wait for the end of pipeline execution before exiting.
If False, only submits job.
If None, default behavior.
The default behavior depends on the type of pipeline:
* for the streaming pipeline, wait for jobs to start,
* for the batch pipeline, wait for the jobs to complete.
.. warning::
You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
always wait until finished. For more information, look at:
`Asynchronous execution
<https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__
The process of starting the Dataflow job in Airflow consists of two steps:
* running a subprocess and reading the stderr/stderr log for the job id.
* loop waiting for the end of the job ID from the previous step.
This loop checks the status of the job.
Step two is started just after step one has finished, so if you have wait_until_finished in your
pipeline code, step two will not start until the process stops. When this process stops,
steps two will run, but it will only execute one iteration as the job will be in a terminal state.
If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
to the operator, the second loop will wait for the job's terminal state.
If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
to the operator, the second loop will check once is job not in terminal state and exit the loop.
"""
[docs] template_fields: Sequence[str] = ("options", "dataflow_default_options", "job_name", "py_file")
def __init__(
self,
*,
py_file: str,
job_name: str = "{{task.task_id}}",
dataflow_default_options: dict | None = None,
options: dict | None = None,
py_interpreter: str = "python3",
py_options: list[str] | None = None,
py_requirements: list[str] | None = None,
py_system_site_packages: bool = False,
project_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
poll_sleep: int = 10,
drain_pipeline: bool = False,
cancel_timeout: int | None = 10 * 60,
wait_until_finished: bool | None = None,
**kwargs,
) -> None:
# TODO: Remove one day
warnings.warn(
f"The `{self.__class__.__name__}` operator is deprecated, "
"please use `providers.apache.beam.operators.beam.BeamRunPythonPipelineOperator` instead.",
AirflowProviderDeprecationWarning,
stacklevel=2,
)
super().__init__(**kwargs)
self.py_file = py_file
self.job_name = job_name
self.py_options = py_options or []
self.dataflow_default_options = dataflow_default_options or {}
self.options = options or {}
self.options.setdefault("labels", {}).update(
{"airflow-version": "v" + version.replace(".", "-").replace("+", "-")}
)
self.py_interpreter = py_interpreter
self.py_requirements = py_requirements
self.py_system_site_packages = py_system_site_packages
self.project_id = project_id
self.location = location
self.gcp_conn_id = gcp_conn_id
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 = None
self.beam_hook: BeamHook | None = None
self.dataflow_hook: DataflowHook | None = None
[docs] def execute(self, context: Context):
"""Execute the python dataflow job."""
self.beam_hook = BeamHook(runner=BeamRunnerType.DataflowRunner)
self.dataflow_hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
poll_sleep=self.poll_sleep,
impersonation_chain=None,
drain_pipeline=self.drain_pipeline,
cancel_timeout=self.cancel_timeout,
wait_until_finished=self.wait_until_finished,
)
job_name = self.dataflow_hook.build_dataflow_job_name(job_name=self.job_name)
pipeline_options = self.dataflow_default_options.copy()
pipeline_options["job_name"] = job_name
pipeline_options["project"] = self.project_id or self.dataflow_hook.project_id
pipeline_options["region"] = self.location
pipeline_options.update(self.options)
# Convert argument names from lowerCamelCase to snake case.
def camel_to_snake(name):
return re.sub("[A-Z]", lambda x: "_" + x.group(0).lower(), name)
formatted_pipeline_options = {camel_to_snake(key): pipeline_options[key] for key in pipeline_options}
def set_current_job_id(job_id):
self.job_id = job_id
process_line_callback = process_line_and_extract_dataflow_job_id_callback(
on_new_job_id_callback=set_current_job_id
)
with ExitStack() as exit_stack:
if self.py_file.lower().startswith("gs://"):
gcs_hook = GCSHook(self.gcp_conn_id)
tmp_gcs_file = exit_stack.enter_context(gcs_hook.provide_file(object_url=self.py_file))
self.py_file = tmp_gcs_file.name
with self.dataflow_hook.provide_authorized_gcloud():
self.beam_hook.start_python_pipeline(
variables=formatted_pipeline_options,
py_file=self.py_file,
py_options=self.py_options,
py_interpreter=self.py_interpreter,
py_requirements=self.py_requirements,
py_system_site_packages=self.py_system_site_packages,
process_line_callback=process_line_callback,
)
self.dataflow_hook.wait_for_done(
job_name=job_name,
location=self.location,
job_id=self.job_id,
multiple_jobs=False,
)
return {"job_id": self.job_id}
[docs] def on_kill(self) -> None:
self.log.info("On kill.")
if self.job_id:
self.dataflow_hook.cancel_job(
job_id=self.job_id, project_id=self.project_id or self.dataflow_hook.project_id
)
[docs]class DataflowStopJobOperator(GoogleCloudBaseOperator):
"""
Stops the job with the specified name prefix or Job ID.
All jobs with provided name prefix will be stopped.
Streaming jobs are drained by default.
Parameter ``job_name_prefix`` and ``job_id`` are mutually exclusive.
.. seealso::
For more details on stopping a pipeline see:
https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:DataflowStopJobOperator`
:param job_name_prefix: Name prefix specifying which jobs are to be stopped.
:param job_id: Job ID specifying which jobs are to be stopped.
: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: Optional, Job location. If set to None or missing, "us-central1" will be used.
:param gcp_conn_id: The connection ID to use connecting to Google Cloud.
:param poll_sleep: The time in seconds to sleep between polling Google
Cloud Platform for the dataflow job status to confirm it's stopped.
: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 drain_pipeline: Optional, set to False if want to stop streaming job by canceling it
instead of draining. See: https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
:param stop_timeout: wait time in seconds for successful job canceling/draining
"""
[docs] template_fields = [
"job_id",
"project_id",
"impersonation_chain",
]
def __init__(
self,
job_name_prefix: str | None = None,
job_id: str | None = None,
project_id: str | None = None,
location: str = DEFAULT_DATAFLOW_LOCATION,
gcp_conn_id: str = "google_cloud_default",
poll_sleep: int = 10,
impersonation_chain: str | Sequence[str] | None = None,
stop_timeout: int | None = 10 * 60,
drain_pipeline: bool = True,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.poll_sleep = poll_sleep
self.stop_timeout = stop_timeout
self.job_name = job_name_prefix
self.job_id = job_id
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
self.drain_pipeline = drain_pipeline
[docs] def execute(self, context: Context) -> None:
self.dataflow_hook = DataflowHook(
gcp_conn_id=self.gcp_conn_id,
poll_sleep=self.poll_sleep,
impersonation_chain=self.impersonation_chain,
cancel_timeout=self.stop_timeout,
drain_pipeline=self.drain_pipeline,
)
if self.job_id or self.dataflow_hook.is_job_dataflow_running(
name=self.job_name,
project_id=self.project_id,
location=self.location,
):
self.dataflow_hook.cancel_job(
job_name=self.job_name,
project_id=self.project_id,
location=self.location,
job_id=self.job_id,
)
else:
self.log.info("No jobs to stop")
return None
