Spark - 图1

Kubernetes 从 v1.8 开始支持 原生的 Apache Spark 应用（需要 Spark 支持 Kubernetes，比如 v2.3），可以通过 spark-submit 命令直接提交 Kubernetes 任务。比如计算圆周率

  1. bin/spark-submit \
  2.   --deploy-mode cluster \
  3.   --class org.apache.spark.examples.SparkPi \
  4.   --master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
  5.   --kubernetes-namespace default \
  6.   --conf spark.executor.instances=5 \
  7.   --conf spark.app.name=spark-pi \
  8.   --conf spark.kubernetes.driver.docker.image=kubespark/spark-driver:v2.2.0-kubernetes-0.4.0 \
  9.   --conf spark.kubernetes.executor.docker.image=kubespark/spark-executor:v2.2.0-kubernetes-0.4.0 \
  10.   local:///opt/spark/examples/jars/spark-examples_2.11-2.2.0-k8s-0.4.0.jar

或者使用 Python 版本

  1. bin/spark-submit \
  2.   --deploy-mode cluster \
  3.   --master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
  4.   --kubernetes-namespace <k8s-namespace> \
  5.   --conf spark.executor.instances=5 \
  6.   --conf spark.app.name=spark-pi \
  7.   --conf spark.kubernetes.driver.docker.image=kubespark/spark-driver-py:v2.2.0-kubernetes-0.4.0 \
  8.   --conf spark.kubernetes.executor.docker.image=kubespark/spark-executor-py:v2.2.0-kubernetes-0.4.0 \
  9.   --jars local:///opt/spark/examples/jars/spark-examples_2.11-2.2.0-k8s-0.4.0.jar \
  10.   --py-files local:///opt/spark/examples/src/main/python/sort.py \
  11.   local:///opt/spark/examples/src/main/python/pi.py 10

Spark on Kubernetes 部署

Kubernetes 示例 github 上提供了一个详细的 spark 部署方法，由于步骤复杂，这里简化一些部分让大家安装的时候不用去多设定一些东西。

部署条件

  • 一个 kubernetes 群集, 可参考 集群部署
  • kube-dns 正常运作

创建一个命名空间

namespace-spark-cluster.yaml

  1. apiVersion: v1
  2. kind: Namespace
  3. metadata:
  4.   name: "spark-cluster"
  5.   labels:
  6.     name: "spark-cluster"
  1. $ kubectl create -f examples/staging/spark/namespace-spark-cluster.yaml

这边原文提到需要将 kubectl 的执行环境转到 spark-cluster, 这边为了方便我们不这样做, 而是将之后的佈署命名空间都加入 spark-cluster

部署 Master Service

建立一个 replication controller, 来运行 Spark Master 服务

  1. kind: ReplicationController
  2. apiVersion: v1
  3. metadata:
  4.   name: spark-master-controller
  5.   namespace: spark-cluster
  6. spec:
  7.   replicas: 1
  8.   selector:
  9.     component: spark-master
  10.   template:
  11.     metadata:
  12.       labels:
  13.         component: spark-master
  14.     spec:
  15.       containers:
  16.         - name: spark-master
  17.           image: gcr.io/google_containers/spark:1.5.2_v1
  18.           command: ["/start-master"]
  19.           ports:
  20.             - containerPort: 7077
  21.             - containerPort: 8080
  22.           resources:
  23.             requests:
  24.               cpu: 100m
  1. $ kubectl create -f spark-master-controller.yaml

创建 master 服务

spark-master-service.yaml

  1. kind: Service
  2. apiVersion: v1
  3. metadata:
  4.   name: spark-master
  5.   namespace: spark-cluster
  6. spec:
  7.   ports:
  8.     - port: 7077
  9.       targetPort: 7077
  10.       name: spark
  11.     - port: 8080
  12.       targetPort: 8080
  13.       name: http
  14.   selector:
  15.     component: spark-master
  1. $ kubectl create -f spark-master-service.yaml

