TobKed commented on a change in pull request #12814: URL: https://github.com/apache/airflow/pull/12814#discussion_r567432715
########## File path: airflow/providers/apache/beam/operators/beam.py ########## @@ -0,0 +1,474 @@ +# +# 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.""" +import re +from contextlib import ExitStack +from typing import Callable, List, Optional, 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.decorators import apply_defaults +from airflow.version import version + + +class BeamRunPythonPipelineOperator(BaseOperator): + """ + 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. + + .. code-block:: python + + default_args = { + 'default_pipeline_options': + { + 'labels': 'example-label' + } + } + + with models.DAG( + "example_beam_native_python", + default_args=default_args, + start_date=days_ago(1), + schedule_interval=None, + tags=['example'], + ) as dag_native_python: + + start_python_job_local_direct_runner = BeamRunPythonPipelineOperator( + task_id="start_python_job_local_direct_runner", + runner="DirectRunner", + py_file='apache_beam.examples.wordcount', + py_options=['-m'], + py_requirements=['apache-beam[gcp]==2.21.0'], + py_interpreter='python3', + py_system_site_packages=False, + ) + + .. 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/ + + If you use Dataflow runner check dedicated operator: + :class:`~providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator` + :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 pyfile 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] + """ + + template_fields = ["py_file", "runner", "pipeline_options", "default_pipeline_options", "dataflow_config"] + template_fields_renderers = {'dataflow_config': 'json'} + + @apply_defaults + 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 + ) + + 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 + + if isinstance(self.dataflow_config, dict): + self.dataflow_config = DataflowConfiguration(**self.dataflow_config) + + if self.runner.lower() == BeamRunnerType.DataflowRunner.lower(): + 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, + ) + self.dataflow_config.project_id = self.dataflow_config.project_id or self.dataflow_hook.project_id + + dataflow_job_name = DataflowHook.build_dataflow_job_name( + self.dataflow_config.job_name, self.dataflow_config.append_job_name + ) + pipeline_options["job_name"] = dataflow_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("+", "-")} + ) + + def set_current_dataflow_job_id(job_id): + self.dataflow_job_id = job_id + + process_line_callback = process_line_and_extract_dataflow_job_id_callback( + on_new_job_id_callback=set_current_dataflow_job_id + ) + + pipeline_options.update(self.pipeline_options) + + # Convert argument names from lowerCamelCase to snake case. + camel_to_snake = lambda name: re.sub(r"[A-Z]", lambda x: "_" + x.group(0).lower(), name) + formatted_pipeline_options = {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( # pylint: disable=no-member + 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 self.runner.lower() == BeamRunnerType.DataflowRunner.lower(): + self.dataflow_hook.wait_for_done( # pylint: disable=no-value-for-parameter + 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} + + def on_kill(self) -> None: + self.log.info("On kill.") + if self.dataflow_hook and self.dataflow_job_id: + self.dataflow_hook.cancel_job( + job_id=self.dataflow_job_id, + project_id=self.dataflow_config.project_id, + ) + + +# pylint: disable=too-many-instance-attributes +class BeamRunJavaPipelineOperator(BaseOperator): + """ + 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. + + It's a good practice to define parameters in the default_args of the dag + like the project, zone and staging location. + + .. code-block:: python + + default_args = { + 'default_pipeline_options': + { + 'labels': 'example-label' + } + } + + 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. + + .. code-block:: python + + t1 = BeamRunJavaPipelineOperator( + task_id='start_java_job_spark_runner', + jar='{{var.value.spark_runner_jar_base}}pipeline/build/libs/pipeline-example-1.0.jar', + pipeline_options={ + 'output': '/tmp/start_java_job_spark_runner', + 'inputFile': 'gs://apache-beam-samples/shakespeare/kinglear.txt, + }, + dag=my-dag) + + .. seealso:: + For more detail on Apache Beam have a look at the reference: + https://beam.apache.org/documentation/ + + :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/ + If you use Dataflow runner check dedicated operator: + :class:`~providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator` + :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] + """ + + template_fields = [ + "jar", + "runner", + "job_class", + "pipeline_options", + "default_pipeline_options", + "dataflow_config", + ] + template_fields_renderers = {'dataflow_config': 'json'} + ui_color = "#0273d4" + + @apply_defaults + 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 + ) + + 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 + + if isinstance(self.dataflow_config, dict): + self.dataflow_config = DataflowConfiguration(**self.dataflow_config) + + if self.runner.lower() == BeamRunnerType.DataflowRunner.lower(): + 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, + ) + self.dataflow_config.project_id = self.dataflow_config.project_id or self.dataflow_hook.project_id + + self._dataflow_job_name = DataflowHook.build_dataflow_job_name( + self.dataflow_config.job_name, self.dataflow_config.append_job_name + ) + pipeline_options["jobName"] = self.dataflow_config.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("+", "-")} + ) + + def set_current_dataflow_job_id(job_id): + self.dataflow_job_id = job_id + + process_line_callback = process_line_and_extract_dataflow_job_id_callback( + on_new_job_id_callback=set_current_dataflow_job_id + ) + + 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( # pylint: disable=no-member + gcs_hook.provide_file(object_url=self.jar) + ) + self.jar = tmp_gcs_file.name + + if self.runner.lower() == BeamRunnerType.DataflowRunner.lower(): + is_running = False + if self.dataflow_config.check_if_running != CheckJobRunning.IgnoreJob: + is_running = ( + self.dataflow_hook.is_job_dataflow_running( # pylint: disable=no-value-for-parameter Review comment: Good point. 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