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