Spark on K8S （Kubernetes Native）

Spark on K8S 的几种模式

• Standalone：在 K8S 启动一个长期运行的集群，所有 Job 都通过 spark-submit 向这个集群提交
• Kubernetes Native：通过 spark-submit 直接向 K8S 的 API Server 提交，申请到资源后启动 Pod 做为 Driver 和 Executor 执行 Job，参考 http://spark.apache.org/docs/2.4.6/running-on-kubernetes.html
• Spark Operator：安装 Spark Operator，然后定义 spark-app.yaml，再执行 kubectl apply -f spark-app.yaml，这种申明式 API 和调用方式是 K8S 的典型应用方式，参考 https://github.com/GoogleCloudPlatform/spark-on-k8s-operator
  
  

Start Minikube

sudo minikube start --driver=none --image-repository=registry.cn-hangzhou.aliyuncs.com/google_containers

如果启动失败可以尝试先删除集群 minikube delete

Spark on K8S 官网

https://spark.apache.org/docs/latest/running-on-kubernetes.html

上面没说 Spark 版本和 K8S 版本的兼容问题，但是是有影响的

Download Spark

https://archive.apache.org/dist/spark/

Spark 可能和 Hadoop 关系比较紧密，可以下载带 Hadoop 的版本，这样会有 Hadoop 的 jar 包可以用，不然可能会出现找不到包和类的错误，哪怕其实没用到 Hadoop

Build Spark Image

Spark 提供 bin/docker-image-tool.sh 工具用于 build image

这个工具会找到 kubernetes/dockerfiles 下的 docker 文件，根据 docker file 会把需要的 Spark 命令、工具、库、jar 包、java、example、entrypoint.sh 等 build 进 image

2.3 只支持 Java/Scala，从 2.4 开始支持 Python 和 R，会有三个 docker file，会 build 出三个 image，其中 Python 和 R 是基于 Java/Scala 版的

sudo ./bin/docker-image-tool.sh -t my_spark_2.4_hadoop_2.7 build

遇到类似下面的错误

WARNING: Ignoring http://dl-cdn.alpinelinux.org/alpine/v3.9/main/x86_64/APKINDEX.tar.gz: temporary error (try again later)
WARNING: Ignoring http://dl-cdn.alpinelinux.org/alpine/v3.9/community/x86_64/APKINDEX.tar.gz: temporary error (try again later)
ERROR: unsatisfiable constraints:
  bash (missing):
    required by: world[bash]

这是网络问题，可以修改 ./bin/docker-image-tool.sh，在里面的 docker build 命令加上 --network=host 使容器使用宿主机网络 (要确保宿主机网络是 OK 的)

在宿主机提交 Job

bin/spark-submit 
    --master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> 
    --deploy-mode cluster 
    --name spark-pi 
    --class org.apache.spark.examples.SparkPi 
    --conf spark.executor.instances=5 
    --conf spark.kubernetes.container.image=<spark-image> 
    local:///path/to/examples.jar

注意这里的 local:///path/to/examples.jar 指的是 容器的文件系统，不是执行 spark-submit 的机器的文件系统，官网的说法：Note that using application dependencies from the submission client's local file system is currently not yet supported.

如果不使用 local 的话，也可以用 HTTP、HDFS 等系统，没指定的话默认是 local 模式

因为一开始没用带 Hadoop 包的 Spark，结果 spark-submit 会报 classNotFound
然后指定 --jars 或是在宿主机的 conf/spark-env.sh 添加

export SPARK_DIST_CLASSPATH=$(/home/lin/Hadoop/hadoop-2.8.3/bin/hadoop classpath)

这样 spark-submit 过了，但容器跑起来后还是报 classNotFound
实际上启动的 driver 容器又调用了 spark-submit，只是改了一些参数，比如把 cluster 模式改成 client 模式
后来改成使用带 Hadoop 包的 Spark，这个问题就没出现了
所以推测 spark-submit 使用 --jars 指定的包，可能也需要在 容器里有

