aaltay commented on a change in pull request #12814: URL: https://github.com/apache/airflow/pull/12814#discussion_r561159658
########## File path: airflow/providers/apache/beam/BACKPORT_PROVIDER_README.md ########## @@ -0,0 +1,101 @@ +<!-- + 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. + --> + + +# Package apache-airflow-backport-providers-apache-beam + +Release: + +**Table of contents** + +- [Backport package](#backport-package) +- [Installation](#installation) +- [PIP requirements](#pip-requirements) +- [Cross provider package dependencies](#cross-provider-package-dependencies) +- [Provider class summary](#provider-classes-summary) + - [Operators](#operators) + - [Moved operators](#moved-operators) + - [Transfer operators](#transfer-operators) + - [Moved transfer operators](#moved-transfer-operators) + - [Hooks](#hooks) + - [Moved hooks](#moved-hooks) +- [Releases](#releases) + - [Release](#release) + +## Backport package + +This is a backport providers package for `apache.beam` provider. All classes for this provider package +are in `airflow.providers.apache.beam` python package. + +**Only Python 3.6+ is supported for this backport package.** + +While Airflow 1.10.* continues to support Python 2.7+ - you need to upgrade python to 3.6+ if you +want to use this backport package. + + + +## Installation + +You can install this package on top of an existing airflow 1.10.* installation via +`pip install apache-airflow-backport-providers-apache-beam` + +## PIP requirements + + +## Cross provider package dependencies + +Those are dependencies that might be needed in order to use all the features of the package. +You need to install the specified backport providers package in order to use them. + +You can install such cross-provider dependencies when installing from PyPI. For example: + +```bash +pip install apache-airflow-backport-providers-apache-beam[google] +``` + +| Dependent package | Extra | +|:--------------------------------------------------------------------------------------------------------------------|:-------| +| [apache-airflow-backport-providers-google](https://github.com/apache/airflow/tree/master/airflow/providers/google/) | google | + +# Provider classes summary + +In Airflow 2.0, all operators, transfers, hooks, sensors, secrets for the `apache.beam` provider +are in the `airflow.providers.apache.beam` package. You can read more about the naming conventions used +in [Naming conventions for provider packages](https://github.com/apache/airflow/blob/master/CONTRIBUTING.rst#naming-conventions-for-provider-packages) + + +## Operators Review comment: There is more that could be added here related to hooks, and operators maybe? ########## File path: airflow/providers/apache/beam/hooks/beam.py ########## @@ -0,0 +1,287 @@ +# +# 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 a Apache Beam Hook.""" +import json +import select +import shlex +import subprocess +import textwrap +from tempfile import TemporaryDirectory +from typing import Callable, List, Optional + +from airflow.exceptions import AirflowException +from airflow.hooks.base_hook import BaseHook +from airflow.utils.log.logging_mixin import LoggingMixin +from airflow.utils.python_virtualenv import prepare_virtualenv + + +class BeamRunnerType: Review comment: Why is this needed? Ideally any Beam supported runners should just work, and there is no need for this duplication. ########## File path: airflow/providers/apache/beam/example_dags/example_beam.py ########## @@ -0,0 +1,305 @@ +# +# 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. + +""" +Example Airflow DAG for Apache Beam operators +""" +import os +from urllib.parse import urlparse + +from airflow import models +from airflow.providers.apache.beam.operators.beam import ( + BeamRunJavaPipelineOperator, + BeamRunPythonPipelineOperator, +) +from airflow.providers.google.cloud.hooks.dataflow import DataflowJobStatus +from airflow.providers.google.cloud.operators.dataflow import DataflowPythonConfiguration +from airflow.providers.google.cloud.sensors.dataflow import DataflowJobStatusSensor +from airflow.providers.google.cloud.transfers.gcs_to_local import GCSToLocalFilesystemOperator +from airflow.utils.dates import days_ago + +GCP_PROJECT_ID = os.environ.get('GCP_PROJECT_ID', 'example-project') +GCS_INPUT = os.environ.get('APACHE_BEAM_PYTHON', 'gs://apache-beam-samples/shakespeare/kinglear.txt') +GCS_TMP = os.environ.get('APACHE_BEAM_GCS_TMP', 'gs://test-dataflow-example/temp/') +GCS_STAGING = os.environ.get('APACHE_BEAM_GCS_STAGING', 