大数据平台搭建：Hadoop-3.1.3+Hive-3.1.2+HBase-2.2.3+Zookeeper-3.5.7+Kafka_2.11-2.4.0+Spark-2.4.5

1.框架选型

hadoop-3.1.3

hive-3.1.2

zookeeper-3.5.7

hbase-2.2.3

kafka_2.11-2.4.0

spark-2.4.5-bin-hadoop2.7

2.安装前准备

1.关闭防火墙

2.安装 JDK

3.安装 Scala

4.配置ssh 免密

5.配置 IP 和 主机名映射

6.Mysql 安装

3.安装

3.1 Hadoop 安装

1.hadoop-env.sh

export JAVA_HOME=/opt/module/jdk1.8.0_121

2.hdfs-site.xml

 <property>
        <name>dfs.replication</name>
        <value>2</value>
  </property>

  <!-- 指定Hadoop辅助名称节点主机配置 -->
  <property>
      <name>dfs.namenode.secondary.http-address</name>
      <value>hadoop102:50090</value>
  </property>

  <property>
       <name>dfs.namenode.name.dir</name>
       <value>/opt/module/hadoop-3.1.3/data/dfs/nn</value>
  </property>
  <property>
       <name>dfs.datanode.data.dir</name>
       <value>/opt/module/hadoop-3.1.3/data/dfs/dn</value>
  </property>
  <property>
    <name>dfs.permissions.enabled</name>
    <value>false</value>
  </property>

3.yarn-env.sh

export JAVA_HOME=/opt/module/jdk1.8.0_121

4.yarn-site.xml

<!-- reducer获取数据的方式 -->
  <property>
	<name>yarn.nodemanager.aux-services</name>
	<value>mapreduce_shuffle</value>
  </property>

  <!-- 指定YARN的ResourceManager的地址 -->
  <property>
	<name>yarn.resourcemanager.hostname</name>
	<value>hadoop103</value>
  </property>

  <!-- 日志聚集功能使能 -->
  <property>
 	<name>yarn.log-aggregation-enable</name>
	<value>true</value>
  </property>

  <!-- 日志保留时间设置3天 -->
  <property>
	<name>yarn.log-aggregation.retain-seconds</name>
	<value>259200</value>
  </property>
  
  <property>
	<name>yarn.nodemanager.vmem-check-enabled</name>
	<value>false</value>
  </property>
  <property>
	<name>yarn.nodemanager.vmem-pmem-ratio</name>
	<value>5</value>
  </property>

5.mapred-env.sh

export JAVA_HOME=/opt/module/jdk1.8.0_121

6.mapred-site.xml

<!-- 指定mr运行在yarn上 -->
  <property>
	<name>mapreduce.framework.name</name>
	<value>yarn</value>
  </property>
  
  <!--配置历史服务器-->
  <property>
	<name>mapreduce.jobhistory.address</name>
	<value>hadoop103:10020</value>
  </property>
  <property>
	<name>mapreduce.jobhistory.webapp.address</name>
	<value>hadoop103:19888</value>
  </property>

  <!-- 在hadoop3.x中需要执行mapreduce的运行环境 -->
  <property>
	<name>yarn.app.mapreduce.am.env</name>
	<value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
  <property>
	<name>mapreduce.map.env</name>
	<value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>
  <property>
	<name>mapreduce.reduce.env</name>
	<value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value>
  </property>

  <property>
	<name>mapreduce.map.memory.mb</name>
	<value>1024</value>
  </property>

7.core-site.xml

<configuration>
  <!-- 指定HDFS中NameNode的地址 -->
  <property>
    <name>fs.defaultFS</name>
    <value>hdfs://hadoop101:9000</value>
  </property>

  <!-- 指定Hadoop运行时产生文件的存储目录 -->
  <property>
    <name>hadoop.tmp.dir</name>
    <value>/opt/module/hadoop-3.1.3/data/tmp</value>
  </property>
</configuration>

