大数据学习（07）——Hadoop3.3高可用环境搭建
前面用了五篇文章来介绍Hadoop的相关模块，理论学完还得操作一把才能加深理解。这一篇我会花相当长的时间从环境搭建开始，到怎么在使用Hadoop，逐步介绍Hadoop的使用。
本篇分这么几段内容：
- 规划部署节点
- 节点免密和网络配置
- zookeeper分布式集群搭建
- Hadoop分布式集群搭建
- IDEA远程提交MapReduce任务到分布式集群
HDFS高可用至少有两个NameNode（NN），副本存三份有 三个DataNode（DN）。Yarn高可用至少有两个Resource Manager（RM），计算向存储移动需要在每个DataNode上部署NodeManager（NM）。Zookeeper（ZK）三节点选主。JournalNode（JN）三节点才有过半成功。
基于以上考虑，规划部署节点如下：
主机
IP
NN
RM
ZKFC
DN
NM
JN
ZK
server01
192.168.0.111
•
•
•
server02
192.168.0.112
•
•
•
server03
192.168.0.113
•
•
•
•
server04
192.168.0.114
•
•
•
•
server05
192.168.0.115
•
•
•
•
注意：1.ZKFC与NameNode必须同节点才能选主。
2.DataNode与NodeManager必须同节点，体现计算向数据移动。
3.新建hadoop用户和组，以上软件全部用hadoop用户启动。
先要关闭五台机的防火墙，这一点非常重要。否则后面启动zookeeper后，zkServer.sh status不能正常查看zk状态，三个zk之间无法通讯。第二个Namenode也无法同步第一个Namenode的数据。
[root@server04 home]# systemctl stop firewalld
[root@server04 home]# systemctl disable firewalld
Removed symlink /etc/systemd/system/multi-user.target.wants/firewalld.service.
Removed symlink /etc/systemd/system/dbus-org.fedoraproject.FirewallD1.service.
按照规划修改/etc/hostname文件中的主机名。
在/etc/hosts文件中增加五台机的主机名解析。
127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.0.111 server01
192.168.0.112 server02
192.168.0.113 server03
192.168.0.114 server04
192.168.0.115 server05
节点免密
由于server01、server02上可能会启动其他节点的脚本，所以server01和server02要对其他节点免密。
以server01为例，依次输入以下命令，将server01的公钥分发给其他节点，达到免密登录的目的。
注意一点，ssh-copy-id生成的authorized_keys权限是600，如果权限是644就没办法免密登录，这个文件权限要求很苛刻。
[hadoop@server01 ~]$ ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa
[hadoop@server01 ~]$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop@server01
[hadoop@server01 ~]$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop@server02
[hadoop@server01 ~]$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop@server03
[hadoop@server01 ~]$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop@server04
[hadoop@server01 ~]$ ssh-copy-id -i ~/.ssh/id_rsa.pub hadoop@server05
下载zookeeper
从官网上下载apache-zookeeper-3.5.8-bin.tar.gz包，注意不带bin的包里面是源码，不能启动QuorumPeerMain。
安装zookeeper
将apache-zookeeper-3.5.8-bin.tar.gz包解压到/usr目录下，把用户和组设置为hadoop。
在环境变量里增加
ZOOKEEPER_HOME=/usr/apache-zookeeper-3.5.8-bin
PATH=$PATH:$JAVA_HOME/bin:$ZOOKEEPER_HOME/bin
将/usr/apache-zookeeper-3.5.8-bin/conf/zoo_sample.cfg复制一份到相同目录，命令为zoo.cfg，这是启动zookeeper时要读取的配置文件。
以server03为例，修改zoo.cfg的内容。
[hadoop@server03 home]$ cat /usr/apache-zookeeper-3.5.8-bin/conf/zoo.cfg
The number of milliseconds of each tick
tickTime=2000
The number of ticks that the initial
synchronization phase can take
initLimit=10
The number of ticks that can pass between
sending a request and getting an acknowledgement
syncLimit=5
the directory where the snapshot is stored.
do not use /tmp for storage, /tmp here is just
example sakes.
这里存放zookeeper的myid文件
dataDir=/opt/zookeeper/data
the port at which the clients will connect
clientPort=2181
the maximum number of client connections.
increase this if you need to handle more clients
#maxClientCnxns=60
Be sure to read the maintenance section of the
administrator guide before turning on autopurge.
http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
Purge task interval in hours
Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1
server.x的数值与myid中的一致，后面接是zookeeper的三个节点。
2888端口是集群节点通讯使用
3888端口是选举leader使用
server.3=server03:2888:3888
server.4=server04:2888:3888
server.5=server05:2888:3888
在/opt/zookeeper/data目录下创建myid文件
[hadoop@server03 home]$ echo 3 > /opt/zookeeper/data/myid
至此，zookeeper搭建完毕，server04和server05的步骤一致。
在三台机器分别启动zookeeper。
[hadoop@server03 home]$ zkServer.sh start
[hadoop@server03 home]$ jps
3762 Jps
1594 QuorumPeerMain
[hadoop@server03 home]$ zkServer.sh status
/bin/java
ZooKeeper JMX enabled by default
Using config: /usr/apache-zookeeper-3.5.8-bin/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost.
