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搭建Spark应用

更新时间: 2022-01-19

本文介绍如何使用阿里云Serverless Kubernetes(ASK)和弹性容器实例(ECI),快速搭建Spark应用。

背景信息

Apache Spark是一个在数据分析领域广泛使用的开源项目,它常被应用于众所周知的大数据和机器学习工作负载中。从Apache Spark 2.3.0版本开始,您可以在Kubernetes上运行和管理Spark资源。

Spark Operator是专门针对Spark on Kubernetes设计的Operator,开发者可以通过使用CRD的方式,提交Spark任务到Kubernetes集群中。使用Spark Operator有以下优势:

  • 能够弥补原生Spark对Kubernetes支持不足的部分。

  • 能够快速和Kubernetes生态中的存储、监控、日志等组件对接。

  • 支持故障恢复、弹性伸缩、调度优化等高阶Kubernetes特性。

准备工作

  1. 创建ASK集群。

    容器服务管理控制台上创建ASK集群。更多信息,请参见创建Serverless Kubernetes集群

    说明

    如果您需要从公网拉取镜像,或者训练任务需要访问公网,请配置NAT网关。

    您可以通过kubectl管理和访问ASK集群,相关操作如下:

  2. 创建OSS存储空间。

    您需要创建一个OSS存储空间(Bucket)用来存放测试数据、测试结果和测试过程中的日志等。关于如何创建OSS Bucket,请参见创建存储空间

安装Spark Operator

  1. 安装Spark Operator。

    1. 容器服务管理控制台的左侧导航栏,选择市场>应用市场

    2. 应用目录页签,找到并单击ack-spark-operator

    3. 单击创建,在弹出面板中完成相关配置。

      参数配置中,sparkJobNamespace为需要部署Spark作业的命名空间,默认为default,如果设置为空字符串则表示所有命名空间都监听。

  2. 创建ServiceAccount、Role和Rolebinding。

    Spark作业需要一个ServiceAccount来获取创建Pod的权限,因此需要创建ServiceAccount、Role和Rolebinding。YAML示例如下,请根据需要修改三者的Namespace。

    apiVersion: v1
    kind: ServiceAccount
    metadata:
      name: spark
      namespace: default
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: Role
    metadata:
      namespace: default
      name: spark-role
    rules:
    - apiGroups: [""]
      resources: ["pods"]
      verbs: ["*"]
    - apiGroups: [""]
      resources: ["services"]
      verbs: ["*"]
    ---
    apiVersion: rbac.authorization.k8s.io/v1
    kind: RoleBinding
    metadata:
      name: spark-role-binding
      namespace: default
    subjects:
    - kind: ServiceAccount
      name: spark
      namespace: default
    roleRef:
      kind: Role
      name: spark-role
      apiGroup: rbac.authorization.k8s.io

构建Spark作业镜像

您需要编译Spark作业的JAR包,使用Dockerfile打包镜像。

以阿里云容器服务的Spark基础镜像为例,设置Dockerfile内容如下:

FROM registry.aliyuncs.com/acs/spark:ack-2.4.5-latest
RUN mkdir -p /opt/spark/jars
# 如果需要使用OSS(读取OSS数据或者离线Event到OSS),可以添加以下JAR包到镜像中
ADD https://repo1.maven.org/maven2/com/aliyun/odps/hadoop-fs-oss/3.3.8-public/hadoop-fs-oss-3.3.8-public.jar $SPARK_HOME/jars
ADD https://repo1.maven.org/maven2/com/aliyun/oss/aliyun-sdk-oss/3.8.1/aliyun-sdk-oss-3.8.1.jar $SPARK_HOME/jars
ADD https://repo1.maven.org/maven2/org/aspectj/aspectjweaver/1.9.5/aspectjweaver-1.9.5.jar $SPARK_HOME/jars
ADD https://repo1.maven.org/maven2/org/jdom/jdom/1.1.3/jdom-1.1.3.jar $SPARK_HOME/jars
COPY SparkExampleScala-assembly-0.1.jar /opt/spark/jars

建议您使用阿里云Spark基础镜像。阿里云提供了Spark2.4.5的基础镜像,针对Kubernetes场景(调度、弹性)进行了优化,能够极大提升调度速度和启动速度。您可以通过设置Helm Chart的变量enableAlibabaCloudFeatureGates: true的方式开启,如果想要达到更快的启动速度,可以设置enableWebhook: falsespark-3