8.workers

hadoop2是slaves,hadoop3是workers
该文件是指明哪些节点运行DataNode,NodeManager
/opt/module/hadoop-3.1.3/etc/hadoop/workers

hadoop101
hadoop102
hadoop103

9.配置环境变量 /etc/profile

#Java
export JAVA_HOME=/opt/module/jdk1.8.0_121
export PATH=$PATH:$JAVA_HOME/bin

#Scala
export SCALA_HOME=/opt/module/scala-2.11.12
export PATH=$PATH:$SCALA_HOME/bin

#Hadoop
export HADOOP_HOME=/opt/module/hadoop-3.1.3
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

3.2 Hive安装

1.hive-env.sh

HADOOP_HOME=/opt/module/hadoop-3.1.3

export HIVE_CONF_DIR=/opt/module/hive-3.1.2/conf

export HIVE_AUX_JARS_PATH=/opt/module/hive-3.1.2/auxlib

2.hive-site.xml

        <property>
	  <name>javax.jdo.option.ConnectionURL</name>
	  <value>jdbc:mysql://hadoop101:3306/metastore?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>123456</value>
	</property>

	<property>
	  <name>hive.cli.print.header</name>
	  <value>true</value>
	</property>

	<property>
	  <name>hive.cli.print.current.db</name>
	  <value>true</value>
	</property>

	<property>
	    <name>hive.metastore.uris</name>
	    <value>thrift://hadoop101:9083</value>
	</property>

	<property>
		<name>hive.server2.webui.host</name>
		<value>hadoop101</value>
	</property>

	<property>
		<name>hive.server2.webui.port</name>
		<value>10002</value>
	</property>

	<!--Hive 3.x 默认打开了ACID，Spark不支持读取 ACID 的 Hive,需要关闭ACID-->
        <property>
                <name>hive.strict.managed.tables</name>
                <value>false</value>
        </property>
        <property>
                <name>hive.create.as.insert.only</name>
                <value>false</value>
        </property>
        <property>
                <name>metastore.create.as.acid</name>
                <value>false</value>
        </property>

	<!--关闭版本验证-->
        <property>
                <name>hive.metastore.schema.verification</name>
                <value>false</value>
        </property>

3.创建 HIVE_AUX_JARS_PATH

mkdir -p /opt/module/hive-3.1.2/auxlib

4.拷贝 mysql-connector-java-5.1.27-bin.jar 到 /opt/module/hive-3.1.2/lib 下

cp /opt/software/mysql-libs/mysql-connector-java-5.1.27/mysql-connector-java-5.1.27-bin.jar  /opt/module/hive-3.1.2/lib

5. 配置环境变量

#HIVE_HOME
export HIVE_HOME=/opt/module/hive-3.1.2
export PATH=$PATH:$HIVE_HOME/bin

6. 第一次执行，进行初始化

schematool -dbType mysql -initSchema

3.3 Zookeeper 安装

1.zoo.cfg

tickTime=2000
initLimit=10
syncLimit=5
dataDir=/opt/module/zookeeper-3.5.7/zkData
clientPort=2181

# 3台机器
server.1=hadoop101:2888:3888
server.2=hadoop102:2888:3888
server.3=hadoop103:2888:3888

2.创建 Zookeeper 数据目录

mkdir -p /opt/module/zookeeper-3.5.7/zkData

3.在 /opt/module/zookeeper-3.5.7/zkData 下创建myid，标识当前主机

echo "1" > /opt/module/zookeeper-3.5.7/zkData/myid

4.配置环境变量 /etc/profile

#Zookeeper
export ZOOKEEPER_HOME=/opt/module/zookeeper-3.5.7
export PATH=$PATH:$ZOOKEEPER_HOME/bin