获取 K8S Api Server 的地址

sudo kubectl cluster-info

假设返回

https://192.168.0.107:8443

那么 spark-submit 命令是

# --master 指定 k8s api server
# --conf spark.kubernetes.container.image 指定通过 docker-image-tool.sh 创建的镜像
# 第一个 wordcount.py 是要执行的命令
# 第二个 wordcount.py 是参数，即统计 wordcount.py 文件的单词量
bin/spark-submit 
    --master k8s://https://192.168.0.107:8443 
    --deploy-mode cluster 
    --name spark-test 
    --conf spark.executor.instances=3 
    --conf spark.kubernetes.container.image=spark-py:my_spark_2.4_hadoop_2.7 
    /opt/spark/examples/src/main/python/wordcount.py 
    /opt/spark/examples/src/main/python/wordcount.py

这样可能会报证书错误，无法启动 Pod，可能需要配置证书
Spark on K8S 官网看到有 spark.kubernetes.authenticate.submission.caCertFile 配置项，不过没试
在测试环境可以用下面的命令使用 proxy，生成一个不需要证书认证的地址

kubectl proxy

然后 spark-submit 命令变成

# Api Server 的地址变成 http://127.0.0.1:8001
bin/spark-submit 
    --master k8s://http://127.0.0.1:8001 
    --deploy-mode cluster 
    --name spark-test 
    --conf spark.executor.instances=3 
    --conf spark.kubernetes.container.image=spark-py:my_spark_2.4_hadoop_2.7 
    /opt/spark/examples/src/main/python/wordcount.py 
    /opt/spark/examples/src/main/python/wordcount.py

这样还是会报错，在宿主机或容器里报，没有权限，需要在 K8S 配置一个有权限的用户

准备一个 role.yaml 文件

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

可以参考 https://github.com/GoogleCloudPlatform/spark-on-k8s-operator/blob/master/manifest/spark-rbac.yaml

执行命令

sudo kubectl apply -f role.yaml

查看配置

sudo kubectl get role
sudo kubectl get role spark-role -o yaml
sudo kubectl get rolebinding
sudo kubectl get rolebinding spark-role-binding -o yaml

重新提交

# 添加了 --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark
bin/spark-submit 
    --master k8s://http://127.0.0.1:8001 
    --deploy-mode cluster 
    --name spark-test 
    --conf spark.executor.instances=3 
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark 
    --conf spark.kubernetes.container.image=spark-py:my_spark_2.4_hadoop_2.7 
    /opt/spark/examples/src/main/python/wordcount.py 
    /opt/spark/examples/src/main/python/wordcount.py

没报权限错误了，但可能还会有其他错误

20/07/09 06:32:23 INFO SparkContext: Successfully stopped SparkContext
Traceback (most recent call last):
  File "/opt/spark/examples/src/main/python/wordcount.py", line 33, in <module>
    .appName("PythonWordCount")
  File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py", line 173, in getOrCreate
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 367, in getOrCreate
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 136, in __init__
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 198, in _do_init
  File "/opt/spark/python/lib/pyspark.zip/pyspark/context.py", line 306, in _initialize_context
  File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1525, in __call__
  File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: org.apache.spark.SparkException: External scheduler cannot be instantiated
        at org.apache.spark.SparkContext$.org$apache$spark$SparkContext$$createTaskScheduler(SparkContext.scala:2794)
        at org.apache.spark.SparkContext.<init>(SparkContext.scala:493)
        at org.apache.spark.api.java.JavaSparkContext.<init>(JavaSparkContext.scala:58)
        at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
        at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
        at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
        at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
        at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:247)
        at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
        at py4j.Gateway.invoke(Gateway.java:238)
        at py4j.commands.ConstructorCommand.invokeConstructor(ConstructorCommand.java:80)
        at py4j.commands.ConstructorCommand.execute(ConstructorCommand.java:69)
        at py4j.GatewayConnection.run(GatewayConnection.java:238)
        at java.lang.Thread.run(Thread.java:748)
Caused by: io.fabric8.kubernetes.client.KubernetesClientException: Operation: [get]  for kind: [Pod]  with name: [spark-test-1594276334218-driver]  in namespace: [default]  failed.
        at io.fabric8.kubernetes.client.KubernetesClientException.launderThrowable(KubernetesClientException.java:64)
        at io.fabric8.kubernetes.client.KubernetesClientException.launderThrowable(KubernetesClientException.java:72)
        at io.fabric8.kubernetes.client.dsl.base.BaseOperation.getMandatory(BaseOperation.java:237)
        at io.fabric8.kubernetes.client.dsl.base.BaseOperation.get(BaseOperation.java:170)
        at org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator$$anonfun$1.apply(ExecutorPodsAllocator.scala:57)
        at org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator$$anonfun$1.apply(ExecutorPodsAllocator.scala:55)
        at scala.Option.map(Option.scala:146)
        at org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.<init>(ExecutorPodsAllocator.scala:55)
        at org.apache.spark.scheduler.cluster.k8s.KubernetesClusterManager.createSchedulerBackend(KubernetesClusterManager.scala:89)
        at org.apache.spark.SparkContext$.org$apache$spark$SparkContext$$createTaskScheduler(SparkContext.scala:2788)
        ... 13 more
Caused by: java.net.SocketException: Broken pipe (Write failed)
        at java.net.SocketOutputStream.socketWrite0(Native Method)

