Source code for tests.system.providers.google.cloud.dataflow.example_dataflow_native_python

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"""
Example Airflow DAG for testing Google Dataflow Beam Pipeline Operator with Python.
"""
from __future__ import annotations

import os
from datetime import datetime
from pathlib import Path

from airflow import models
from airflow.providers.apache.beam.operators.beam import BeamRunPythonPipelineOperator
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
from airflow.utils.trigger_rule import TriggerRule


[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")


[docs]DAG_ID = "dataflow_native_python"



[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"



[docs]PYTHON_FILE_NAME = "wordcount_debugging.txt"


[docs]GCS_TMP = f"gs://{BUCKET_NAME}/temp/"


[docs]GCS_STAGING = f"gs://{BUCKET_NAME}/staging/"


[docs]GCS_OUTPUT = f"gs://{BUCKET_NAME}/output"


[docs]GCS_PYTHON_SCRIPT = f"gs://{BUCKET_NAME}/{PYTHON_FILE_NAME}"


[docs]PYTHON_FILE_LOCAL_PATH = str(Path(__file__).parent / "resources" / PYTHON_FILE_NAME)


[docs]LOCATION = "europe-west3"



[docs]default_args = {
    "dataflow_default_options": {
        "tempLocation": GCS_TMP,
        "stagingLocation": GCS_STAGING,

    }
}

with models.DAG(
    DAG_ID,
    default_args=default_args,
    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)


    upload_file = LocalFilesystemToGCSOperator(
        task_id="upload_file_to_bucket",
        src=PYTHON_FILE_LOCAL_PATH,
        dst=PYTHON_FILE_NAME,
        bucket=BUCKET_NAME,
    )

    # [START howto_operator_start_python_job]
    start_python_job = BeamRunPythonPipelineOperator(
        task_id="start_python_job",
        py_file=GCS_PYTHON_SCRIPT,
        py_options=[],
        pipeline_options={
            "output": GCS_OUTPUT,
        },
        py_requirements=["apache-beam[gcp]==2.36.0"],
        py_interpreter="python3",
        py_system_site_packages=False,
        dataflow_config={"location": LOCATION},
    )
    # [END howto_operator_start_python_job]

    start_python_job_local = BeamRunPythonPipelineOperator(
        task_id="start_python_job_local",
        py_file="apache_beam.examples.wordcount",
        py_options=["-m"],
        pipeline_options={
            "output": GCS_OUTPUT,
        },
        py_requirements=["apache-beam[gcp]==2.36.0"],
        py_interpreter="python3",
        py_system_site_packages=False,
    )

    delete_bucket = GCSDeleteBucketOperator(
        task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE
    )

    (
        # TEST SETUP
        create_bucket
        >> upload_file
        # TEST BODY
        >> start_python_job
        >> start_python_job_local
        # 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/README.md#run_via_pytest)

[docs]test_run = get_test_run(dag)

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