基于docker的spark-hadoop分布式集群之一：环境搭建

一、软件准备

1、基础docker镜像：ubuntu，目前最新的版本是18

2、需准备的环境软件包：

(1) spark-2.3.0-bin-hadoop2.7.tgz

(2) hadoop-2.7.3.tar.gz

(3) apache-hive-2.3.2-bin.tar.gz

(4) jdk-8u101-linux-x64.tar.gz

(5) mysql-5.5.45-linux2.6-x86_64.tar.gz、mysql-connector-java-5.1.37-bin.jar

(6) scala-2.11.8.tgz

(7) zeppelin-0.8.0-bin-all.tgz

二、ubuntu镜像准备

1、获取官方的镜像：

docker pull ubuntu

2、因官方镜像中的apt源是国外资源，后续扩展安装软件包时较麻烦。先修改为国内源：

（1）启动ubuntu容器，并进入容器中的apt配置目录

docker run -it -d ubuntu
docker exec -it ubuntu /bin/bash
cd /etc/apt

（2）先将原有的源文件备份：

mv sources.list sources.list.bak

（3）换为国内源，这里提供阿里的资源。因官方的ubuntu没有艰装vi等软件，使用echo指令写入。需注意一点，资源必须与系统版本匹配。

echo deb http://mirrors.aliyun.com/ubuntu/ bionic main restricted universe multiverse  >> sources.list

echo deb http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse >> sources.list

echo deb http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse >> sources.list

echo deb http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse >> sources.list

echo deb http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse >> sources.list

echo deb-src http://mirrors.aliyun.com/ubuntu/ bionic main restricted universe multiverse >> sources.list

echo deb-src http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse >> sources.list

echo deb-src http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse >> sources.list

echo deb-src http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse >> sources.list

echo deb-src http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse >> sources.list

3、退出容器，提交镜像

exit

docker commit 容器id ubuntu:latest

生成的ubuntu镜像，就可以做为基础镜像使用。

三、spark-hadoop集群配置

先前所准备的一列系软件包，在构建镜像时，直接用RUN ADD指令添加到镜像中，这里先将一些必要的配置处理好。这些配置文件，都来自于各个软件包中的conf目录下。

1、Spark配置

（1）spark-env.sh

声明Spark需要的环境变量

SPARK_MASTER_WEBUI_PORT=8888

export SPARK_HOME=$SPARK_HOME

export HADOOP_HOME=$HADOOP_HOME

export MASTER=spark://hadoop-maste:7077

export SCALA_HOME=$SCALA_HOME

export SPARK_MASTER_HOST=hadoop-maste

export JAVA_HOME=/usr/local/jdk1.8.0_101

export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

（2）spark-default.conf

关于spark的默认配置

spark.executor.memory=2G

spark.driver.memory=2G

spark.executor.cores=2

#spark.sql.codegen.wholeStage=false

#spark.memory.offHeap.enabled=true

#spark.memory.offHeap.size=4G

#spark.memory.fraction=0.9

#spark.memory.storageFraction=0.01

#spark.kryoserializer.buffer.max=64m

#spark.shuffle.manager=sort

#spark.sql.shuffle.partitions=600

spark.speculation=true

spark.speculation.interval=5000

spark.speculation.quantile=0.9

spark.speculation.multiplier=2

spark.default.parallelism=1000

spark.driver.maxResultSize=1g

#spark.rdd.compress=false

spark.task.maxFailures=8

spark.network.timeout=300

spark.yarn.max.executor.failures=200

spark.shuffle.service.enabled=true

spark.dynamicAllocation.enabled=true

spark.dynamicAllocation.minExecutors=4

spark.dynamicAllocation.maxExecutors=8

spark.dynamicAllocation.executorIdleTimeout=60

#spark.serializer=org.apache.spark.serializer.JavaSerializer

#spark.sql.adaptive.enabled=true

#spark.sql.adaptive.shuffle.targetPostShuffleInputSize=100000000

#spark.sql.adaptive.minNumPostShufflePartitions=1

##for spark2.0

#spark.sql.hive.verifyPartitionPath=true

#spark.sql.warehouse.dir

spark.sql.warehouse.dir=/spark/warehouse

（3）节点声明文件，包括masters文件及slaves文件

主节点声明文件：masters

hadoop-maste

从节点文件：slaves

hadoop-node1

hadoop-node2

2、Hadoop配置

（1）hadoop-env.sh

声明Hadoop需要的环境变量

export JAVA_HOME=/usr/local/jdk1.8.0_101

export HADOOP_CONF_DIR=${HADOOP_CONF_DIR:-"/etc/hadoop"}

for f in $HADOOP_HOME/contrib/capacity-scheduler/*.jar; do

  if [ "$HADOOP_CLASSPATH" ]; then

    export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$f

  else

    export HADOOP_CLASSPATH=$f

  fi

done

export HADOOP_OPTS="$HADOOP_OPTS -Djava.net.preferIPv4Stack=true"