这个 Broken pipe 应该是 Spark 使用的代码和 jar 包，跟 K8S 不兼容导致的
尝试替换 spark 下面的 jar 目录下的 k8s 包

https://repo1.maven.org/maven2/io/fabric8/kubernetes-client/4.4.2/kubernetes-client-4.4.2.jar
https://gitee.com/everworking/kubernetes-client

但还是有其他问题，应该是版本不兼容导致的
查看 Spark 2.4.6 的 jars 目录可以看到使用的 K8S jar 包

kubernetes-client-4.6.1.jar
kubernetes-model-4.6.1.jar
kubernetes-model-common-4.6.1.jar

查看 Kubernetes Client 的说明
https://github.com/fabric8io/kubernetes-client#compatibility-matrix
可以看到 4.6.1 可以匹配的 Kubernetes 最高版本是 15（Spark 官网对 K8S 版本的兼容就没说清楚）
而当前最新的 Minikube 默认安装的版本是 18

删除并重新启动 15 版本的 Kubernetes 集群

sudo minikube stop
sudo minikube delete

sudo rm -rf ~/.kube
sudo rm -rf ~/.minikube

sudo minikube start --driver=none 
                    --image-repository=registry.cn-hangzhou.aliyuncs.com/google_containers 
                    --kubernetes-version="v1.15.3"

同时下载 15 版本的 kubectl

curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.15.3/bin/linux/amd64/kubectl

查看版本确保都是 15 的

sudo kubectl version --client

同样的命令重新提交

# 添加了 --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark
bin/spark-submit 
    --master k8s://http://127.0.0.1:8001 
    --deploy-mode cluster 
    --name spark-test 
    --conf spark.executor.instances=3 
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark 
    --conf spark.kubernetes.container.image=spark-py:my_spark_2.4_hadoop_2.7 
    /opt/spark/examples/src/main/python/wordcount.py 
    /opt/spark/examples/src/main/python/wordcount.py

这次成功了，可以看到 Driver 和 Executor 的 Pod 都启动了

NAME                                   READY   STATUS    RESTARTS   AGE
pythonwordcount-1595818025111-exec-1   1/1     Running   0          12s
pythonwordcount-1595818025401-exec-2   1/1     Running   0          12s
pythonwordcount-1595818025443-exec-3   0/1     Pending   0          12s
spark-test-1595818015819-driver        1/1     Running   0          20s

查看相应的 Service

NAME                                  TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)             AGE
kubernetes                            ClusterIP   10.96.0.1    <none>        443/TCP             27m
spark-test-1595818015819-driver-svc   ClusterIP   None         <none>        7078/TCP,7079/TCP   22s