'gs://test-dataflow-example/staging/') +GCS_OUTPUT = os.environ.get('APACHE_BEAM_GCS_OUTPUT', 'gs://test-dataflow-example/output') +GCS_PYTHON = os.environ.get('APACHE_BEAM_PYTHON', 'gs://test-dataflow-example/wordcount_debugging.py') +GCS_PYTHON_DATAFLOW_ASYNC = os.environ.get( + 'APACHE_BEAM_PYTHON_DATAFLOW_ASYNC', 'gs://test-dataflow-example/wordcount_debugging.py' +) + +GCS_JAR_DIRECT_RUNNER = os.environ.get( + 'APACHE_BEAM_DIRECT_RUNNER_JAR', + 'gs://test-dataflow-example/tests/dataflow-templates-bundled-java=11-beam-v2.25.0-DirectRunner.jar', +) +GCS_JAR_DATAFLOW_RUNNER = os.environ.get( + 'APACHE_BEAM_DATAFLOW_RUNNER_JAR', 'gs://test-dataflow-example/word-count-beam-bundled-0.1.jar' +) +GCS_JAR_SPARK_RUNNER = os.environ.get( + 'APACHE_BEAM_SPARK_RUNNER_JAR', + 'gs://test-dataflow-example/tests/dataflow-templates-bundled-java=11-beam-v2.25.0-SparkRunner.jar', +) +GCS_JAR_FLINK_RUNNER = os.environ.get( + 'APACHE_BEAM_FLINK_RUNNER_JAR', + 'gs://test-dataflow-example/tests/dataflow-templates-bundled-java=11-beam-v2.25.0-FlinkRunner.jar', +) + +GCS_JAR_DIRECT_RUNNER_PARTS = urlparse(GCS_JAR_DIRECT_RUNNER) +GCS_JAR_DIRECT_RUNNER_BUCKET_NAME = GCS_JAR_DIRECT_RUNNER_PARTS.netloc +GCS_JAR_DIRECT_RUNNER_OBJECT_NAME = GCS_JAR_DIRECT_RUNNER_PARTS.path[1:] +GCS_JAR_DATAFLOW_RUNNER_PARTS = urlparse(GCS_JAR_DATAFLOW_RUNNER) +GCS_JAR_DATAFLOW_RUNNER_BUCKET_NAME = GCS_JAR_DATAFLOW_RUNNER_PARTS.netloc +GCS_JAR_DATAFLOW_RUNNER_OBJECT_NAME = GCS_JAR_DATAFLOW_RUNNER_PARTS.path[1:] +GCS_JAR_SPARK_RUNNER_PARTS = urlparse(GCS_JAR_SPARK_RUNNER) +GCS_JAR_SPARK_RUNNER_BUCKET_NAME = GCS_JAR_SPARK_RUNNER_PARTS.netloc +GCS_JAR_SPARK_RUNNER_OBJECT_NAME = GCS_JAR_SPARK_RUNNER_PARTS.path[1:] +GCS_JAR_FLINK_RUNNER_PARTS = urlparse(GCS_JAR_FLINK_RUNNER) +GCS_JAR_FLINK_RUNNER_BUCKET_NAME = GCS_JAR_FLINK_RUNNER_PARTS.netloc +GCS_JAR_FLINK_RUNNER_OBJECT_NAME = GCS_JAR_FLINK_RUNNER_PARTS.path[1:] + + +default_args = { + 'default_pipeline_options': { + 'output': '/tmp/example_beam', + }, + "trigger_rule": "all_done", +} + + +with models.DAG( + "example_beam_native_java_direct_runner", + schedule_interval=None, # Override to match your needs + start_date=days_ago(1), + tags=['example'], +) as dag_native_java_direct_runner: + + jar_to_local_direct_runner = GCSToLocalFilesystemOperator( + task_id="jar_to_local_direct_runner", + bucket=GCS_JAR_DIRECT_RUNNER_BUCKET_NAME, + object_name=GCS_JAR_DIRECT_RUNNER_OBJECT_NAME, + filename="/tmp/beam_wordcount_direct_runner_{{ ds_nodash }}.jar", + ) + + start_java_pipeline_direct_runner = BeamRunJavaPipelineOperator( + task_id="start_java_pipeline_direct_runner", + runner="DirectRunner", Review comment: Is DirectRunner the default option? (If yes, you can omit this arg.) ########## File path: airflow/providers/apache/beam/operators/beam.py ########## @@ -0,0 +1,469 @@ +# +# 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, + DataflowJavaConfiguration, + DataflowPythonConfiguration, +) +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.DataflowPythonConfiguration] + """ + + 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[DataflowPythonConfiguration, 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("+", "-")} Review comment: Does the operator always have the same version as airflow? Would it make sense to include what is the operator version that is used? ########## File path: airflow/providers/apache/beam/operators/beam.py ########## @@ -0,0 +1,469 @@ +# +# 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, + DataflowJavaConfiguration, + DataflowPythonConfiguration, +) +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.DataflowPythonConfiguration] + """ + + 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[DataflowPythonConfiguration, 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 + self._dataflow_job_name: Optional[str] = None Review comment: _dataflow_job_name - I think this does not need to be a member. It seems to be local only to execute(). ########## File path: airflow/providers/apache/beam/operators/beam.py ########## @@ -0,0 +1,469 @@ +# +# 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, + DataflowJavaConfiguration, + DataflowPythonConfiguration, +) +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.DataflowPythonConfiguration] + """ + + 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[DataflowPythonConfiguration, 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 + self._dataflow_job_name: Optional[str] = None + + 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 = DataflowPythonConfiguration(**self.dataflow_config) Review