TobKed commented on a change in pull request #12814: URL: https://github.com/apache/airflow/pull/12814#discussion_r537712551
########## 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( Review comment: Another role of example dags is bo be part of documentation and cen be visible from docs (example from PR for [databricks](https://github.com/apache/airflow/pull/12175/files#diff-e287093666061b3eb90c0c75ea0bf551563bcb6ee00d3bf6028b0239cade2f34R64)). I refactored it slightly so local task is "fully" local and in second task both input and output will be on GCS. My idea was to show working example showing usage different sources/outputs. Also executing system tests will confirm that both tasks works. ---------------------------------------------------------------- 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]
