airflow beam 源码
airflow beam 代码
文件路径：/airflow/providers/apache/beam/operators/beam.py
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains Apache Beam operators."""
from __future__ import annotations
import copy
import tempfile
from abc import ABC, ABCMeta
from contextlib import ExitStack
from typing import TYPE_CHECKING, Callable, Sequence
from airflow import AirflowException
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.links.dataflow import DataflowJobLink
from airflow.providers.google.cloud.operators.dataflow import CheckJobRunning, DataflowConfiguration
from airflow.utils.helpers import convert_camel_to_snake
from airflow.version import version
if TYPE_CHECKING:
from airflow.utils.context import Context
class BeamDataflowMixin(metaclass=ABCMeta):
"""
Helper class to store common, Dataflow specific logic for both
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunPythonPipelineOperator`,
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator` and
:class:`~airflow.providers.apache.beam.operators.beam.BeamRunGoPipelineOperator`.
"""
dataflow_hook: DataflowHook | None
dataflow_config: DataflowConfiguration
gcp_conn_id: str
delegate_to: str | None
dataflow_support_impersonation: bool = True
def _set_dataflow(
self,
pipeline_options: dict,
job_name_variable_key: str | None = 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: str | None = None
) -> dict:
pipeline_options = copy.deepcopy(pipeline_options)
if job_name_key is not None:
pipeline_options[job_name_key] = job_name
if self.dataflow_config.service_account:
pipeline_options["serviceAccount"] = self.dataflow_config.service_account
if self.dataflow_support_impersonation and self.dataflow_config.impersonation_chain:
if isinstance(self.dataflow_config.impersonation_chain, list):
pipeline_options["impersonateServiceAccount"] = ",".join(
self.dataflow_config.impersonation_chain
)
else:
pipeline_options["impersonateServiceAccount"] = self.dataflow_config.impersonation_chain
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
)
class BeamBasePipelineOperator(BaseOperator, BeamDataflowMixin, ABC):
"""
Abstract base class for Beam Pipeline Operators.
: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 default_pipeline_options: Map of default pipeline options.
: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.
:param gcp_conn_id: Optional.
The connection ID to use connecting to Google Cloud Storage if python file is on GCS.
: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.
:param dataflow_config: Dataflow configuration, used when runner type is set to DataflowRunner,
(optional) defaults to None.
"""
def __init__(
self,
*,
runner: str = "DirectRunner",
default_pipeline_options: dict | None = None,
pipeline_options: dict | None = None,
gcp_conn_id: str = "google_cloud_default",
delegate_to: str | None = None,
dataflow_config: DataflowConfiguration | dict | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.runner = runner
self.default_pipeline_options = default_pipeline_options or {}
self.pipeline_options = pipeline_options or {}
self.gcp_conn_id = gcp_conn_id
self.delegate_to = delegate_to
if isinstance(dataflow_config, dict):
self.dataflow_config = DataflowConfiguration(**dataflow_config)
else:
self.dataflow_config = dataflow_config or DataflowConfiguration()
self.beam_hook: BeamHook | None = None
self.dataflow_hook: DataflowHook | None = None
self.dataflow_job_id: str | None = 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
)
def _init_pipeline_options(
self,
format_pipeline_options: bool = False,
job_name_variable_key: str | None = None,
) -> tuple[bool, str | None, dict, Callable[[str], None] | None]:
self.beam_hook = BeamHook(runner=self.runner)
pipeline_options = self.default_pipeline_options.copy()
process_line_callback: Callable[[str], None] | None = None
is_dataflow = self.runner.lower() == BeamRunnerType.DataflowRunner.lower()
dataflow_job_name: str | None = None
if is_dataflow:
dataflow_job_name, pipeline_options, process_line_callback = self._set_dataflow(
pipeline_options=pipeline_options,
job_name_variable_key=job_name_variable_key,
)
self.log.info(pipeline_options)
pipeline_options.update(self.pipeline_options)
if format_pipeline_options:
snake_case_pipeline_options = {
convert_camel_to_snake(key): pipeline_options[key] for key in pipeline_options
}
return is_dataflow, dataflow_job_name, snake_case_pipeline_options, process_line_callback
return is_dataflow, dataflow_job_name, pipeline_options, process_line_callback
class BeamRunPythonPipelineOperator(BeamBasePipelineOperator):
"""
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)
:param py_options: Additional python options, e.g., ["-m", "-v"].
