centos7配置hadoop集群

一：测试环境搭建规划：

主机名称	IP	用户	HDFS	YARN
hadoop11	192.168.1.101	hadoop	NameNode,DataNode	NodeManager
hadoop12	192.168.1.102	hadoop	DataNode	NodeManager
hadoop13	192.168.1.103	hadoop	DataNode,SecondaryNameNode	NodeManager
hadoop14	192.168.1.104	hadoop	DataNode	ResourceManager,NodeManager

二：hadoop安装：

1:新建hadoop用户：

useradd hadoop

passwd hadoop

su hadoop

mkdir /home/hadoop/apps

mkdir /home/hadoop/data

三：关闭防火墙：

systemctl stop firewalld

systemctl disable firewalld

四：关闭selinux：

vim /etc/sysconfig/selinux

修改SELINUX=enforcing为SELINUX=disabled

五：安装Java：

5-1下载JDK：

https://www.oracle.com/technetwork/java/javase/downloads/index.html

https://www.oracle.com/technetwork/java/javase/downloads/jdk11-downloads-5066655.html

点选 Accept License Agreement

下载jdk-11.0.1_linux-x64_bin.tar.gz

把下载好的jdk-11.0.1_linux-x64_bin.tar.gz，上传到服务器 /usr/local目录

tar -zxvf jdk-11.0.1_linux-x64_bin.tar.gz

5-2添加环境变量：

vim /etc/profile

在结尾添加：

#java
export JAVA_HOME='/usr/local/jdk-11.0.1'
export PATH=$JAVA_HOME/bin:$PATH

保存退出后，执行：

source /etc/profile

查看版本，确定环境变量是否生效：

java -version

六：安装hadoop

6-1下载hadoop：

https://hadoop.apache.org/releases.html

选择binary文件

拷贝镜像地址，下载

wget http://mirror.bit.edu.cn/apache/hadoop/common/hadoop-2.8.5/hadoop-2.8.5.tar.gz

6-2 解压到指定目录：

tar -zxvf hadoop-2.8.5.tar.gz -C /home/hadoop/apps

 6-3修改配置文件：

  cd /home/hadoop/apps/hadoop-2.8.5/etc/hadoop

  配置Hadoop JDK路径修改hadoop-env.sh、mapred-env.sh、yarn-env.sh文件中的JDK路径：

 #export JAVA_HOME='/usr/local/jdk-11.0.1'

 export JAVA_HOME='/usr/local/jdk1.8.0_191'

  

6-3-1 vim core-site.xml

<!-- Put site-specific property overrides in this file. -->

<configuration>
 <property>
   <name>fs.defaultFS</name>
   <value>hdfs://hadoop11.zm.com:8020</value>
 </property>
 <property>
   <name>hadoop.tmp.dir</name>
   <value>/home/hadoop/data/hadoop/tmp</value>
 </property>
</configuration>

fs.fefaultFS是NameNode的地址，

hadoop.tmp.dir是hadoop临时目录的地址，

应保证目录是已存在的，如不存在，需先创建。

6-3-2 vim hdfs-site.xml

<!-- Put site-specific property overrides in this file. -->

<configuration>

  <property>
    <name>dfs.namenode.name.dir</name>
    <value>/home/hadoop/data/hadoop/name</value>
    <description>为了保证元数据的安全一般配置多个不同目录</description>
  </property>

  <property>
    <name>dfs.datanode.data.dir</name>
    <value>/home/hadoop/data/hadoop/data</value>
    <description>datanode 的数据存储目录</description>
  </property>

  <property>
    <name>dfs.replication</name>
    <value>2</value>
     <description>HDFS 的数据块的副本存储个数, 默认是3</description>
  </property>

  <property>
    <name>dfs.namenode.secondary.http-address</name>
    <value>hadoop13.zm.com:50090</value>
    </property>

