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"""This module contains Apache Beam operators."""
import copy
from abc import ABCMeta
from contextlib import ExitStack
from typing import Callable, List, Optional, Tuple, Union
from airflow.models import BaseOperator
from airflow.providers.apache.beam.hooks.beam import BeamHook, BeamRunnerType
from airflow.providers.google.cloud.hooks.dataflow import (
DataflowHook,
process_line_and_extract_dataflow_job_id_callback,
)
from airflow.providers.google.cloud.hooks.gcs import GCSHook
from airflow.providers.google.cloud.operators.dataflow import CheckJobRunning, DataflowConfiguration
from airflow.utils.helpers import convert_camel_to_snake
from airflow.version import version
[docs]class BeamDataflowMixin(metaclass=ABCMeta):
"""
Helper class to store common, Dataflow specific logic for both
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunPythonPipelineOperator` and
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`.
"""
[docs] dataflow_hook: Optional[DataflowHook]
[docs] dataflow_config: Optional[DataflowConfiguration]
def _set_dataflow(
self, pipeline_options: dict, job_name_variable_key: Optional[str] = None
) -> Tuple[str, dict, Callable[[str], None]]:
self.dataflow_hook = self.__set_dataflow_hook()
self.dataflow_config.project_id = self.dataflow_config.project_id or self.dataflow_hook.project_id
dataflow_job_name = self.__get_dataflow_job_name()
pipeline_options = self.__get_dataflow_pipeline_options(
pipeline_options, dataflow_job_name, job_name_variable_key
)
process_line_callback = self.__get_dataflow_process_callback()
return dataflow_job_name, pipeline_options, process_line_callback
def __set_dataflow_hook(self) -> DataflowHook:
self.dataflow_hook = DataflowHook(
gcp_conn_id=self.dataflow_config.gcp_conn_id or self.gcp_conn_id,
delegate_to=self.dataflow_config.delegate_to or self.delegate_to,
poll_sleep=self.dataflow_config.poll_sleep,
impersonation_chain=self.dataflow_config.impersonation_chain,
drain_pipeline=self.dataflow_config.drain_pipeline,
cancel_timeout=self.dataflow_config.cancel_timeout,
wait_until_finished=self.dataflow_config.wait_until_finished,
)
return self.dataflow_hook
def __get_dataflow_job_name(self) -> str:
return DataflowHook.build_dataflow_job_name(
self.dataflow_config.job_name, self.dataflow_config.append_job_name
)
def __get_dataflow_pipeline_options(
self, pipeline_options: dict, job_name: str, job_name_key: Optional[str] = None
) -> dict:
pipeline_options = copy.deepcopy(pipeline_options)
if job_name_key is not None:
pipeline_options[job_name_key] = job_name
pipeline_options["project"] = self.dataflow_config.project_id
pipeline_options["region"] = self.dataflow_config.location
pipeline_options.setdefault("labels", {}).update(
{"airflow-version": "v" + version.replace(".", "-").replace("+", "-")}
)
return pipeline_options
def __get_dataflow_process_callback(self) -> Callable[[str], None]:
def set_current_dataflow_job_id(job_id):
self.dataflow_job_id = job_id
return process_line_and_extract_dataflow_job_id_callback(
on_new_job_id_callback=set_current_dataflow_job_id
)
[docs]class BeamRunPythonPipelineOperator(BaseOperator, BeamDataflowMixin):
"""
Launching Apache Beam pipelines written in Python. Note that both
``default_pipeline_options`` and ``pipeline_options`` will be merged to specify pipeline
execution parameter, and ``default_pipeline_options`` is expected to save
high-level options, for instances, project and zone information, which
apply to all beam operators in the DAG.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BeamRunPythonPipelineOperator`
.. seealso::
For more detail on Apache Beam have a look at the reference:
https://beam.apache.org/documentation/
:param py_file: Reference to the python Apache Beam pipeline file.py, e.g.,
/some/local/file/path/to/your/python/pipeline/file. (templated)
:type py_file: str
:param runner: Runner on which pipeline will be run. By default "DirectRunner" is being used.
Other possible options: DataflowRunner, SparkRunner, FlinkRunner.
See: :class:`~providers.apache.beam.hooks.beam.BeamRunnerType`
See: https://beam.apache.org/documentation/runners/capability-matrix/
:type runner: str
:param py_options: Additional python options, e.g., ["-m", "-v"].
:type py_options: list[str]
:param default_pipeline_options: Map of default pipeline options.
:type default_pipeline_options: dict
:param pipeline_options: Map of pipeline 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.
:type pipeline_options: dict
: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
:type py_interpreter: str
: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.
:type py_requirements: List[str]
: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: Optional.
The connection ID to use connecting to Google Cloud Storage if python file is on GCS.
:type gcp_conn_id: str
:param delegate_to: Optional.
The account to impersonate using domain-wide delegation of authority,
if any. For this to work, the service account making the request must have
domain-wide delegation enabled.