comment: DataflowPythonConfiguration is getting a bit strange in Beam operator. I wonder if it can be merged into regular options? Another question is there a reason for DataflowPythonConfiguration / and its java equivalent. Presumably they are similar and this is more like "DataflowConfiguration" ########## File path: airflow/providers/google/cloud/hooks/dataflow.py ########## @@ -50,6 +47,35 @@ T = TypeVar("T", bound=Callable) # pylint: disable=invalid-name +def process_line_and_extract_dataflow_job_id_callback( + on_new_job_id_callback: Optional[Callable[[str], None]] +) -> Callable[[str], None]: + """ + Returns callback which triggers function passed as `on_new_job_id_callback` when Dataflow job_id is found. + To be used for `process_line_callback` in + :py:class:`~airflow.providers.apache.beam.hooks.beam.BeamCommandRunner` + + :param on_new_job_id_callback: Callback called when the job ID is known + :type on_new_job_id_callback: callback + """ + + def _process_line_and_extract_job_id( + line: str, + # on_new_job_id_callback: Optional[Callable[[str], None]] + ) -> None: + # Job id info: https://goo.gl/SE29y9. + matched_job = JOB_ID_PATTERN.search(line) + if matched_job: + job_id = matched_job.group("job_id_java") or matched_job.group("job_id_python") Review comment: What is the difference between job_id_java and job_id_python? ########## File path: airflow/providers/google/cloud/operators/dataflow.py ########## @@ -43,6 +48,214 @@ class CheckJobRunning(Enum): WaitForRun = 3 +class DataflowConfiguration(metaclass=ABCMeta): + """Abstract class for Dataflow configuration to be passed to Beam operators""" + + template_fields = ["job_name", "location"] + + def __init__( + self, + *, + job_name: Optional[str] = "{{task.task_id}}", + append_job_name: bool = True, + project_id: Optional[str] = None, + location: Optional[str] = DEFAULT_DATAFLOW_LOCATION, + gcp_conn_id: str = "google_cloud_default", + delegate_to: Optional[str] = None, + poll_sleep: int = 10, + impersonation_chain: Optional[Union[str, Sequence[str]]] = None, + drain_pipeline: bool = False, + cancel_timeout: Optional[int] = 5 * 60, + wait_until_finished: Optional[bool] = 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.delegate_to = delegate_to + 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 + + +class DataflowPythonConfiguration(DataflowConfiguration): Review comment: I wonder if you can remove DataflowPythonConfiguration and java version, in favor of only keeping the base version. (E.g. we could avoid adding another one when Go is available.) ########## File path: airflow/providers/google/cloud/hooks/dataflow.py ########## @@ -680,22 +582,36 @@ def start_java_dataflow( :param location: Job location. :type location: str """ - name = self._build_dataflow_job_name(job_name, append_job_name) + warnings.warn( + """"This method is deprecated. Review comment: Are the new versions fully backward compatible with these ones? ########## File path: airflow/providers/apache/beam/operators/beam.py ########## @@ -0,0 +1,469 @@ +# +# 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, + DataflowJavaConfiguration, + DataflowPythonConfiguration, +) +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.DataflowPythonConfiguration] + """ + + 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[DataflowPythonConfiguration, 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 + self._dataflow_job_name: Optional[str] = None + + 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 = DataflowPythonConfiguration(**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["job_name"] = self._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=self._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.DataflowJavaConfiguration] + """ + + 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[DataflowJavaConfiguration, 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 + + 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 = DataflowJavaConfiguration(**self.dataflow_config) Review comment: what happens if dataflow config is not none, but the runner is something other than dataflow runner. Should it be an error/ warning? ########## File path: airflow/providers/google/cloud/operators/dataflow.py ########## @@ -43,6 +48,214 @@ class CheckJobRunning(Enum): WaitForRun = 3 +class DataflowConfiguration(metaclass=ABCMeta): + """Abstract class for Dataflow configuration to be passed to Beam operators""" + + template_fields = ["job_name", "location"] + + def __init__( + self, + *, + job_name: Optional[str] = "{{task.task_id}}", + append_job_name: bool = True, + project_id: Optional[str] = None, + location: Optional[str] = DEFAULT_DATAFLOW_LOCATION, + gcp_conn_id: str = "google_cloud_default", + delegate_to: Optional[str] = None, + poll_sleep: int = 10, + impersonation_chain: Optional[Union[str, Sequence[str]]] = None, + drain_pipeline: bool = False, + cancel_timeout: Optional[int] = 5 * 60, + wait_until_finished: Optional[bool] = 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.delegate_to = delegate_to + 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 + + +class DataflowPythonConfiguration(DataflowConfiguration): + """ + Dataflow configuration that can be passed to + :py: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. + :type job_name: str + :param append_job_name: True if unique suffix has to be appended to job name. + :type append_job_name: bool + :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. + :type project_id: str + :param location: Job location. + :type location: str + :param gcp_conn_id: The connection ID to use connecting to Google Cloud. + :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 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. + :type poll_sleep: int + :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). + :type impersonation_chain: Union[str, Sequence[str]] + :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it + instead of canceling during during killing task instance. See: + https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline + :type drain_pipeline: bool + :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be + successfully cancelled when task is being killed. + :type cancel_timeout: Optional[int] + :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. + :type wait_until_finished: Optional[bool] + """ + + +class DataflowJavaConfiguration(DataflowConfiguration): + """ + Dataflow configuration that can be passed to + :py:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator` + + :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. + :type job_name: str + :param append_job_name: True if unique suffix has to be appended to job name. + :type append_job_name: bool + :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. + :type project_id: str + :param location: Job location. + :type location: str + :param gcp_conn_id: The connection ID to use connecting to Google Cloud. + :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 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. + :type poll_sleep: int + :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). + :type impersonation_chain: Union[str, Sequence[str]] + :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it + instead of canceling during during killing task instance. See: + https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline + :type drain_pipeline: bool + :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be + successfully cancelled when task is being killed. + :type cancel_timeout: Optional[int] + :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. + :type wait_until_finished: Optional[bool] + :param multiple_jobs: If pipeline creates multiple jobs then monitor all jobs + :type multiple_jobs: boolean + :param check_if_running: before running job, validate that a previous run is not in process + :type check_if_running: CheckJobRunning(IgnoreJob = do not check if running, FinishIfRunning= + 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. + """ + + def __init__( + self, + *, + multiple_jobs: Optional[bool] = None, + check_if_running: CheckJobRunning = CheckJobRunning.WaitForRun, + **kwargs, + ) -> None: + super().__init__(**kwargs) + self.multiple_jobs = multiple_jobs + self.check_if_running = check_if_running + + # pylint: disable=too-many-instance-attributes class DataflowCreateJavaJobOperator(BaseOperator): Review comment: Should this have a deprecation warning? ########## File path: airflow/providers/apache/beam/operators/beam.py ########## @@ -0,0 +1,469 @@ +# +# 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, + DataflowJavaConfiguration, + DataflowPythonConfiguration, +) +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.DataflowPythonConfiguration] + """ + + 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[DataflowPythonConfiguration, 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("+", "-")} Review comment: why not use version as is, instead of adding a "v" and changing . and +'s to dashes? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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