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# 设置 spark-submit 的服务账户（IRSA）的 IAM 角色
<a name="spark-submit-security-irsa"></a>

以下部分介绍了如何设置服务账户的 IAM 角色（IRSA），对 Kubernetes 服务账户进行身份验证和授权，从而运行存储在 Amazon S3 中的 Spark 应用程序。

## 先决条件
<a name="spark-submit-security-irsa-prereqs"></a>

在尝试本文档中的任何示例之前，请确保已满足以下先决条件：
+ [完成设置 spark-submit](https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/spark-submit-setup.html)
+ [已创建 S3 存储桶](https://docs.aws.amazon.com/AmazonS3/latest/userguide/creating-bucket.html)并[上传](https://docs.aws.amazon.com/AmazonS3/latest/userguide/uploading-an-object-bucket.html) Spark 应用程序 jar 文件

## 配置 Kubernetes 服务账户以代入 IAM 角色
<a name="spark-submit-security-irsa-configure-kubernetes"></a>

以下步骤介绍如何配置 Kubernetes 服务账户以代入 AWS Identity and Access Management （IAM）角色。将这些 pod 配置为使用服务帐号后，他们就可以访问 AWS 服务 该角色有权访问的任何内容。

1. 创建一个策略文件，允许对[上传的](https://docs.aws.amazon.com/AmazonS3/latest/userguide/uploading-an-object-bucket.html) Amazon S3 对象进行只读访问：

   ```
   cat >my-policy.json <<EOF
   {
       "Version": "2012-10-17",		 	 	 
       "Statement": [
           {
               "Effect": "Allow",
               "Action": [
                   "s3:GetObject",
                   "s3:ListBucket"
               ],
               "Resource": [
                   "arn:aws:s3:::<my-spark-jar-bucket>",
                   "arn:aws:s3:::<my-spark-jar-bucket>/*"
               ]
           }
       ]
   }
   EOF
   ```

1. 创建 IAM 策略。

   ```
   aws iam create-policy --policy-name my-policy --policy-document file://my-policy.json
   ```

1. 创建一个 IAM 角色，将该角色与 Spark 驱动程序的 Kubernetes 服务账户关联：

   ```
   eksctl create iamserviceaccount --name my-spark-driver-sa --namespace spark-operator \
   --cluster my-cluster --role-name "my-role" \
   --attach-policy-arn arn:aws:iam::111122223333:policy/my-policy --approve
   ```

1. 创建一个 YAML 文件，其中包含 Spark 驱动程序服务账户所需的[权限](https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/spark-submit-security.html)：

   ```
   cat >spark-rbac.yaml <<EOF
   apiVersion: rbac.authorization.k8s.io/v1
   kind: Role
   metadata:
     namespace: default
     name: emr-containers-role-spark
   rules:
   - apiGroups:
     - ""
     resources:
     - pods
     verbs:
     - "*"
   - apiGroups:
     - ""
     resources:
     - services
     verbs:
     - "*"
   - apiGroups:
     - ""
     resources:
     - configmaps
     verbs:
     - "*"
   - apiGroups:
     - ""
     resources:
     - persistentvolumeclaims
     verbs:
     - "*"
   ---
   apiVersion: rbac.authorization.k8s.io/v1
   kind: RoleBinding
   metadata:
     name: spark-role-binding
     namespace: default
   roleRef:
     apiGroup: rbac.authorization.k8s.io
     kind: Role
     name: emr-containers-role-spark
   subjects:
   - kind: ServiceAccount
     name: emr-containers-sa-spark
     namespace: default
   EOF
   ```

1. 应用集群角色绑定配置。

   ```
   kubectl apply -f spark-rbac.yaml
   ```

1. `kubectl` 命令将返回已创建账户的确认信息。

   ```
   serviceaccount/emr-containers-sa-spark created
   clusterrolebinding.rbac.authorization.k8s.io/emr-containers-role-spark configured
   ```

