在Ubuntu Kylin15.04中配置Hadoop单机/伪分布式系统经验分享
一、工作准备
首先，明确工作的重心，在Ubuntu Kylin15.04中配置Hadoop集群，这里我是用的双系统中的Ubuntu来配制的，不是虚拟机。在网上有很多配置的方案，我看了一下Ubuntu的版本有14.x,16.x等等，唯独缺少15.x，后来我也了解到，15.x出来一段时间就被下一个版本所替代了，可能有一定的问题吧，可是我还是觉得这个版本的用起来很舒服，但是当我安装了Ubuntu kylin15.04之后，网络配置成功，我开始使用sudo apt-get update更新一下软件源的时候，就遇到了非常大的麻烦，具体的介绍可以参考我的拙作Ubuntu版本更替所引发的“血案”，经过了一番斗争，我总算在打算安装16.x之前找到了解决办法，实现了一次技术上的沉淀！之后安装Hadoop集群总算是踏上了高速列车。解决了系统的问题，我们需要使用的原来还有vim或者gedit文本编辑工具，SSH,openssh-server,当然了Ubuntu默认是安装了openssh-client的，我们可以再安装一次，之后需要java的jre和jdk，需要hadoop，基本上需要这么多基本的原料，有了这些东西，我们就可以使用shell来尽情的发挥了。
1、vim或者gedit文本编辑工具；
2、ssh,openssh-server,openssh-client；
3、jre和jdk,这里安装的是openjdk-7-jre openjdk-7-jdk；
4、Hadoop 2.x.y；
5、Ubuntu Kylin15.04；
二、创建hadoop用户
这一步是保证操作的纯洁性，至于是不是必须要以hadoop为用户名，这个地方还有待考证，不过作为初学者，我们就先从最基本的开始理解，主要的操作如下，增加用户名为hadoop，并且使用bash作为shell，之后设置密码，然后为hadoop赋予sudo权限，最后退出原系统，登录我们新创建的系统。
sudo useradd -m hadoop -s /bin/bash
sudo passwd hadoop
sudo adduser hadoop sudo
三、更新apt，并且安装一些工具软件
3.1、到了这里，我们使用新创建的用户登录系统，然后打开shell，在shell中运行如下命令,更新软件源：
sudo apt-get clean
sudo apt-get update
sudo apt-get upgrade
如果中途失败，提示get不到源，或者网络失败，我们的排查思路是，首先ping 公网，看看能不能够连接成功，其次检查DNS，/etc/hosts等，判断是不是域名系统的问题，最后我们使用源的IP来ping，如果都没有问题，那我们的问题可能就在于‘源’已经失去维护了，从以前的仓库中移除了，遇到这个问题，请参考我的拙作Ubuntu版本更替所引发的“血案”，基本上可以解决问题。
3.2、然后更新vim,安装ssh工具，具体操作如下：
sudo apt-get install vim sudo apt-get install ssh
sudo apt-get install openssh-server
sudo apt-get install openssh-client
安装完成以后测试一下是否能够登陆localhost，自己登陆自己来测试是否可以使用ssh协议。如果不成功，我们启动一下ssh，并且使用ps和grep来看一下是否出现sshd，如果有代表程序启动成功，登录localhost会显示登录的结果，如果提示要更新或什么的不用理会。
ssh localhost
sudo /etc/init.d/ssh start
ps -e | grep ssh
之后我们生成并导出公钥，使得公钥可信任，我们每一次ssh就不用输入密码了。
cd ~/.ssh/
ssh-keygen -t rsa
Generating public/private rsa key pair.
Enter file in which to save the key (/home/hadoop/.ssh/id_rsa):
Enter passphrase (empty for no passphrase):
Enter same passphrase again:
Your identification has been saved in /home/hadoop/.ssh/id_rsa.
Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub.
The key fingerprint is:
35:1f:b0:20:dc:03:0d:52:00:9b:34:51:7f:95:60:b6 hadoop@zyr-Aspire-V5-551G
cat ./id_rsa.pub >> ./authorized_keys
⦁ hadoop@zyr-Aspire-V5-551G:~$ ssh localhost
⦁ Welcome to Ubuntu 15.04 (GNU/Linux 3.19.0-15-generic x86_64)
⦁
⦁ * Documentation: https://help.ubuntu.com/
⦁
⦁ 15 packages can be updated.
⦁ 9 updates are security updates.
⦁
⦁ Your Ubuntu release is not supported anymore.