检查 Master 是否正常运行

  1. $ kubectl get pod -n spark-cluster
  2. spark-master-controller-qtwm8     1/1       Running   0          6d
  1. $ kubectl logs spark-master-controller-qtwm8 -n spark-cluster
  2. 17/08/07 02:34:54 INFO Master: Registered signal handlers for [TERM, HUP, INT]
  3. 17/08/07 02:34:54 INFO SecurityManager: Changing view acls to: root
  4. 17/08/07 02:34:54 INFO SecurityManager: Changing modify acls to: root
  5. 17/08/07 02:34:54 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
  6. 17/08/07 02:34:55 INFO Slf4jLogger: Slf4jLogger started
  7. 17/08/07 02:34:55 INFO Remoting: Starting remoting
  8. 17/08/07 02:34:55 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkMaster@spark-master:7077]
  9. 17/08/07 02:34:55 INFO Utils: Successfully started service 'sparkMaster' on port 7077.
  10. 17/08/07 02:34:55 INFO Master: Starting Spark master at spark://spark-master:7077
  11. 17/08/07 02:34:55 INFO Master: Running Spark version 1.5.2
  12. 17/08/07 02:34:56 INFO Utils: Successfully started service 'MasterUI' on port 8080.
  13. 17/08/07 02:34:56 INFO MasterWebUI: Started MasterWebUI at http://10.2.6.12:8080
  14. 17/08/07 02:34:56 INFO Utils: Successfully started service on port 6066.
  15. 17/08/07 02:34:56 INFO StandaloneRestServer: Started REST server for submitting applications on port 6066
  16. 17/08/07 02:34:56 INFO Master: I have been elected leader! New state: ALIVE

若 master 已经被建立与运行, 我们可以透过 Spark 开发的 webUI 来察看我们 spark 的群集状况, 我们将佈署 specialized proxy

spark-ui-proxy-controller.yaml

  1. kind: ReplicationController
  2. apiVersion: v1
  3. metadata:
  4.   name: spark-ui-proxy-controller
  5.   namespace: spark-cluster
  6. spec:
  7.   replicas: 1
  8.   selector:
  9.     component: spark-ui-proxy
  10.   template:
  11.     metadata:
  12.       labels:
  13.         component: spark-ui-proxy
  14.     spec:
  15.       containers:
  16.         - name: spark-ui-proxy
  17.           image: elsonrodriguez/spark-ui-proxy:1.0
  18.           ports:
  19.             - containerPort: 80
  20.           resources:
  21.             requests:
  22.               cpu: 100m
  23.           args:
  24.             - spark-master:8080
  25.           livenessProbe:
  26.               httpGet:
  27.                 path: /
  28.                 port: 80
  29.               initialDelaySeconds: 120
  30.               timeoutSeconds: 5
  1. $ kubectl create -f spark-ui-proxy-controller.yaml

提供一个 service 做存取, 这边原文是使用 LoadBalancer type, 这边我们改成 NodePort, 如果你的 kubernetes 运行环境是在 cloud provider, 也可以参考原文作法

spark-ui-proxy-service.yaml

  1. kind: Service
  2. apiVersion: v1
  3. metadata:
  4.   name: spark-ui-proxy
  5.   namespace: spark-cluster
  6. spec:
  7.   ports:
  8.     - port: 80
  9.       targetPort: 80
  10.       nodePort: 30080
  11.   selector:
  12.     component: spark-ui-proxy
  13.   type: NodePort
  1. $ kubectl create -f spark-ui-proxy-service.yaml

部署完后你可以利用 kubecrl proxy 来察看你的 Spark 群集状态

  1. $ kubectl proxy --port=8001

可以透过 http://localhost:8001/api/v1/proxy/namespaces/spark-cluster/services/spark-master:8080/ 察看, 若 kubectl 中断就无法这样观察了, 但我们再先前有设定 nodeport 所以也可以透过任意台 node 的端口 30080 去察看（例如 http://10.201.2.34:30080）。

