Hadoop记录-Apache hadoop+spark集群部署
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Hadoop+Spark集群部署指南
（多节点文件分发、集群操作建议salt/ansible）
1.集群规划
节点名称 主机名 IP地址 操作系统
Master centos1 192.168.0.1 CentOS 7.2
Slave1 centos2 192.168.0.2 CentOS 7.2
Slave2 centos2 192.168.0.3 Centos 7.2
2.基础环境配置
2.1 hostname配置
1）修改主机名
在192.168.0.1 root用户下执行：
hostnamectl set-hostname centos1
在192.168.0.2 root用户下执行：
hostnamectl set-hostname centos2
在192.168.0.3 root用户下执行：
hostnamectl set-hostname centos3
2）加入主机映射
在目标服务器（192.168.0.1 192.168.0.2 192.168.0.3）root用户下执行：
vim /etc/hosts
192.168.0.1 centos1
192.168.0.2 centos2
192.168.0.3 centos3
2.2 关闭selinux
在目标服务器（192.168.0.1 192.168.0.2 192.168.0.3）root用户下执行：
sed -i ‘/^SELINUX/s/=.*/=disabled/‘ /etc/selinux/config
setenforce 0
2.3 修改Linux最大打开文件数
在目标服务器（192.168.0.1 192.168.0.2 192.168.0.3）root用户下执行：
vim /etc/security/limits.conf
* soft nofile 65536
* hard nofile 65536
2.4 关闭防火墙
在目标服务器（192.168.0.1 192.168.0.2 192.168.0.3）root用户下执行
systemctl disable firewalld.service
systemctl stop firewalld.service
systemctl status firewalld.service
2.5初始化服务器
1）初始化服务器
在目标服务器（192.168.0.1 192.168.0.2 192.168.0.1 192.168.0.3）root用户下执行
groupadd -g 6000 hadoop
useradd -s /bin/bash -G hadoop -m hadoop
passwd hadoop
mkdir -p /usr/app/jdk
chown –R hadoop:hadoop /usr/app
2）配置sudo
在目标服务器（192.168.0.1 192.168.0.2 192.168.0.3）root用户下执行
vim /etc/sudoers.d/hadoop
hadoop ALL=(ALL) ALL
hadoop ALL=(ALL) NOPASSWD: ALL
Defaults !env_reset
3）配置ssh无密登录
在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下执行
su hadoop
ssh-keygen -t rsa
2）合并id_rsa_pub文件
在192.168.0.1 hadoop用户下执行
cat ~/.ssh/id_rsa.pub >> /home/hadoop/.ssh/authorized_keys
chmod 600 ~/.ssh/authorized_keys
scp ~/.ssh/authorized_keys hadoop@192.168.0.2:/home/hadoop/.ssh
输入密码：hadoop
在192.168.0.2 hadoop用户下执行
cat ~/.ssh/id_rsa.pub >> /home/hadoop/.ssh/authorized_keys
scp ~/.ssh/authorized_keys hadoop@192.168.0.3:/home/hadoop/.ssh
输入密码：hadoop
在192.168.0.3 hadoop用户下执行
cat ~/.ssh/id_rsa.pub >> /home/hadoop/.ssh/authorized_keys
scp ~/.ssh/authorized_keys hadoop@192.168.0.1:/home/hadoop/.ssh
scp ~/.ssh/authorized_keys hadoop@192.168.0.2:/home/hadoop/.ssh
覆盖之前的文件
输入密码：hadoop
3）在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下执行
ssh hadoop@192.168.0.1
ssh hadoop@192.168.0.2
ssh hadoop@192.168.0.3
3.程序包准备
#上传以下程序包到服务器上
jdk-8u192-linux-x64.tar.gz
hadoop-2.8.5.tar.gz
scala-2.11.12.tar.gz
spark-2.4.1-bin-hadoop2.7.tar.gz
zookeeper-3.4.5.tar.gz
#解压
tar xvf hadoop-2.8.5.tar.gz -C /usr/app
tar xvf scala-2.11.12.tar.gz -C /usr/app
tar xvf spark-2.4.1-bin-hadoop2.7.tar.gz -C /usr/app
tar xvf zookeeper-3.4.5.tar.gz -C /usr/app
tar xvf jdk-8u192-linux-x64.tar.gz -C /usr/app/jdk
mv hadoop-2.8.5 hadoop
mv scala-2.11.12 scala
mv spark-2.4.1-bin-hadoop2.7 spark
mv zookeeper-3.4.5 zookeeper
#配置/etc/profile
export JAVA_HOME=/usr/app/jdk/jdk1.8.0_192
export PATH=$JAVA_HOME/bin:$PATH
export HADOOP_HOME=/usr/app/hadoop
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
export SPARK_HOME=/usr/app/spark
export PATH=$SPARK_HOME/bin:$PATH
