前往小程序，Get更优阅读体验！
立即前往
文档建议反馈控制台
首页
学习
活动
专区
工具
TVP
最新优惠活动
文章/答案/技术大牛
发布
首页
学习
活动
专区
工具
TVP最新优惠活动
返回腾讯云官网
社区首页 >专栏 >k8s安装spark

k8s安装spark

作者头像
summerking
发布于 2022-10-27 13:44:30
1K0
发布于 2022-10-27 13:44:30
举报
文章被收录于专栏：summerking的专栏summerking的专栏

这段时间已经基本实现了产品应用层从原生的springboot微服务架构迁移到k8s上，过程可谓是瞎子过河一步一个坑，但是好在系统总体能跑起来了；今天研究了下产品计算层（spark集群）如何基于k8s部署操作，过程有些取巧了，但总的来说有些进展。 本次部署spark on k8s集群，基于kubeapps，简单便捷且一步到胃：

提示

Client启动一个 pod 运行Spark Driver Spark Driver中运行main函数，并创建SparkSession，后者使用KubernetesClusterManager作为SchedulerBackend，启动Kubernetes pod，创建Executor。 每个Kubernetes pod创建Executor，并执行应用程序代码 运行完程序代码，Spark Driver 清理 Executor 所在的 pod，并保持为“Complete”状态

# 1.安装kubeapps

看这里哟

# 2.选择spark版本

# 3.yml配置

点击查看

代码语言：javascript
复制
## Global Docker image parameters
## Please, note that this will override the image parameters, including dependencies, configured to use the global value
## Current available global Docker image parameters: imageRegistry and imagePullSecrets
##
# global:
#   imageRegistry: myRegistryName
#   imagePullSecrets:
#     - myRegistryKeySecretName

## Bitnami Spark image version
## ref: https://hub.docker.com/r/bitnami/spark/tags/
##
image:
  registry: docker.io
  repository: bitnami/spark
  tag: 2.4.3-debian-9-r78
  ## Specify a imagePullPolicy
  ## Defaults to 'Always' if image tag is 'latest', else set to 'IfNotPresent'
  ## ref: http://kubernetes.io/docs/user-guide/images/#pre-pulling-images
  ##
  pullPolicy: IfNotPresent
  ## Pull secret for this image
  # pullSecrets:
  #   - myRegistryKeySecretName

## String to partially override spark.fullname template (will maintain the release name)
##
# nameOverride:
## String to fully override spark.fullname template
##
# fullnameOverride:
## Spark Components configuration
##
master:
  ## Spark master specific configuration
  ## Set a custom configuration by using an existing configMap with the configuration file.
  # configurationConfigMap:
  webPort: 8080
  clusterPort: 7077

  ## Set the master daemon memory limit.
  # daemonMemoryLimit:
  ## Use a string to set the config options for in the form "-Dx=y"
  # configOptions:
  ## Set to true if you would like to see extra information on logs
  ## It turns BASH and NAMI debugging in minideb
  ## ref:  https://github.com/bitnami/minideb-extras/#turn-on-bash-debugging
  debug: false

  ## An array to add extra env vars
  ## For example:
  ## extraEnvVars:
  ##  - name: SPARK_DAEMON_JAVA_OPTS
  ##    value: -Dx=y
  # extraEnvVars:
  ## Kubernetes Security Context
  ## https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
  ##
  securityContext:
    enabled: true
    fsGroup: 1001
    runAsUser: 1001
  ## Node labels for pod assignment
  ## Ref: https://kubernetes.io/docs/user-guide/node-selection/
  ##
  nodeSelector: {}
  ## Tolerations for pod assignment
  ## Ref: https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/
  ##
  tolerations: []
  ## Affinity for pod assignment
  ## Ref: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity
  ##
  affinity: {}
  ## Configure resource requests and limits
  ## ref: http://kubernetes.io/docs/user-guide/compute-resources/
  ##
  resources: 
  #  limits:
  #    cpu: 200m
  #    memory: 1Gi
  #  requests:
  #    memory: 256Mi
  #    cpu: 250m
  ## Configure extra options for liveness and readiness probes
  ## ref: https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-probes/#configure-probes)
  livenessProbe:
    enabled: true
    initialDelaySeconds: 180
    periodSeconds: 20
    timeoutSeconds: 5
    failureThreshold: 6
    successThreshold: 1