通过 docker 命令查看相应的容器

CONTAINER ID        IMAGE                                                                     COMMAND                  CREATED             STATUS              PORTS               NAMES
a55500cdd00f        9cdc285a4fbb                                                              "/opt/entrypoint.sh …"   9 seconds ago       Up 8 seconds                            k8s_executor_pythonwordcount-1595818025401-exec-2_default_0624eb2d-aeab-454e-bce5-15c38b46f970_0
37ddc67f3527        9cdc285a4fbb                                                              "/opt/entrypoint.sh …"   9 seconds ago       Up 8 seconds                            k8s_executor_pythonwordcount-1595818025111-exec-1_default_0d8fa5ac-07dc-41ea-a7fb-a75d1f5dfdf9_0
5d9d9c5517e4        registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.1             "/pause"                 10 seconds ago      Up 8 seconds                            k8s_POD_pythonwordcount-1595818025401-exec-2_default_0624eb2d-aeab-454e-bce5-15c38b46f970_0
210ebc82c274        registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.1             "/pause"                 10 seconds ago      Up 8 seconds                            k8s_POD_pythonwordcount-1595818025111-exec-1_default_0d8fa5ac-07dc-41ea-a7fb-a75d1f5dfdf9_0
400f155d78f2        9cdc285a4fbb                                                              "/opt/entrypoint.sh …"   15 seconds ago      Up 14 seconds                           k8s_spark-kubernetes-driver_spark-test-1595818015819-driver_default_3198bca3-fcb7-4a5c-8821-c5fe7ef02dfa_0
d4c7f82d90de        registry.cn-hangzhou.aliyuncs.com/google_containers/pause:3.1             "/pause"                 16 seconds ago      Up 14 seconds                           k8s_POD_spark-test-1595818015819-driver_default_3198bca3-fcb7-4a5c-8821-c5fe7ef02dfa_0

这里只有两个 Executor 容器，而提交的时候是指定 3 个
上面的 Pod 也可以看到有一个处于 Pending 状态
查看 Pending 的 Pod

sudo kubectl describe pod pythonwordcount-1595818025443-exec-3

返回很多信息，最后面可以看到

Events:
  Type     Reason            Age                From               Message
  ----     ------            ----               ----               -------
  Warning  FailedScheduling  23s (x2 over 23s)  default-scheduler  0/1 nodes are available: 1 Insufficient cpu.

所以 Pending 的原因是 CPU 不够
不过这不影响 Job 的正常运行

Spark Job 结束后 Executor 和 Driver 容器都会变成 Exit 状态
但是 Executor 变成 Exit 一小段时间后就不见了，相应的 Pod 也被删除了
而 Driver 一直都在，且相应的 pod 会变成 Completed 状态

NAME                              READY   STATUS      RESTARTS   AGE
spark-test-1595818015819-driver   0/1     Completed   0          32s

如果出错了会是 Error 状态

在容器里提交 Job

定义 deployment，注意指定 serviceAccountName 使用前面创建的 spark role

apiVersion: apps/v1
kind: Deployment
metadata:
  name: spark-client
spec:
  replicas: 1
  selector:
    matchLabels:
      app: spark
      component: client
  template:
    metadata:
      labels:
        app: spark
        component: client
    spec:
      containers:
      - name: sparkclient
        image: spark-py:2.4.6
        workingDir: /opt/spark
        command: ["/bin/bash", "-c", "while true;do echo hello;sleep 6000;done"]
      serviceAccountName: spark

部署

sudo kubectl create -f client-deployment.yaml

查看并登陆 pod

sudo kubectl exec -t -i spark-client-6479b76776-l5bzw /bin/bash

通过 env 命令可以看到容器里有定义 Kubernetes API Server 的地址

KUBERNETES_SERVICE_HOST=10.96.0.1
KUBERNETES_SERVICE_PORT_HTTPS=443

实际上容器上还有相应的 token 和证书，可以用来访问 API Server

TOKEN=$(cat /var/run/secrets/kubernetes.io/serviceaccount/token)

curl --cacert /var/run/secrets/kubernetes.io/serviceaccount/ca.crt 
     -H "Authorization: Bearer $TOKEN" 
     -s https://${KUBERNETES_SERVICE_HOST}:${KUBERNETES_SERVICE_PORT_HTTPS}/api/v1/namespaces/default/pods

但通过 spark-submit 提交 Job 报错了，说是没权限获取 configMap，看来要求的权限和在宿主机提交不一样
改变 spark role 的配置，允许操作所有资源，然后重新执行 kubectl create

- apiGroups: [""]
  resources: ["*"]
  verbs: ["*"]

重新提交 Job，可以看到成功启动运行了

# 第二个 wordcount.py 是作为参数用
bin/spark-submit 
    --master k8s://https://${KUBERNETES_SERVICE_HOST}:${KUBERNETES_SERVICE_PORT_HTTPS} 
    --deploy-mode cluster 
    --name spark-test 
    --conf spark.executor.instances=3 
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark 
    --conf spark.kubernetes.container.image=spark-py:2.4.6 
    /opt/spark/examples/src/main/python/wordcount.py 
    /opt/spark/examples/src/main/python/wordcount.py

 转自：https://www.cnblogs.com/moonlight-lin/p/13296909.html