export HADOOP_NAMENODE_OPTS="-Dhadoop.security.logger=${HADOOP_SECURITY_LOGGER:-INFO,RFAS} -Dhdfs.audit.logger=${HDFS_AUDIT_LOGGER:-INFO,NullAppender} $HADOOP_NAMENODE_OPTS"

export HADOOP_DATANODE_OPTS="-Dhadoop.security.logger=ERROR,RFAS $HADOOP_DATANODE_OPTS"

export HADOOP_SECONDARYNAMENODE_OPTS="-Dhadoop.security.logger=${HADOOP_SECURITY_LOGGER:-INFO,RFAS} -Dhdfs.audit.logger=${HDFS_AUDIT_LOGGER:-INFO,NullAppender} $HADOOP_SECONDARYNAMENODE_OPTS"

export HADOOP_NFS3_OPTS="$HADOOP_NFS3_OPTS"

export HADOOP_PORTMAP_OPTS="-Xmx512m $HADOOP_PORTMAP_OPTS"

export HADOOP_CLIENT_OPTS="-Xmx512m $HADOOP_CLIENT_OPTS"

export HADOOP_SECURE_DN_USER=${HADOOP_SECURE_DN_USER}

export HADOOP_SECURE_DN_LOG_DIR=${HADOOP_LOG_DIR}/${HADOOP_HDFS_USER}

export HADOOP_PID_DIR=${HADOOP_PID_DIR}

export HADOOP_SECURE_DN_PID_DIR=${HADOOP_PID_DIR}

export HADOOP_IDENT_STRING=$USER

（2）hdfs-site.xml

主要设置了Hadoop的name及data节点。name节点存储的是元数据，data存储的是数据文件

<?xml version="1.0"?>

<configuration>

    <property>

        <name>dfs.namenode.name.dir</name>

        <value>file:/usr/local/hadoop2.7/dfs/name</value>

    </property>

    <property>

        <name>dfs.datanode.data.dir</name>

        <value>file:/usr/local/hadoop2.7/dfs/data</value>

    </property>

    <property>

        <name>dfs.webhdfs.enabled</name>

        <value>true</value>

    </property>

    <property>

        <name>dfs.replication</name>

        <value>2</value>

    </property>

    <property>

        <name>dfs.permissions.enabled</name>

        <value>false</value>

    </property>

 </configuration>

（3）core-site.xml

设置主节点的地址：hadoop-maste。与后面启动容器时，设置的主节点hostname要一致。

<?xml version="1.0"?>

<configuration>

    <property>

        <name>fs.defaultFS</name>

        <value>hdfs://hadoop-maste:9000/</value>

    </property>

    <property>

         <name>hadoop.tmp.dir</name>

         <value>file:/usr/local/hadoop/tmp</value>

    </property>

    <property>

        <name>hadoop.proxyuser.root.hosts</name>

        <value>*</value>

    </property>

    <property>

        <name>hadoop.proxyuser.root.groups</name>

        <value>*</value>

    </property>

    <property>

        <name>hadoop.proxyuser.oozie.hosts</name>

        <value>*</value>

    </property>

    <property>

        <name>hadoop.proxyuser.oozie.groups</name>

        <value>*</value>

    </property>

</configuration>

（4）yarn-site.xml

<?xml version="1.0"?>

<configuration>

    <property>

        <name>yarn.nodemanager.aux-services</name>

        <value>mapreduce_shuffle</value>

    </property>

    <property>

        <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>

        <value>org.apache.hadoop.mapred.ShuffleHandler</value>

    </property>

    <property>

        <name>yarn.resourcemanager.hostname</name>

        <value>hadoop-maste</value>

    </property>

    <property>

        <name>yarn.resourcemanager.address</name>

        <value>hadoop-maste:8032</value>

    </property>

    <property>

        <name>yarn.resourcemanager.scheduler.address</name>

        <value>hadoop-maste:8030</value>

    </property>

    <property>

        <name>yarn.resourcemanager.resource-tracker.address</name>

        <value>hadoop-maste:8035</value>

    </property>

    <property>

        <name>yarn.resourcemanager.admin.address</name>

        <value>hadoop-maste:8033</value>

    </property>

    <property>

        <name>yarn.resourcemanager.webapp.address</name>

        <value>hadoop-maste:8088</value>

    </property>

    <property>

        <name>yarn.log-aggregation-enable</name>

        <value>true</value>

   </property>

    <property>

       <name>yarn.nodemanager.vmem-pmem-ratio</name>

       <value>5</value>

    </property>

<property>

    <name>yarn.nodemanager.resource.memory-mb</name>

    <value>22528</value>

    <discription>每个节点可用内存,单位MB</discription>

  </property>

  <property>

    <name>yarn.scheduler.minimum-allocation-mb</name>

    <value>4096</value>

    <discription>单个任务可申请最少内存，默认1024MB</discription>

  </property>

  <property>

    <name>yarn.scheduler.maximum-allocation-mb</name>

    <value>16384</value>

    <discription>单个任务可申请最大内存，默认8192MB</discription>

  </property>

</configuration>

（5）mapred-site.xml

<?xml version="1.0"?>

<configuration>

    <property>

        <name>mapreduce.framework.name</name>

        <value>yarn</value>

    </property>

    <property>

        <name>mapreduce.jobhistory.address</name>

        <!-- 配置实际的Master主机名和端口-->

        <value>hadoop-maste:10020</value>

    </property>

    <property>

        <name>mapreduce.map.memory.mb</name>

        <value>4096</value>

    </property>

    <property>

        <name>mapreduce.reduce.memory.mb</name>

        <value>8192</value>

    </property>

    <property>