:type delegate_to: str
:param dataflow_config: Dataflow configuration, used when runner type is set to DataflowRunner
:type dataflow_config: Union[dict, providers.google.cloud.operators.dataflow.DataflowConfiguration]
"""
[docs] template_fields = ["py_file", "runner", "pipeline_options", "default_pipeline_options", "dataflow_config"]
[docs] template_fields_renderers = {'dataflow_config': 'json', 'pipeline_options': 'json'}
def __init__(
self,
*,
py_file: str,
runner: str = "DirectRunner",
default_pipeline_options: Optional[dict] = None,
pipeline_options: Optional[dict] = None,
py_interpreter: str = "python3",
py_options: Optional[List[str]] = None,
py_requirements: Optional[List[str]] = None,
py_system_site_packages: bool = False,
gcp_conn_id: str = "google_cloud_default",
delegate_to: Optional[str] = None,
dataflow_config: Optional[Union[DataflowConfiguration, dict]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.py_file = py_file
self.runner = runner
self.py_options = py_options or []
self.default_pipeline_options = default_pipeline_options or {}
self.pipeline_options = pipeline_options or {}
self.pipeline_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.gcp_conn_id = gcp_conn_id
self.delegate_to = delegate_to
self.dataflow_config = dataflow_config or {}
self.beam_hook: Optional[BeamHook] = None
self.dataflow_hook: Optional[DataflowHook] = None
self.dataflow_job_id: Optional[str] = None
if self.dataflow_config and self.runner.lower() != BeamRunnerType.DataflowRunner.lower():
self.log.warning(
"dataflow_config is defined but runner is different than DataflowRunner (%s)", self.runner
)
[docs] def execute(self, context):
"""Execute the Apache Beam Pipeline."""
self.beam_hook = BeamHook(runner=self.runner)
pipeline_options = self.default_pipeline_options.copy()
process_line_callback: Optional[Callable] = None
is_dataflow = self.runner.lower() == BeamRunnerType.DataflowRunner.lower()
dataflow_job_name: Optional[str] = None
if isinstance(self.dataflow_config, dict):
self.dataflow_config = DataflowConfiguration(**self.dataflow_config)
if is_dataflow:
dataflow_job_name, pipeline_options, process_line_callback = self._set_dataflow(
pipeline_options=pipeline_options, job_name_variable_key="job_name"
)
pipeline_options.update(self.pipeline_options)
# Convert argument names from lowerCamelCase to snake case.
formatted_pipeline_options = {
convert_camel_to_snake(key): pipeline_options[key] for key in pipeline_options
}
with ExitStack() as exit_stack:
if self.py_file.lower().startswith("gs://"):
gcs_hook = GCSHook(self.gcp_conn_id, self.delegate_to)
tmp_gcs_file = exit_stack.enter_context(gcs_hook.provide_file(object_url=self.py_file))
self.py_file = tmp_gcs_file.name
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,
)
if is_dataflow:
self.dataflow_hook.wait_for_done(
job_name=dataflow_job_name,
location=self.dataflow_config.location,
job_id=self.dataflow_job_id,
multiple_jobs=False,
)
return {"dataflow_job_id": self.dataflow_job_id}
[docs] def on_kill(self) -> None:
if self.dataflow_hook and self.dataflow_job_id:
self.log.info('Dataflow job with id: `%s` was requested to be cancelled.', self.dataflow_job_id)
self.dataflow_hook.cancel_job(
job_id=self.dataflow_job_id,
project_id=self.dataflow_config.project_id,
)
[docs]class BeamRunJavaPipelineOperator(BaseOperator, BeamDataflowMixin):
"""
Launching Apache Beam pipelines written in Java.
Note that both
``default_pipeline_options`` and ``pipeline_options`` will be merged to specify pipeline
execution parameter, and ``default_pipeline_options`` is expected to save
high-level pipeline_options, for instances, project and zone information, which
apply to all Apache Beam operators in the DAG.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:BeamRunJavaPipelineOperator`
.. seealso::
For more detail on Apache Beam have a look at the reference:
https://beam.apache.org/documentation/
You need to pass the path to your jar file 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 ``pipeline_options`` to pass on pipeline_options to your job.
:param jar: The reference to a self executing Apache Beam jar (templated).
:type jar: str
:param runner: Runner on which pipeline will be run. By default "DirectRunner" is being used.
See:
https://beam.apache.org/documentation/runners/capability-matrix/
:type runner: str
:param job_class: The name of the Apache Beam pipeline class to be executed, it
is often not the main class configured in the pipeline jar file.
:type job_class: str
:param default_pipeline_options: Map of default job pipeline_options.
:type default_pipeline_options: dict
:param pipeline_options: Map of job specific pipeline_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 pipeline_options will be added for each key.
If the value is ``['A', 'B']`` and the key is ``key`` then the ``--key=A --key-B`` pipeline_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.