⦁ For upgrade information, please visit:
⦁ http://www.ubuntu.com/releaseendoflife
⦁
⦁ New release '16.04.4 LTS' available.
⦁ Run 'do-release-upgrade' to upgrade to it.
⦁
⦁ Last login: Sat Mar 3 10:32:05 2018 from localhost
3.3、安装JAVA环境
在这里我们使用openjdk和openjre，这是非官方的开源的，安装起来更容易，更方便。
sudo apt-get install openjdk-7-jre openjdk-7-jdk
之后我们需要找到这些文件的安装路径：
hadoop@zyr-Aspire-V5-551G:~$ dpkg -L openjdk-7-jdk | grep '/bin/javac'
/usr/lib/jvm/java-7-openjdk-amd64/bin/javac
可以看到就是/usr/lib/jvm/java-7-openjdk-amd64安装路径，在这里我们使用的hadoop是2.9.0，java的环境是1.7.x，亲测通过，在官网上有这样的说法，当hadoop版本超过一定的级别的时候(2.7)，必须使用java1.7以及之上的版本。之后我们修改环境变量，在开头增加export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64/，并且保存退出，然后使用source ~/.bashrc进行更新，通过下面的命令来测试java是否安装成功，环境变量是否匹配，系统是否正在使用我们配置的环境变量等信息。至此，java环境设置完成。
vim ~/.bashrc
hadoop@zyr-Aspire-V5-551G:~$ cat ~/.bashrc
export JAVA_HOME=/usr/lib/jvm/java-7-openjdk-amd64/ # ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples
…… source ~/.bashrc hadoop@zyr-Aspire-V5-551G:~$ echo $JAVA_HOME
/usr/lib/jvm/java-7-openjdk-amd64/ hadoop@zyr-Aspire-V5-551G:~$ java -version
java version "1.7.0_95"
OpenJDK Runtime Environment (IcedTea 2.6.4) (7u95-2.6.4-0ubuntu0.15.04.1)
OpenJDK 64-Bit Server VM (build 24.95-b01, mixed mode) hadoop@zyr-Aspire-V5-551G:~$ $JAVA_HOME/bin/java -version
java version "1.7.0_95"
OpenJDK Runtime Environment (IcedTea 2.6.4) (7u95-2.6.4-0ubuntu0.15.04.1)
OpenJDK 64-Bit Server VM (build 24.95-b01, mixed mode)
四、安装Hadoop
4.1、下载Hadoop
在官网，或者通过 http://mirror.bit.edu.cn/apache/hadoop/common/ 或者 http://mirrors.cnnic.cn/apache/hadoop/common/ 下载Hadoop的所有版本，一般选择下载最新的稳定版本，下载 “stable” 下的 hadoop-2.x.y.tar.gz 这个格式的文件，我们可以直接使用，简单的解压，并且放到相应的文件夹即可；另一个包含 src 的则是 Hadoop 源代码，需要进行编译才可使用，我们可以拿来作为学习，在后期研究Hadoop的架构，因为Hadoop是用java语言写的，所以通俗易读。另外要保证下载文件的安全性、完整性、可用性、不可否认性、可控性等，最好的是找到一个含有hash校验码的下载源，不过笔者亲测这个下载源是可靠的。通过浏览器下载即可，之后进行保存，记住保存的位置，便于我们后期的操作。在这里笔者使用的是次新版的2.9.0，如下图所示。
4.2、安装Hadoop
下载之后，我们将该压缩文件解压到/usr/local这个文件夹下，其实别的地方也是可以的，但是放在这里见名知意，恰到好处。之后我们进入这个文件夹下，通过mv的重命名功能将版本号去掉，改为hadoop，并且修改该文件夹的权限，使得该文件夹拥有hadoop的权限。并且我们使用ll命令来查看一下local下面的文件布局。
hadoop@zyr-Aspire-V5-551G:~$ sudo tar -zxf ~/Downloads/hadoop-2.9.0.tar.gz -C /usr/local
[sudo] password for hadoop:
hadoop@zyr-Aspire-V5-551G:~$ cd /usr/local/
hadoop@zyr-Aspire-V5-551G:/usr/local$ sudo mv ./hadoop-2.9.0/ ./hadoop
hadoop@zyr-Aspire-V5-551G:/usr/local$ sudo chown -R hadoop ./hadoop
hadoop@zyr-Aspire-V5-551G:/usr/local$ ll
total 44