4.Zookeeper集群部署
#在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下执行
cd /usr/app/zookeeper/conf
cat >> zoo.cfg << EOF
tickTime=2000
initLimit=10
syncLimit=5
dataDir=/usr/app/zookeeper/data/zookeeper
dataLogDir=/usr/app/zookeeper/logs
clientPort=2181
maxClientCnxns=1000
server.1= 192.168.0.1:2888:3888
server.2= 192.168.0.2:2888:3888
server.3= 192.168.0.3:2888:3888
EOF
#master节点写1 slave节点依次类推
echo 1>> /usr/app/zookeeper/data/zookeeper/myid
#启动
nohup /usr/app/zookeeper/bin/zkServer.sh start &
5.Hadoop集群部署
#在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下执行
cd /usr/app/hadoop/etc/hadoop
在hadoop-env.sh、yarn-env.sh
加入：export JAVA_HOME=/usr/app/jdk/jdk1.8.0_192
到/usr/app/Hadoop/etc/hadoop目录下，根据实际情况修改里面的IP主机名、目录等。
core-sit.xml
<configuration>
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/app/hadoop/tmp</value>
</property>
<property>
<name>fs.default.name</name>
<value>hdfs://mycluster</value>
</property>
<property>
<name>io.compression.codecs</name>
<value>org.apache.hadoop.io.compress.GzipCodec,
org.apache.hadoop.io.compress.DefaultCodec,
org.apache.hadoop.io.compress.BZip2Codec,
org.apache.hadoop.io.compress.SnappyCodec
</value>
</property>
<property>
<name>hadoop.proxyuser.root.hosts</name>
<value>*</value>
</property>
<property>
<name>hadoop.proxyuser.root.groups</name>
<value>*</value>
</property>
<property>
<name>ha.zookeeper.quorum</name>
<value>192.168.0.1:2181,192.168.0.2:2181,192.168.0.3:2181</value>
</property>
</configuration>
hdfs-site.xml
<configuration>
<property>
<name>dfs.replication</name>
<value>3</value>
</property>
<property>
<name>dfs.permissions.enabled</name>
<value>false</value>
</property>
<property>
<name>dfs.nameservices</name>
<value>mycluster</value>
</property>
<property>
<name>dfs.ha.namenodes.mycluster</name>
<value>nn1,nn2</value>
</property>
<property>
<name>dfs.namenode.rpc-address.mycluster.nn1</name>
<value>192.168.0.1:9000</value>
</property>
<property>
<name>dfs.namenode.http-address.mycluster.nn1</name>
<value>192.168.0.1:50070</value>
</property>
<property>
<name>dfs.namenode.rpc-address.mycluster.nn2</name>
<value>192.168.0.2:9000</value>
</property>
<property>
<name>dfs.namenode.http-address.mycluster.nn2</name>
<value>192.168.0.2:50070</value>
</property>
<property>
<name>dfs.namenode.shared.edits.dir</name>
<value>qjournal://192.168.0.1:8485;192.168.0.2:8485;192.168.0.3:8485/mycluster</value>
</property>
<property>
<name>dfs.journalnode.edits.dir</name>
<value>/usr/app/hadoop/data/journaldata</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:///usr/app/hadoop/data/dfs/nn/local</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>/usr/app/hadoop/data/dfs/dn/local</value>
</property>
<property>
<name>dfs.client.failover.proxy.provider.mycluster</name>
<value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
</property>
<property>
<name>dfs.ha.fencing.methods</name>
<value>shell(/bin/true)</value>
</property>
<property>
<name>dfs.ha.fencing.ssh.private-key-files</name>