: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.
"""
template_fields: Sequence[str] = (
"py_file",
"runner",
"pipeline_options",
"default_pipeline_options",
"dataflow_config",
)
template_fields_renderers = {'dataflow_config': 'json', 'pipeline_options': 'json'}
operator_extra_links = (DataflowJobLink(),)
def __init__(
self,
*,
py_file: str,
runner: str = "DirectRunner",
default_pipeline_options: dict | None = None,
pipeline_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,
gcp_conn_id: str = "google_cloud_default",
delegate_to: str | None = None,
dataflow_config: DataflowConfiguration | dict | None = None,
**kwargs,
) -> None:
super().__init__(
runner=runner,
default_pipeline_options=default_pipeline_options,
pipeline_options=pipeline_options,
gcp_conn_id=gcp_conn_id,
delegate_to=delegate_to,
dataflow_config=dataflow_config,
**kwargs,
)
self.py_file = py_file
self.py_options = py_options or []
self.py_interpreter = py_interpreter
self.py_requirements = py_requirements
self.py_system_site_packages = py_system_site_packages
self.pipeline_options.setdefault("labels", {}).update(
{"airflow-version": "v" + version.replace(".", "-").replace("+", "-")}
)
def execute(self, context: Context):
"""Execute the Apache Beam Pipeline."""
(
is_dataflow,
dataflow_job_name,
snake_case_pipeline_options,
process_line_callback,
) = self._init_pipeline_options(format_pipeline_options=True, job_name_variable_key="job_name")
if not self.beam_hook:
raise AirflowException("Beam hook is not defined.")
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
if is_dataflow and self.dataflow_hook:
with self.dataflow_hook.provide_authorized_gcloud():
self.beam_hook.start_python_pipeline(
variables=snake_case_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,
)
DataflowJobLink.persist(
self,
context,
self.dataflow_config.project_id,
self.dataflow_config.location,
self.dataflow_job_id,
)
if dataflow_job_name and self.dataflow_config.location:
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,
project_id=self.dataflow_config.project_id,
)
return {"dataflow_job_id": self.dataflow_job_id}
else:
self.beam_hook.start_python_pipeline(
variables=snake_case_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,
)
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,
)
class BeamRunJavaPipelineOperator(BeamBasePipelineOperator):
"""
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).
: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.
"""
template_fields: Sequence[str] = (
"jar",
"runner",
"job_class",
"pipeline_options",
"default_pipeline_options",
"dataflow_config",
)
template_fields_renderers = {'dataflow_config': 'json', 'pipeline_options': 'json'}
ui_color = "#0273d4"
operator_extra_links = (DataflowJobLink(),)
def __init__(
self,
*,
jar: str,
runner: str = "DirectRunner",
job_class: str | None = None,
default_pipeline_options: dict | None = None,
pipeline_options: dict | None = None,
gcp_conn_id: str = "google_cloud_default",
delegate_to: str | None = None,
dataflow_config: DataflowConfiguration | dict | None = None,
**kwargs,
) -> None:
super().__init__(
runner=runner,
default_pipeline_options=default_pipeline_options,
pipeline_options=pipeline_options,
gcp_conn_id=gcp_conn_id,
delegate_to=delegate_to,
dataflow_config=dataflow_config,
**kwargs,
)
self.jar = jar
self.job_class = job_class
def execute(self, context: Context):
"""Execute the Apache Beam Pipeline."""
(
is_dataflow,
dataflow_job_name,
pipeline_options,
process_line_callback,
) = self._init_pipeline_options()
if not self.beam_hook:
raise AirflowException("Beam hook is not defined.")
