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"""
Example Airflow DAG for Managed Spark batch operators.
"""
from __future__ import annotations
import os
from datetime import datetime
from google.api_core.retry import Retry
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.dataproc import (
DataprocCancelOperationOperator,
DataprocCreateBatchOperator,
DataprocDeleteBatchOperator,
DataprocGetBatchOperator,
DataprocListBatchesOperator,
)
from airflow.providers.google.cloud.sensors.dataproc import DataprocBatchSensor
try:
from airflow.sdk import TriggerRule
except ImportError:
# Compatibility for Airflow < 3.1
from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
from system.google import DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
DAG_ID = "dataproc_batch"
REGION = "europe-west3"
BATCH_ID = f"batch-{ENV_ID}-{DAG_ID}".replace("_", "-")
BATCH_ID_2 = f"batch-{ENV_ID}-{DAG_ID}-2".replace("_", "-")
BATCH_ID_3 = f"batch-{ENV_ID}-{DAG_ID}-3".replace("_", "-")
BATCH_ID_4 = f"batch-{ENV_ID}-{DAG_ID}-4".replace("_", "-")
BATCH_ID_PREFIX = f"batch-{ENV_ID}-pfx".replace("_", "-")
BATCH_CONFIG = {
"spark_batch": {
"jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
"main_class": "org.apache.spark.examples.SparkPi",
},
}
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "managed-spark"],
) as dag:
# [START how_to_cloud_dataproc_create_batch_operator]
create_batch = DataprocCreateBatchOperator(
task_id="create_batch",
project_id=PROJECT_ID,
region=REGION,
batch=BATCH_CONFIG,
batch_id=BATCH_ID,
result_retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
num_retries_if_resource_is_not_ready=3,
)
create_batch_2 = DataprocCreateBatchOperator(
task_id="create_batch_2",
project_id=PROJECT_ID,
region=REGION,
batch=BATCH_CONFIG,
batch_id=BATCH_ID_2,
result_retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
num_retries_if_resource_is_not_ready=3,
)
create_batch_3 = DataprocCreateBatchOperator(
task_id="create_batch_3",
project_id=PROJECT_ID,
region=REGION,
batch=BATCH_CONFIG,
batch_id=BATCH_ID_3,
asynchronous=True,
result_retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
num_retries_if_resource_is_not_ready=3,
)
# [END how_to_cloud_dataproc_create_batch_operator]
# [START how_to_cloud_dataproc_create_batch_operator_with_prefix]
create_batch_with_prefix = DataprocCreateBatchOperator(
task_id="create_batch_with_prefix",
project_id=PROJECT_ID,
region=REGION,
batch=BATCH_CONFIG,
batch_id_prefix=BATCH_ID_PREFIX,
result_retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
)
# [END how_to_cloud_dataproc_create_batch_operator_with_prefix]
# [START how_to_cloud_dataproc_batch_async_sensor]
batch_async_sensor = DataprocBatchSensor(
task_id="batch_async_sensor",
region=REGION,
project_id=PROJECT_ID,
batch_id=BATCH_ID_3,
poke_interval=10,
)
# [END how_to_cloud_dataproc_batch_async_sensor]
# [START how_to_cloud_dataproc_get_batch_operator]
get_batch = DataprocGetBatchOperator(
task_id="get_batch", project_id=PROJECT_ID, region=REGION, batch_id=BATCH_ID
)
# [END how_to_cloud_dataproc_get_batch_operator]
# [START how_to_cloud_dataproc_list_batches_operator]
list_batches = DataprocListBatchesOperator(
task_id="list_batches",
project_id=PROJECT_ID,
region=REGION,
)
# [END how_to_cloud_dataproc_list_batches_operator]
create_batch_4 = DataprocCreateBatchOperator(
task_id="create_batch_4",
project_id=PROJECT_ID,
region=REGION,
batch=BATCH_CONFIG,
batch_id=BATCH_ID_4,
asynchronous=True,
num_retries_if_resource_is_not_ready=3,
)
# [START how_to_cloud_dataproc_cancel_operation_operator]
cancel_operation = DataprocCancelOperationOperator(
task_id="cancel_operation",
project_id=PROJECT_ID,
region=REGION,
operation_name="{{ task_instance.xcom_pull('create_batch_4')['operation'] }}",
)
# [END how_to_cloud_dataproc_cancel_operation_operator]
# [START how_to_cloud_dataproc_delete_batch_operator]
delete_batch = DataprocDeleteBatchOperator(
task_id="delete_batch", project_id=PROJECT_ID, region=REGION, batch_id=BATCH_ID
)
delete_batch_2 = DataprocDeleteBatchOperator(
task_id="delete_batch_2", project_id=PROJECT_ID, region=REGION, batch_id=BATCH_ID_2
)
delete_batch_3 = DataprocDeleteBatchOperator(
task_id="delete_batch_3", project_id=PROJECT_ID, region=REGION, batch_id=BATCH_ID_3
)
delete_batch_4 = DataprocDeleteBatchOperator(
task_id="delete_batch_4", project_id=PROJECT_ID, region=REGION, batch_id=BATCH_ID_4
)
# [END how_to_cloud_dataproc_delete_batch_operator]
# The prefixed batch gets a generated suffix, so its ID is only known at runtime.
delete_batch_with_prefix = DataprocDeleteBatchOperator(
task_id="delete_batch_with_prefix",
project_id=PROJECT_ID,
region=REGION,
batch_id="{{ task_instance.xcom_pull('create_batch_with_prefix')['name'].split('/') | last }}",
)
delete_batch.trigger_rule = TriggerRule.ALL_DONE
delete_batch_2.trigger_rule = TriggerRule.ALL_DONE
delete_batch_3.trigger_rule = TriggerRule.ALL_DONE
delete_batch_4.trigger_rule = TriggerRule.ALL_FAILED
delete_batch_with_prefix.trigger_rule = TriggerRule.ALL_DONE
(
# TEST SETUP
create_batch
>> create_batch_2
>> create_batch_3
# TEST BODY
>> batch_async_sensor
>> get_batch
>> list_batches
>> create_batch_with_prefix
>> create_batch_4
>> cancel_operation
# TEST TEARDOWN
>> delete_batch
>> delete_batch_2
>> delete_batch_3
>> delete_batch_4
>> delete_batch_with_prefix
)
from tests_common.test_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_common.test_utils.system_tests import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)