  readinessProbe:
    enabled: true
    initialDelaySeconds: 30
    periodSeconds: 10
    timeoutSeconds: 5
    failureThreshold: 6
    successThreshold: 1

worker:
  ## Spark worker specific configuration
  ## Set a custom configuration by using an existing configMap with the configuration file.
  # configurationConfigMap:
  webPort: 8081
  ## Set to true to use a custom cluster port instead of a random port.
  # clusterPort:
  ## Set the daemonMemoryLimit as the daemon max memory
  # daemonMemoryLimit:
  ## Set the worker memory limit
  # memoryLimit:
  ## Set the maximun number of cores
  # coreLimit:
  ## Working directory for the application
  # dir:
  ## Options for the JVM as "-Dx=y"
  # javaOptions:
  ## Configuraion options in the form "-Dx=y"
  # configOptions:
  ## Number of spark workers (will be the min number when autoscaling is enabled)
  replicaCount: 3

  autoscaling:
    ## Enable replica autoscaling depending on CPU
    enabled: false
    CpuTargetPercentage: 50
    ## Max number of workers when using autoscaling
    replicasMax: 5

  ## Set to true if you would like to see extra information on logs
  ## It turns BASH and NAMI debugging in minideb
  ## ref:  https://github.com/bitnami/minideb-extras/#turn-on-bash-debugging
  debug: false

  ## An array to add extra env vars
  ## For example:
  ## extraEnvVars:
  ##  - name: SPARK_DAEMON_JAVA_OPTS
  ##    value: -Dx=y
  # extraEnvVars:
  ## Kubernetes Security Context
  ## https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
  ##
  securityContext:
    enabled: true
    fsGroup: 1001
    runAsUser: 1001
  ## Node labels for pod assignment
  ## Ref: https://kubernetes.io/docs/user-guide/node-selection/
  ##
  nodeSelector: {}
  ## Tolerations for pod assignment
  ## Ref: https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/
  ##
  tolerations: []
  ## Affinity for pod assignment
  ## Ref: https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity
  ##
  affinity: {}
  ## Configure resource requests and limits
  ## ref: http://kubernetes.io/docs/user-guide/compute-resources/
  ##
  resources: 
  #  limits:
  #    cpu: 200m
  #    memory: 1Gi
  #  requests:
  #    memory: 256Mi
  #    cpu: 250m
  ## Configure extra options for liveness and readiness probes
  ## ref: https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-probes/#configure-probes)
  livenessProbe:
    enabled: true
    initialDelaySeconds: 180
    periodSeconds: 20
    timeoutSeconds: 5
    failureThreshold: 6
    successThreshold: 1

  readinessProbe:
    enabled: true
    initialDelaySeconds: 30
    periodSeconds: 10
    timeoutSeconds: 5
    failureThreshold: 6
    successThreshold: 1

## Security configuration
security:
  ## Name of the secret that contains all the passwords. This is optional, by default random passwords are generated.
  # passwordsSecretName:
  ## RPC configuration
  rpc:
    authenticationEnabled: false
    encryptionEnabled: false

  ## Enables local storage encryption
  storageEncryptionEnabled: false

  ## SSL configuration
  ssl:
    enabled: false
    needClientAuth: false
    protocol: TLSv1.2
  ## Name of the secret that contains the certificates
  ## It should contains two keys called "spark-keystore.jks" and "spark-truststore.jks" with the files in JKS format.
  # certificatesSecretName:
## Service to access the master from the workers and to the WebUI
##
service:
  type: NodePort
  clusterPort: 7077
  webPort: 80
  ## Specify the NodePort value for the LoadBalancer and NodePort service types.
  ## ref: https://kubernetes.io/docs/concepts/services-networking/service/#type-nodeport
  ##
  # nodePort:
  ## Use loadBalancerIP to request a specific static IP,
  # loadBalancerIP:
  ## Service annotations done as key:value pairs
  annotations: 

