目录

  • 版本要求
  • 启动 minikube
  • 安装 Helm
  • 使用 helm 安装 spark-operator
  • 提交 spark 任务
  • Schedule 机制
  • Metric



版本要求

Spark 官方没有开发 Spark Operator，现在是由 Google 开发的

这个 Operator 使用的 Spark on K8S 方案和 Spark 官方一样，只是包了一层，使得可以像声明其他 K8S 的应用（比如声明 Service）一样声明 Spark 任务，也可以像其他 K8S 应用一样，支持自动重启、失败重试、更新、挂载配置文件等功能

缺点就是受 Operator 自身的版本和实现的制约

如果使用官方 Spark on K8S 的 cluster 模式，需要自己实现一个用于提交 spark 任务的 pod（起类似 operator 的作用）

如果使用官方 Spark on K8S 的 client 模式，就不需要额外的 pod 或 operator，但每个 Spark 任务需要自己为 driver 配置用于和 executor 之间连接的端口

对 Helm 和 K8S 的版本要求参考官网

https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/tree/master/charts/spark-operator-chart

Prerequisites

Helm >= 3
Kubernetes >= 1.13

Operator 和 Spark 版本

https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/#version-matrix

spark中worker node的作用 spark operator_jar

Spark Operator 启动后也是一个 Pod，会在调用 K8S 的 kubectl apply -f test.yml 命令的时候，读取配置文件的内容，然后调用 spark-submit 命令启动 spark 任务，所以特定版本的 operator 是基于特定版本的 spark 的，除非这个 operator 安装了多个版本的 spark 然后在 test.yml 指定版本，但现在的实现应该是没有的

启动 minikube

sudo minikube start --driver=none \
                    --image-repository=registry.cn-hangzhou.aliyuncs.com/google_containers \
                    --kubernetes-version="v1.16.3"

这是轻量的 K8S 环境

安装 Helm

下载

wget https://get.helm.sh/helm-v3.0.2-linux-amd64.tar.gz
tar zxvf helm-v3.0.2-linux-amd64.tar.gz
sudo cp ./linux-amd64/helm /usr/local/bin/

查看 helm 命令

> helm version
version.BuildInfo{Version:"v3.0.2", GitCommit:"19e47ee3283ae98139d98460de796c1be1e3975f", GitTreeState:"clean", GoVersion:"go1.13.5"}

添加常用源

helm repo add stable https://kubenetes-charts.storage.googleapis.com
helm repo add incubator https://kubernetes-charts-incubator.storage.googleapis.com
helm repo add bitnami https://charts.bitnami.com/bitnami
helm repo add aliyuncs https://apphub.aliyuncs.com

查找 chart

helm search repo tomcat

返回

NAME            CHART VERSION   APP VERSION     DESCRIPTION
aliyuncs/tomcat 6.2.3           9.0.31          Chart for Apache Tomcat
bitnami/tomcat  9.5.3           10.0.12         Chart for Apache Tomcat

google 的源可能访问不了

使用 helm 安装 spark-operator

添加源

helm repo add spark-operator https://googlecloudplatform.github.io/spark-on-k8s-operator

安装 spark operator

helm install my-release spark-operator/spark-operator

返回

NAME: my-release
LAST DEPLOYED: Fri Nov  5 11:53:04 2021
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None

如果要卸载

helm uninstall my-release

# 可能有残留的东西要删除
kubectl delete serviceaccounts my-release-spark-operator
kubectl delete clusterrole my-release-spark-operator
kubectl delete clusterrolebindings my-release-spark-operator

成功启动后可以看到有个 spark operator 的 pod 在运行

spark operator 的 image 可能会没权限下载，导致 operator 的 pod 报错起不来

message: Back-off pulling image "gcr.io/spark-operator/spark-operator:latest"
        reason: ImagePullBackOff

可以通过其他方式下载下来后，用 docker tag 20144a306214 gcr.io/spark-operator/spark-operator:latest 命令改 tag

通过 helm 创建 spark operator 的时候会自动创建 spark 的 service account 用于申请和操作 pod，也可以自己创建 service account

apiVersion: v1
kind: ServiceAccount
metadata:
  name: spark
  namespace: spark
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: spark
  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: spark
subjects:
- kind: ServiceAccount
  name: spark
  namespace: spark
roleRef:
  kind: Role
  name: spark-role
  apiGroup: rbac.authorization.k8s.io

