def test_execute_async(self, mock_hook):
job = {}
job_id = "job_id"
mock_hook.return_value.wait_for_job.return_value = None
mock_hook.return_value.submit_job.return_value.reference.job_id = job_id
op = DataprocSubmitJobOperator(
task_id=TASK_ID,
location=GCP_LOCATION,
project_id=GCP_PROJECT,
job=job,
gcp_conn_id=GCP_CONN_ID,
retry=RETRY,
asynchronous=True,
timeout=TIMEOUT,
metadata=METADATA,
request_id=REQUEST_ID,
impersonation_chain=IMPERSONATION_CHAIN,
)
op.execute(context={})
mock_hook.assert_called_once_with(
gcp_conn_id=GCP_CONN_ID,
impersonation_chain=IMPERSONATION_CHAIN,
)
mock_hook.return_value.submit_job.assert_called_once_with(
project_id=GCP_PROJECT,
location=GCP_LOCATION,
job=job,
request_id=REQUEST_ID,
retry=RETRY,
timeout=TIMEOUT,
metadata=METADATA,
)
mock_hook.return_value.wait_for_job.assert_not_called()
project_id=PROJECT_ID,
cluster=CLUSTER,
region=REGION)
scale_cluster = DataprocUpdateClusterOperator(
task_id="scale_cluster",
cluster_name=CLUSTER_NAME,
cluster=CLUSTER_UPDATE,
update_mask=UPDATE_MASK,
graceful_decommission_timeout=TIMEOUT,
project_id=PROJECT_ID,
location=REGION,
)
pig_task = DataprocSubmitJobOperator(task_id="pig_task",
job=PIG_JOB,
location=REGION,
project_id=PROJECT_ID)
spark_sql_task = DataprocSubmitJobOperator(
task_id="spark_sql_task",
job=SPARK_SQL_JOB,
location=REGION,
project_id=PROJECT_ID,
)
spark_task = DataprocSubmitJobOperator(task_id="spark_task",
job=SPARK_JOB,
location=REGION,
project_id=PROJECT_ID)
pyspark_task = DataprocSubmitJobOperator(task_id="pyspark_task",
template=WORKFLOW_TEMPLATE,
project_id=PROJECT_ID,
region=REGION,
)
# [END how_to_cloud_dataproc_create_workflow_template]
# [START how_to_cloud_dataproc_trigger_workflow_template]
trigger_workflow = DataprocInstantiateWorkflowTemplateOperator(
task_id="trigger_workflow",
region=REGION,
project_id=PROJECT_ID,
template_id=WORKFLOW_NAME)
# [END how_to_cloud_dataproc_trigger_workflow_template]
pig_task = DataprocSubmitJobOperator(task_id="pig_task",
job=PIG_JOB,
region=REGION,
project_id=PROJECT_ID)
spark_sql_task = DataprocSubmitJobOperator(task_id="spark_sql_task",
job=SPARK_SQL_JOB,
region=REGION,
project_id=PROJECT_ID)
spark_task = DataprocSubmitJobOperator(task_id="spark_task",
job=SPARK_JOB,
region=REGION,
project_id=PROJECT_ID)
