Implement consistency workflow

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Giambattista Bloisi 2024-10-21 15:25:48 +02:00
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#
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# to you under the Apache License, Version 2.0 (the
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# http://www.apache.org/licenses/LICENSE-2.0
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"""
This is an example DAG which uses SparkKubernetesOperator and SparkKubernetesSensor.
In this example, we create two tasks which execute sequentially.
The first task is to submit sparkApplication on Kubernetes cluster(the example uses spark-pi application).
and the second task is to check the final state of the sparkApplication that submitted in the first state.
Spark-on-k8s operator is required to be already installed on Kubernetes
https://github.com/GoogleCloudPlatform/spark-on-k8s-operator
"""
# [START import_module]
# The DAG object; we'll need this to instantiate a DAG
from airflow import DAG
# Operators; we need this to operate!
from airflow.providers.cncf.kubernetes.operators.spark_kubernetes import SparkKubernetesOperator
from airflow.utils.dates import days_ago
from spark_configurator import SparkConfigurator
# [END import_module]
# [START default_args]
# These args will get passed on to each operator
# You can override them on a per-task basis during operator initialization
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': days_ago(1),
'email': ['airflow@example.com'],
'email_on_failure': False,
'email_on_retry': False,
'max_active_runs': 1,
'retries': 3
}
dag = DAG(
'consistency_graph',
default_args=default_args,
schedule_interval=None,
tags=['example', 'spark']
)
propagaterel = SparkKubernetesOperator(
task_id='PropagateRelation',
namespace='dnet-spark-jobs',
template_spec=SparkConfigurator(
name="propagaterels-{{ ds }}-{{ task_instance.try_number }}",
mainClass="eu.dnetlib.dhp.oa.dedup.SparkPropagateRelation",
jarLocation='s3a://binaries/dhp-shade-package-1.2.5-SNAPSHOT.jar',
arguments=["--graphBasePath", "s3a://graph/tmp/prod_provision/graph/06_graph_dedup",
"--graphOutputPath", "s3a://graph/tmp/prod_provision/graph/07_graph_consistent",
"--workingPath", "s3a://graph/tmp/prod_provision/working_dir/dedup"
],
executor_cores=8,
executor_memory="4G",
executor_instances=1,
executor_memoryOverhead="3G").get_configuration(),
kubernetes_conn_id="kubernetes_default",
dag=dag
)
group_entities = SparkKubernetesOperator(
task_id='GroupEntities',
namespace='dnet-spark-jobs',
template_spec=SparkConfigurator(
name="groupentities-{{ ds }}-{{ task_instance.try_number }}",
mainClass="eu.dnetlib.dhp.oa.merge.GroupEntitiesSparkJob",
jarLocation='s3a://binaries/dhp-shade-package-1.2.5-SNAPSHOT.jar',
arguments=["--graphInputPath", "s3a://graph/tmp/prod_provision/graph/06_graph_dedup",
"--checkpointPath", "s3a://graph/tmp/prod_provision/working_dir/dedup/grouped_entities",
"--outputPath", "s3a://graph/tmp/prod_provision/graph/07_graph_consistent",
"--isLookupUrl", "http://services.openaire.eu:8280/is/services/isLookUp?wsdl",
"--filterInvisible", "true"
],
#
executor_cores=8,
executor_memory="4G",
executor_instances=1,
executor_memoryOverhead="3G").get_configuration(),
kubernetes_conn_id="kubernetes_default",
dag=dag
)
propagaterel >> group_entities