构建ImageCache

Spark镜像如果较大,则拉取需要较长时间,您可以通过ImageCache加速镜像拉取。更多信息,请参见管理ImageCache使用ImageCache加速创建Pod

编写作业模板并提交作业

创建一个Spark作业的YMAL配置文件,并进行部署。

  1. 创建spark-pi.yaml文件。

    一个典型的作业模板示例如下。更多信息,请参见spark-on-k8s-operator

    apiVersion: "sparkoperator.k8s.io/v1beta2"
    kind: SparkApplication
    metadata:
      name: spark-pi
      namespace: default
    spec:
      type: Scala
      mode: cluster
      image: "registry.aliyuncs.com/acs/spark-pi:ack-2.4.5-latest"
      imagePullPolicy: Always
      mainClass: org.apache.spark.examples.SparkPi
      mainApplicationFile: "local:///opt/spark/examples/jars/spark-examples_2.11-2.4.5.jar"
      sparkVersion: "2.4.5"
      restartPolicy:
        type: Never
      driver:
        cores: 2
        coreLimit: "2"
        memory: "3g"
        memoryOverhead: "1g"
        labels:
          version: 2.4.5
        serviceAccount: spark
        annotations:
          k8s.aliyun.com/eci-kube-proxy-enabled: 'true'
          k8s.aliyun.com/eci-image-cache: "true"
        tolerations:
        - key: "virtual-kubelet.io/provider"
          operator: "Exists"
      executor:
        cores: 2
        instances: 1
        memory: "3g"
        memoryOverhead: "1g"
        labels:
          version: 2.4.5
        annotations:
          k8s.aliyun.com/eci-kube-proxy-enabled: 'true'
          k8s.aliyun.com/eci-image-cache: "true"
        tolerations:
        - key: "virtual-kubelet.io/provider"
          operator: "Exists"
  2. 部署一个Spark计算任务。

    kubectl apply -f spark-pi.yaml

配置日志采集

以采集Spark的标准输出日志为例,您可以在Spark driver和Spark executor的envVars字段中注入环境变量,实现日志的自动采集。更多信息,请参见自定义配置ECI日志采集

envVars:
   aliyun_logs_test-stdout_project: test-k8s-spark
   aliyun_logs_test-stdout_machinegroup: k8s-group-app-spark
   aliyun_logs_test-stdout: stdout

提交作业时,您可以按上述方式设置driver和executor的环境变量,即可实现日志的自动采集。spark-1

配置历史服务器

历史服务器用于审计Spark作业,您可以通过在Spark Applicaiton的CRD中增加SparkConf字段的方式,将event写入到OSS,再通过历史服务器读取OSS的方式进行展现。配置示例如下:

sparkConf:
   "spark.eventLog.enabled": "true"
   "spark.eventLog.dir": "oss://bigdatastore/spark-events"
   "spark.hadoop.fs.oss.impl": "org.apache.hadoop.fs.aliyun.oss.AliyunOSSFileSystem"
   # oss bucket endpoint such as oss-cn-beijing.aliyuncs.com
   "spark.hadoop.fs.oss.endpoint": "oss-cn-beijing.aliyuncs.com"
   "spark.hadoop.fs.oss.accessKeySecret": ""
   "spark.hadoop.fs.oss.accessKeyId": ""

阿里云也提供了spark-history-server的Chart,您可以在容器服务管理控制台的市场>应用市场页面,搜索ack-spark-history-server进行安装。安装时需在参数中配置OSS的相关信息,示例如下:

oss:
  enableOSS: true
  # Please input your accessKeyId
  alibabaCloudAccessKeyId: ""
  # Please input your accessKeySecret
  alibabaCloudAccessKeySecret: ""
  # oss bucket endpoint such as oss-cn-beijing.aliyuncs.com
  alibabaCloudOSSEndpoint: "oss-cn-beijing.aliyuncs.com"
  # oss file path such as oss://bucket-name/path
  eventsDir: "oss://bigdatastore/spark-events"