  </configuration>

 dfs.namenode.name.dir：namenode数据的存放地点。也就是namenode元数据存放的地方，记录了hdfs系统中文件的元数据。

 dfs.datanode.data.dir： datanode数据的存放地点。也就是block块存放的目录了。

 dfs.replication：hdfs的副本数设置。也就是上传一个文件，其分割为block块后，每个block的冗余副本个数，默认配置是3。

 dfs.secondary.http.address：secondarynamenode 运行节点的信息，和 namenode 不同节点

 dfs.namenode.secondary.http-address是指定secondaryNameNode的http访问地址和端口号，因为在规划中，我们将hadoop13规划为SecondaryNameNode服务器。

所以这里设置为：hadoop13.zm.com:50090

6-3-3 配置slave文件,slaves文件是指定HDFS上有哪些DataNode节点：

cd /home/hadoop/apps/hadoop-2.8.5

[hadoop@hadoop11 hadoop-2.8.5]$ vim slaves

hadoop11.zm.com
hadoop12.zm.com
hadoop13.zm.com
hadoop14.zm.com

6-3-4 cd /home/hadoop/apps/hadoop-2.8.5/etc/hadoop

vim yarn-site.xml

<configuration>
<!-- Site specific YARN configuration properties -->
<property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>hadoop14.zm.com</value>
    </property>
    <property>
        <name>yarn.log-aggregation-enable</name>
        <value>true</value>
    </property>
    <property>
        <name>yarn.log-aggregation.retain-seconds</name>
        <value>106800</value>
    </property>
</configuration>

yarn.resourcemanager.hostname这个指定resourcemanager服务器指向hadoop14.zm.com，

yarn.log-aggregation-enable是配置是否启用日志聚集功能，

yarn.log-aggregation.retain-seconds是配置聚集的日志在HDFS上最多保存多长时间。

6-3-5 cp mapred-site.xml.template mapred-site.xml

vim mapred-site.xml

<!-- Put site-specific property overrides in this file. -->

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.address</name>
        <value>hadoop11.zm.com:10020</value>
    </property>
    <property>
        <name>mapreduce.jobhistory.webapp.address</name>
        <value>hadoop11.zm.com:19888</value>
    </property>
</configuration>

mapreduce.framework.name设置mapreduce任务运行在yarn上。

mapreduce.jobhistory.address是设置mapreduce的历史服务器安装在hadoop11机器上。

mapreduce.jobhistory.webapp.address是设置历史服务器的web页面地址和端口号。

 七：配置hosts文件：

回到root权限：su

vim /etc/hosts

127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
::1         localhost localhost.localdomain localhost6 localhost6.localdomain6

192.168.1.201     hadoop11     hadoop11.zm.com

192.168.1.202     hadoop12     hadoop12.zm.com 

192.168.1.203     hadoop13     hadoop13.zm.com 

192.168.1.204     hadoop14     hadoop14.zm.com

 八：SSH免密登陆

su hadoop

所有hadoop集群服务器：

su hadoop

cd /home/hadoop/.ssh

ssh-keygen -t rsa

将所有终端公钥文件拷贝到hadoop11：

　　所有终端运行：

　　ssh-copy-id -i hadoop11

登陆hadoop11终端的/home/hadoop/.ssh修改权限：

　　chmod 600 authorized_keys

分发授权文件到其他终端：

　　scp /home/hadoop/.ssh/authorized_keys hadoop12:/home/hadoop/.ssh/

       scp /home/hadoop/.ssh/authorized_keys hadoop13:/home/hadoop/.ssh/

       scp /home/hadoop/.ssh/authorized_keys hadoop14:/home/hadoop/.ssh/

每台机器用ssh命令连接下所有机器，第一次连接要输入yes，以后不用输入

九：hadoop分发到其他终端

拷贝hadoop11 /home/hadoop/apps目录内容到其他终端:

scp -rq data hadoop12:/home/hadoop/

拷贝hadoop11 /home/hadoop/data目录内容到其他终端:

scp -rq data hadoop12:/home/hadoop/

 十：配置hadoop环境变量（所有节点均需配置）

注意：

1、如果使用root用户进行安装。 vi /etc/profile 即可 系统变量

2、如果使用普通用户进行安装。 vi ~/.bashrc 用户变量

vim .bashrc

# User specific aliases and functions

export HADOOP_HOME=/home/hadoop/apps/hadoop-2.8.5
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