5.分发zookeeper；注意：每台zookeeper节点的 myid 必须唯一

3.4 HBase 安装

1.hbase-env.sh

export JAVA_HOME=/opt/module/jdk1.8.0_121

#是否使用Hbase内置的Zookeeper.改成false,使用我们以前配置的Zookeeper
export HBASE_MANAGES_ZK=false

2.hbase-site.xml

  <!--hbase在hdfs上存储数据时的目录-->
  <property>
    	<name>hbase.rootdir</name>
   	<value>hdfs://hadoop101:9000/hbase</value>
  </property>
  <!--是否开启集群-->
  <property>
    	<name>hbase.cluster.distributed</name>
    	<value>true</value>
  </property>
  <property>
	<name>hbase.tmp.dir</name>
	<value>/opt/module/hbase-2.2.3/tmp</value>
  </property>
  <!--配置Zookeeper-->
  <property>
    	<name>hbase.zookeeper.quorum</name>
    	<value>hadoop101,hadoop102,hadoop103</value>
  </property>
  <property>
        <name>hbase.zookeeper.property.clientPort</name>
        <value>2181</value>
  </property>
  <!--Zookeeper的dataDir目录-->
  <property>
    	<name>hbase.zookeeper.property.dataDir</name>
    	<value>/opt/module/zookeeper-3.5.7/zkData</value>
  </property>
  <property>
        <name>zookeeper.znode.parent</name>
        <value>/hbase</value>
  </property>

  <property>
       <name>hbase.unsafe.stream.capability.enforce</name>
       <value>false</value>
   </property>

3.regionservers

hadoop101
hadoop102
hadoop102

4.配置环境变量 /etc/profile

#HBase
export HBASE_HOME=/opt/module/hbase-2.2.3
export PATH=$PATH:$HBASE_HOME/bin

3.5 Kafka 安装

1.server.properties

broker.id=0
log.dirs=/opt/module/kafka_2.11-2.4.0/logs
zookeeper.connect=hadoop101:2181,hadoop102:2181,hadoop103:2181/kafka

2.分发 kafka 到其他kafka节点；注意 server.properties中的 broker.id 必须全局唯一

3.配置环境变量 /etc/profile

#KAFKA_HOME
export KAFKA_HOME=/opt/module/kafka_2.11-2.4.0
export PATH=$PATH:$KAFKA_HOME/bin

3.6 Spark安装

1.spark-env.sh

export JAVA_HOME=/opt/module/jdk1.8.0_121

export SCALA_HOME=/opt/module/scala-2.11.12

export SPARK_MASTER_IP=hadoop101

export HADOOP_CONF_DIR=/opt/module/hadoop-3.1.3/etc/hadoop

#history.retainedApplications=3  //内存中历史副本存1份
export SPARK_HISTORY_OPTS="-Dspark.history.retainedApplications=1 -Dspark.history.fs.logDirectory=hdfs://hadoop101:9000/spark/log/"

2.spark-defalt.conf

spark.eventLog.enabled           true
spark.eventLog.dir               hdfs://hadoop101:9000/spark/log/
spark.yarn.historyServer.address hadoop102:18080

3.slaves

hadoop101
hadoop102
hadoop103

4.创建 hdfs-site.xml,hdfs-site.xml,hive-site.xml的软连接到 /opt/module/spark-2.4.5-bin-hadoop2.7/conf 下

ln -s /opt/module/hadoop-3.1.3/etc/hadoop/core-site.xml
ln -s /opt/module/hadoop-3.1.3/etc/hadoop/hdfs-site.xml
ln -s /opt/module/hive-3.1.2/conf/hive-site.xml

5. 拷贝 mysql-connector-java-5.1.27-bin.jar 到 /opt/module/spark-2.4.5-bin-hadoop2.7/jars 下

cp /opt/software/mysql-libs/mysql-connector-java-5.1.27/mysql-connector-java-5.1.27-bin.jar /opt/module/spark-2.4.5-bin-hadoop2.7/jars

6.配置环境变量 /etc/profile

#Spark
export SPARK_HOME=/opt/module/spark-2.4.5-bin-hadoop2.7
export PATH=$PATH:$SPARK_HOME/bin

#spark 提示 unable to load native-hadoop library for your platform... using builtin-java classes where applicable
export LD_LIBRARY_PATH=$HADOOP_HOME/lib/native/:$LD_LIBRARY_PATH

4. 总结

本次安装，经测试全部可用！