example_dataflow.py

#
# 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 Google Cloud Dataflow service
"""
import os
from typing import Callable, Dict, List
from urllib.parse import urlparse

from airflow import models
from airflow.exceptions import AirflowException
from airflow.providers.google.cloud.hooks.dataflow import DataflowJobStatus
from airflow.providers.google.cloud.operators.dataflow import (
    CheckJobRunning,
    DataflowCreateJavaJobOperator,
    DataflowCreatePythonJobOperator,
    DataflowTemplatedJobStartOperator,
)
from airflow.providers.google.cloud.sensors.dataflow import (
    DataflowJobAutoScalingEventsSensor,
    DataflowJobMessagesSensor,
    DataflowJobMetricsSensor,
    DataflowJobStatusSensor,
)
from airflow.providers.google.cloud.transfers.gcs_to_local import GCSToLocalFilesystemOperator
from airflow.utils.dates import days_ago

GCS_TMP = os.environ.get('GCP_DATAFLOW_GCS_TMP', 'gs://test-dataflow-example/temp/')
GCS_STAGING = os.environ.get('GCP_DATAFLOW_GCS_STAGING', 'gs://test-dataflow-example/staging/')
GCS_OUTPUT = os.environ.get('GCP_DATAFLOW_GCS_OUTPUT', 'gs://test-dataflow-example/output')
GCS_JAR = os.environ.get('GCP_DATAFLOW_JAR', 'gs://test-dataflow-example/word-count-beam-bundled-0.1.jar')
GCS_PYTHON = os.environ.get('GCP_DATAFLOW_PYTHON', 'gs://test-dataflow-example/wordcount_debugging.py')

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

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

with models.DAG(
    "example_gcp_dataflow_native_java",
    schedule_interval=None,  # Override to match your needs
    start_date=days_ago(1),
    tags=['example'],
) as dag_native_java:

    # [START howto_operator_start_java_job]
    start_java_job = DataflowCreateJavaJobOperator(
        task_id="start-java-job",
        jar=GCS_JAR,
        job_name='{{task.task_id}}',
        options={
            'output': GCS_OUTPUT,
        },
        poll_sleep=10,
        job_class='org.apache.beam.examples.WordCount',
        check_if_running=CheckJobRunning.IgnoreJob,
        location='europe-west3',
    )
    # [END howto_operator_start_java_job]

    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 = DataflowCreateJavaJobOperator(
        task_id="start-java-job-local",
        jar="/tmp/dataflow-{{ ds_nodash }}.jar",
        job_name='{{task.task_id}}',
        options={
            'output': GCS_OUTPUT,
        },
        poll_sleep=10,
        job_class='org.apache.beam.examples.WordCount',
        check_if_running=CheckJobRunning.WaitForRun,
    )
    jar_to_local >> start_java_job_local

with models.DAG(
    "example_gcp_dataflow_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 howto_operator_start_python_job]
    start_python_job = DataflowCreatePythonJobOperator(
        task_id="start-python-job",
        py_file=GCS_PYTHON,
        py_options=[],
        job_name='{{task.task_id}}',
        options={
            'output': GCS_OUTPUT,
        },
        py_requirements=['apache-beam[gcp]==2.21.0'],
        py_interpreter='python3',
        py_system_site_packages=False,
        location='europe-west3',
    )
    # [END howto_operator_start_python_job]

    start_python_job_local = DataflowCreatePythonJobOperator(
        task_id="start-python-job-local",
        py_file='apache_beam.examples.wordcount',
        py_options=['-m'],
        job_name='{{task.task_id}}',
        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=days_ago(1),
    schedule_interval=None,  # Override to match your needs
    tags=['example'],
) as dag_native_python_async:
    start_python_job_async = DataflowCreatePythonJobOperator(
        task_id="start-python-job-async",
        py_file=GCS_PYTHON,
        py_options=[],
        job_name='{{task.task_id}}',
        options={
            'output': GCS_OUTPUT,
        },
        py_requirements=['apache-beam[gcp]==2.25.0'],
        py_interpreter='python3',
        py_system_site_packages=False,
        location='europe-west3',
        wait_until_finished=False,
    )

    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')['job_id']}}",
        expected_statuses={DataflowJobStatus.JOB_STATE_DONE},
        location='europe-west3',
    )

    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')['job_id']}}",
        location='europe-west3',
        callback=check_metric_scalar_gte(metric_name="Service-cpu_num_seconds", value=100),
    )

    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')['job_id']}}",
        location='europe-west3',
        callback=check_message,
    )

    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')['job_id']}}",
        location='europe-west3',
        callback=check_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=days_ago(1),
    schedule_interval=None,  # Override to match your needs
    tags=['example'],
) as dag_template:
    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',
    )