Mode: leader
server03被选为主节点了。
Hadoop基于Java开发，必须先安装jdk。本次实验使用的版本是jdk-11.0.8，安装完成后需要在环境变量里加入JAVA_HOME。
下载Hadoop
从官网下载hadoop-3.3.0.tar.gz，解压到/usr目录下，并将用户和组修改为hadoop。
配置Hadoop
官方网站上有详细的说明 Apache Hadoop 3.3.0，把单节点的配置和高可用配置放在相应的文件下就行，这里我再梳理一遍。在此之前，需要修改/usr/hadoop-3.3.0/etc/hadoop/hadoop-env.sh，增加export JAVA_HOME=/usr/java/jdk-11.0.8，让hadoop运行命令时能调用jdk。
在环境变量里增加
HADOOP_HOME=/usr/hadoop-3.3.0
PATH=$PATH:$JAVA_HOME/bin:$ZOOKEEPER_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
1. /usr/hadoop-3.3.0/etc/hadoop/core-site.xml
2./usr/hadoop-3.3.0/etc/hadoop/hdfs-site.xml
3./usr/hadoop-3.3.0/etc/hadoop/mapred-site.xml
4./usr/hadoop-3.3.0/etc/hadoop/yarn-site.xml
5./usr/hadoop-3.3.0/etc/hadoop/works
server03
server04
server05
启动zookeeper
在serverr03、server04、server05三台机器上使用hadoop用户执行zkServer.sh start启动zookeeper。三台都启动之后，在每台机执行zkServer.sh status可以查看本节点是主节点（leader）还是从节点（follower）。
[hadoop@server03 ~]$ zkServer.sh start
/bin/java
ZooKeeper JMX enabled by default
Using config: /usr/apache-zookeeper-3.5.8-bin/bin/../conf/zoo.cfg
Starting zookeeper … STARTED
[hadoop@server03 ~]$ jps
3383 QuorumPeerMain
3422 Jps
[hadoop@server03 ~]$ zkServer.sh status
/bin/java
ZooKeeper JMX enabled by default
Using config: /usr/apache-zookeeper-3.5.8-bin/bin/../conf/zoo.cfg
Client port found: 2181. Client address: localhost.
Mode: follower
启动JournalNode
在启动journalnode之前，我们在hdfs-site.xml里做了如下配置
<property>
<name>dfs.journalnode.edits.dir</name>
<value>/opt/hadoop/journaldata</value>
</property>我们先用root用户创建这个路径，再把用户和组修改成hadoop
[root@server04 ~]# cd /opt
[root@server04 opt]# ll
总用量 0
drwxr-xr-x. 3 hadoop hadoop 18 8月 8 23:37 zookeeper
[root@server04 opt]# mkdir -p hadoop/journaldata
[root@server04 opt]# chown -R hadoop:hadoop hadoop/
[root@server04 opt]# ll
总用量 0
drwxr-xr-x. 3 hadoop hadoop 25 8月 11 22:42 hadoop
drwxr-xr-x. 3 hadoop hadoop 18 8月 8 23:37 zookeeper
在server03、server04、server05三台机执行hadoop-daemon.sh start journalnode命令，启动journalnode
[hadoop@server03 sbin]$ hadoop-daemon.sh start journalnode
WARNING: Use of this script to start HDFS daemons is deprecated.
WARNING: Attempting to execute replacement "hdfs --daemon start" instead.
[hadoop@server03 sbin]$ jps
3383 QuorumPeerMain
4439 JournalNode
4476 Jps
格式化HDFS
我们在core-site.xml里有这么一段配置
<property>
<name>hadoop.tmp.dir</name>
<value>/opt/hadoop/tmp</value>
</property>先用root创建这个目录，然后修改用户和组为hadoop
[root@server01 ~]# cd /opt
[root@server01 opt]# ll
总用量 0
[root@server01 opt]# mkdir -p hadoop/tmp
[root@server01 opt]# chown -R hadoop:hadoop hadoop/
[root@server01 opt]# ll
总用量 0
drwxr-xr-x. 3 hadoop hadoop 17 8月 11 22:54 hadoop
在server01上执行hadoop namenode -format，对HDFS格式化。
启动Hadoop
第一次启动，在server01上执行hadoop-daemon.sh start namenode。
[hadoop@server01 tmp]$ hadoop-daemon.sh start namenode
WARNING: Use of this script to start HDFS daemons is deprecated.
