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
| 1 | docker run -it -d ubuntu<br data-filtered="filtered">docker exec -it ubuntu /bin/bash<br data-filtered="filtered">cd /etc/apt |
（2）先将原有的源文件备份：
| 1 | 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.listecho deb http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse >> sources.listecho deb http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse >> sources.listecho deb http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse >> sources.listecho deb http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse >> sources.listecho deb-src http://mirrors.aliyun.com/ubuntu/ bionic main restricted universe multiverse >> sources.listecho deb-src http://mirrors.aliyun.com/ubuntu/ bionic-security main restricted universe multiverse >> sources.listecho deb-src http://mirrors.aliyun.com/ubuntu/ bionic-updates main restricted universe multiverse >> sources.listecho deb-src http://mirrors.aliyun.com/ubuntu/ bionic-proposed main restricted universe multiverse >> sources.listecho deb-src http://mirrors.aliyun.com/ubuntu/ bionic-backports main restricted universe multiverse >> sources.list |
3、退出容器，提交镜像
| 12 | exitdocker 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=600spark.speculation=truespark.speculation.interval=5000spark.speculation.quantile=0.9spark.speculation.multiplier=2spark.default.parallelism=1000spark.driver.maxResultSize=1g #spark.rdd.compress=falsespark.task.maxFailures=8spark.network.timeout=300spark.yarn.max.executor.failures=200spark.shuffle.service.enabled=truespark.dynamicAllocation.enabled=truespark.dynamicAllocation.minExecutors=4spark.dynamicAllocation.maxExecutors=8spark.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
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
5、Spark测试
访问：http://localhost:38080
（2）新建一个note，使用Spark环境
（3）测试
加载zeppelin的例程：
加载数据：
import org.apache.commons.io.IOUtilsimport java.net.URLimport 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 dataval 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
OVER！
原文出处：https://www.cnblogs.com/Fordestiny/p/9401161.html
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