## 运行 Spark 应用程序
<a name="spark-submit-security-irsa-app-run"></a>

Amazon EMR 6.10.0 及更高版本都支持 spark-submit 在 Amazon EKS 集群上运行 Spark 应用程序。要运行 Spark 应用程序，请按照下述步骤操作：

1. 确保已完成[设置 Amazon EMR on EKS 的 spark-submit](https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/spark-submit-setup.html) 中的步骤。

1. 设置以下环境变量的值：

   ```
   export SPARK_HOME=spark-home
   export MASTER_URL=k8s://Amazon EKS-cluster-endpoint
   ```

1. 现在，使用以下命令提交 Spark 应用程序：

   ```
   $SPARK_HOME/bin/spark-submit \
    --class org.apache.spark.examples.SparkPi \
    --master $MASTER_URL \
    --conf spark.kubernetes.container.image=895885662937.dkr.ecr.us-west-2.amazonaws.com/spark/emr-6.15.0:latest \
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=emr-containers-sa-spark \
    --deploy-mode cluster \
    --conf spark.kubernetes.namespace=default \
    --conf "spark.driver.extraClassPath=/usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar:/usr/share/aws/sagemaker-spark-sdk/lib/sagemaker-spark-sdk.jar:/home/hadoop/extrajars/*" \
    --conf "spark.driver.extraLibraryPath=/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native:/docker/usr/lib/hadoop/lib/native:/docker/usr/lib/hadoop-lzo/lib/native" \
    --conf "spark.executor.extraClassPath=/usr/lib/hadoop-lzo/lib/*:/usr/lib/hadoop/hadoop-aws.jar:/usr/share/aws/aws-java-sdk/*:/usr/share/aws/emr/emrfs/conf:/usr/share/aws/emr/emrfs/lib/*:/usr/share/aws/emr/emrfs/auxlib/*:/usr/share/aws/emr/security/conf:/usr/share/aws/emr/security/lib/*:/usr/share/aws/hmclient/lib/aws-glue-datacatalog-spark-client.jar:/usr/share/java/Hive-JSON-Serde/hive-openx-serde.jar:/usr/share/aws/sagemaker-spark-sdk/lib/sagemaker-spark-sdk.jar:/home/hadoop/extrajars/*" \
    --conf "spark.executor.extraLibraryPath=/usr/lib/hadoop/lib/native:/usr/lib/hadoop-lzo/lib/native:/docker/usr/lib/hadoop/lib/native:/docker/usr/lib/hadoop-lzo/lib/native" \
    --conf spark.hadoop.fs.s3.customAWSCredentialsProvider=com.amazonaws.auth.WebIdentityTokenCredentialsProvider \
    --conf spark.hadoop.fs.s3.impl=com.amazon.ws.emr.hadoop.fs.EmrFileSystem \
    --conf spark.hadoop.fs.AbstractFileSystem.s3.impl=org.apache.hadoop.fs.s3.EMRFSDelegate \
    --conf spark.hadoop.fs.s3.buffer.dir=/mnt/s3 \
    --conf spark.hadoop.fs.s3.getObject.initialSocketTimeoutMilliseconds="2000" \
    --conf spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version.emr_internal_use_only.EmrFileSystem="2" \
    --conf spark.hadoop.mapreduce.fileoutputcommitter.cleanup-failures.ignored.emr_internal_use_only.EmrFileSystem="true" \
    s3://my-pod-bucket/spark-examples.jar 20
   ```

1. Spark 驱动程序完成 Spark 作业后，您应该会在提交结束时看到一个日志行，指示 Spark 作业已完成。

   ```
   23/11/24 17:02:14 INFO LoggingPodStatusWatcherImpl: Application org.apache.spark.examples.SparkPi with submission ID default:org-apache-spark-examples-sparkpi-4980808c03ff3115-driver finished
   23/11/24 17:02:14 INFO ShutdownHookManager: Shutdown hook called
   ```

## 清理
<a name="spark-submit-security-irsa-cleanup"></a>

运行完应用程序后，可使用以下命令执行清理。

```
kubectl delete -f spark-rbac.yaml
```