      <name>yarn.app.mapreduce.am.staging-dir</name>

      <value>/stage</value>

    </property>

    <property>

      <name>mapreduce.jobhistory.done-dir</name>

      <value>/mr-history/done</value>

    </property>

    <property>

      <name>mapreduce.jobhistory.intermediate-done-dir</name>

      <value>/mr-history/tmp</value>

    </property>

</configuration>

（6）主节点声明文件：master

hadoop-maste

3、hive配置

（1）hive-site.xml

主要两个：一是hive.server2.transport.mode设为binary，使其支持JDBC连接；二是设置mysql的地址。

<?xml version="1.0" encoding="UTF-8" standalone="no"?>

<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>

        <property>

                <name>hive.metastore.warehouse.dir</name>

                <value>/home/hive/warehouse</value>

        </property>

        <property>

                <name>hive.exec.scratchdir</name>

                <value>/tmp/hive</value>

        </property>

        <property>

                <name>hive.metastore.uris</name>

                <value>thrift://hadoop-hive:9083</value>

                <description>Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.</description>

        </property>

        <property>

                <name>hive.server2.transport.mode</name>

                <value>binary</value>

        </property>

        <property>

                <name>hive.server2.thrift.http.port</name>

                <value>10001</value>

        </property>

        <property>

                <name>javax.jdo.option.ConnectionURL</name>

                <value>jdbc:mysql://hadoop-mysql:3306/hive?createDatabaseIfNotExist=true</value>

        </property>

        <property>

                <name>javax.jdo.option.ConnectionDriverName</name>

                <value>com.mysql.jdbc.Driver</value>

        </property>

        <property>

                <name>javax.jdo.option.ConnectionUserName</name>

                <value>root</value>

        </property>

        <property>

                <name>javax.jdo.option.ConnectionPassword</name>

                <value>root</value>

        </property>

        <property>

                <name>hive.metastore.schema.verification</name>

                <value>false</value>

        </property>

        <property>

               <name>hive.server2.authentication</name>

               <value>NONE</value>

        </property>

</configuration>

4、Zeppelin配置

（1）zeppelin-env.sh

export JAVA_HOME=/usr/local/jdk1.8.0_101

export MASTER=spark://hadoop-maste:7077

export SPARK_HOME=$SPARK_HOME

export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop

（2）zeppelin-site.xml

http端口默认8080，这里改为18080。为方便加载第三方包，mvnRepo也改为阿里的源。

<?xml version="1.0"?>

<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<configuration>

<property>

  <name>zeppelin.server.addr</name>

  <value>0.0.0.0</value>

  <description>Server address</description>

</property>

<property>

  <name>zeppelin.server.port</name>

  <value>18080</value>

  <description>Server port.</description>

</property>

<property>

  <name>zeppelin.server.ssl.port</name>

  <value>18443</value>

  <description>Server ssl port. (used when ssl property is set to true)</description>

</property>

<property>

  <name>zeppelin.server.context.path</name>

  <value>/</value>

  <description>Context Path of the Web Application</description>

</property>

<property>

  <name>zeppelin.war.tempdir</name>

  <value>webapps</value>

  <description>Location of jetty temporary directory</description>

</property>

<property>

  <name>zeppelin.notebook.dir</name>

  <value>notebook</value>

  <description>path or URI for notebook persist</description>

</property>

<property>

  <name>zeppelin.notebook.homescreen</name>

  <value></value>

  <description>id of notebook to be displayed in homescreen. ex) 2A94M5J1Z Empty value displays default home screen</description>

</property>

<property>

  <name>zeppelin.notebook.homescreen.hide</name>

  <value>false</value>

  <description>hide homescreen notebook from list when this value set to true</description>

</property>

<property>

  <name>zeppelin.notebook.storage</name>

  <value>org.apache.zeppelin.notebook.repo.GitNotebookRepo</value>

  <description>versioned notebook persistence layer implementation</description>

</property>

<property>

  <name>zeppelin.notebook.one.way.sync</name>

  <value>false</value>

  <description>If there are multiple notebook storages, should we treat the first one as the only source of truth?</description>

</property>

<property>

  <name>zeppelin.interpreter.dir</name>

  <value>interpreter</value>

  <description>Interpreter implementation base directory</description>

</property>

<property>

  <name>zeppelin.interpreter.localRepo</name>

  <value>local-repo</value>

  <description>Local repository for interpreter's additional dependency loading</description>