[hadoop@hadoop11 ~]$ source .bashrc

查看hadoop版本：hadoop version

[hadoop@hadoop11 ~]$ hadoop version
Hadoop 2.8.5
Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r 0b8464d75227fcee2c6e7f2410377b3d53d3d5f8
Compiled by jdu on 2018-09-10T03:32Z
Compiled with protoc 2.5.0
From source with checksum 9942ca5c745417c14e318835f420733
This command was run using /home/hadoop/apps/hadoop-2.8.5/share/hadoop/common/hadoop-common-2.8.5.jar

十一：hadoop初始化

HDFS初始化只能在主节点上进行

如果需要重新初始化，先删除相关目录文件，

粗略做法是删除/home/hadoop/data/hadoop路径下

[hadoop@hadoop14 hadoop]$ ll
总用量 0
drwx------. 3 hadoop hadoop 40 10月 31 18:14 data
drwxrwxr-x. 3 hadoop hadoop 21 10月 31 18:07 name
drwxrwxr-x. 3 hadoop hadoop 26 10月 31 18:18 tmp
[hadoop@hadoop14 hadoop]$ pwd
/home/hadoop/data/hadoop

这几个目录的子目录；

[hadoop@hadoop11 ~]$ hadoop namenode -format

18/10/31 00:01:14 INFO namenode.FSImage: Allocated new BlockPoolId: BP-1163964159-198.8.8.201-1540915274705
18/10/31 00:01:14 INFO common.Storage: Storage directory /home/hadoop/data/hadoop/name has been successfully formatted.
18/10/31 00:01:14 INFO namenode.FSImageFormatProtobuf: Saving image file /home/hadoop/data/hadoop/name/current/fsimage.ckpt_0000000000000000000 using no compression
18/10/31 00:01:15 INFO namenode.FSImageFormatProtobuf: Image file /home/hadoop/data/hadoop/name/current/fsimage.ckpt_0000000000000000000 of size 323 bytes saved in 0 seconds.
18/10/31 00:01:15 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
18/10/31 00:01:15 INFO util.ExitUtil: Exiting with status 0
18/10/31 00:01:15 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at hadoop11/198.8.8.201
************************************************************/

初始化后会自动在/home/hadoop/data/hadoop/name路径

建立current目录

生成：

-rw-rw-r-- 1 hadoop hadoop 323 10月 31 18:07 fsimage_0000000000000000000
-rw-rw-r-- 1 hadoop hadoop  62 10月 31 18:07 fsimage_0000000000000000000.md5
-rw-rw-r-- 1 hadoop hadoop   2 10月 31 18:07 seen_txid
-rw-rw-r-- 1 hadoop hadoop 213 10月 31 18:07 VERSION

十二：启动HDFS：

可以在集群中任何节点执行：

[hadoop@hadoop12 ~]$ start-dfs.sh

报错

[hadoop@hadoop12 ~]$ start-dfs.sh
WARNING: An illegal reflective access operation has occurred
WARNING: Illegal reflective access by org.apache.hadoop.security.authentication.util.KerberosUtil (file:/home/hadoop/apps/hadoop-2.8.5/share/hadoop/common/lib/hadoop-auth-2.8.5.jar) to method sun.security.krb5.Config.getInstance()
WARNING: Please consider reporting this to the maintainers of org.apache.hadoop.security.authentication.util.KerberosUtil
WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations
WARNING: All illegal access operations will be denied in a future release
Starting namenodes on [hadoop11.zm.com]
The authenticity of host 'hadoop11.zm.com (198.8.8.201)' can't be established.
ECDSA key fingerprint is SHA256:RlH2tSlCWblQ5jZ6DMuCt4+yEmYuWo5MZuqUTGesM9I.
ECDSA key fingerprint is MD5:de:e9:61:07:c7:14:35:0c:e6:8a:70:0e:93:5f:2b:8d.
Are you sure you want to continue connecting (yes/no)? no