提交 spark 应用的时候需要指定这个 service account

提交 spark 任务

配置文件像这样

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: spark-pi
  namespace: default
spec:
  type: Scala
  mode: cluster
  image: "gcr.io/spark-operator/spark:v3.1.1"
  imagePullPolicy: Always
  mainClass: org.apache.spark.examples.SparkPi
  mainApplicationFile: "local:///opt/spark/examples/jars/spark-examples_2.12-3.1.1.jar"
  sparkVersion: "3.1.1"
  restartPolicy:
    type: Never
  volumes:
    - name: "test-volume"
      hostPath:
        path: "/tmp"
        type: Directory
  driver:
    cores: 1
    coreLimit: "1200m"
    memory: "512m"
    labels:
      version: 3.1.1
    serviceAccount: spark
    volumeMounts:
      - name: "test-volume"
        mountPath: "/tmp"
  executor:
    cores: 1
    instances: 1
    memory: "512m"
    labels:
      version: 3.1.1
    volumeMounts:
      - name: "test-volume"
        mountPath: "/tmp"

这里的 image 必须包含要执行的 spark 代码，local 指的是 image 里面的文件

这里虽然可以配 sparkVersion 但从 operator 的代码看，应该是没用到的，所以 operator 应该只用固定版本的 spark-submit 命令

启动 spark 任务

kubectl apply -f spark-test.yaml

如果启动成功，就可以看到相应的 driver pod 和 executor pod 在运行

Schedule 机制

spark operator 支持 cron 机制，只需要改成 ScheduledSparkApplication 类型

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: ScheduledSparkApplication
metadata:
  name: spark-pi-scheduled
  namespace: default
spec:
  schedule: "@every 5m"
  concurrencyPolicy: Allow
  template:
    type: Scala
    mode: cluster
    image: "gcr.io/spark-operator/spark:v3.1.1"
    imagePullPolicy: Always
    mainClass: org.apache.spark.examples.SparkPi
    mainApplicationFile: "local:///opt/spark/examples/jars/spark-examples_2.12-3.1.1.jar"
    sparkVersion: "3.1.1"
    restartPolicy:
      type: Never
    driver:
      cores: 1
      coreLimit: "1200m"
      memory: "512m"
      labels:
        version: 3.1.1
      serviceAccount: spark
    executor:
      cores: 1
      instances: 1
      memory: "512m"
      labels:
        version: 3.1.1

这里的 schedule 也可以是

"*/10 * * * *"

最小单位是分钟

Metric

https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/blob/master/docs/quick-start-guide.md#enable-metric-exporting-to-prometheus

spark operator 有 metric 接口可以暴露一系列 metrics (比如成功数量、失败数量、运行数量等等) 给 Prometheus

Helm 安装 operator 的时候默认打开了 metric，如果要关掉可以

helm install my-release spark-operator/spark-operator --namespace spark-operator --set metrics.enable=false

可以配置 spark-operator 的 deployment 修改 metric 的路径、端口等

apiVersion: apps/v1
kind: Deployment
metadata:
  name: sparkoperator
  namespace: spark-operator
  labels:
    app.kubernetes.io/name: sparkoperator
    app.kubernetes.io/version: v1beta2-1.3.0-3.1.1
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/name: sparkoperator
      app.kubernetes.io/version: v1beta2-1.3.0-3.1.1
  strategy:
    type: Recreate
  template:
    metadata:
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "10254"
        prometheus.io/path: "/metrics"
      labels:
        app.kubernetes.io/name: sparkoperator
        app.kubernetes.io/version: v1beta2-1.3.0-3.1.1
    spec:
      serviceAccountName: sparkoperator
      containers:
      - name: sparkoperator
        image: gcr.io/spark-operator/spark-operator:v1beta2-1.3.0-3.1.1
        imagePullPolicy: Always
        ports:
          - containerPort: 10254
        args:
        - -logtostderr
        - -enable-metrics=true
        - -metrics-labels=app_type

可以配置 spark app 要不要暴露 metric

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: spark-pi
  namespace: default
spec:
  type: Scala
  mode: cluster
  image: "gcr.io/spark-operator/spark:v3.1.1-gcs-prometheus"
  imagePullPolicy: Always
  mainClass: org.apache.spark.examples.SparkPi
  mainApplicationFile: "local:///opt/spark/examples/jars/spark-examples_2.12-3.1.1.jar"
  arguments:
    - "100000"
  sparkVersion: "3.1.1"
  restartPolicy:
    type: Never
  driver:
    cores: 1
    coreLimit: "1200m"
    memory: "512m"
    labels:
      version: 3.1.1
    serviceAccount: spark
  executor:
    cores: 1
    instances: 1
    memory: "512m"
    labels:
      version: 3.1.1
  monitoring:
    exposeDriverMetrics: true
    exposeExecutorMetrics: true
    prometheus:
      jmxExporterJar: "/prometheus/jmx_prometheus_javaagent-0.11.0.jar"
      port: 8090

spark operator 如果重启这些 metric 会被重置