added workflow test
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Sandro La Bruzzo
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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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from os import path
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from datetime import timedelta, datetime
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from spark_configurator import SparkConfigurator
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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.providers.cncf.kubernetes.sensors.spark_kubernetes import SparkKubernetesSensor
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from airflow.utils.dates import days_ago
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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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spec =SparkConfigurator(
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name="spark-scholix-{{ ds }}-{{ task_instance.try_number }}",
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mainClass="eu.dnetlib.dhp.sx.graph.SparkCreateScholexplorerDump",
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jarLocation = 's3a://deps/dhp-shade-package-1.2.5-SNAPSHOT.jar',
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arguments =[ "--sourcePath", "s3a://raw-graph/01", "--targetPath", "s3a://scholix"],\
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executor_cores=10,
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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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dag = DAG(
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'spark_scholix',
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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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submit = SparkKubernetesOperator(
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task_id='spark-scholix',
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namespace='dnet-spark-jobs',
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template_spec=spec,
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kubernetes_conn_id="kubernetes_default",
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# do_xcom_push=True,
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# delete_on_termination=True,
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# base_container_name="spark-kubernetes-driver",
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dag=dag
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)
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submit
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class SparkConfigurator:
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def __init__(self,
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name,
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mainClass,
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jarLocation:str,
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arguments,
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apiVersion=None,
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namespace="dnet-spark-jobs",
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image= "dnet-spark:1.0.0",
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driver_cores=1,
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driver_memory='1G',
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executor_cores=1,
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executor_memory="1G",
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executor_memoryOverhead= "1G",
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executor_instances=1
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) -> None:
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if apiVersion:
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self.apiVersion = apiVersion
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else:
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self.apiVersion = "sparkoperator.k8s.io/v1beta2"
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self.namespace= namespace
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self.name = name
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self.image= image
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self.mainClass = mainClass
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self.jarLocation = jarLocation
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self.arguments= arguments
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self.s3Configuration = {
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"spark.driver.extraJavaOptions": "-Divy.cache.dir=/tmp -Dcom.amazonaws.sdk.disableCertChecking=true -Dcom.cloudera.com.amazonaws.sdk.disableCertChecking=true",
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"spark.executor.extraJavaOptions": "-Divy.cache.dir=/tmp -Dcom.amazonaws.sdk.disableCertChecking=true -Dcom.cloudera.com.amazonaws.sdk.disableCertChecking=true",
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"spark.hadoop.fs.defaultFS": "s3a://spark",
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"spark.hadoop.fs.s3a.access.key": "minio",
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"spark.hadoop.fs.s3a.secret.key": "minio123",
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"spark.hadoop.fs.s3a.endpoint": "https://minio.dnet-minio-tenant.svc.cluster.local",
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"spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
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"spark.hadoop.fs.s3a.path.style.access": "true",
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"spark.hadoop.fs.s3a.attempts.maximum": "1",
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"spark.hadoop.fs.s3a.connection.establish.timeout": "5000",
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"spark.hadoop.fs.s3a.connection.timeout": "10001",
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"spark.hadoop.fs.s3a.connection.ssl.enabled": "false",
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"com.amazonaws.sdk.disableCertChecking": "true",
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"com.cloudera.com.amazonaws.sdk.disableCertChecking": "true",
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"fs.s3a.connection.ssl.strictverify": "false",
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"fs.s3a.connection.ssl.enabled": "false",
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"fs.s3a.ssl.enabled": "false",
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"spark.hadoop.fs.s3a.ssl.enabled": "false"
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}
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self.sparkResoruceConf= {
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'driver_cores':driver_cores,
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'driver_memory':driver_memory,
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'executor_cores':executor_cores,
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'executor_memory':executor_memory,
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'executor_instances':executor_instances,
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'memoryOverhead':executor_memoryOverhead
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}
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def get_configuration(self) -> dict:
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return {
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"apiVersion": self.apiVersion,
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"kind": "SparkApplication",
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"metadata": {
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"name": self.name,
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"namespace": self.namespace
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},
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"spec": {
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"type": "Scala",
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"mode": "cluster",
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"image":self.image,
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"imagePullPolicy": "IfNotPresent",
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"mainClass": self.mainClass,
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"mainApplicationFile": self.jarLocation,
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"arguments": self.arguments,
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"sparkVersion": "3.5.1",
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"sparkConf": self.s3Configuration,
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"restartPolicy": {
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"type": "Never"
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},
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"volumes": [
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{
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"name": "test-volume",
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"persistentVolumeClaim": {
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"claimName": "my-spark-pvc-tmp"
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}
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}
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],
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"driver": {
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"javaOptions": "-Dcom.amazonaws.sdk.disableCertChecking=true -Dcom.cloudera.com.amazonaws.sdk.disableCertChecking=true",
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"cores": self.sparkResoruceConf['driver_cores'],
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"coreLimit": "1200m",
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"memory": self.sparkResoruceConf['driver_memory'],
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"labels": {
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"version": "3.5.1"
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},
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"serviceAccount": "spark",
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"volumeMounts": [
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{
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"name": "test-volume",
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"mountPath": "/tmp"
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}
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]
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},
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"executor": {
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"javaOptions": "-Dcom.amazonaws.sdk.disableCertChecking=true -Dcom.cloudera.com.amazonaws.sdk.disableCertChecking=true",
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"cores": self.sparkResoruceConf['executor_cores'],
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"memoryOverhead": self.sparkResoruceConf['memoryOverhead'],
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"memory": self.sparkResoruceConf['executor_memory'],
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"instances": self.sparkResoruceConf['executor_instances'],
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"labels": {
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"version": "3.5.1"
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},
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"volumeMounts": [
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{
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"name": "test-volume",
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"mountPath": "/tmp"
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}
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]
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}
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}
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}
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