使用服务账户的 IAM 角色（IRSA）设置集群访问权限

完成后先决条件 ，使用创建允许 Amazon Command Line Interface 对您上传到 Amazon S3 的文件进行只读访问的文件：example-policy.json

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

然后，创建 IAM policy example-policy：

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

接下来，创建一个 IAM 角色 example-role，将该角色与 Spark 驱动程序的 Kubernetes 服务账户关联起来：

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

创建一个 yaml 文件，其中包含 Spark 驱动程序服务账户所需的集群角色绑定：

cat >spark-rbac.yaml <<EOF
apiVersion: v1
kind: ServiceAccount
metadata:
  name: driver-account-sa
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: spark-role
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: edit
subjects:
  - kind: ServiceAccount
    name: driver-account-sa
    namespace: spark-operator
EOF

应用集群角色绑定配置：

kubectl apply -f spark-rbac.yaml

创建一个新文件 word-count.yaml，其中包含字数统计应用程序的 SparkApplication 定义。

cat >word-count.yaml <<EOF
apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: word-count
  namespace: spark-operator
spec:
  type: Java
  mode: cluster
  image: "895885662937.dkr.ecr.us-west-2.amazonaws.com/spark/emr-6.10.0:latest"
  imagePullPolicy: Always
  mainClass: org.apache.spark.examples.JavaWordCount
  mainApplicationFile: local:///usr/lib/spark/examples/jars/spark-examples.jar
  arguments:
    - s3://my-pod-bucket/poem.txt
  hadoopConf:
   # EMRFS filesystem
    fs.s3.customAWSCredentialsProvider: com.amazonaws.auth.WebIdentityTokenCredentialsProvider
    fs.s3.impl: com.amazon.ws.emr.hadoop.fs.EmrFileSystem
    fs.AbstractFileSystem.s3.impl: org.apache.hadoop.fs.s3.EMRFSDelegate
    fs.s3.buffer.dir: /mnt/s3
    fs.s3.getObject.initialSocketTimeoutMilliseconds: "2000"
    mapreduce.fileoutputcommitter.algorithm.version.emr_internal_use_only.EmrFileSystem: "2"
    mapreduce.fileoutputcommitter.cleanup-failures.ignored.emr_internal_use_only.EmrFileSystem: "true"
  sparkConf:
    # Required for EMR Runtime
    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/*
    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
    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/*
    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
  sparkVersion: "3.3.1"
  restartPolicy:
    type: Never
  driver:
    cores: 1
    coreLimit: "1200m"
    memory: "512m"
    labels:
      version: 3.3.1
    serviceAccount: my-spark-driver-sa
  executor:
    cores: 1
    instances: 1
    memory: "512m"
    labels:
      version: 3.3.1
EOF

提交 Spark 应用程序。

kubectl apply -f word-count.yaml

kubectl 命令应返回您已成功创建名为 word-count 的 SparkApplication 对象的确认信息。

sparkapplication.sparkoperator.k8s.io/word-count configured

运行以下命令检查 SparkApplication 对象的事件：

kubectl describe sparkapplication word-count -n spark-operator

kubectl 命令应返回 SparkApplication 的描述和事件：

Events:
  Type     Reason                               Age                    From            Message
  ----     ------                               ----                   ----            -------
  Normal   SparkApplicationSpecUpdateProcessed  3m2s (x2 over 17h)     spark-operator  Successfully processed spec update for SparkApplication word-count
  Warning  SparkApplicationPendingRerun         3m2s (x2 over 17h)     spark-operator  SparkApplication word-count is pending rerun
  Normal   SparkApplicationSubmitted            2m58s (x2 over 17h)    spark-operator  SparkApplication word-count was submitted successfully
  Normal   SparkDriverRunning                   2m56s (x2 over 17h)    spark-operator  Driver word-count-driver is running
  Normal   SparkExecutorPending                 2m50s                  spark-operator  Executor [javawordcount-fdd1698807392c66-exec-1] is pending
  Normal   SparkExecutorRunning                 2m48s                  spark-operator  Executor [javawordcount-fdd1698807392c66-exec-1] is running
  Normal   SparkDriverCompleted                 2m31s (x2 over 17h)    spark-operator  Driver word-count-driver completed
  Normal   SparkApplicationCompleted            2m31s (x2 over 17h)    spark-operator  SparkApplication word-count completed
  Normal   SparkExecutorCompleted               2m31s (x2 over 2m31s)  spark-operator  Executor [javawordcount-fdd1698807392c66-exec-1] completed