安装完成后,您可以在集群详情页面的服务中看到ack-spark-history-server的对外地址,访问对外地址即可查看历史任务归档。spark-2

查看作业结果

  1. 查看Pod的执行情况。

    kubectl get pods

    预期返回结果:

    NAME                            READY      STATUS     RESTARTS   AGE
    spark-pi-1547981232122-driver   1/1       Running    0          12s
    spark-pi-1547981232122-exec-1   1/1       Running    0          3s
  2. 查看实时Spark UI。

    kubectl port-forward spark-pi-1547981232122-driver 4040:4040
  3. 查看Spark Applicaiton的状态。

    kubectl describe sparkapplication spark-pi

    预期返回结果:

    Name:         spark-pi
    Namespace:    default
    Labels:       <none>
    Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                    {"apiVersion":"sparkoperator.k8s.io/v1alpha1","kind":"SparkApplication","metadata":{"annotations":{},"name":"spark-pi","namespace":"defaul...
    API Version:  sparkoperator.k8s.io/v1alpha1
    Kind:         SparkApplication
    Metadata:
      Creation Timestamp:  2019-01-20T10:47:08Z
      Generation:          1
      Resource Version:    4923532
      Self Link:           /apis/sparkoperator.k8s.io/v1alpha1/namespaces/default/sparkapplications/spark-pi
      UID:                 bbe7445c-1ca0-11e9-9ad4-062fd7c19a7b
    Spec:
      Deps:
      Driver:
        Core Limit:  200m
        Cores:       0.1
        Labels:
          Version:        2.4.0
        Memory:           512m
        Service Account:  spark
        Volume Mounts:
          Mount Path:  /tmp
          Name:        test-volume
      Executor:
        Cores:      1
        Instances:  1
        Labels:
          Version:  2.4.0
        Memory:     512m
        Volume Mounts:
          Mount Path:         /tmp
          Name:               test-volume
      Image:                  gcr.io/spark-operator/spark:v2.4.0
      Image Pull Policy:      Always
      Main Application File:  local:///opt/spark/examples/jars/spark-examples_2.11-2.4.0.jar
      Main Class:             org.apache.spark.examples.SparkPi
      Mode:                   cluster
      Restart Policy:
        Type:  Never
      Type:    Scala
      Volumes:
        Host Path:
          Path:  /tmp
          Type:  Directory
        Name:    test-volume
    Status:
      Application State:
        Error Message:
        State:          COMPLETED
      Driver Info:
        Pod Name:             spark-pi-driver
        Web UI Port:          31182
        Web UI Service Name:  spark-pi-ui-svc
      Execution Attempts:     1
      Executor State:
        Spark - Pi - 1547981232122 - Exec - 1:  COMPLETED
      Last Submission Attempt Time:             2019-01-20T10:47:14Z
      Spark Application Id:                     spark-application-1547981285779
      Submission Attempts:                      1
      Termination Time:                         2019-01-20T10:48:56Z
    Events:
      Type    Reason                     Age                 From            Message
      ----    ------                     ----                ----            -------
      Normal  SparkApplicationAdded      55m                 spark-operator  SparkApplication spark-pi was added, Enqueuing it for submission
      Normal  SparkApplicationSubmitted  55m                 spark-operator  SparkApplication spark-pi was submitted successfully
      Normal  SparkDriverPending         55m (x2 over 55m)   spark-operator  Driver spark-pi-driver is pending
      Normal  SparkExecutorPending       54m (x3 over 54m)   spark-operator  Executor spark-pi-1547981232122-exec-1 is pending
      Normal  SparkExecutorRunning       53m (x4 over 54m)   spark-operator  Executor spark-pi-1547981232122-exec-1 is running
      Normal  SparkDriverRunning         53m (x12 over 55m)  spark-operator  Driver spark-pi-driver is running
      Normal  SparkExecutorCompleted     53m (x2 over 53m)   spark-operator  Executor spark-pi-1547981232122-exec-1 completed
  4. 查看日志获取结果。

    NAME                                      READY     STATUS      RESTARTS   AGE
    spark-pi-1547981232122-driver   0/1       Completed   0          1m

    当Spark Applicaiton的状态为Succeed或者Spark driver对应的Pod状态为Completed时,可以查看日志获取结果。

    kubectl logs spark-pi-1547981232122-driver
    Pi is roughly 3.152155760778804