drwxr-xr-x 11 root root 4096 3月 3 11:07 ./
drwxr-xr-x 10 root root 4096 4月 23 2015 ../
drwxr-xr-x 2 root root 4096 4月 23 2015 bin/
drwxr-xr-x 2 root root 4096 4月 23 2015 etc/
drwxr-xr-x 2 root root 4096 4月 23 2015 games/
drwxr-xr-x 9 hadoop zyr 4096 11月 14 07:28 hadoop/
drwxr-xr-x 2 root root 4096 4月 23 2015 include/
drwxr-xr-x 4 root root 4096 4月 23 2015 lib/
lrwxrwxrwx 1 root root 9 3月 2 20:16 man -> share/man/
drwxr-xr-x 2 root root 4096 4月 23 2015 sbin/
drwxr-xr-x 8 root root 4096 4月 23 2015 share/
drwxr-xr-x 2 root root 4096 4月 23 2015 src/
解压之后就相当于安装了，这点我们要记住，特别的方便，之后我们开始检验一下安装的结果，通过 ./bin/hadoop version命令来判断是否安装成功，如下是安装成功之后的结果。到这里我们总算是安装好了hadoop，其实也并不复杂，但是从无到有的过程，每一步的细节都是非常值得我们注意的。
hadoop@zyr-Aspire-V5-551G:/usr/local$ cd hadoop/
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hadoop version
Hadoop 2.9.0
Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r 756ebc8394e473ac25feac05fa493f6d612e6c50
Compiled by arsuresh on 2017-11-13T23:15Z
Compiled with protoc 2.5.0
From source with checksum 0a76a9a32a5257331741f8d5932f183
This command was run using /usr/local/hadoop/share/hadoop/common/hadoop-common-2.9.0.jar
五、单机Hadoop测试
到了这里，我们其实只是完成了单机上的Hadoop的安装，但是这些步骤在分布式上面是一样的，需要勤加练习，这样的Hadoop系统远不是集群系统，但是却迈出了关键性的一步，因为在一些学术研究中，到了这里我们就可以开发map reduce程序了，如果程序不是非常复杂，我们在单机上就可以完成，值得喜悦的是在Hadoop的安装包中早就集成了一些样例，我们可以通过这些样例来测试一下我们的Hadoop，比如WordCount、GREP 【正则表达式】等等，但是在我们兴奋之前，需要认识到，我们这样的程序并没有用到HDFS，而是使用的我们OS自带的文件系统FS，但是至少说这是一个里程碑。
我们首先切换到相关目录，然后创建一个input文件夹（名字无特殊要求），然后将一些文件放进去，这里我们放入的是一些配置文件来作为数据源，并且通过Hadoop自带的样例程序来测试一下我们的安装是不是成功的。
cd /usr/local/hadoop
mkdir ./input
cp ./etc/hadoop/*.xml ./input hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ls ./etc/hadoop/
capacity-scheduler.xml httpfs-env.sh mapred-env.sh
configuration.xsl httpfs-log4j.properties mapred-queues.xml.template
container-executor.cfg httpfs-signature.secret mapred-site.xml.template
core-site.xml httpfs-site.xml slaves
hadoop-env.cmd kms-acls.xml ssl-client.xml.example
hadoop-env.sh kms-env.sh ssl-server.xml.example
hadoop-metrics2.properties kms-log4j.properties yarn-env.cmd
hadoop-metrics.properties kms-site.xml yarn-env.sh
hadoop-policy.xml log4j.properties yarn-site.xml
hdfs-site.xml mapred-env.cmd
我们使用如下命令来测试我们的程序，首先我们可以执行一下./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar来看一下我们有哪些样例程序，然后我们使用其中的grep程序来从所有的输入文件中统计满足'dfs[a-z.]+'正则表达式的单词的个数是多少。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.0.jar
An example program must be given as the first argument.