WARNING: Attempting to execute replacement "hdfs --daemon start" instead.
[hadoop@server01 tmp]$ jps
24664 NameNode
24700 Jps
在server02上执行hadoop namenode -bootstrapStandby同步server01的信息。
在server01上执行hdfs zkfc -formatZK格式化zkfc。
以上操作完成后，在server01上执行stop-all.sh，把相关的服务全部停掉。
在server01上执行start-all.sh，启动集群。
[hadoop@server01 hadoop]$ start-all.sh
WARNING: Attempting to start all Apache Hadoop daemons as hadoop in 10 seconds.
WARNING: This is not a recommended production deployment configuration.
WARNING: Use CTRL-C to abort.
Starting namenodes on [server01 server02]
Starting datanodes
Starting journal nodes [server04 server03 server05]
Starting ZK Failover Controllers on NN hosts [server01 server02]
Starting resourcemanagers on [ server01 server02]
Starting nodemanagers
[hadoop@server01 hadoop]$ jps
6323 DFSZKFailoverController
5977 NameNode
6668 ResourceManager
6797 Jps
[hadoop@server02 ~]$ jps
6736 ResourceManager
6600 DFSZKFailoverController
6492 NameNode
7007 Jps
[hadoop@server03 current]$ jps
27705 NodeManager
27978 Jps
5531 QuorumPeerMain
27580 JournalNode
27470 DataNode
[hadoop@server04 tmp]$ jps
5104 QuorumPeerMain
26819 NodeManager
26694 JournalNode
27094 Jps
26584 DataNode
[hadoop@server05 tmp]$ jps
4663 QuorumPeerMain
26394 NodeManager
26268 JournalNode
26684 Jps
26159 DataNode
看到这些结果，我忍不住给自己点个赞，让开发人员来搭环境真不容易啊。搭建环境的过程遇到了很多坑，查找资料一一解决，最后成功，这种感觉挺棒的。
环境搭好了就该实战一把了。
出一个题目，给出一些日期的气温，从中找出每个月温度最高的两天所对应的气温。注意：一天的气温可能会记录多次，但是结果只能输出最高的一条。
气温文件
1949-10-01 34c
1949-10-01 38c
1949-10-02 36c
1950-01-01 32c
1950-10-01 37c
1951-12-01 23c
1950-10-02 41c
1950-10-03 27c
1951-07-01 45c
1951-07-02 46c
1951-07-03 47c
结题思路：每一行记录可以全部作为Map阶段输出的Key值，按照Key值中的年、月分组，再按照年、月、温度排序，最后去掉一天有多条的记录，输出结果。
HadoopClient.java
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import java.net.URI;
public class HadoopClient {
public static void main(String[] args) throws Exception {
//1,conf
System.setProperty("HADOOP\_USER\_NAME", "hadoop");
Configuration conf = new Configuration(true);
conf.set("mapreduce.app-submission.cross-platform", "true");
conf.set("mapreduce.job.jar","D:\\\\IDEAProject\\\\hadoopmr\\\\target\\\\hadoopmr-1.0-SNAPSHOT.jar");
//2,job
Job job = Job.getInstance(conf);
job.setJarByClass(HadoopClient.class);
job.setJobName("FirstMR");
//3,输入输出路径
Path input = new Path("/tq/input/data.txt");
FileInputFormat.addInputPath(job, input);
Path output = new Path(URI.create("/tq/output/"));
//
if(output.getFileSystem(conf).exists(output)){
output.getFileSystem(conf).delete(output, true);
}
FileOutputFormat.setOutputPath(job, output );
//4,map
job.setMapperClass(MyMapper.class);
job.setMapOutputKeyClass(MyKey.class);
job.setMapOutputValueClass(IntWritable.class);
job.setPartitionerClass(MyPartitioner.class);
job.setSortComparatorClass(MySortComparator.class);
//5,reduce
// 分组比较器
job.setGroupingComparatorClass(MyGroupingComparator.class);
job.setReducerClass(MyReducer.class);
job.setNumReduceTasks(1);
job.setCombinerKeyGroupingComparatorClass(MyGroupingComparator.class);
//7,submit
job.waitForCompletion(true);
} }
MyKey.java
import org.apache.hadoop.io.WritableComparable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class MyKey implements WritableComparable
private int year;
private int month;
private int day;
private int temperature;
public int getYear() {
return year;
}
public void setYear(int year) {
this.year = year;
}
public int getMonth() {
return month;
}
public void setMonth(int month) {
this.month = month;
}
public int getDay() {
return day;
}
public void setDay(int day) {
this.day = day;
}
public int getTemperature() {
return temperature;
}
public void setTemperature(int temperature) {
this.temperature = temperature;
}
@Override
public void write(DataOutput out) throws IOException {
out.writeInt(year);
out.writeInt(month);
out.writeInt(day);
out.writeInt(temperature);
}
@Override
public void readFields(DataInput in) throws IOException {