## Ingress controller to access the web UI.
ingress:
  enabled: false

  ## Set this to true in order to add the corresponding annotations for cert-manager
  certManager: false

  ## If certManager is set to true, annotation kubernetes.io/tls-acme: "true" will automatically be set
  annotations: 

  ## The list of hostnames to be covered with this ingress record.
  ## Most likely this will be just one host, but in the event more hosts are needed, this is an array
  hosts:
    - name: spark.local
      path: /

# 4.执行后耐心等待即可

代码语言：javascript
复制
[root@master ~]# kubectl get pod -n kspark
NAME                             READY   STATUS    RESTARTS   AGE
sulky-selection-spark-master-0   1/1     Running   0          22h
sulky-selection-spark-worker-0   1/1     Running   0          22h
sulky-selection-spark-worker-1   1/1     Running   0          22h
sulky-selection-spark-worker-2   1/1     Running   0          22h

# 5.验证

代码语言：javascript
复制
1. Get the Spark master WebUI URL by running these commands:

  export NODE_PORT=$(kubectl get --namespace kspark -o jsonpath="{.spec.ports[?(@.name=='http')].nodePort}" services sulky-selection-spark-master-svc)
  export NODE_IP=$(kubectl get nodes --namespace kspark -o jsonpath="{.items[0].status.addresses[0].address}")
  echo http://$NODE_IP:$NODE_PORT

2. Submit an application to the cluster:

  To submit an application to the cluster the spark-submit script must be used. That script can be
  obtained at https://github.com/apache/spark/tree/master/bin. Also you can use kubectl run.

  Run the commands below to obtain the master IP and submit your application.

  export EXAMPLE_JAR=$(kubectl exec -ti --namespace kspark sulky-selection-spark-worker-0 -- find examples/jars/ -name 'spark-example*\.jar' | tr -d '\r')
  export SUBMIT_PORT=$(kubectl get --namespace kspark -o jsonpath="{.spec.ports[?(@.name=='cluster')].nodePort}" services sulky-selection-spark-master-svc)
  export SUBMIT_IP=$(kubectl get nodes --namespace kspark -o jsonpath="{.items[0].status.addresses[0].address}")

  kubectl run --namespace kspark sulky-selection-spark-client --rm --tty -i --restart='Never' \
    --image docker.io/bitnami/spark:2.4.3-debian-9-r78 \
    -- spark-submit --master spark://$SUBMIT_IP:$SUBMIT_PORT \
    --class org.apache.spark.examples.SparkPi \
    --deploy-mode cluster \
    $EXAMPLE_JAR 1000

** IMPORTANT: When submit an application the --master parameter should be set to the service IP, if not, the application will not resolve the master. **

