Source code for airflow.providers.google.cloud.example_dags.example_dataflow

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"""
Example Airflow DAG for Google Cloud Dataflow service
"""
import os
from datetime import datetime
from typing import Callable, Dict, List
from urllib.parse import urlparse

from airflow import models
from airflow.exceptions import AirflowException
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 (
    CheckJobRunning,
    DataflowTemplatedJobStartOperator,
)
from airflow.providers.google.cloud.sensors.dataflow import (
    DataflowJobAutoScalingEventsSensor,
    DataflowJobMessagesSensor,
    DataflowJobMetricsSensor,
    DataflowJobStatusSensor,
)
from airflow.providers.google.cloud.transfers.gcs_to_local import GCSToLocalFilesystemOperator

[docs]START_DATE = datetime(2021, 1, 1)

[docs]GCS_TMP = os.environ.get('GCP_DATAFLOW_GCS_TMP', 'gs://INVALID BUCKET NAME/temp/')
[docs]GCS_STAGING = os.environ.get('GCP_DATAFLOW_GCS_STAGING', 'gs://INVALID BUCKET NAME/staging/')
[docs]GCS_OUTPUT = os.environ.get('GCP_DATAFLOW_GCS_OUTPUT', 'gs://INVALID BUCKET NAME/output')
[docs]GCS_JAR = os.environ.get('GCP_DATAFLOW_JAR', 'gs://INVALID BUCKET NAME/word-count-beam-bundled-0.1.jar')
[docs]GCS_PYTHON = os.environ.get('GCP_DATAFLOW_PYTHON', 'gs://INVALID BUCKET NAME/wordcount_debugging.py')

[docs]GCS_JAR_PARTS = urlparse(GCS_JAR)
[docs]GCS_JAR_BUCKET_NAME = GCS_JAR_PARTS.netloc
[docs]GCS_JAR_OBJECT_NAME = GCS_JAR_PARTS.path[1:]

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

with models.DAG(
    "example_gcp_dataflow_native_java",
    schedule_interval='@once',  # Override to match your needs
    start_date=START_DATE,
    catchup=False,
    tags=['example'],
) as dag_native_java:

    # [START howto_operator_start_java_job_jar_on_gcs]
[docs]    start_java_job = BeamRunJavaPipelineOperator(
        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": 'europe-west3',
            "poll_sleep": 10,
        },
    )
    # [END howto_operator_start_java_job_jar_on_gcs]

    # [START howto_operator_start_java_job_local_jar]
    jar_to_local = GCSToLocalFilesystemOperator(
        task_id="jar-to-local",
        bucket=GCS_JAR_BUCKET_NAME,
        object_name=GCS_JAR_OBJECT_NAME,
        filename="/tmp/dataflow-{{ ds_nodash }}.jar",
    )

    start_java_job_local = BeamRunJavaPipelineOperator(
        task_id="start-java-job-local",
        jar="/tmp/dataflow-{{ ds_nodash }}.jar",
        pipeline_options={
            'output': GCS_OUTPUT,
        },
        job_class='org.apache.beam.examples.WordCount',
        dataflow_config={
            "check_if_running": CheckJobRunning.WaitForRun,
            "location": 'europe-west3',
            "poll_sleep": 10,
        },
    )
    jar_to_local >> start_java_job_local
    # [END howto_operator_start_java_job_local_jar]

with models.DAG(
    "example_gcp_dataflow_native_python",
    default_args=default_args,
    start_date=START_DATE,
    catchup=False,
    schedule_interval='@once',  # Override to match your needs
    tags=['example'],
) as dag_native_python:

    # [START howto_operator_start_python_job]
[docs]    start_python_job = BeamRunPythonPipelineOperator(
        task_id="start-python-job",
        py_file=GCS_PYTHON,
        py_options=[],
        pipeline_options={
            'output': GCS_OUTPUT,
        },
        py_requirements=['apache-beam[gcp]==2.21.0'],
        py_interpreter='python3',
        py_system_site_packages=False,
        dataflow_config={'location': 'europe-west3'},
    )
    # [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]==2.14.0'],
        py_interpreter='python3',
        py_system_site_packages=False,
    )