关闭HDFS:

stop-dfs.sh 

降低java版本到jdk1.8.0_191

 如步骤6-3修改集群所有节点配置文件：

  cd /home/hadoop/apps/hadoop-2.8.5/etc/hadoop

  配置Hadoop JDK路径修改hadoop-env.sh、mapred-env.sh、yarn-env.sh文件中的JDK路径：

 #export JAVA_HOME='/usr/local/jdk-11.0.1'

 export JAVA_HOME='/usr/local/jdk1.8.0_191'

再次启动HDFS（任何节点均可执行）:

start-dfs.sh

[hadoop@hadoop11 sbin]$ start-dfs.sh 
Starting namenodes on [hadoop11.zm.com]
hadoop11.zm.com: starting namenode, logging to /home/hadoop/apps/hadoop-2.8.5/logs/hadoop-hadoop-namenode-hadoop11.out
hadoop11: starting datanode, logging to /home/hadoop/apps/hadoop-2.8.5/logs/hadoop-hadoop-datanode-hadoop11.out
hadoop13: starting datanode, logging to /home/hadoop/apps/hadoop-2.8.5/logs/hadoop-hadoop-datanode-hadoop13.out
hadoop14: starting datanode, logging to /home/hadoop/apps/hadoop-2.8.5/logs/hadoop-hadoop-datanode-hadoop14.out
hadoop12: starting datanode, logging to /home/hadoop/apps/hadoop-2.8.5/logs/hadoop-hadoop-datanode-hadoop12.out
Starting secondary namenodes [hadoop13.zm.com]
hadoop13.zm.com: starting secondarynamenode, logging to /home/hadoop/apps/hadoop-2.8.5/logs/hadoop-hadoop-secondarynamenode-hadoop13.out

 在其他节点启动时会报询问信息

The authenticity of host 'hadoop13.zm.com (198.8.8.203)' can't be established.
ECDSA key fingerprint is SHA256:RlH2tSlCWblQ5jZ6DMuCt4+yEmYuWo5MZuqUTGesM9I.
ECDSA key fingerprint is MD5:de:e9:61:07:c7:14:35:0c:e6:8a:70:0e:93:5f:2b:8d.
Are you sure you want to continue connecting (yes/no)? 

解决办法（所有节点执行）：

进入root用户

su

vim /etc/ssh/ssh_config

加入如下两句

#   StrictHostKeyChecking ask
StrictHostKeyChecking no
UserKnownHostsFile /dev/null

 start-dfs.sh执行后：

在/home/hadoop/data/hadoop/data中自动生成

[hadoop@hadoop11 data]$ ll
总用量 4
drwxrwxr-x 3 hadoop hadoop 66 10月 31 18:14 current
-rw-rw-r-- 1 hadoop hadoop 13 10月 31 18:14 in_use.lock

在/home/hadoop/data/hadoop/name中自动生成

[hadoop@hadoop11 name]$ ll
总用量 4
drwxrwxr-x 2 hadoop hadoop 156 10月 31 18:13 current
-rw-rw-r-- 1 hadoop hadoop  13 10月 31 18:13 in_use.lock

十三：启动YARN（只能在ResourceManager主节点执行）：

[hadoop@hadoop14 ~]$ start-yarn.sh

[hadoop@hadoop14 ~]$ start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-resourcemanager-hadoop11.out
hadoop12: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop12.out
hadoop13: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop13.out
hadoop14: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop14.out
hadoop11: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop11.out

 start-yarn.sh执行后：

会在/home/hadoop/data/hadoop/tmp中自动生成

[hadoop@hadoop14 tmp]$ ll
总用量 0
drwxr-xr-x 5 hadoop hadoop 57 10月 31 18:18 nm-local-dir