Valid program names are:
aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount: An example job that count the pageview counts from a database.
distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
grep: A map/reduce program that counts the matches of a regex in the input.
join: A job that effects a join over sorted, equally partitioned datasets
multifilewc: A job that counts words from several files.
pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.
randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
randomwriter: A map/reduce program that writes 10GB of random data per node.
secondarysort: An example defining a secondary sort to the reduce.
sort: A map/reduce program that sorts the data written by the random writer.
sudoku: A sudoku solver.
teragen: Generate data for the terasort
terasort: Run the terasort
teravalidate: Checking results of terasort
wordcount: A map/reduce program that counts the words in the input files.
wordmean: A map/reduce program that counts the average length of the words in the input files.
wordmedian: A map/reduce program that counts the median length of the words in the input files.
wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files. Eg:
./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.0.jar wordcount ./input ./output
真正MapReduce命令：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep ./input ./output 'dfs[a-z.]+'
执行的结果是喜人的，我在这里将结果贴出来，但因为太长了，所以就缩进了。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep ./input ./output 'dfs[a-z.]+'
18/03/03 11:20:28 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
18/03/03 11:20:28 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
18/03/03 11:20:28 INFO input.FileInputFormat: Total input files to process : 8
18/03/03 11:20:28 INFO mapreduce.JobSubmitter: number of splits:8
18/03/03 11:20:30 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local325822439_0001
18/03/03 11:20:31 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
18/03/03 11:20:31 INFO mapreduce.Job: Running job: job_local325822439_0001
18/03/03 11:20:31 INFO mapred.LocalJobRunner: OutputCommitter set in config null
18/03/03 11:20:31 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:31 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:31 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
18/03/03 11:20:31 INFO mapred.LocalJobRunner: Waiting for map tasks
18/03/03 11:20:31 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000000_0
18/03/03 11:20:31 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:31 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:31 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:31 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/hadoop-policy.xml:0+10206
18/03/03 11:20:31 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:31 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:31 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:31 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:31 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:31 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:31 INFO mapred.LocalJobRunner:
18/03/03 11:20:31 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:31 INFO mapred.MapTask: Spilling map output
18/03/03 11:20:31 INFO mapred.MapTask: bufstart = 0; bufend = 17; bufvoid = 104857600
18/03/03 11:20:31 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214396(104857584); length = 1/6553600
18/03/03 11:20:32 INFO mapred.MapTask: Finished spill 0
18/03/03 11:20:32 INFO mapred.Task: Task:attempt_local325822439_0001_m_000000_0 is done. And is in the process of committing
18/03/03 11:20:32 INFO mapred.LocalJobRunner: map
18/03/03 11:20:32 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000000_0' done.
18/03/03 11:20:32 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000000_0
18/03/03 11:20:32 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000001_0
18/03/03 11:20:32 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:32 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:32 INFO mapreduce.Job: Job job_local325822439_0001 running in uber mode : false
18/03/03 11:20:32 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:32 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/capacity-scheduler.xml:0+7861
18/03/03 11:20:32 INFO mapreduce.Job: map 100% reduce 0%
18/03/03 11:20:32 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:32 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:32 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:32 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:32 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:32 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:32 INFO mapred.LocalJobRunner:
18/03/03 11:20:32 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:32 INFO mapred.Task: Task:attempt_local325822439_0001_m_000001_0 is done. And is in the process of committing
18/03/03 11:20:32 INFO mapred.LocalJobRunner: map
18/03/03 11:20:32 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000001_0' done.
18/03/03 11:20:32 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000001_0
18/03/03 11:20:32 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000002_0
18/03/03 11:20:32 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:32 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:32 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:32 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/kms-site.xml:0+5939
18/03/03 11:20:32 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:32 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:32 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:32 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:32 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:32 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:32 INFO mapred.LocalJobRunner:
18/03/03 11:20:32 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:33 INFO mapred.Task: Task:attempt_local325822439_0001_m_000002_0 is done. And is in the process of committing
18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
18/03/03 11:20:33 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000002_0' done.
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000002_0
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000003_0
18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:33 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/kms-acls.xml:0+3518
18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:33 INFO mapred.LocalJobRunner:
18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:33 INFO mapreduce.Job: map 38% reduce 0%
18/03/03 11:20:33 INFO mapred.Task: Task:attempt_local325822439_0001_m_000003_0 is done. And is in the process of committing
18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
18/03/03 11:20:33 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000003_0' done.
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000003_0
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000004_0
18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:33 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/hdfs-site.xml:0+775
18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:33 INFO mapred.LocalJobRunner:
18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:33 INFO mapred.Task: Task:attempt_local325822439_0001_m_000004_0 is done. And is in the process of committing
18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
18/03/03 11:20:33 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000004_0' done.
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000004_0
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000005_0
18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:33 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/core-site.xml:0+774
18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:33 INFO mapred.LocalJobRunner:
18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:33 INFO mapred.Task: Task:attempt_local325822439_0001_m_000005_0 is done. And is in the process of committing
18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
18/03/03 11:20:33 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000005_0' done.