with models.DAG(
    "example_gcp_dataflow_native_python_async",
    default_args=default_args,
    start_date=START_DATE,
    catchup=False,
    schedule_interval='@once',  # Override to match your needs
    tags=['example'],
) as dag_native_python_async:
    # [START howto_operator_start_python_job_async]
[docs]    start_python_job_async = BeamRunPythonPipelineOperator(
        task_id="start-python-job-async",
        runner="DataflowRunner",
        py_file=GCS_PYTHON,
        py_options=[],
        pipeline_options={
            'output': GCS_OUTPUT,
        },
        py_requirements=['apache-beam[gcp]==2.25.0'],
        py_interpreter='python3',
        py_system_site_packages=False,
        dataflow_config={
            "job_name": "start-python-job-async",
            "location": 'europe-west3',
            "wait_until_finished": False,
        },
    )
    # [END howto_operator_start_python_job_async]

    # [START howto_sensor_wait_for_job_status]
    wait_for_python_job_async_done = DataflowJobStatusSensor(
        task_id="wait-for-python-job-async-done",
        job_id="{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}",
        expected_statuses={DataflowJobStatus.JOB_STATE_DONE},
        location='europe-west3',
    )
    # [END howto_sensor_wait_for_job_status]

    # [START howto_sensor_wait_for_job_metric]
    def check_metric_scalar_gte(metric_name: str, value: int) -> Callable:
        """Check is metric greater than equals to given value."""

        def callback(metrics: List[Dict]) -> bool:
            dag_native_python_async.log.info("Looking for '%s' >= %d", metric_name, value)
            for metric in metrics:
                context = metric.get("name", {}).get("context", {})
                original_name = context.get("original_name", "")
                tentative = context.get("tentative", "")
                if original_name == "Service-cpu_num_seconds" and not tentative:
                    return metric["scalar"] >= value
            raise AirflowException(f"Metric '{metric_name}' not found in metrics")

        return callback

    wait_for_python_job_async_metric = DataflowJobMetricsSensor(
        task_id="wait-for-python-job-async-metric",
        job_id="{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}",
        location='europe-west3',
        callback=check_metric_scalar_gte(metric_name="Service-cpu_num_seconds", value=100),
        fail_on_terminal_state=False,
    )
    # [END howto_sensor_wait_for_job_metric]

    # [START howto_sensor_wait_for_job_message]
    def check_message(messages: List[dict]) -> bool:
        """Check message"""
        for message in messages:
            if "Adding workflow start and stop steps." in message.get("messageText", ""):
                return True
        return False

    wait_for_python_job_async_message = DataflowJobMessagesSensor(
        task_id="wait-for-python-job-async-message",
        job_id="{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}",
        location='europe-west3',
        callback=check_message,
        fail_on_terminal_state=False,
    )
    # [END howto_sensor_wait_for_job_message]

    # [START howto_sensor_wait_for_job_autoscaling_event]
    def check_autoscaling_event(autoscaling_events: List[dict]) -> bool:
        """Check autoscaling event"""
        for autoscaling_event in autoscaling_events:
            if "Worker pool started." in autoscaling_event.get("description", {}).get("messageText", ""):
                return True
        return False

    wait_for_python_job_async_autoscaling_event = DataflowJobAutoScalingEventsSensor(
        task_id="wait-for-python-job-async-autoscaling-event",
        job_id="{{task_instance.xcom_pull('start-python-job-async')['dataflow_job_id']}}",
        location='europe-west3',
        callback=check_autoscaling_event,
        fail_on_terminal_state=False,
    )
    # [END howto_sensor_wait_for_job_autoscaling_event]

    start_python_job_async >> wait_for_python_job_async_done
    start_python_job_async >> wait_for_python_job_async_metric
    start_python_job_async >> wait_for_python_job_async_message
    start_python_job_async >> wait_for_python_job_async_autoscaling_event


with models.DAG(
    "example_gcp_dataflow_template",
    default_args=default_args,
    start_date=START_DATE,
    catchup=False,
    schedule_interval='@once',  # Override to match your needs
    tags=['example'],
) as dag_template:
    # [START howto_operator_start_template_job]
[docs]    start_template_job = DataflowTemplatedJobStartOperator(
        task_id="start-template-job",
        template='gs://dataflow-templates/latest/Word_Count',
        parameters={'inputFile': "gs://dataflow-samples/shakespeare/kinglear.txt", 'output': GCS_OUTPUT},
        location='europe-west3',
    )
    # [END howto_operator_start_template_job]