Spark

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

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

或者使用 Python 版本

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

apiVersion: v1
kind: Namespace
metadata:
name: "spark-cluster"
labels:
name: "spark-cluster"
$ 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

kind: ReplicationController
apiVersion: v1
metadata:
name: spark-master-controller
namespace: spark-cluster
spec:
replicas: 1
selector:
component: spark-master
template:
metadata:
labels:
component: spark-master
spec:
containers:
- name: spark-master
image: gcr.io/google_containers/spark:1.5.2_v1
command: ["/start-master"]
ports:
- containerPort: 7077
- containerPort: 8080
resources:
requests:
cpu: 100m
$ kubectl create -f spark-master-controller.yaml

创建 master 服务

spark-master-service.yaml

kind: Service
apiVersion: v1
metadata:
name: spark-master
namespace: spark-cluster
spec:
ports:
- port: 7077
targetPort: 7077
name: spark
- port: 8080
targetPort: 8080
name: http
selector:
component: spark-master
$ kubectl create -f spark-master-service.yaml

检查 Master 是否正常运行

$ kubectl get pod -n spark-cluster
spark-master-controller-qtwm8 1/1 Running 0 6d
$ kubectl logs spark-master-controller-qtwm8 -n spark-cluster
17/08/07 02:34:54 INFO Master: Registered signal handlers for [TERM, HUP, INT]
17/08/07 02:34:54 INFO SecurityManager: Changing view acls to: root
17/08/07 02:34:54 INFO SecurityManager: Changing modify acls to: root
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)
17/08/07 02:34:55 INFO Slf4jLogger: Slf4jLogger started
17/08/07 02:34:55 INFO Remoting: Starting remoting
17/08/07 02:34:55 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkMaster@spark-master:7077]
17/08/07 02:34:55 INFO Utils: Successfully started service 'sparkMaster' on port 7077.
17/08/07 02:34:55 INFO Master: Starting Spark master at spark://spark-master:7077
17/08/07 02:34:55 INFO Master: Running Spark version 1.5.2
17/08/07 02:34:56 INFO Utils: Successfully started service 'MasterUI' on port 8080.
17/08/07 02:34:56 INFO MasterWebUI: Started MasterWebUI at http://10.2.6.12:8080
17/08/07 02:34:56 INFO Utils: Successfully started service on port 6066.
17/08/07 02:34:56 INFO StandaloneRestServer: Started REST server for submitting applications on port 6066
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

kind: ReplicationController
apiVersion: v1
metadata:
name: spark-ui-proxy-controller
namespace: spark-cluster
spec:
replicas: 1
selector:
component: spark-ui-proxy
template:
metadata:
labels:
component: spark-ui-proxy
spec:
containers:
- name: spark-ui-proxy
image: elsonrodriguez/spark-ui-proxy:1.0
ports:
- containerPort: 80
resources:
requests:
cpu: 100m
args:
- spark-master:8080
livenessProbe:
httpGet:
path: /
port: 80
initialDelaySeconds: 120
timeoutSeconds: 5
$ 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

kind: Service
apiVersion: v1
metadata:
name: spark-ui-proxy
namespace: spark-cluster
spec:
ports:
- port: 80
targetPort: 80
nodePort: 30080
selector:
component: spark-ui-proxy
type: NodePort
$ 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

$ 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

kind: ReplicationController
apiVersion: v1
metadata:
name: spark-worker-controller
namespace: spark-cluster
spec:
replicas: 2
selector:
component: spark-worker
template:
metadata:
labels:
component: spark-worker
spec:
containers:
- name: spark-worker
image: gcr.io/google_containers/spark:1.5.2_v1
command: ["/start-worker"]
ports:
- containerPort: 8081
resources:
requests:
cpu: 100m
$ kubectl create -f spark-worker-controller.yaml
replicationcontroller "spark-worker-controller" created

透过指令察看运行状况

$ kubectl get pod -n spark-cluster
spark-master-controller-qtwm8 1/1 Running 0 6d
spark-worker-controller-4rxrs 1/1 Running 0 6d
spark-worker-controller-z6f21 1/1 Running 0 6d
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 UISpark architecture

zeppelin-controller.yaml

kind: ReplicationController
apiVersion: v1
metadata:
name: zeppelin-controller
namespace: spark-cluster
spec:
replicas: 1
selector:
component: zeppelin
template:
metadata:
labels:
component: zeppelin
spec:
containers:
- name: zeppelin
image: gcr.io/google_containers/zeppelin:v0.5.6_v1
ports:
- containerPort: 8080
resources:
requests:
cpu: 100m
$ kubectl create -f zeppelin-controller.yaml
replicationcontroller "zeppelin-controller" created

然后一样佈署 Service

zeppelin-service.yaml

kind: Service
apiVersion: v1
metadata:
name: zeppelin
namespace: spark-cluster
spec:
ports:
- port: 80
targetPort: 8080
nodePort: 30081
selector:
component: zeppelin
type: NodePort
$ 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):

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

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