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000005_0
18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000006_0
18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:33 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/yarn-site.xml:0+690
18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:33 INFO mapred.LocalJobRunner:
18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:34 INFO mapred.Task: Task:attempt_local325822439_0001_m_000006_0 is done. And is in the process of committing
18/03/03 11:20:34 INFO mapred.LocalJobRunner: map
18/03/03 11:20:34 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000006_0' done.
18/03/03 11:20:34 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000006_0
18/03/03 11:20:34 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000007_0
18/03/03 11:20:34 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:34 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:34 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:34 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/httpfs-site.xml:0+620
18/03/03 11:20:34 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:34 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:34 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:34 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:34 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:34 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:34 INFO mapred.LocalJobRunner:
18/03/03 11:20:34 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:34 INFO mapred.Task: Task:attempt_local325822439_0001_m_000007_0 is done. And is in the process of committing
18/03/03 11:20:34 INFO mapred.LocalJobRunner: map
18/03/03 11:20:34 INFO mapred.Task: Task 'attempt_local325822439_0001_m_000007_0' done.
18/03/03 11:20:34 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000007_0
18/03/03 11:20:34 INFO mapred.LocalJobRunner: map task executor complete.
18/03/03 11:20:34 INFO mapred.LocalJobRunner: Waiting for reduce tasks
18/03/03 11:20:34 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_r_000000_0
18/03/03 11:20:34 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:34 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:34 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:34 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@362850fb
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=369937600, maxSingleShuffleLimit=92484400, mergeThreshold=244158832, ioSortFactor=10, memToMemMergeOutputsThreshold=10
18/03/03 11:20:34 INFO reduce.EventFetcher: attempt_local325822439_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
18/03/03 11:20:34 INFO mapreduce.Job: map 100% reduce 0%
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000003_0 decomp: 2 len: 6 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000003_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->2
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000000_0 decomp: 21 len: 25 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 21 bytes from map-output for attempt_local325822439_0001_m_000000_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 21, inMemoryMapOutputs.size() -> 2, commitMemory -> 2, usedMemory ->23
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000006_0 decomp: 2 len: 6 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000006_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 3, commitMemory -> 23, usedMemory ->25
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000005_0 decomp: 2 len: 6 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000005_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 4, commitMemory -> 25, usedMemory ->27
18/03/03 11:20:34 WARN io.ReadaheadPool: Failed readahead on ifile
EBADF: Bad file descriptor
at org.apache.hadoop.io.nativeio.NativeIO$POSIX.posix_fadvise(Native Method)
at org.apache.hadoop.io.nativeio.NativeIO$POSIX.posixFadviseIfPossible(NativeIO.java:267)
at org.apache.hadoop.io.nativeio.NativeIO$POSIX$CacheManipulator.posixFadviseIfPossible(NativeIO.java:146)
at org.apache.hadoop.io.ReadaheadPool$ReadaheadRequestImpl.run(ReadaheadPool.java:208)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000001_0 decomp: 2 len: 6 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000001_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 5, commitMemory -> 27, usedMemory ->29
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000004_0 decomp: 2 len: 6 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000004_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 6, commitMemory -> 29, usedMemory ->31
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000007_0 decomp: 2 len: 6 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000007_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 7, commitMemory -> 31, usedMemory ->33
18/03/03 11:20:34 INFO reduce.LocalFetcher: localfetcher#1 about to shuffle output of map attempt_local325822439_0001_m_000002_0 decomp: 2 len: 6 to MEMORY
18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000002_0
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 8, commitMemory -> 33, usedMemory ->35
18/03/03 11:20:34 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
18/03/03 11:20:34 INFO mapred.LocalJobRunner: 8 / 8 copied.
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: finalMerge called with 8 in-memory map-outputs and 0 on-disk map-outputs
18/03/03 11:20:34 INFO mapred.Merger: Merging 8 sorted segments
18/03/03 11:20:34 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 10 bytes
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: Merged 8 segments, 35 bytes to disk to satisfy reduce memory limit
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: Merging 1 files, 25 bytes from disk
18/03/03 11:20:34 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
18/03/03 11:20:34 INFO mapred.Merger: Merging 1 sorted segments
18/03/03 11:20:34 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 10 bytes
18/03/03 11:20:34 INFO mapred.LocalJobRunner: 8 / 8 copied.
18/03/03 11:20:34 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
18/03/03 11:20:34 INFO mapred.Task: Task:attempt_local325822439_0001_r_000000_0 is done. And is in the process of committing
18/03/03 11:20:34 INFO mapred.LocalJobRunner: 8 / 8 copied.