</property>

<property>

  <name>zeppelin.interpreter.dep.mvnRepo</name>

  <value>http://maven.aliyun.com/nexus/content/groups/public/</value>

  <description>Remote principal repository for interpreter's additional dependency loading</description>

</property>

<property>

  <name>zeppelin.dep.localrepo</name>

  <value>local-repo</value>

  <description>Local repository for dependency loader</description>

</property>

<property>

  <name>zeppelin.helium.node.installer.url</name>

  <value>https://nodejs.org/dist/</value>

  <description>Remote Node installer url for Helium dependency loader</description>

</property>

<property>

  <name>zeppelin.helium.npm.installer.url</name>

  <value>http://registry.npmjs.org/</value>

  <description>Remote Npm installer url for Helium dependency loader</description>

</property>

<property>

  <name>zeppelin.helium.yarnpkg.installer.url</name>

  <value>https://github.com/yarnpkg/yarn/releases/download/</value>

  <description>Remote Yarn package installer url for Helium dependency loader</description>

</property>

<property>

  <name>zeppelin.interpreters</name>

  <value>org.apache.zeppelin.spark.SparkInterpreter,org.apache.zeppelin.spark.PySparkInterpreter,org.apache.zeppelin.rinterpreter.RRepl,org.apache.zeppelin.rinterpreter.KnitR,org.apache.zeppelin.spark.SparkRInterpreter,org.apache.zeppelin.spark.SparkSqlInterpreter,org.apache.zeppelin.spark.DepInterpreter,org.apache.zeppelin.markdown.Markdown,org.apache.zeppelin.angular.AngularInterpreter,org.apache.zeppelin.shell.ShellInterpreter,org.apache.zeppelin.file.HDFSFileInterpreter,org.apache.zeppelin.flink.FlinkInterpreter,,org.apache.zeppelin.python.PythonInterpreter,org.apache.zeppelin.python.PythonInterpreterPandasSql,org.apache.zeppelin.python.PythonCondaInterpreter,org.apache.zeppelin.python.PythonDockerInterpreter,org.apache.zeppelin.lens.LensInterpreter,org.apache.zeppelin.ignite.IgniteInterpreter,org.apache.zeppelin.ignite.IgniteSqlInterpreter,org.apache.zeppelin.cassandra.CassandraInterpreter,org.apache.zeppelin.geode.GeodeOqlInterpreter,org.apache.zeppelin.jdbc.JDBCInterpreter,org.apache.zeppelin.kylin.KylinInterpreter,org.apache.zeppelin.elasticsearch.ElasticsearchInterpreter,org.apache.zeppelin.scalding.ScaldingInterpreter,org.apache.zeppelin.alluxio.AlluxioInterpreter,org.apache.zeppelin.hbase.HbaseInterpreter,org.apache.zeppelin.livy.LivySparkInterpreter,org.apache.zeppelin.livy.LivyPySparkInterpreter,org.apache.zeppelin.livy.LivyPySpark3Interpreter,org.apache.zeppelin.livy.LivySparkRInterpreter,org.apache.zeppelin.livy.LivySparkSQLInterpreter,org.apache.zeppelin.bigquery.BigQueryInterpreter,org.apache.zeppelin.beam.BeamInterpreter,org.apache.zeppelin.pig.PigInterpreter,org.apache.zeppelin.pig.PigQueryInterpreter,org.apache.zeppelin.scio.ScioInterpreter,org.apache.zeppelin.groovy.GroovyInterpreter</value>

  <description>Comma separated interpreter configurations. First interpreter become a default</description>

</property>

<property>

  <name>zeppelin.interpreter.group.order</name>

  <value>spark,md,angular,sh,livy,alluxio,file,psql,flink,python,ignite,lens,cassandra,geode,kylin,elasticsearch,scalding,jdbc,hbase,bigquery,beam,groovy</value>

  <description></description>

</property>

<property>

  <name>zeppelin.interpreter.connect.timeout</name>

  <value>30000</value>

  <description>Interpreter process connect timeout in msec.</description>

</property>

<property>

  <name>zeppelin.interpreter.output.limit</name>

  <value>102400</value>

  <description>Output message from interpreter exceeding the limit will be truncated</description>

</property>

<property>

  <name>zeppelin.ssl</name>

  <value>false</value>

  <description>Should SSL be used by the servers?</description>

</property>

<property>

  <name>zeppelin.ssl.client.auth</name>

  <value>false</value>

  <description>Should client authentication be used for SSL connections?</description>

</property>

<property>

  <name>zeppelin.ssl.keystore.path</name>

  <value>keystore</value>

  <description>Path to keystore relative to Zeppelin configuration directory</description>

</property>

<property>

  <name>zeppelin.ssl.keystore.type</name>

  <value>JKS</value>

  <description>The format of the given keystore (e.g. JKS or PKCS12)</description>

</property>

<property>

  <name>zeppelin.ssl.keystore.password</name>

  <value>change me</value>

  <description>Keystore password. Can be obfuscated by the Jetty Password tool</description>