部署 Spark workers

要先确定 Matser 是再运行的状态

spark-worker-controller.yaml

  1. kind: ReplicationController
  2. apiVersion: v1
  3. metadata:
  4.   name: spark-worker-controller
  5.   namespace: spark-cluster
  6. spec:
  7.   replicas: 2
  8.   selector:
  9.     component: spark-worker
  10.   template:
  11.     metadata:
  12.       labels:
  13.         component: spark-worker
  14.     spec:
  15.       containers:
  16.         - name: spark-worker
  17.           image: gcr.io/google_containers/spark:1.5.2_v1
  18.           command: ["/start-worker"]
  19.           ports:
  20.             - containerPort: 8081
  21.           resources:
  22.             requests:
  23.               cpu: 100m
  1. $ kubectl create -f spark-worker-controller.yaml
  2. replicationcontroller "spark-worker-controller" created

透过指令察看运行状况

  1. $ kubectl get pod -n spark-cluster
  2. spark-master-controller-qtwm8     1/1       Running   0          6d
  3. spark-worker-controller-4rxrs     1/1       Running   0          6d
  4. spark-worker-controller-z6f21     1/1       Running   0          6d
  5. spark-ui-proxy-controller-d4br2   1/1       Running   4          6d

也可以透过上面建立的 WebUI 服务去察看

基本上到这边 Spark 的群集已经建立完成了

创建 Zeppelin UI

我们可以利用 Zeppelin UI 经由 web notebook 直接去执行我们的任务, 详情可以看 Zeppelin UI 与 Spark architecture

zeppelin-controller.yaml

  1. kind: ReplicationController
  2. apiVersion: v1
  3. metadata:
  4.   name: zeppelin-controller
  5.   namespace: spark-cluster
  6. spec:
  7.   replicas: 1
  8.   selector:
  9.     component: zeppelin
  10.   template:
  11.     metadata:
  12.       labels:
  13.         component: zeppelin
  14.     spec:
  15.       containers:
  16.         - name: zeppelin
  17.           image: gcr.io/google_containers/zeppelin:v0.5.6_v1
  18.           ports:
  19.             - containerPort: 8080
  20.           resources:
  21.             requests:
  22.               cpu: 100m
  1. $ kubectl create -f zeppelin-controller.yaml
  2. replicationcontroller "zeppelin-controller" created

然后一样佈署 Service

zeppelin-service.yaml

  1. kind: Service
  2. apiVersion: v1
  3. metadata:
  4.   name: zeppelin
  5.   namespace: spark-cluster
  6. spec:
  7.   ports:
  8.     - port: 80
  9.       targetPort: 8080
  10.       nodePort: 30081
  11.   selector:
  12.     component: zeppelin
  13.   type: NodePort
  1. $ kubectl create -f zeppelin-service.yaml

可以看到我们把 NodePort 设再 30081, 一样可以透过任意台 node 的 30081 port 访问 zeppelin UI。

通过命令行访问 pyspark（记得把 pod 名字换成你自己的）：

  1. $ kubectl exec -it zeppelin-controller-8f14f -n spark-cluster pyspark
  2. Python 2.7.9 (default, Mar  1 2015, 12:57:24)
  3. [GCC 4.9.2] on linux2
  4. Type "help", "copyright", "credits" or "license" for more information.
  5. 17/08/14 01:59:22 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
  6. Welcome to
  7.       ____              __
  8.      / __/__  ___ _____/ /__
  9.     _\ \/ _ \/ _ `/ __/  '_/
  10.    /__ / .__/\_,_/_/ /_/\_\   version 1.5.2
  11.       /_/
  13. Using Python version 2.7.9 (default, Mar  1 2015 12:57:24)
  14. SparkContext available as sc, HiveContext available as sqlContext.
  15. >>>

接着就能使用 Spark 的服务了, 如有错误欢迎更正。

zeppelin 常见问题

  • zeppelin 的镜像非常大, 所以再 pull 时会花上一些时间, 而 size 大小的问题现在也正在解决中, 详情可参考 issue #17231
  • 在 GKE 的平台上, kubectl post-forward 可能有些不稳定, 如果你看现 zeppelin 的状态为 Disconnected,port-forward 可能已经失败你需要去重新启动它, 详情可参考 #12179

参考文档