[hadoop@hadoop14 nm-local-dir]$ ll
总用量 0
drwxr-xr-x 2 hadoop hadoop 6 10月 31 18:18 filecache
drwx------ 2 hadoop hadoop 6 10月 31 18:18 nmPrivate
drwxr-xr-x 2 hadoop hadoop 6 10月 31 18:18 usercache

 此配置启动后，ResourceManager没有启动，无法访问：http://198.8.8.204:8088/

需要在所有节点添加ResourceManager配置：

vim vim yarn-site.xml

  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->
<configuration>

<!-- Site specific YARN configuration properties -->

<property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.resourcemanager.hostname</name>
        <value>hadoop14</value>
    </property>

    <property>
        <name>yarn.resourcemanager.address</name>
        <value>${yarn.resourcemanager.hostname}:8032</value>
#ResourceManager 对客户端暴露的地址。客户端通过该地址向RM提交应用程序，杀死应用程序等。
    </property>
    <property>
        <name>yarn.resourcemanager.scheduler.address</name>
        <value>${yarn.resourcemanager.hostname}:8030</value>
#ResourceManager 对ApplicationMaster暴露的访问地址。ApplicationMaster通过该地址向RM申请资源、释放资源等。
    </property>
    <property>
        <name>yarn.resourcemanager.resource-tracker.address</name>
        <value>${yarn.resourcemanager.hostname}:8031</value>
#ResourceManager 对NodeManager暴露的地址。NodeManager通过该地址向RM汇报心跳，领取任务等。
    </property>
    <property>
        <name>yarn.resourcemanager.admin.address</name>
        <value>${yarn.resourcemanager.hostname}:8033</value>
#ResourceManager 对管理员暴露的访问地址。管理员通过该地址向RM发送管理命令等。
    </property>

    <property>
        <name>yarn.resourcemanager.webapp.address</name>
#        <value>198.8.8.204:8088</value>
        <value>${yarn.resourcemanager.hostname}:8088</value>
    </property>
    <property>
        <name>yarn.log-aggregation-enable</name>
        <value>true</value>
    </property>
    <property>
        <name>yarn.log-aggregation.retain-seconds</name>
        <value>106800</value>
    </property>

</configuration>

保存配置，

在ResourceManager主节点hadoop14重启yarn：

stop-yarn.sh

start-yarn.sh

[hadoop@hadoop14 hadoop]$ start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-resourcemanager-hadoop14.out
hadoop11: Warning: Permanently added 'hadoop11,198.8.8.201' (ECDSA) to the list of known hosts.
hadoop12: Warning: Permanently added 'hadoop12,198.8.8.202' (ECDSA) to the list of known hosts.
hadoop14: Warning: Permanently added 'hadoop14,198.8.8.204' (ECDSA) to the list of known hosts.
hadoop13: Warning: Permanently added 'hadoop13,198.8.8.203' (ECDSA) to the list of known hosts.
hadoop14: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop14.out
hadoop11: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop11.out
hadoop13: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop13.out
hadoop12: starting nodemanager, logging to /home/hadoop/apps/hadoop-2.8.5/logs/yarn-hadoop-nodemanager-hadoop12.out
[hadoop@hadoop14 hadoop]$ jps
2659 ResourceManager
1511 DataNode
2791 NodeManager
3180 Jps

可以访问：http://198.8.8.204:8088/

十四：查看所有节点进程

 jps

[hadoop@hadoop11 nm-local-dir]$ jps
3201 NodeManager
2934 DataNode
3351 Jps
2824 NameNode

[hadoop@hadoop12 nm-local-dir]$ jps
2358 NodeManager
2216 DataNode
2490 Jps

[hadoop@hadoop13 nm-local-dir]$ jps
2642 NodeManager
2410 DataNode
2778 Jps
2524 SecondaryNameNode