</property>

<!--

<property>

  <name>zeppelin.ssl.key.manager.password</name>

  <value>change me</value>

  <description>Key Manager password. Defaults to keystore password. Can be obfuscated.</description>

</property>

-->

<property>

  <name>zeppelin.ssl.truststore.path</name>

  <value>truststore</value>

  <description>Path to truststore relative to Zeppelin configuration directory. Defaults to the keystore path</description>

</property>

<property>

  <name>zeppelin.ssl.truststore.type</name>

  <value>JKS</value>

  <description>The format of the given truststore (e.g. JKS or PKCS12). Defaults to the same type as the keystore type</description>

</property>

<!--

<property>

  <name>zeppelin.ssl.truststore.password</name>

  <value>change me</value>

  <description>Truststore password. Can be obfuscated by the Jetty Password tool. Defaults to the keystore password</description>

</property>

-->

<property>

  <name>zeppelin.server.allowed.origins</name>

  <value>*</value>

  <description>Allowed sources for REST and WebSocket requests (i.e. http://onehost:8080,http://otherhost.com). If you leave * you are vulnerable to https://issues.apache.org/jira/browse/ZEPPELIN-173</description>

</property>

<property>

  <name>zeppelin.anonymous.allowed</name>

  <value>true</value>

  <description>Anonymous user allowed by default</description>

</property>

<property>

  <name>zeppelin.username.force.lowercase</name>

  <value>false</value>

  <description>Force convert username case to lower case, useful for Active Directory/LDAP. Default is not to change case</description>

</property>

<property>

  <name>zeppelin.notebook.default.owner.username</name>

  <value></value>

  <description>Set owner role by default</description>

</property>

<property>

  <name>zeppelin.notebook.public</name>

  <value>true</value>

  <description>Make notebook public by default when created, private otherwise</description>

</property>

<property>

  <name>zeppelin.websocket.max.text.message.size</name>

  <value>1024000</value>

  <description>Size in characters of the maximum text message to be received by websocket. Defaults to 1024000</description>

</property>

<property>

  <name>zeppelin.server.default.dir.allowed</name>

  <value>false</value>

  <description>Enable directory listings on server.</description>

</property>

</configuration>

三、集群启动脚本

整套环境启动较为烦琐，这里将需要的操作写成脚本，在容器启动时，自动运行。

1、环境变量

先前在处理集群配置中，用到许多环境变量，这里统一做定义profile文件，构建容器时，用它替换系统的配置文件，即/etc/profile

profile文件：

if [ "$PS1" ]; then

  if [ "$BASH" ] && [ "$BASH" != "/bin/sh" ]; then

    # The file bash.bashrc already sets the default PS1.

    # PS1='\h:\w\$ '

    if [ -f /etc/bash.bashrc ]; then

      . /etc/bash.bashrc

    fi

  else

    if [ "`id -u`" -eq 0 ]; then

      PS1='# '

    else

      PS1='$ '

    fi

  fi

fi

if [ -d /etc/profile.d ]; then

  for i in /etc/profile.d/*.sh; do

    if [ -r $i ]; then

      . $i

    fi

  done

  unset i

fi

export JAVA_HOME=/usr/local/jdk1.8.0_101

export SCALA_HOME=/usr/local/scala-2.11.8

export HADOOP_HOME=/usr/local/hadoop-2.7.3

export SPARK_HOME=/usr/local/spark-2.3.0-bin-hadoop2.7

export HIVE_HOME=/usr/local/apache-hive-2.3.2-bin

export MYSQL_HOME=/usr/local/mysql

export PATH=$HIVE_HOME/bin:$MYSQL_HOME/bin:$JAVA_HOME/bin:$SCALA_HOME/bin:$HADOOP_HOME/bin:$SPARK_HOME/bin:$PATH

2、SSH配置

各个容器需要通过网络端口连接在一起，为方便连接访问，使用SSH无验证登录

ssh_config文件：

Host localhost

  StrictHostKeyChecking no

Host 0.0.0.0

  StrictHostKeyChecking no

Host hadoop-*

   StrictHostKeyChecking no

3、Hadoop集群脚本

（1）启动脚本：start-hadoop.sh

#!/bin/bash

echo -e "\n"

hdfs namenode -format -force

echo -e "\n"

$HADOOP_HOME/sbin/start-dfs.sh

echo -e "\n"

$HADOOP_HOME/sbin/start-yarn.sh

echo -e "\n"

$SPARK_HOME/sbin/start-all.sh

echo -e "\n"

hdfs dfs -mkdir /mr-history

hdfs dfs -mkdir /stage

echo -e "\n":

（2）重启脚本：restart-hadoop.sh

#!/bin/bash

echo -e "\n"

echo -e "\n"

$HADOOP_HOME/sbin/start-dfs.sh

echo -e "\n"

$HADOOP_HOME/sbin/start-yarn.sh

echo -e "\n"

$SPARK_HOME/sbin/start-all.sh

echo -e "\n"

hdfs dfs -mkdir /mr-history

hdfs dfs -mkdir /stage

echo -e "\n"