<value>/home/hadodp/.ssh/id_rsa</value>
</property>
<property>
<name>dfs.ha.fencing.ssh.connect-timeout</name>
<value>10000</value>
</property>
<property>
<name>dfs.ha.automatic-failover.enabled</name>
<value>true</value>
</property>
</configuration>
mapred-site.xml
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
yarn-site.xml
<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.resourcemanager.ha.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.cluster-id</name>
<value>rmCluster</value>
</property>
<property>
<name>yarn.resourcemanager.ha.rm-ids</name>
<value>rm1,rm2</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm1</name>
<value>192.168.0.1</value>
</property>
<property>
<name>yarn.resourcemanager.hostname.rm2</name>
<value>192.168.0.2</value>
</property>
<property>
<name>yarn.resourcemanager.zk-address</name>
<value>192.168.0.1:2181,192.168.0.2:2181,192.168.0.3:2181</value>
</property>
<property>
<name>yarn.resourcemanager.recovery.enabled</name>
<value>true</value>
</property>
<property>
<name>yarn.resourcemanager.store.class</name>
<value>org.apache.hadoop.yarn.server.resourcemanager.recovery.ZKRMStateStore</value>
</property>
<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.nodemanager.pmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>20480</value>
</property>
<property>
<name>yarn.nodemanager.disk-health-checker.max-disk-utilization-per-disk-percentage</name>
<value>97.0</value>
</property>
</configuration>
#新建目录
mkdir –p /usr/app/Hadoop/tmp
mkdir –p /usr/app/Hadoop/data/dfs/nn/local
mkdir –p /usr/app/Hadoop/data/dfs/nn/local
#启动
在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下执行
hadoop-daemon.sh start journalnode
在192.168.0.1 hadoop用户下执行
hdfs namenode –format
hadoop-daemon.sh start namenode
在192.168.0.2 hadoop用户下操作
hdfs namenode –bootstrapStandby
在192.168.0.1 hadoop用户下执行
hdfs zkfc –formatZK
在192.168.0.2 hadoop用户下操作
hadoop-daemon.sh start namenode
在192.168.0.1 192.168.0.2 hadoop用户下操作
hadoop-daemon.sh start zkfc
在192.168.0.1 192.168.0.2 hadoop用户下操作
yarn-daemon.sh start resourcemanager
在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下操作
yarn-daemon.sh start nodemanager
在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下操作
hadoop-daemon.sh start datanode
#验证
http://192.168.0.1:50070查看hadoop状态
http://192.168.0.1:8088查看yarn集群状态
6.Spark集群部署
#在192.168.0.1 192.168.0.2 192.168.0.3 hadoop用户下执行
cd /usr/app/spark/conf
在spark-env.sh加入
export JAVA_HOME=/usr/app/jdk/jdk1.8.0_192
export SCALA_HOME=/usr/app/scala
export HADOOP_HOME=/usr/app/hadoop
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export SPARK_HISTORY_OPTS="-Dspark.history.fs.logDirectory=hdfs://cpu-cluster/tmp/spark/event"
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib/native"
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${HADOOP_HOME}/lib/native
在slaves加入192.168.0.2 192.168.0.3
#启动
/usr/app/spark/sbin/start-all.sh
#验证
/usr/app/spark/bin/spark-shell --master yarn --deploy-mode client
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