Implement consistency workflow
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#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""
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This is an example DAG which uses SparkKubernetesOperator and SparkKubernetesSensor.
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In this example, we create two tasks which execute sequentially.
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The first task is to submit sparkApplication on Kubernetes cluster(the example uses spark-pi application).
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and the second task is to check the final state of the sparkApplication that submitted in the first state.
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Spark-on-k8s operator is required to be already installed on Kubernetes
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https://github.com/GoogleCloudPlatform/spark-on-k8s-operator
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"""
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# [START import_module]
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# The DAG object; we'll need this to instantiate a DAG
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from airflow import DAG
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# Operators; we need this to operate!
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from airflow.providers.cncf.kubernetes.operators.spark_kubernetes import SparkKubernetesOperator
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from airflow.utils.dates import days_ago
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from spark_configurator import SparkConfigurator
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# [END import_module]
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# [START default_args]
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# These args will get passed on to each operator
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# You can override them on a per-task basis during operator initialization
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default_args = {
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'owner': 'airflow',
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'depends_on_past': False,
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'start_date': days_ago(1),
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'email': ['airflow@example.com'],
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'email_on_failure': False,
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'email_on_retry': False,
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'max_active_runs': 1,
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'retries': 3
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}
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dag = DAG(
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'consistency_graph',
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default_args=default_args,
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schedule_interval=None,
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tags=['example', 'spark']
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)
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propagaterel = SparkKubernetesOperator(
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task_id='PropagateRelation',
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namespace='dnet-spark-jobs',
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template_spec=SparkConfigurator(
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name="propagaterels-{{ ds }}-{{ task_instance.try_number }}",
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mainClass="eu.dnetlib.dhp.oa.dedup.SparkPropagateRelation",
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jarLocation='s3a://binaries/dhp-shade-package-1.2.5-SNAPSHOT.jar',
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arguments=["--graphBasePath", "s3a://graph/tmp/prod_provision/graph/06_graph_dedup",
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"--graphOutputPath", "s3a://graph/tmp/prod_provision/graph/07_graph_consistent",
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"--workingPath", "s3a://graph/tmp/prod_provision/working_dir/dedup"
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],
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executor_cores=8,
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executor_memory="4G",
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executor_instances=1,
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executor_memoryOverhead="3G").get_configuration(),
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kubernetes_conn_id="kubernetes_default",
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dag=dag
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)
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group_entities = SparkKubernetesOperator(
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task_id='GroupEntities',
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namespace='dnet-spark-jobs',
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template_spec=SparkConfigurator(
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name="groupentities-{{ ds }}-{{ task_instance.try_number }}",
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mainClass="eu.dnetlib.dhp.oa.merge.GroupEntitiesSparkJob",
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jarLocation='s3a://binaries/dhp-shade-package-1.2.5-SNAPSHOT.jar',
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arguments=["--graphInputPath", "s3a://graph/tmp/prod_provision/graph/06_graph_dedup",
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"--checkpointPath", "s3a://graph/tmp/prod_provision/working_dir/dedup/grouped_entities",
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"--outputPath", "s3a://graph/tmp/prod_provision/graph/07_graph_consistent",
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"--isLookupUrl", "http://services.openaire.eu:8280/is/services/isLookUp?wsdl",
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"--filterInvisible", "true"
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],
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#
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executor_cores=8,
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executor_memory="4G",
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executor_instances=1,
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executor_memoryOverhead="3G").get_configuration(),
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kubernetes_conn_id="kubernetes_default",
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dag=dag
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)
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propagaterel >> group_entities
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