3、Mysql脚本

（1）mysql 初始化脚本：init_mysql.sh

#!/bin/bash

cd /usr/local/mysql/

echo ..........mysql_install_db --user=root.................

nohup ./scripts/mysql_install_db --user=root &

sleep 3

echo ..........mysqld_safe --user=root.................

nohup ./bin/mysqld_safe --user=root &

sleep 3

echo ..........mysqladmin -u root password 'root'.................

nohup ./bin/mysqladmin -u root password 'root' &

sleep 3

echo ..........mysqladmin -uroot -proot shutdown.................

nohup ./bin/mysqladmin -uroot -proot shutdown &

sleep 3

echo ..........mysqld_safe.................

nohup ./bin/mysqld_safe --user=root &

sleep 3

echo ...........................

nohup ./bin/mysql -uroot -proot -e "grant all privileges on *.* to root@'%' identified by 'root' with grant option;"

sleep 3

echo ........grant all privileges on *.* to root@'%' identified by 'root' with grant option...............

4、Hive脚本

（1）hive初始化：init_hive.sh

#!/bin/bash

cd /usr/local/apache-hive-2.3.2-bin/bin

sleep 3

nohup ./schematool -initSchema -dbType mysql &

sleep 3

nohup ./hive --service metastore  &

sleep 3

nohup ./hive --service hiveserver2 &

sleep 5

echo Hive has initiallized!

四、镜像构建

（1）Dockfile

FROM ubuntu:lin

MAINTAINER reganzm 183943842@qq.com

ENV BUILD_ON 2018-03-04

COPY config /tmp

#RUN mv /tmp/apt.conf /etc/apt/

RUN mkdir -p ~/.pip/

RUN mv /tmp/pip.conf ~/.pip/pip.conf

RUN apt-get update -qqy

RUN apt-get -qqy install netcat-traditional vim wget net-tools  iputils-ping  openssh-server libaio-dev apt-utils

RUN pip install pandas  numpy  matplotlib  sklearn  seaborn  scipy tensorflow  gensim #--proxy http://root:1qazxcde32@192.168.0.4:7890/

#添加JDK

ADD ./software/jdk-8u101-linux-x64.tar.gz /usr/local/

#添加hadoop

ADD ./software/hadoop-2.7.3.tar.gz  /usr/local

#添加scala

ADD ./software/scala-2.11.8.tgz /usr/local

#添加spark

ADD ./software/spark-2.3.0-bin-hadoop2.7.tgz /usr/local

#添加Zeppelin

ADD ./software/zeppelin-0.8.0-bin-all.tgz /usr/local

#添加mysql

ADD ./software/mysql-5.5.45-linux2.6-x86_64.tar.gz /usr/local

RUN mv /usr/local/mysql-5.5.45-linux2.6-x86_64  /usr/local/mysql

ENV MYSQL_HOME /usr/local/mysql

#添加hive

ADD ./software/apache-hive-2.3.2-bin.tar.gz /usr/local

ENV HIVE_HOME /usr/local/apache-hive-2.3.2-bin

RUN echo "HADOOP_HOME=/usr/local/hadoop-2.7.3"  | cat >> /usr/local/apache-hive-2.3.2-bin/conf/hive-env.sh

#添加mysql-connector-java-5.1.37-bin.jar到hive的lib目录中

ADD ./software/mysql-connector-java-5.1.37-bin.jar /usr/local/apache-hive-2.3.2-bin/lib

RUN cp /usr/local/apache-hive-2.3.2-bin/lib/mysql-connector-java-5.1.37-bin.jar /usr/local/spark-2.3.0-bin-hadoop2.7/jars

#增加JAVA_HOME环境变量

ENV JAVA_HOME /usr/local/jdk1.8.0_101

#hadoop环境变量

ENV HADOOP_HOME /usr/local/hadoop-2.7.3

#scala环境变量

ENV SCALA_HOME /usr/local/scala-2.11.8

#spark环境变量

ENV SPARK_HOME /usr/local/spark-2.3.0-bin-hadoop2.7

#Zeppelin环境变量

ENV ZEPPELIN_HOME /usr/local/zeppelin-0.8.0-bin-all

#将环境变量添加到系统变量中

ENV PATH $HIVE_HOME/bin:$MYSQL_HOME/bin:$SCALA_HOME/bin:$SPARK_HOME/bin:$ZEPPELIN_HOME/bin:$HADOOP_HOME/bin:$JAVA_HOME/bin:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar:$PATH

