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Example Airflow DAG for testing Google Dataflow Beam Pipeline Operator with Java.

Important Note:
    This test downloads Java JAR file from the public bucket. In case the JAR file cannot be downloaded
    or is not compatible with the Java version used in the test, the source code for this test can be
    downloaded from here ( and needs to be compiled
    manually in order to work.

    You can follow the instructions on how to pack a self-executing jar here:

    These operators require the gcloud command and Java's JRE to run.

from __future__ import annotations

import os
from datetime import datetime

from airflow.models.dag import DAG
from airflow.providers.apache.beam.hooks.beam import BeamRunnerType
from airflow.providers.apache.beam.operators.beam import BeamRunJavaPipelineOperator
from import CheckJobRunning
from import GCSCreateBucketOperator, GCSDeleteBucketOperator
from import GCSToLocalFilesystemOperator
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "dataflow_native_java"
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]PUBLIC_BUCKET = "airflow-system-tests-resources"
[docs]JAR_FILE_NAME = "word-count-beam-bundled-0.1.jar"
[docs]REMOTE_JAR_FILE_PATH = f"dataflow/java/{JAR_FILE_NAME}"
[docs]GCS_OUTPUT = f"gs://{BUCKET_NAME}"
[docs]GCS_JAR = f"gs://{PUBLIC_BUCKET}/dataflow/java/{JAR_FILE_NAME}"
[docs]LOCATION = "europe-west3"
with DAG( DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "dataflow"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator(task_id="create_bucket", bucket_name=BUCKET_NAME)
download_file = GCSToLocalFilesystemOperator( task_id="download_file", object_name=REMOTE_JAR_FILE_PATH, bucket=PUBLIC_BUCKET, filename=JAR_FILE_NAME, ) # [START howto_operator_start_java_job_local_jar] start_java_job_local = BeamRunJavaPipelineOperator( task_id="start_java_job_local", jar=JAR_FILE_NAME, pipeline_options={ "output": GCS_OUTPUT, }, job_class="org.apache.beam.examples.WordCount", dataflow_config={ "check_if_running": CheckJobRunning.WaitForRun, "location": LOCATION, "poll_sleep": 10, }, ) # [END howto_operator_start_java_job_local_jar] # [START howto_operator_start_java_job_jar_on_gcs] start_java_job = BeamRunJavaPipelineOperator( runner=BeamRunnerType.DataflowRunner, task_id="start-java-job", jar=GCS_JAR, pipeline_options={ "output": GCS_OUTPUT, }, job_class="org.apache.beam.examples.WordCount", dataflow_config={ "check_if_running": CheckJobRunning.IgnoreJob, "location": LOCATION, "poll_sleep": 10, }, ) # [END howto_operator_start_java_job_jar_on_gcs] # [START howto_operator_start_java_job_jar_on_gcs_deferrable] start_java_deferrable = BeamRunJavaPipelineOperator( runner=BeamRunnerType.DataflowRunner, task_id="start-java-job-deferrable", jar=GCS_JAR, pipeline_options={ "output": GCS_OUTPUT, }, job_class="org.apache.beam.examples.WordCount", dataflow_config={ "check_if_running": CheckJobRunning.WaitForRun, "location": LOCATION, "poll_sleep": 10, "append_job_name": False, }, deferrable=True, ) # [END howto_operator_start_java_job_jar_on_gcs_deferrable] delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE ) ( # TEST SETUP create_bucket >> download_file # TEST BODY >> [start_java_job_local, start_java_job, start_java_deferrable] # TEST TEARDOWN >> delete_bucket ) from tests.system.utils.watcher import watcher # This test needs watcher in order to properly mark success/failure # when "teardown" task with trigger rule is part of the DAG list(dag.tasks) >> watcher() from tests.system.utils import get_test_run # noqa: E402 # Needed to run the example DAG with pytest (see: tests/system/
[docs]test_run = get_test_run(dag)

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