TobKed commented on a change in pull request #12814: URL: https://github.com/apache/airflow/pull/12814#discussion_r537697679
########## File path: airflow/providers/apache/beam/example_dags/example_beam.py ########## @@ -0,0 +1,262 @@ +# +# 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.transfers.gcs_to_local import GCSToLocalFilesystemOperator +from airflow.utils.dates import days_ago + +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_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_job_direct_runner = BeamRunJavaPipelineOperator( + task_id="start_java_job_direct_runner", + runner="DirectRunner", + jar="/tmp/beam_wordcount_direct_runner_{{ ds_nodash }}.jar", + job_name='{{task.task_id}}', + pipeline_options={ + 'output': '/tmp/start_java_job_direct_runner', + 'inputFile': GCS_INPUT, + }, + job_class='org.apache.beam.examples.WordCount', + ) + + jar_to_local_direct_runner >> start_java_job_direct_runner + +with models.DAG( + "example_beam_native_java_dataflow_runner", + schedule_interval=None, # Override to match your needs + start_date=days_ago(1), + tags=['example'], +) as dag_native_java_dataflow_runner: + + jar_to_local_dataflow_runner = GCSToLocalFilesystemOperator( + task_id="jar_to_local_dataflow_runner", + bucket=GCS_JAR_DATAFLOW_RUNNER_BUCKET_NAME, + object_name=GCS_JAR_DATAFLOW_RUNNER_OBJECT_NAME, + filename="/tmp/beam_wordcount_dataflow_runner_{{ ds_nodash }}.jar", + ) + + start_java_job_dataflow = BeamRunJavaPipelineOperator( + task_id="start_java_job_dataflow", + runner="DataflowRunner", + jar="/tmp/beam_wordcount_dataflow_runner_{{ ds_nodash }}.jar", + job_name='{{task.task_id}}', + pipeline_options={ + 'tempLocation': GCS_TMP, + 'stagingLocation': GCS_STAGING, + 'output': GCS_OUTPUT, + }, + job_class='org.apache.beam.examples.WordCount', + ) + + jar_to_local_dataflow_runner >> start_java_job_dataflow + +with models.DAG( + "example_beam_native_java_spark_runner", + schedule_interval=None, # Override to match your needs + start_date=days_ago(1), + tags=['example'], +) as dag_native_java_spark_runner: + + jar_to_local_spark_runner = GCSToLocalFilesystemOperator( + task_id="jar_to_local_spark_runner", + bucket=GCS_JAR_SPARK_RUNNER_BUCKET_NAME, + object_name=GCS_JAR_SPARK_RUNNER_OBJECT_NAME, + filename="/tmp/beam_wordcount_spark_runner_{{ ds_nodash }}.jar", + ) + + start_java_job_spark_runner = BeamRunJavaPipelineOperator( + task_id="start_java_job_spark_runner", + runner="SparkRunner", + jar="/tmp/beam_wordcount_spark_runner_{{ ds_nodash }}.jar", + job_name='{{task.task_id}}', + pipeline_options={ + 'output': '/tmp/start_java_job_spark_runner', + 'inputFile': GCS_INPUT, + }, + job_class='org.apache.beam.examples.WordCount', + ) + + jar_to_local_spark_runner >> start_java_job_spark_runner + +with models.DAG( + "example_beam_native_java_flink_runner", + schedule_interval=None, # Override to match your needs + start_date=days_ago(1), + tags=['example'], +) as dag_native_java_flink_runner: + + jar_to_local_flink_runner = GCSToLocalFilesystemOperator( + task_id="jar_to_local_flink_runner", + bucket=GCS_JAR_FLINK_RUNNER_BUCKET_NAME, + object_name=GCS_JAR_FLINK_RUNNER_OBJECT_NAME, + filename="/tmp/beam_wordcount_flink_runner_{{ ds_nodash }}.jar", + ) + + start_java_job_flink_runner = BeamRunJavaPipelineOperator( + task_id="start_java_job_flink_runner", + runner="FlinkRunner", + jar="/tmp/beam_wordcount_flink_runner_{{ ds_nodash }}.jar", + job_name='{{task.task_id}}', + pipeline_options={ + 'output': '/tmp/start_java_job_flink_runner', + 'inputFile': GCS_INPUT, + }, + job_class='org.apache.beam.examples.WordCount', + ) + + jar_to_local_flink_runner >> start_java_job_flink_runner + + +with models.DAG( + "example_beam_native_python", + default_args=default_args, + start_date=days_ago(1), + schedule_interval=None, # Override to match your needs + tags=['example'], +) as dag_native_python: + + start_python_job_local_direct_runner = BeamRunPythonPipelineOperator( + task_id="start_python_job_local_direct_runner", + py_file='apache_beam.examples.wordcount', + py_options=['-m'], + job_name='{{task.task_id}}', + py_requirements=['apache-beam[gcp]==2.21.0'], + py_interpreter='python3', + py_system_site_packages=False, + ) + + start_python_job_direct_runner = BeamRunPythonPipelineOperator( + task_id="start_python_job_direct_runner", + py_file=GCS_PYTHON, + py_options=[], + job_name='{{task.task_id}}', + py_requirements=['apache-beam[gcp]==2.21.0'], + py_interpreter='python3', + py_system_site_packages=False, + ) + + start_python_job_dataflow_runner = BeamRunPythonPipelineOperator( + task_id="start_python_job_dataflow_runner", + runner="DataflowRunner", + py_file=GCS_PYTHON, + pipeline_options={ + 'tempLocation': GCS_TMP, + 'stagingLocation': GCS_STAGING, + 'output': GCS_OUTPUT, + }, + py_options=[], + job_name='{{task.task_id}}', + py_requirements=['apache-beam[gcp]==2.21.0'], + py_interpreter='python3', + py_system_site_packages=False, + ) + + start_python_job_local_spark_runner = BeamRunPythonPipelineOperator( + task_id="start_python_job_local_spark_runner", + py_file='apache_beam.examples.wordcount', + runner="SparkRunner", + py_options=['-m'], + job_name='{{task.task_id}}', + py_requirements=['apache-beam[gcp]==2.21.0'], + py_interpreter='python3', + py_system_site_packages=False, + ) + + start_python_job_local_flink_runner = BeamRunPythonPipelineOperator( + task_id="start_python_job_local_flink_runner", + py_file='apache_beam.examples.wordcount', + runner="FlinkRunner", + py_options=['-m'], + job_name='{{task.task_id}}', + pipeline_options={ + 'output': '/tmp/start_python_job_local_flink_runner', + }, + py_requirements=['apache-beam[gcp]==2.21.0'], + py_interpreter='python3', + py_system_site_packages=False, + ) + + start_python_job_local_direct_runner >> start_python_job_local_flink_runner Review comment: You are right. It will be easier to follow the DAG, i made small refactor of this part. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected]