RUN ssh-keygen -t rsa -f ~/.ssh/id_rsa -P '' && \

    cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys && \

    chmod 600 ~/.ssh/authorized_keys

COPY config /tmp

#将配置移动到正确的位置

RUN mv /tmp/ssh_config    ~/.ssh/config && \

    mv /tmp/profile /etc/profile && \

    mv /tmp/masters $SPARK_HOME/conf/masters && \

    cp /tmp/slaves $SPARK_HOME/conf/ && \

    mv /tmp/spark-defaults.conf $SPARK_HOME/conf/spark-defaults.conf && \

    mv /tmp/spark-env.sh $SPARK_HOME/conf/spark-env.sh && \

    mv /tmp/zeppelin-env.sh $ZEPPELIN_HOME/conf/zeppelin-env.sh && \

    mv /tmp/zeppelin-site.xml $ZEPPELIN_HOME/conf/zeppelin-site.xml && \

    cp /tmp/hive-site.xml $SPARK_HOME/conf/hive-site.xml && \

    mv /tmp/hive-site.xml $HIVE_HOME/conf/hive-site.xml && \

    mv /tmp/hadoop-env.sh $HADOOP_HOME/etc/hadoop/hadoop-env.sh && \

    mv /tmp/hdfs-site.xml $HADOOP_HOME/etc/hadoop/hdfs-site.xml && \

    mv /tmp/core-site.xml $HADOOP_HOME/etc/hadoop/core-site.xml && \

    mv /tmp/yarn-site.xml $HADOOP_HOME/etc/hadoop/yarn-site.xml && \

    mv /tmp/mapred-site.xml $HADOOP_HOME/etc/hadoop/mapred-site.xml && \

    mv /tmp/master $HADOOP_HOME/etc/hadoop/master && \

    mv /tmp/slaves $HADOOP_HOME/etc/hadoop/slaves && \

    mv /tmp/start-hadoop.sh ~/start-hadoop.sh && \

    mkdir -p /usr/local/hadoop2.7/dfs/data && \

    mkdir -p /usr/local/hadoop2.7/dfs/name && \

    mv /tmp/init_mysql.sh ~/init_mysql.sh && chmod 700 ~/init_mysql.sh && \

    mv /tmp/init_hive.sh ~/init_hive.sh && chmod 700 ~/init_hive.sh && \

    mv /tmp/restart-hadoop.sh ~/restart-hadoop.sh && chmod 700 ~/restart-hadoop.sh && \

    mv /tmp/zeppelin-daemon.sh ~/zeppelin-daemon.sh && chmod 700 ~/zeppelin-daemon.sh

#创建Zeppelin环境需要的目录,设置在zeppelin-env.sh中

RUN mkdir /var/log/zeppelin && mkdir /var/run/zeppelin && mkdir /var/tmp/zeppelin

RUN echo $JAVA_HOME

#设置工作目录

WORKDIR /root

#启动sshd服务

RUN /etc/init.d/ssh start

#修改start-hadoop.sh权限为700

RUN chmod 700 start-hadoop.sh

#修改root密码

RUN echo "root:555555" | chpasswd

CMD ["/bin/bash"]

（2）构建脚本：build.sh

echo build Spark-hadoop images

docker build -t="spark" .

（3）构建镜像，执行：

./build.sh

五、容器构建脚本

（1）创建子网

所有的网络，通过内网连接，这里构建一个名为spark的子网：build_network.sh

echo create network

docker network create --subnet=172.16.0.0/16 spark

echo create success

docker network ls

（2）容器启动脚本：start_container.sh

echo start hadoop-hive container...

docker run -itd --restart=always --net spark --ip 172.16.0.5 --privileged --name hive --hostname hadoop-hive --add-host hadoop-node1:172.16.0.3 --add-host hadoop-node2:172.16.0.4 --add-host hadoop-mysql:172.16.0.6 --add-host hadoop-maste:172.16.0.2 --add-host zeppelin:172.16.0.7 spark-lin /bin/bash

echo start hadoop-mysql container ...

docker run -itd --restart=always --net spark --ip 172.16.0.6 --privileged --name mysql --hostname hadoop-mysql --add-host hadoop-node1:172.16.0.3 --add-host hadoop-node2:172.16.0.4 --add-host hadoop-hive:172.16.0.5 --add-host hadoop-maste:172.16.0.2 --add-host zeppelin:172.16.0.7  spark-lin /bin/bash

echo start hadoop-maste container ...

docker run -itd --restart=always --net spark --ip 172.16.0.2 --privileged -p 18032:8032 -p 28080:18080 -p 29888:19888 -p 17077:7077 -p 51070:50070 -p 18888:8888 -p 19000:9000 -p 11100:11000 -p 51030:50030 -p 18050:8050 -p 18081:8081 -p 18900:8900 -p 18088:8088 --name hadoop-maste --hostname hadoop-maste  --add-host hadoop-node1:172.16.0.3 --add-host hadoop-node2:172.16.0.4 --add-host hadoop-hive:172.16.0.5 --add-host hadoop-mysql:172.16.0.6 --add-host zeppelin:172.16.0.7  spark-lin /bin/bash

echo "start hadoop-node1 container..."

docker run -itd --restart=always --net spark  --ip 172.16.0.3 --privileged -p 18042:8042 -p 51010:50010 -p 51020:50020 --name hadoop-node1 --hostname hadoop-node1  --add-host hadoop-hive:172.16.0.5 --add-host hadoop-mysql:172.16.0.6 --add-host hadoop-maste:172.16.0.2 --add-host hadoop-node2:172.16.0.4 --add-host zeppelin:172.16.0.7 spark-lin  /bin/bash

echo "start hadoop-node2 container..."