18/03/03 11:20:34 INFO mapred.Task: Task attempt_local325822439_0001_r_000000_0 is allowed to commit now
18/03/03 11:20:34 INFO output.FileOutputCommitter: Saved output of task 'attempt_local325822439_0001_r_000000_0' to file:/usr/local/hadoop/grep-temp-876870354/_temporary/0/task_local325822439_0001_r_000000
18/03/03 11:20:34 INFO mapred.LocalJobRunner: reduce > reduce
18/03/03 11:20:34 INFO mapred.Task: Task 'attempt_local325822439_0001_r_000000_0' done.
18/03/03 11:20:34 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_r_000000_0
18/03/03 11:20:34 INFO mapred.LocalJobRunner: reduce task executor complete.
18/03/03 11:20:35 INFO mapreduce.Job: map 100% reduce 100%
18/03/03 11:20:35 INFO mapreduce.Job: Job job_local325822439_0001 completed successfully
18/03/03 11:20:35 INFO mapreduce.Job: Counters: 30
File System Counters
FILE: Number of bytes read=2993922
FILE: Number of bytes written=7026239
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
Map-Reduce Framework
Map input records=840
Map output records=1
Map output bytes=17
Map output materialized bytes=67
Input split bytes=869
Combine input records=1
Combine output records=1
Reduce input groups=1
Reduce shuffle bytes=67
Reduce input records=1
Reduce output records=1
Spilled Records=2
Shuffled Maps =8
Failed Shuffles=0
Merged Map outputs=8
GC time elapsed (ms)=120
Total committed heap usage (bytes)=3988258816
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=30383
File Output Format Counters
Bytes Written=123
18/03/03 11:20:35 INFO jvm.JvmMetrics: Cannot initialize JVM Metrics with processName=JobTracker, sessionId= - already initialized
18/03/03 11:20:35 INFO input.FileInputFormat: Total input files to process : 1
18/03/03 11:20:35 INFO mapreduce.JobSubmitter: number of splits:1
18/03/03 11:20:35 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1695778912_0002
18/03/03 11:20:36 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
18/03/03 11:20:36 INFO mapreduce.Job: Running job: job_local1695778912_0002
18/03/03 11:20:36 INFO mapred.LocalJobRunner: OutputCommitter set in config null
18/03/03 11:20:36 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:36 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:36 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
18/03/03 11:20:36 INFO mapred.LocalJobRunner: Waiting for map tasks
18/03/03 11:20:36 INFO mapred.LocalJobRunner: Starting task: attempt_local1695778912_0002_m_000000_0
18/03/03 11:20:36 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:36 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:36 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:36 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/grep-temp-876870354/part-r-00000:0+111
18/03/03 11:20:36 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
18/03/03 11:20:36 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
18/03/03 11:20:36 INFO mapred.MapTask: soft limit at 83886080
18/03/03 11:20:36 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
18/03/03 11:20:36 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
18/03/03 11:20:36 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
18/03/03 11:20:36 INFO mapred.LocalJobRunner:
18/03/03 11:20:36 INFO mapred.MapTask: Starting flush of map output
18/03/03 11:20:36 INFO mapred.MapTask: Spilling map output
18/03/03 11:20:36 INFO mapred.MapTask: bufstart = 0; bufend = 17; bufvoid = 104857600
18/03/03 11:20:36 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214396(104857584); length = 1/6553600
18/03/03 11:20:36 INFO mapred.MapTask: Finished spill 0
18/03/03 11:20:36 INFO mapred.Task: Task:attempt_local1695778912_0002_m_000000_0 is done. And is in the process of committing
18/03/03 11:20:36 INFO mapred.LocalJobRunner: map
18/03/03 11:20:36 INFO mapred.Task: Task 'attempt_local1695778912_0002_m_000000_0' done.
18/03/03 11:20:36 INFO mapred.LocalJobRunner: Finishing task: attempt_local1695778912_0002_m_000000_0
18/03/03 11:20:36 INFO mapred.LocalJobRunner: map task executor complete.