** Please be patient while the chart is being deployed **
  1. 访问NodePort

这里可以看到NodePort指向的是30423

代码语言：javascript
复制
[root@master ~]# kubectl get svc --namespace kspark
NAME                               TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)                       AGE
sulky-selection-spark-headless     ClusterIP   None             <none>        <none>                        22h
sulky-selection-spark-master-svc   NodePort    10.107.246.253   <none>        7077:30028/TCP,80:30423/TCP   22h
  1. 进入master启动spark shell
代码语言：javascript
复制
[root@master home]# kubectl exec -ti sulky-selection-spark-master-0 -n kspark /bin/sh
kubectl exec [POD] [COMMAND] is DEPRECATED and will be removed in a future version. Use kubectl kubectl exec [POD] -- [COMMAND] instead.
$ ls
LICENSE  NOTICE  R  README.md  RELEASE	bin  conf  data  examples  jars  kubernetes  licenses  logs  python  sbin  tmp	work  yarn
$ ls
LICENSE  NOTICE  R  README.md  RELEASE	bin  conf  data  examples  jars  kubernetes  licenses  logs  python  sbin  tmp	work  yarn
$ cd bin
$ ls
beeline		      find-spark-home	   load-spark-env.sh  pyspark2.cmd     spark-class	 spark-shell	   spark-sql	   spark-submit       sparkR
beeline.cmd	      find-spark-home.cmd  pyspark	      run-example      spark-class.cmd	 spark-shell.cmd   spark-sql.cmd   spark-submit.cmd   sparkR.cmd
docker-image-tool.sh  load-spark-env.cmd   pyspark.cmd	      run-example.cmd  spark-class2.cmd  spark-shell2.cmd  spark-sql2.cmd  spark-submit2.cmd  sparkR2.cmd
$ cd ../sbin
$ ls
slaves.sh	  start-all.sh		     start-mesos-shuffle-service.sh  start-thriftserver.sh   stop-mesos-dispatcher.sh	    stop-slaves.sh
spark-config.sh   start-history-server.sh    start-shuffle-service.sh	     stop-all.sh	     stop-mesos-shuffle-service.sh  stop-thriftserver.sh
spark-daemon.sh   start-master.sh	     start-slave.sh		     stop-history-server.sh  stop-shuffle-service.sh
spark-daemons.sh  start-mesos-dispatcher.sh  start-slaves.sh		     stop-master.sh	     stop-slave.sh
$ pwd
/opt/bitnami/spark/sbin 
$ ./spark-shell --master spark://sturdy-cars-spark-master-0.sturdy-cars-spark-headless.kspark.svc.cluster.local:7077
20/12/29 08:11:21 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://sturdy-cars-spark-master-0.sturdy-cars-spark-headless.kspark.svc.cluster.local:4040
Spark context available as 'sc' (master = spark://sturdy-cars-spark-master-0.sturdy-cars-spark-headless.kspark.svc.cluster.local:7077, app id = app-20201229081130-0000).
Spark session available as 'spark'.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.4.3
      /_/
         
Using Scala version 2.11.12 (OpenJDK 64-Bit Server VM, Java 1.8.0_222)
Type in expressions to have them evaluated.
Type :help for more information.

scala>
  1. 测试提交jar到spark
代码语言：javascript
复制
[root@master]# export EXAMPLE_JAR=$(kubectl exec -ti --namespace kspark sulky-selection-spark-worker-0 -- find examples/jars/ -name 'spark-example*\.jar' | tr -d '\r')
[root@master]# export SUBMIT_PORT=$(kubectl get --namespace kspark -o jsonpath="{.spec.ports[?(@.name=='cluster')].nodePort}" services sulky-selection-spark-master-svc)
[root@master]# export SUBMIT_IP=$(kubectl get nodes --namespace kspark -o jsonpath="{.items[0].status.addresses[0].address}")
[root@master]# kubectl run --namespace kspark sulky-selection-spark-client --rm --tty -i --restart='Never' \
>     --image docker.io/bitnami/spark:2.4.3-debian-9-r78 \
>     -- spark-submit --master spark://$SUBMIT_IP:$SUBMIT_PORT \
>     --class org.apache.spark.examples.SparkPi \
>     --deploy-mode cluster \
>     $EXAMPLE_JAR 1000
If you don't see a command prompt, try pressing enter.
log4j:WARN No appenders could be found for logger (org.apache.hadoop.util.NativeCodeLoader).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
21/01/28 01:34:21 INFO SecurityManager: Changing view acls to: spark
21/01/28 01:34:21 INFO SecurityManager: Changing modify acls to: spark
21/01/28 01:34:21 INFO SecurityManager: Changing view acls groups to: 
21/01/28 01:34:21 INFO SecurityManager: Changing modify acls groups to: 
21/01/28 01:34:21 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(spark); groups with view permissions: Set(); users  with modify permissions: Set(spark); groups with modify permissions: Set()
21/01/28 01:34:22 INFO Utils: Successfully started service 'driverClient' on port 44922.
21/01/28 01:34:22 INFO TransportClientFactory: Successfully created connection to /192.168.0.177:30028 after 58 ms (0 ms spent in bootstraps)
21/01/28 01:34:22 INFO ClientEndpoint: Driver successfully submitted as driver-20210128013422-0000
21/01/28 01:34:22 INFO ClientEndpoint: ... waiting before polling master for driver state
21/01/28 01:34:27 INFO ClientEndpoint: ... polling master for driver state
21/01/28 01:34:27 INFO ClientEndpoint: State of driver-20210128013422-0000 is RUNNING
21/01/28 01:34:27 INFO ClientEndpoint: Driver running on 100.67.224.69:42072 (worker-20210127034500-100.67.224.69-42072)
21/01/28 01:34:27 INFO ShutdownHookManager: Shutdown hook called
21/01/28 01:34:27 INFO ShutdownHookManager: Deleting directory /tmp/spark-7667114a-6d54-48a9-83b7-174cabce632a
pod "sulky-selection-spark-client" deleted
[root@master]#