:type pipeline_options: dict
:param gcp_conn_id: The connection ID to use connecting to Google Cloud Storage if jar is on GCS
:type gcp_conn_id: str
:param delegate_to: The account to impersonate using domain-wide delegation of authority,
if any. For this to work, the service account making the request must have
domain-wide delegation enabled.
:type delegate_to: str
:param dataflow_config: Dataflow configuration, used when runner type is set to DataflowRunner
:type dataflow_config: Union[dict, providers.google.cloud.operators.dataflow.DataflowConfiguration]
"""
[docs] template_fields = [
"jar",
"runner",
"job_class",
"pipeline_options",
"default_pipeline_options",
"dataflow_config",
]
[docs] template_fields_renderers = {'dataflow_config': 'json', 'pipeline_options': 'json'}
def __init__(
self,
*,
jar: str,
runner: str = "DirectRunner",
job_class: Optional[str] = None,
default_pipeline_options: Optional[dict] = None,
pipeline_options: Optional[dict] = None,
gcp_conn_id: str = "google_cloud_default",
delegate_to: Optional[str] = None,
dataflow_config: Optional[Union[DataflowConfiguration, dict]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.jar = jar
self.runner = runner
self.default_pipeline_options = default_pipeline_options or {}
self.pipeline_options = pipeline_options or {}
self.job_class = job_class
self.dataflow_config = dataflow_config or {}
self.gcp_conn_id = gcp_conn_id
self.delegate_to = delegate_to
self.dataflow_job_id = None
self.dataflow_hook: Optional[DataflowHook] = None
self.beam_hook: Optional[BeamHook] = None
self._dataflow_job_name: Optional[str] = None
if self.dataflow_config and self.runner.lower() != BeamRunnerType.DataflowRunner.lower():
self.log.warning(
"dataflow_config is defined but runner is different than DataflowRunner (%s)", self.runner
)
[docs] def execute(self, context):
"""Execute the Apache Beam Pipeline."""
self.beam_hook = BeamHook(runner=self.runner)
pipeline_options = self.default_pipeline_options.copy()
process_line_callback: Optional[Callable] = None
is_dataflow = self.runner.lower() == BeamRunnerType.DataflowRunner.lower()
dataflow_job_name: Optional[str] = None
if isinstance(self.dataflow_config, dict):
self.dataflow_config = DataflowConfiguration(**self.dataflow_config)
if is_dataflow:
dataflow_job_name, pipeline_options, process_line_callback = self._set_dataflow(
pipeline_options=pipeline_options, job_name_variable_key=None
)
pipeline_options.update(self.pipeline_options)
with ExitStack() as exit_stack:
if self.jar.lower().startswith("gs://"):
gcs_hook = GCSHook(self.gcp_conn_id, self.delegate_to)
tmp_gcs_file = exit_stack.enter_context(gcs_hook.provide_file(object_url=self.jar))
self.jar = tmp_gcs_file.name
if is_dataflow:
is_running = False
if self.dataflow_config.check_if_running != CheckJobRunning.IgnoreJob:
is_running = (
# The reason for disable=no-value-for-parameter is that project_id parameter is
# required but here is not passed, moreover it cannot be passed here.
# This method is wrapped by @_fallback_to_project_id_from_variables decorator which
# fallback project_id value from variables and raise error if project_id is
# defined both in variables and as parameter (here is already defined in variables)
self.dataflow_hook.is_job_dataflow_running(
name=self.dataflow_config.job_name,
variables=pipeline_options,
)
)
while is_running and self.dataflow_config.check_if_running == CheckJobRunning.WaitForRun:
# The reason for disable=no-value-for-parameter is that project_id parameter is
# required but here is not passed, moreover it cannot be passed here.
# This method is wrapped by @_fallback_to_project_id_from_variables decorator which
# fallback project_id value from variables and raise error if project_id is
# defined both in variables and as parameter (here is already defined in variables)
is_running = self.dataflow_hook.is_job_dataflow_running(
name=self.dataflow_config.job_name,
variables=pipeline_options,
)
if not is_running:
pipeline_options["jobName"] = dataflow_job_name
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=dataflow_job_name,
location=self.dataflow_config.location,
job_id=self.dataflow_job_id,
multiple_jobs=self.dataflow_config.multiple_jobs,
project_id=self.dataflow_config.project_id,
)
else:
self.beam_hook.start_java_pipeline(
variables=pipeline_options,
jar=self.jar,
job_class=self.job_class,
process_line_callback=process_line_callback,
)
return {"dataflow_job_id": self.dataflow_job_id}
[docs] def on_kill(self) -> None:
if self.dataflow_hook and self.dataflow_job_id:
self.log.info('Dataflow job with id: `%s` was requested to be cancelled.', self.dataflow_job_id)
self.dataflow_hook.cancel_job(
job_id=self.dataflow_job_id,
project_id=self.dataflow_config.project_id,
)