[hadoop@hadoop14 nm-local-dir]$ jps
2577 DataNode
2853 NodeManager
2985 Jps

十五：HDFS和YARN的WEB管理界面：

 http://198.8.8.201:50070

十六：hadoop简单应用：

创建目录

[hadoop@hadoop11 ~]$ hadoop fs -mkdir -p /test/input

查看创建文件

hadoop fs -ls /

新建文件

vim words.txt

hello zhangsan
hello lisi
hello wangwu

上传文件

hadoop fs -put ~/words.txt /test/input

查看是否上传成功

hadoop fs -ls /test/input

下载文件

hadoop fs -get /test/input/words.txt /home/hadoop/

查看是否下载成功

cd /home/hadoop

ll

用wordcount测试：

 [hadoop@hadoop11 hadoop]$ hadoop jar /home/hadoop/apps/hadoop-2.8.5/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.8.5.jar wordcount /test/input /test/output

18/11/02 15:46:58 INFO client.RMProxy: Connecting to ResourceManager at hadoop14/198.8.8.204:8032
18/11/02 15:46:59 INFO input.FileInputFormat: Total input files to process : 1
18/11/02 15:46:59 INFO mapreduce.JobSubmitter: number of splits:1
18/11/02 15:47:00 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1541142769004_0001
18/11/02 15:47:00 INFO impl.YarnClientImpl: Submitted application application_1541142769004_0001
18/11/02 15:47:01 INFO mapreduce.Job: The url to track the job: http://hadoop14:8088/proxy/application_1541142769004_0001/
18/11/02 15:47:01 INFO mapreduce.Job: Running job: job_1541142769004_0001
18/11/02 15:47:10 INFO mapreduce.Job: Job job_1541142769004_0001 running in uber mode : false
18/11/02 15:47:10 INFO mapreduce.Job:  map 0% reduce 0%
18/11/02 15:47:19 INFO mapreduce.Job:  map 100% reduce 0%
18/11/02 15:47:27 INFO mapreduce.Job:  map 100% reduce 100%
18/11/02 15:47:27 INFO mapreduce.Job: Job job_1541142769004_0001 completed successfully
18/11/02 15:47:27 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=57
        FILE: Number of bytes written=315855
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=152
        HDFS: Number of bytes written=35
        HDFS: Number of read operations=6
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters 
        Launched map tasks=1
        Launched reduce tasks=1
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=5816
        Total time spent by all reduces in occupied slots (ms)=4662
        Total time spent by all map tasks (ms)=5816
        Total time spent by all reduce tasks (ms)=4662
        Total vcore-milliseconds taken by all map tasks=5816
        Total vcore-milliseconds taken by all reduce tasks=4662
        Total megabyte-milliseconds taken by all map tasks=5955584
        Total megabyte-milliseconds taken by all reduce tasks=4773888
    Map-Reduce Framework
        Map input records=3
        Map output records=6
        Map output bytes=63
        Map output materialized bytes=57
        Input split bytes=113
        Combine input records=6
        Combine output records=4
        Reduce input groups=4
        Reduce shuffle bytes=57
        Reduce input records=4
        Reduce output records=4
        Spilled Records=8
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=449
        CPU time spent (ms)=2730
        Physical memory (bytes) snapshot=445227008
        Virtual memory (bytes) snapshot=4208754688
        Total committed heap usage (bytes)=278396928
    Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters 
        Bytes Read=39
    File Output Format Counters 
        Bytes Written=35

查看输出文件：

[hadoop@hadoop11 hadoop]$ hadoop fs -ls /test/output

[hadoop@hadoop11 hadoop]$ hadoop fs -ls /test/output
Found 2 items
-rw-r--r--   2 hadoop supergroup          0 2018-11-02 15:47 /test/output/_SUCCESS
-rw-r--r--   2 hadoop supergroup         35 2018-11-02 15:47 /test/output/part-r-00000
[hadoop@hadoop11 hadoop]$ hadoop fs -cat /test/output/part-r-00000
hello    3
lisi    1
wangwu    1
zhangsan    1

参考文档：

https://www.cnblogs.com/qingyunzong/p/8496127.html

https://blog.csdn.net/hliq5399/article/details/78193113