标签：nload binding info export github stop yam uber 环境
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
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
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
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)
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
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
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
在测试环境可以用下面的命令使用 proxy
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，应该直接把 K8S 的版本降低，到 1.16 (官网里没写 Spark 和 K8S 的版本匹配问题)
Spark on K8S （Kubernetes Native）
标签：nload binding info export github stop yam uber 环境
原文地址：https://www.cnblogs.com/moonlight-lin/p/13296909.html
