基于Yarn的Spark环境,统计哈姆雷特词频(1)

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基于Yarn的Spark环境,统计哈姆雷特词频(1)

白头雁 2018-07-23 10:40:00 浏览657
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一、最流行的大数据框架Spark

  1. Yarn 环境搭建
  2. Spark History Server 以及 Yarn MapReduce History Servcer
  3. Spark-submit 提交到Yarn 运行

二、Docker部署Hadoop Yarn

部署结果:

  • 一台namenode节点,运行

namenode
resourcemanager
JobHistoryServer
HistoryServer

  • 2台datanode节点,运行

datanode
nodemanager

  • 主机Mac

docker宿主机(virtualbox)
Intellij idea
spark client
hdfs client

三、网络结构

  • mac 192.168.99.1
  • namenode 172.18.0.11
  • datanode1,datanode2 172.18.0.13 172.18.0.14
  • virtualbox 网桥 192.168.99.100

建立192.168.99.1 ~ 172.18.0.0 路由

sudo route -n add 172.18.0.0/24 192.168.99.100

docker 创建 172.18 网段,命名hadoopnet,docker设置ip必须先创建网络

docker network create --subnet=172.18.0.0/16 hadoopnet

四、启动docker

本地文件夹,整理好的工作空间

hadoop文件夹

说明:每个文件夹包含一个启动脚本和一个hdfs挂载的共享卷data


etc/hadoop

说明:本地的hadoop目录会挂载到docker中hadoop/etc/hadoop配置文件目录。

1. NameNode

docker run --name namenode \
--hostname namenode \
--network  hadoopnet \
--ip 172.18.0.11 \
-d \
-v $PWD/data:/opt/tmp \
-v /Users/wangsen/hadoop/datanode/hadoop:/opt/hadoop-2.7.3/etc/hadoop \
-v $PWD/spark-2.1.1-bin-hadoop2.7:/opt/spark  \
--rm dbp/hadoop 

dbp/hadoop是docker镜像的名字,共加载了3个共享卷(文件夹)

  • /opt/tmp hdfs 存储路径
  • etc/hadoop hadpoop配置路径
  • 主节点挂载spark

在创建镜像的时候没有装载spark,hadoop是通过Dockerfile创建dbp/hadoop时,装载到镜像中的;设置spark采用装载模式,也可以重新commit或build dockerfile生成包含spark的镜像。

2. DataNode(datanode1、datanode2)

docker run --name datanode1 --hostname datanode1 --network  hadoopnet --ip 172.18.0.13 -d -v $PWD/data:/opt/tmp -v /Users/wangsen/hadoop/datanode/hadoop:/opt/hadoop-2.7.3/etc/hadoop   --rm dbp/hadoop 
docker run --name datanode2 --hostname datanode2 --network  hadoopnet --ip 172.18.0.14 -d -v $PWD/data:/opt/tmp -v /Users/wangsen/hadoop/datanode/hadoop:/opt/hadoop-2.7.3/etc/hadoop   --rm dbp/hadoop 

五、启动HDFS、YARN

  • etc/hadoop/core-site.xml

   ## 配置HDFS路径 
   <property>
        <name>fs.defaultFS</name>
        <value>hdfs://namenode:9000</value>
    </property>
  • etc/hadoop/hdfs-site.xml

    <property>
        <name>dfs.replication</name>
        <value>3</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/opt/tmp</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>/opt/tmp</value>
    </property>
  • etc/hadoop/yarn-site.xml

    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value> 
    </property>
    <property> 
        <name>yarn.resourcemanager.address</name> 
        <value>namenode:18040</value>
    </property> 
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>namenode:18030</value> 
    </property>
    <property> 
        <name>yarn.resourcemanager.resource-tracker.address</name> 
        <value>namenode:18025</value>
    </property> 
    <property>
        <name>yarn.resourcemanager.admin.address</name>
        <value>namenode:18141</value> 
    </property>
    <property>
        <name>yarn.resourcemanager.webapp.address</name> 
        <value>namenode:18088</value>
   </property> 
   <property>
        <name>yarn.log-aggregation-enable</name>
        <value>true</value> 
    </property>
    <property>
        <name>yarn.log.server.url</name> 
        <value>http://namenode:19888/jobhistory/logs</value>
   </property> 
   <property>
        <name>yarn.nodemanager.vmem-check-enabled</name>
        <value>false</value> 
    </property>
    <property>
        <name>yarn.nodemanager.pmem-check-enabled</name>
        <value>false</value> 
    </property>
  • spark/conf/spark-env

export HADOOP_CONF_DIR=/opt/hadoop-2.7.3/etc/hadoop
  • spark/conf/spark-defaults.conf

## 配置spark ui 页面,通过yarn history服务查看spark任务运行结果
## hdfs:///tmp/spark/events是hdfs上的路径,保存spark运行信息
spark.master=local
spark.yarn.historyServer.address=namenode:18080
spark.history.ui.port=18080
spark.eventLog.enabled=true
spark.eventLog.dir=hdfs:///tmp/spark/events
spark.history.fs.logDirectory=hdfs:///tmp/spark/events
  • hadoop/etc/hadoop-env.sh

修改JAVA_HOME,填写java_home的绝对路径

启动顺序

  • HDFS
    namenode -->sbin/hadoop-daemon.sh start namenode
    datanode -->sbin/hadoop-daemon.sh start datanode
    (已经设置好ssh免密码登录,docker共享了public_key文件。)
  • Yarn
    namenode --> sbin/yarn-daemon.sh start resourcemanager
    datanode -->sbin/yarn-daemon.sh start nodemanager
  • Spark jobserver
    namenode--> sbin/mr-jobhistory-daemon.sh start historyserver
    namenode--> spart/sbin/start-history-server.sh

六、浏览spark histroy页面

http://namenode:18080

spark history

附录 Dockerfile

如果你希望按作者的思路,搭建自己的spark docker集群,那么你可以从Dockerfile 创建image开始。

FROM       ubuntu:16.04
MAINTAINER wsn

RUN apt-get update

RUN apt-get install -y openjdk-8-jdk
RUN apt-get install -y vim
RUN apt install -y net-tools
RUN apt install -y iputils-ping 

RUN apt-get install -y openssh-server
RUN mkdir /var/run/sshd

RUN echo 'root:root' |chpasswd

RUN sed -ri 's/^PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config
RUN sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config
RUN sed -ri 's/#   StrictHostKeyChecking ask/StrictHostKeyChecking no/' /etc/ssh/ssh_config

RUN mkdir /root/.ssh
RUN ssh-keygen -t rsa -P "" -f /root/.ssh/id_rsa
RUN cat /root/.ssh/id_rsa.pub >> /root/.ssh/authorized_keys
 
ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64
ENV JRE_HOME /usr/lib/jvm/java-8-openjdk-amd64/jre
ENV PATH /opt/hadoop-2.7.3/bin:/opt/hadoop-2.7.3/sbin:/usr/lib/jvm/java-8-openjdk-amd64/bin:$PATH
ENV CLASSPATH ./:/usr/lib/jvm/java-8-openjdk-amd64/lib:/usr/lib/jvm/java-8-openjdk-amd64/jre/lib

ADD hadoop-2.7.3.tar.gz /opt/
EXPOSE 22

CMD  ["/usr/sbin/sshd", "-D"]

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