Client启动一个名为sulky-selection-spark-client的 pod 运行Spark Driver Spark Driver中运行SparkPi的main函数，并创建SparkSession，后者使用KubernetesClusterManager作为SchedulerBackend，启动Kubernetes pod，创建Executor。 每个Kubernetes pod创建Executor，并执行应用程序代码 运行完程序代码，Spark Driver 清理 Executor 所在的 pod，并保持为“Complete”状态

  1. web-UI查看
代码语言：javascript
复制
[root@master ~]# kubectl get pod -n kspark
NAME                             READY   STATUS    RESTARTS   AGE
sulky-selection-spark-master-0   1/1     Running   0          22h
sulky-selection-spark-worker-0   1/1     Running   0          22h
sulky-selection-spark-worker-1   1/1     Running   0          22h
sulky-selection-spark-worker-2   1/1     Running   0          22h
[root@master ~]# kubectl get pod -n kspark
NAME                             READY   STATUS    RESTARTS   AGE
sulky-selection-spark-client     1/1     Running   0          8s
sulky-selection-spark-master-0   1/1     Running   0          22h
sulky-selection-spark-worker-0   1/1     Running   0          22h
sulky-selection-spark-worker-1   1/1     Running   0          22h
sulky-selection-spark-worker-2   1/1     Running   0          22h
[root@master ~]# kubectl get pod -n kspark
NAME                             READY   STATUS    RESTARTS   AGE
sulky-selection-spark-master-0   1/1     Running   0          22h
sulky-selection-spark-worker-0   1/1     Running   0          22h
sulky-selection-spark-worker-1   1/1     Running   0          22h
sulky-selection-spark-worker-2   1/1     Running   0          22h
本文参与 腾讯云自媒体分享计划，分享自作者个人站点/博客。
原始发表：2020-12-29，如有侵权请联系 cloudcommunity@tencent.com 删除
jar
kubernetes
spark
client
driver

本文分享自 作者个人站点/博客 前往查看

如有侵权，请联系 cloudcommunity@tencent.com 删除。

本文参与 腾讯云自媒体分享计划  ，欢迎热爱写作的你一起参与！

jar
kubernetes
spark
client
driver
评论
登录后参与评论
0 条评论
热度
最新
登录 后参与评论
推荐阅读
LV.
文章
0
获赞
0
目录
  • # 1.安装kubeapps
  • # 2.选择spark版本
  • # 3.yml配置
  • # 4.执行后耐心等待即可
  • # 5.验证
相关产品与服务
容器服务
腾讯云容器服务（Tencent Kubernetes Engine, TKE）基于原生 kubernetes 提供以容器为核心的、高度可扩展的高性能容器管理服务，覆盖 Serverless、边缘计算、分布式云等多种业务部署场景，业内首创单个集群兼容多种计算节点的容器资源管理模式。同时产品作为云原生 Finops 领先布道者，主导开源项目Crane，全面助力客户实现资源优化、成本控制。
产品介绍产品文档
精选特惠 用云无忧
领券

腾讯云开发者

扫码关注腾讯云开发者

扫码关注腾讯云开发者

领取腾讯云代金券

热门产品

热门推荐

更多推荐

Copyright © 2013 - 2024 Tencent Cloud. All Rights Reserved. 腾讯云 版权所有 

深圳市腾讯计算机系统有限公司 ICP备案/许可证号：粤B2-20090059 深公网安备号 44030502008569

腾讯云计算（北京）有限责任公司 京ICP证150476号 |  京ICP备11018762号 | 京公网安备号11010802020287

问题归档专栏文章快讯文章归档关键词归档开发者手册归档开发者手册 Section 归档

Copyright © 2013 - 2024 Tencent Cloud.

All Rights Reserved. 腾讯云 版权所有

登录 后参与评论