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from __future__ import annotations
import asyncio
from typing import Any, AsyncIterator, Sequence
from deprecated import deprecated
from google.cloud.dataflow_v1beta3 import ListJobsRequest
from airflow.exceptions import AirflowProviderDeprecationWarning
from airflow.providers.apache.beam.hooks.beam import BeamAsyncHook
from airflow.providers.google.cloud.hooks.dataflow import AsyncDataflowHook
from airflow.triggers.base import BaseTrigger, TriggerEvent
[docs]class BeamPipelineBaseTrigger(BaseTrigger):
"""Base class for Beam Pipeline Triggers."""
@staticmethod
def _get_async_hook(*args, **kwargs) -> BeamAsyncHook:
return BeamAsyncHook(*args, **kwargs)
@staticmethod
def _get_sync_dataflow_hook(**kwargs) -> AsyncDataflowHook:
return AsyncDataflowHook(**kwargs)
[docs]class BeamPythonPipelineTrigger(BeamPipelineBaseTrigger):
"""
Trigger to perform checking the Python pipeline status until it reaches terminate state.
:param variables: Variables passed to the pipeline.
:param py_file: Path to the python file to execute.
:param py_options: Additional options.
:param py_interpreter: Python version of the Apache 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 runner: Runner on which pipeline will be run. By default, "DirectRunner" is being used.
Other possible options: DataflowRunner, SparkRunner, FlinkRunner, PortableRunner.
See: :class:`~providers.apache.beam.hooks.beam.BeamRunnerType`
See: https://beam.apache.org/documentation/runners/capability-matrix/
"""
def __init__(
self,
variables: dict,
py_file: str,
py_options: list[str] | None = None,
py_interpreter: str = "python3",
py_requirements: list[str] | None = None,
py_system_site_packages: bool = False,
runner: str = "DirectRunner",
):
super().__init__()
self.variables = variables
self.py_file = py_file
self.py_options = py_options
self.py_interpreter = py_interpreter
self.py_requirements = py_requirements
self.py_system_site_packages = py_system_site_packages
self.runner = runner
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serialize BeamPythonPipelineTrigger arguments and classpath."""
return (
"airflow.providers.apache.beam.triggers.beam.BeamPythonPipelineTrigger",
{
"variables": self.variables,
"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,
"runner": self.runner,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Get current pipeline status and yields a TriggerEvent."""
hook = self._get_async_hook(runner=self.runner)
try:
return_code = await hook.start_python_pipeline_async(
variables=self.variables,
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,
)
except Exception as e:
self.log.exception("Exception occurred while checking for pipeline state")
yield TriggerEvent({"status": "error", "message": str(e)})
else:
if return_code == 0:
yield TriggerEvent(
{
"status": "success",
"message": "Pipeline has finished SUCCESSFULLY",
}
)
else:
yield TriggerEvent({"status": "error", "message": "Operation failed"})
return
[docs]class BeamJavaPipelineTrigger(BeamPipelineBaseTrigger):
"""
Trigger to perform checking the Java pipeline status until it reaches terminate state.
:param variables: Variables passed to the job.
:param jar: Name of the jar for the pipeline.
:param job_class: Optional. Name of the java class for the pipeline.
:param runner: Runner on which pipeline will be run. By default, "DirectRunner" is being used.
Other possible options: DataflowRunner, SparkRunner, FlinkRunner, PortableRunner.
See: :class:`~providers.apache.beam.hooks.beam.BeamRunnerType`
See: https://beam.apache.org/documentation/runners/capability-matrix/
:param check_if_running: Optional. Before running job, validate that a previous run is not in process.
:param project_id: Optional. The Google Cloud project ID in which to start a job.
:param location: Optional. Job location.
:param job_name: Optional. The 'jobName' to use when executing the Dataflow job.
:param gcp_conn_id: Optional. The connection ID to use connecting to Google Cloud.
:param impersonation_chain: Optional. GCP 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 poll_sleep: Optional. The time in seconds to sleep between polling GCP for the dataflow job status.
Default value is 10s.
:param cancel_timeout: Optional. How long (in seconds) operator should wait for the pipeline to be
successfully cancelled when task is being killed. Default value is 300s.
"""
def __init__(
self,
variables: dict,
jar: str,
job_class: str | None = None,
runner: str = "DirectRunner",
check_if_running: bool = False,
project_id: str | None = None,
location: str | None = None,
job_name: str | None = None,
gcp_conn_id: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
poll_sleep: int = 10,
cancel_timeout: int | None = None,
):
super().__init__()
self.variables = variables
self.jar = jar
self.job_class = job_class
self.runner = runner
self.check_if_running = check_if_running
self.project_id = project_id
self.location = location
self.job_name = job_name
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.poll_sleep = poll_sleep
self.cancel_timeout = cancel_timeout
[docs] def serialize(self) -> tuple[str, dict[str, Any]]:
"""Serialize BeamJavaPipelineTrigger arguments and classpath."""
return (
"airflow.providers.apache.beam.triggers.beam.BeamJavaPipelineTrigger",
{
"variables": self.variables,
"jar": self.jar,
"job_class": self.job_class,
"runner": self.runner,
"check_if_running": self.check_if_running,
"project_id": self.project_id,
"location": self.location,
"job_name": self.job_name,
"gcp_conn_id": self.gcp_conn_id,
"impersonation_chain": self.impersonation_chain,
"poll_sleep": self.poll_sleep,
"cancel_timeout": self.cancel_timeout,
},
)
[docs] async def run(self) -> AsyncIterator[TriggerEvent]: # type: ignore[override]
"""Get current Java pipeline status and yields a TriggerEvent."""
hook = self._get_async_hook(runner=self.runner)
return_code = 0
if self.check_if_running:
dataflow_hook = self._get_sync_dataflow_hook(
gcp_conn_id=self.gcp_conn_id,
poll_sleep=self.poll_sleep,
impersonation_chain=self.impersonation_chain,
cancel_timeout=self.cancel_timeout,
)
is_running = True
while is_running:
try:
jobs = await dataflow_hook.list_jobs(
project_id=self.project_id,
location=self.location,
jobs_filter=ListJobsRequest.Filter.ACTIVE,
)
is_running = bool([job async for job in jobs if job.name == self.job_name])
except Exception as e:
self.log.exception("Exception occurred while requesting jobs with name %s", self.job_name)
yield TriggerEvent({"status": "error", "message": str(e)})
return
if is_running:
await asyncio.sleep(self.poll_sleep)
try:
return_code = await hook.start_java_pipeline_async(
variables=self.variables, jar=self.jar, job_class=self.job_class
)
except Exception as e:
self.log.exception("Exception occurred while starting the Java pipeline")
yield TriggerEvent({"status": "error", "message": str(e)})
if return_code == 0:
yield TriggerEvent(
{
"status": "success",
"message": "Pipeline has finished SUCCESSFULLY",
}
)
else:
yield TriggerEvent({"status": "error", "message": "Operation failed"})
return
@deprecated(
reason="`BeamPipelineTrigger` is deprecated. Please use `BeamPythonPipelineTrigger`.",
category=AirflowProviderDeprecationWarning,
)
[docs]class BeamPipelineTrigger(BeamPythonPipelineTrigger):
"""
Trigger to perform checking the Python pipeline status until it reaches terminate state.
This class is deprecated. Please use
:class:`airflow.providers.apache.beam.triggers.beam.BeamPythonPipelineTrigger`
instead.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)