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 and self.dataflow_hook:
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
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,
)
if dataflow_job_name and self.dataflow_config.location:
multiple_jobs = self.dataflow_config.multiple_jobs or False
DataflowJobLink.persist(
self,
context,
self.dataflow_config.project_id,
self.dataflow_config.location,
self.dataflow_job_id,
)
self.dataflow_hook.wait_for_done(
job_name=dataflow_job_name,
location=self.dataflow_config.location,
job_id=self.dataflow_job_id,
multiple_jobs=multiple_jobs,
project_id=self.dataflow_config.project_id,
)
return {"dataflow_job_id": self.dataflow_job_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,
)
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,
)
class BeamRunGoPipelineOperator(BeamBasePipelineOperator):
"""
Launching Apache Beam pipelines written in Go. 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:BeamRunGoPipelineOperator`
.. seealso::
For more detail on Apache Beam have a look at the reference:
https://beam.apache.org/documentation/
:param go_file: Reference to the Go Apache Beam pipeline e.g.,
/some/local/file/path/to/your/go/pipeline/file.go
"""
template_fields = [
"go_file",
"runner",
"pipeline_options",
"default_pipeline_options",
"dataflow_config",
]
template_fields_renderers = {'dataflow_config': 'json', 'pipeline_options': 'json'}
operator_extra_links = (DataflowJobLink(),)
def __init__(
self,
*,
go_file: str,
runner: str = "DirectRunner",
default_pipeline_options: dict | None = None,
pipeline_options: dict | None = None,
gcp_conn_id: str = "google_cloud_default",
delegate_to: str | None = None,
dataflow_config: DataflowConfiguration | dict | None = None,
**kwargs,
) -> None:
super().__init__(
runner=runner,
default_pipeline_options=default_pipeline_options,
pipeline_options=pipeline_options,
gcp_conn_id=gcp_conn_id,
delegate_to=delegate_to,
dataflow_config=dataflow_config,
**kwargs,
)
if self.dataflow_config.impersonation_chain:
self.log.info(
"Impersonation chain parameter is not supported for Apache Beam GO SDK and will be skipped "
"in the execution"
)
self.dataflow_support_impersonation = False
self.go_file = go_file
self.should_init_go_module = False
self.pipeline_options.setdefault("labels", {}).update(
{"airflow-version": "v" + version.replace(".", "-").replace("+", "-")}
)
def execute(self, context: Context):
"""Execute the Apache Beam Pipeline."""
(
is_dataflow,
dataflow_job_name,
snake_case_pipeline_options,
process_line_callback,
) = self._init_pipeline_options(format_pipeline_options=True, job_name_variable_key="job_name")
if not self.beam_hook:
raise AirflowException("Beam hook is not defined.")
with ExitStack() as exit_stack:
if self.go_file.lower().startswith("gs://"):
gcs_hook = GCSHook(self.gcp_conn_id, self.delegate_to)
with tempfile.TemporaryDirectory(prefix="apache-beam-go") as tmp_dir:
tmp_gcs_file = exit_stack.enter_context(
gcs_hook.provide_file(object_url=self.go_file, dir=tmp_dir)
)
self.go_file = tmp_gcs_file.name
self.should_init_go_module = True
if is_dataflow and self.dataflow_hook:
with self.dataflow_hook.provide_authorized_gcloud():
self.beam_hook.start_go_pipeline(
variables=snake_case_pipeline_options,
go_file=self.go_file,
process_line_callback=process_line_callback,
should_init_module=self.should_init_go_module,
)
DataflowJobLink.persist(
self,
context,
self.dataflow_config.project_id,
self.dataflow_config.location,
self.dataflow_job_id,
)
if dataflow_job_name and self.dataflow_config.location:
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,
project_id=self.dataflow_config.project_id,
)
return {"dataflow_job_id": self.dataflow_job_id}
else:
self.beam_hook.start_go_pipeline(
variables=snake_case_pipeline_options,
go_file=self.go_file,
process_line_callback=process_line_callback,
should_init_module=self.should_init_go_module,
)
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,
)
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