18/03/03 11:20:36 INFO mapred.LocalJobRunner: Waiting for reduce tasks
18/03/03 11:20:36 INFO mapred.LocalJobRunner: Starting task: attempt_local1695778912_0002_r_000000_0
18/03/03 11:20:36 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
18/03/03 11:20:36 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
18/03/03 11:20:36 INFO mapred.Task: Using ResourceCalculatorProcessTree : [ ]
18/03/03 11:20:36 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@18e6a4dc
18/03/03 11:20:36 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=370304608, maxSingleShuffleLimit=92576152, mergeThreshold=244401056, ioSortFactor=10, memToMemMergeOutputsThreshold=10
18/03/03 11:20:36 INFO reduce.EventFetcher: attempt_local1695778912_0002_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
18/03/03 11:20:36 INFO reduce.LocalFetcher: localfetcher#2 about to shuffle output of map attempt_local1695778912_0002_m_000000_0 decomp: 21 len: 25 to MEMORY
18/03/03 11:20:36 INFO reduce.InMemoryMapOutput: Read 21 bytes from map-output for attempt_local1695778912_0002_m_000000_0
18/03/03 11:20:36 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 21, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->21
18/03/03 11:20:36 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
18/03/03 11:20:36 INFO mapred.LocalJobRunner: 1 / 1 copied.
18/03/03 11:20:36 INFO reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
18/03/03 11:20:36 INFO mapred.Merger: Merging 1 sorted segments
18/03/03 11:20:36 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 11 bytes
18/03/03 11:20:36 INFO reduce.MergeManagerImpl: Merged 1 segments, 21 bytes to disk to satisfy reduce memory limit
18/03/03 11:20:36 INFO reduce.MergeManagerImpl: Merging 1 files, 25 bytes from disk
18/03/03 11:20:36 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
18/03/03 11:20:36 INFO mapred.Merger: Merging 1 sorted segments
18/03/03 11:20:36 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 11 bytes
18/03/03 11:20:36 INFO mapred.LocalJobRunner: 1 / 1 copied.
18/03/03 11:20:36 INFO mapred.Task: Task:attempt_local1695778912_0002_r_000000_0 is done. And is in the process of committing
18/03/03 11:20:36 INFO mapred.LocalJobRunner: 1 / 1 copied.
18/03/03 11:20:36 INFO mapred.Task: Task attempt_local1695778912_0002_r_000000_0 is allowed to commit now
18/03/03 11:20:36 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1695778912_0002_r_000000_0' to file:/usr/local/hadoop/output/_temporary/0/task_local1695778912_0002_r_000000
18/03/03 11:20:36 INFO mapred.LocalJobRunner: reduce > reduce
18/03/03 11:20:36 INFO mapred.Task: Task 'attempt_local1695778912_0002_r_000000_0' done.
18/03/03 11:20:36 INFO mapred.LocalJobRunner: Finishing task: attempt_local1695778912_0002_r_000000_0
18/03/03 11:20:36 INFO mapred.LocalJobRunner: reduce task executor complete.
18/03/03 11:20:37 INFO mapreduce.Job: Job job_local1695778912_0002 running in uber mode : false
18/03/03 11:20:37 INFO mapreduce.Job: map 100% reduce 100%
18/03/03 11:20:37 INFO mapreduce.Job: Job job_local1695778912_0002 completed successfully
18/03/03 11:20:37 INFO mapreduce.Job: Counters: 30
File System Counters
FILE: Number of bytes read=1286912
FILE: Number of bytes written=3123146
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
Map-Reduce Framework
Map input records=1
Map output records=1
Map output bytes=17
Map output materialized bytes=25
Input split bytes=120
Combine input records=0
Combine output records=0
Reduce input groups=1
Reduce shuffle bytes=25
Reduce input records=1
Reduce output records=1
Spilled Records=2
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=0
Total committed heap usage (bytes)=1058013184
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=123
File Output Format Counters
Bytes Written=23
然后我们使用如下命令来查看运行的结果，可以看到程序运行成功，找到一个符合这样规则的答案。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ cat ./output/*
1 dfsadmin
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ll ./output/
total 20
drwxrwxr-x 2 hadoop hadoop 4096 3月 3 11:20 ./