this.year=in.readInt();
this.month=in.readInt();
this.day=in.readInt();
this.temperature=in.readInt();
}
@Override
public int compareTo(MyKey that) {
// 约定俗成：日期正序
int c1=Integer.compare(this.getYear(), that.getYear());
if(c1==0) {
int c2 = Integer.compare(this.getMonth(), that.getMonth());
if(c2==0) {
// 比完日期，就没事了
return Integer.compare(this.getDay(), that.getDay());
}
return c2;
}
return c1;
} }
MyMapper.java
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.util.StringUtils;
import java.io.IOException;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Calendar;
import java.util.Date;
public class MyMapper extends Mapper
// 放外面，不用每次都创建！
MyKey mkey = new MyKey();
IntWritable mval = new IntWritable();
@Override
protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException {
try {
// value: 1949-10-01 14:21:02 34c >> Mykey
String\[\] strs = StringUtils.split(value.toString(), ' ');
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd");
Date date = sdf.parse(strs\[0\]);
Calendar cal = Calendar.getInstance();
cal.setTime(date);
mkey.setYear(cal.get(Calendar.YEAR));
mkey.setMonth(cal.get(Calendar.MONTH)+1);
mkey.setDay(cal.get(Calendar.DAY\_OF\_MONTH));
int temperature = Integer.parseInt(strs\[1\].substring(0, strs\[1\].length()-1));
mkey.setTemperature(temperature);
mval.set(temperature);
context.write(mkey, mval);
} catch (ParseException e) {
e.printStackTrace();
}
} }
MyPartitioner.java
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Partitioner;
public class MyPartitioner extends Partitioner
@Override
public int getPartition(MyKey mykey, IntWritable intWritable, int i) {
return mykey.getYear() % i;
} }
MySortComparator.java
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
public class MySortComparator extends WritableComparator {
public MySortComparator() {
super(MyKey.class, true);
}
@Override
public int compare(WritableComparable a, WritableComparable b) {
MyKey t1 = (MyKey)a;
MyKey t2 = (MyKey)b;
int c1=Integer.compare(t1.getYear(), t2.getYear());
if(c1==0){
int c2=Integer.compare(t1.getMonth(), t2.getMonth());
if(c2==0){
// 从大到小，倒序
return -Integer.compare(t1.getTemperature(), t2.getTemperature());
}
return c2;
}
return c1;
} }
MyGroupingComparator.java
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
public class MyGroupingComparator extends WritableComparator {
public MyGroupingComparator() {
super(MyKey.class,true);
}
public int compare(WritableComparable a, WritableComparable b) {
MyKey t1 = (MyKey)a;
MyKey t2 = (MyKey)b;
int c1=Integer.compare(t1.getYear(), t2.getYear());
if(c1==0){
return Integer.compare(t1.getMonth(), t2.getMonth());
}
return c1;
} }
MyReducer.java
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class MyReducer extends Reducer
Text rkey = new Text();
IntWritable rval = new IntWritable();
@Override
protected void reduce(MyKey key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int flg = 0;
int day = 0;
// 1970 01 20 34 34
// 1970 01 12 28 28
for (IntWritable v : values) { // 根本就不用v，key跟着变动的
if (flg == 0) {
// 1970-01-20:34
rkey.set(key.getYear()+"-"+key.getMonth()+"-"+key.getDay());
rval.set(key.getTemperature());
context.write(rkey,rval );
day = key.getDay();
flg++;
}
// 将同一天，多条记录排除
if(flg!=0 && day != key.getDay()){
rkey.set(key.getYear()+"-"+key.getMonth()+"-"+key.getDay());
rval.set(key.getTemperature());
context.write(rkey,rval);
break;
}
}
} }
注意，要把core-site.xml、hdfs-stie.xml、mapred-sit.xml、yarn-site.xml这4个文件放到IDEA工程的src/main/resources目录下，resources目录设置为Resources Root。
pom里面增加配置
以上配置完毕后，执行客户端主程序，用浏览器打开server01:8088查看Applications里的执行结果。执行成功之后，到server01上用命令行查看结果文件。
[hadoop@server01 ~]$ hdfs dfs -cat /tq/output/part-r-00000
1949-10-1 38
1949-10-2 36
1950-1-1 32
1950-10-2 41
1950-10-1 37
1951-7-3 47
1951-7-2 46
1951-12-1 23
结果无误。回过头来再慢慢看看每一个步骤的代码逻辑是怎么写的，多练习就熟悉了。
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