# [START cloud_dataproc_async_submit_sensor]
spark_task_async = DataprocSubmitJobOperator(task_id="spark_task_async",
job=SPARK_JOB,
region=REGION,
# By default you won't have access to use `gcloud dataproc jobs submit` on the cluster that you created.
# Running this script would let you submit jobs to the cluster through gcloud.
# Be sure to give the correct cluster-name, cluster-region and your group entity
assign_permissions = BashOperator(
task_id="assign_permissions_for_dataproc_cluster",
bash_command=f"bash {DAGS_FOLDER}/dataproc-set-iam.sh {CLUSTER_NAME} {REGION} group:{GROUP_NAME}",
)
# BashOperator to hold the Dataproc delete operator for specified sleep time
# sleep_task = BashOperator(task_id="sleep_task_to_keep_dataproc_cluster_alive_3h", bash_command="sleep 8h",)
cg_producer_pyspark_task = DataprocSubmitJobOperator(
task_id="run_cee_cg_producer",
impersonation_chain=CONNECT_SA,
job=CG_PYSPARK_JOB,
location=REGION,
cluster_name=CLUSTER_NAME,
cluster_config=CLUSTER_CONFIG,
labels={"tenant": TENANT, "created-by": USER, },
)
pr_producer_pyspark_task = DataprocSubmitJobOperator(
task_id="run_cee_pr_producer",
impersonation_chain=CONNECT_SA,
job=PR_PYSPARK_JOB,
location=REGION,
cluster_name=CLUSTER_NAME,
cluster_config=CLUSTER_CONFIG,
labels={"tenant": TENANT, "created-by": USER, },
)
},
}
with models.DAG("gcp_dataproc_spark", start_date=days_ago(1), schedule_interval=None) as dag:
# [START how_to_cloud_dataproc_create_cluster_operator]
create_cluster = DataprocCreateClusterOperator(
task_id="create_cluster",
project_id=PROJECT_ID,
cluster_config=CLUSTER_CONFIG,
region=REGION,
cluster_name=CLUSTER_NAME,
)
# [END how_to_cloud_dataproc_create_cluster_operator]
spark_task = DataprocSubmitJobOperator(
task_id="spark_task", job=SPARK_JOB, location=REGION, project_id=PROJECT_ID
)
# [START how_to_cloud_dataproc_delete_cluster_operator]
delete_cluster = DataprocDeleteClusterOperator(
task_id="delete_cluster", project_id=PROJECT_ID, cluster_name=CLUSTER_NAME, region=REGION
)
# [END how_to_cloud_dataproc_delete_cluster_operator]
create_cluster >> spark_task >> delete_cluster
default_args=default_dag_args) as dag:
create_dataproc_acme_sales_cluster = dataproc_operator.DataprocClusterCreateOperator(
task_id='create_dataproc_acme_sales_cluster',
cluster_name=DATAPROC_CLUSTER_NAME,
region=REGION,
zone=ZONE,
num_workers=3,
master_machine_type=DATAPROC_MASTER_MACHINE_TYPE,
worker_machine_type=DATAPROC_MASTER_MACHINE_TYPE,
image_version=IMAGE_VERSION,
project_id=PROJECT_ID)
locations_staging_spark_job = DataprocSubmitJobOperator(
task_id="locations_staging_spark_job",
job=LOCATIONS_STAGING_SPARK_JOB,
location=REGION,
project_id=PROJECT_ID)
products_staging_spark_job = DataprocSubmitJobOperator(
task_id="products_staging_spark_job",
job=PRODUCTS_STAGING_SPARK_JOB,
location=REGION,
project_id=PROJECT_ID)
transactions_staging_spark_job = DataprocSubmitJobOperator(
task_id="transactions_staging_spark_job",
job=TRANSACTIONS_STAGING_SPARK_JOB,
location=REGION,
project_id=PROJECT_ID)
schedule_interval='00 6 * * *',
max_active_runs=1,
default_args=default_args
) as dag_daily:
create_cluster = DataprocCreateClusterOperator(
task_id="create_cluster",
cluster_name=CLUSTER_NAME_DAILY,
region=REGION,
project_id=PROJECT_ID,
cluster_config=CLUSTER_CONFIGURATION,
)
pig_job_nltk_stopwords = DataprocSubmitJobOperator(
task_id="pig_job_nltk_stopwords",
job=get_pig_job_config("sh python -m nltk.downloader stopwords",
CLUSTER_NAME_DAILY),
location=REGION,
project_id=PROJECT_ID
)
pig_job_spacy_vocabulary = DataprocSubmitJobOperator(
task_id="pig_job_spacy_vocabulary",
job=get_pig_job_config("sh python -m spacy download es_core_news_lg",
CLUSTER_NAME_DAILY),
location=REGION,
project_id=PROJECT_ID
)
python_task_config_file = PythonOperator(
task_id='python_task_config_file',
python_callable=write_str_to_gcp,