docker run -itd --restart=always --net spark  --ip 172.16.0.4 --privileged -p 18043:8042 -p 51011:50011 -p 51021:50021 --name hadoop-node2 --hostname hadoop-node2 --add-host hadoop-maste:172.16.0.2 --add-host hadoop-node1:172.16.0.3 --add-host hadoop-mysql:172.16.0.6 --add-host hadoop-hive:172.16.0.5 --add-host zeppelin:172.16.0.7 spark-lin /bin/bash

echo "start Zeppeline container..."

docker run -itd --restart=always --net spark  --ip 172.16.0.7 --privileged -p 38080:18080 -p 38443:18443  --name zeppelin --hostname zeppelin --add-host hadoop-maste:172.16.0.2 --add-host hadoop-node1:172.16.0.3  --add-host hadoop-node2:172.16.0.4 --add-host hadoop-mysql:172.16.0.6 --add-host hadoop-hive:172.16.0.5 spark-lin /bin/bash

echo start sshd...

docker exec -it hadoop-maste /etc/init.d/ssh start

docker exec -it hadoop-node1 /etc/init.d/ssh start

docker exec -it hadoop-node2 /etc/init.d/ssh start

docker exec -it hive  /etc/init.d/ssh start

docker exec -it mysql /etc/init.d/ssh start

docker exec -it zeppelin /etc/init.d/ssh start

echo start service...

docker exec -it mysql bash -c "sh ~/init_mysql.sh"

docker exec -it hadoop-maste bash -c "sh ~/start-hadoop.sh"

docker exec -it hive  bash -c "sh ~/init_hive.sh"

docker exec -it zeppelin bash -c "$ZEPPELIN_HOME/bin/zeppelin-daemon.sh start"

echo finished

docker ps

（3）容器停止并移除：stop_container.sh

docker stop hadoop-maste

docker stop hadoop-node1

docker stop hadoop-node2

docker stop hive

docker stop mysql

docker stop zeppelin

echo stop containers

docker rm hadoop-maste

docker rm hadoop-node1

docker rm hadoop-node2

docker rm hive

docker rm mysql

docker rm zeppelin

echo rm containers

docker ps

六、运行测试

依次执行如下脚本：

1、创建子网

./build_network.sh

2、启动容器

./start_container.sh

3、进入主节点：

docker exec -it hadoop-maste /bin/bash

jps一下，进程是正常的

基于docker的spark-hadoop分布式集群之一：环境搭建

4、访问集群子节点

ssh hadoop-node2

一样可以看到，与主节点类似的进程信息

基于docker的spark-hadoop分布式集群之一：环境搭建

说明集群已经是搭建起来。

5、Spark测试

访问：http://localhost:38080

进入Zeppelin交互界面，新建一个note，使用Spark为默认的解释器

import org.apache.commons.io.IOUtils

import java.net.URL

import java.nio.charset.Charset

// Zeppelin creates and injects sc (SparkContext) and sqlContext (HiveContext or SqlContext)

// So you don't need create them manually

// load bank data

val bankText = sc.parallelize(

    IOUtils.toString(

        new URL("http://emr-sample-projects.oss-cn-hangzhou.aliyuncs.com/bank.csv"),

        Charset.forName("utf8")).split("\n"))

case class Bank(age: Integer, job: String, marital: String, education: String, balance: Integer)

val bank = bankText.map(s => s.split(";")).filter(s => s(0) != "\"age\"").map(

    s => Bank(s(0).toInt,

            s(1).replaceAll("\"", ""),

            s(2).replaceAll("\"", ""),

            s(3).replaceAll("\"", ""),

            s(5).replaceAll("\"", "").toInt

        )

).toDF()

bank.registerTempTable("bank")

可视化报表，如下图：

基于docker的spark-hadoop分布式集群之一：环境搭建

说明Spark已经是成功运行。

下一章，将对本套环境各个模块做测试。

OVER！

秒客网

基于docker的spark-hadoop分布式集群之一：环境搭建

一、软件准备

二、ubuntu镜像准备

三、spark-hadoop集群配置

1、Spark配置

2、Hadoop配置

3、hive配置

4、Zeppelin配置

三、集群启动脚本

1、环境变量

2、SSH配置

3、Hadoop集群脚本

3、Mysql脚本

4、Hive脚本

四、镜像构建

五、容器构建脚本

六、运行测试

相关文章

基于docker的spark-hadoop分布式集群之一： 环境搭建

一、软件准备

二、ubuntu镜像准备

三、spark-hadoop集群配置

1、Spark配置

2、Hadoop配置

3、hive配置

4、Zeppelin配置

三、集群启动脚本

1、环境变量

2、SSH配置

3、Hadoop集群脚本

3、Mysql脚本

4、Hive脚本

四、镜像构建

五、容器构建脚本

六、运行测试

相关文章

基于docker的spark-hadoop分布式集群之一：环境搭建