drwxr-xr-x 11 hadoop zyr 4096 3月 3 11:20 ../
-rw-r--r-- 1 hadoop hadoop 11 3月 3 11:20 part-r-00000
-rw-r--r-- 1 hadoop hadoop 12 3月 3 11:20 .part-r-00000.crc
-rw-r--r-- 1 hadoop hadoop 0 3月 3 11:20 _SUCCESS
-rw-r--r-- 1 hadoop hadoop 8 3月 3 11:20 ._SUCCESS.crc
需要注意的是，我们接下来如果还要运行的计划，如果命令中的output不变是会出错的，错误是系统中已经存在这样的文件夹了，这里我们需要删除output文件夹然后再运行就好了！
rm -r ./output
再比如下面的WordCount程序，执行之后输入相应的结果。
./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.0.jar wordcount ./input ./output
在这个地方我们需要注意有的版本之中需要把hadoop目录下的etc/hadoop/hadoop-env.sh中的JAVA_HOME改成绝对路径，不然会报找不到JAVA_HOME的错误，另外我们还要注意如果说创建文件失败，权限不足，我们在命令的前面加入sudo来执行。
六、Hadoop伪分布式实例测试，HDFS
到这里，我们还没用到HDFS，下面就需要配置相关的文件，使得我们可以使用网络浏览器来查看程序运行情况，并且监控HDFS了。在修改文件之前，我们要养成好习惯，先备份再修改，这样我们就算是错误了还是可以回滚的，在这里需要在:/usr/local/hadoop/etc/hadoop/下修改两个文件：core-site.xml和hdfs-site.xml，具体的修改代码如下：
hadoop@zyr-Aspire-V5-551G:~$ cd /usr/local/hadoop/ hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ cp ./etc/hadoop/core-site.xml ./etc/hadoop/core-site.xml.backup
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ gedit ./etc/hadoop/core-site.xml
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ cp ./etc/hadoop/hdfs-site.xml ./etc/hadoop/hdfs-site.xml.backup
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ gedit ./etc/hadoop/hdfs-site.xml
在core-site.xml文件下，我们加入如下代码，其实就是将配置里面填充数据，里面默认为空。
<configuration>
<property>
<name>hadoop.tmp.dir</name>
<value>file:/usr/local/hadoop/tmp</value>
<description>Abase for other temporary directories.</description>
</property>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>
在hdfs-site.xml文件下，我们加入：
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:/usr/local/hadoop/tmp/dfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>file:/usr/local/hadoop/tmp/dfs/data</value>
</property>
</configuration>
然后我们使用./bin/hdfs namenode -format 格式化namenode节点
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs namenode -format
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: host = zyr-Aspire-V5-551G/127.0.1.1
STARTUP_MSG: args = [-format]
STARTUP_MSG: version = 2.9.0
STARTUP_MSG: classpath =
……
18/03/03 12:48:03 INFO common.Storage: Storage directory /usr/local/hadoop/tmp/dfs/name has been successfully formatted.
…...
18/03/03 12:48:03 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at zyr-Aspire-V5-551G/127.0.1.1
************************************************************/
开启 NameNode 和 DataNode 守护进程:
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ls
bin include lib LICENSE.txt output sbin tmp
etc input libexec NOTICE.txt README.txt share hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./sbin/start-dfs.sh
Starting namenodes on [localhost]
localhost: starting namenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-namenode-zyr-Aspire-V5-551G.out
localhost: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-zyr-Aspire-V5-551G.out
Starting secondary namenodes [0.0.0.0]
The authenticity of host '0.0.0.0 (0.0.0.0)' can't be established.
ECDSA key fingerprint is ca:78:98:94:a3:ae:56:dc:57:18:87:3e:d3:a6:13:cf.
Are you sure you want to continue connecting (yes/no)? yes
0.0.0.0: Warning: Permanently added '0.0.0.0' (ECDSA) to the list of known hosts.
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-secondarynamenode-zyr-Aspire-V5-551G.out
通过jps命令查看，必须全部出现才算安装成功：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ jps
12225 SecondaryNameNode
11865 NameNode
11989 DataNode
12376 Jps
如果出现错误，我们可以查看相关的日志来判断：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ cd /usr/local/hadoop/logs/
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop/logs$ ll
total 112
drwxrwxr-x 2 hadoop hadoop 4096 3月 3 12:56 ./
drwxr-xr-x 13 hadoop zyr 4096 3月 3 12:56 ../
-rw-rw-r-- 1 hadoop hadoop 27917 3月 3 12:56 hadoop-hadoop-datanode-zyr-Aspire-V5-551G.log
-rw-rw-r-- 1 hadoop hadoop 718 3月 3 12:56 hadoop-hadoop-datanode-zyr-Aspire-V5-551G.out
-rw-rw-r-- 1 hadoop hadoop 33782 3月 3 12:58 hadoop-hadoop-namenode-zyr-Aspire-V5-551G.log
-rw-rw-r-- 1 hadoop hadoop 718 3月 3 12:56 hadoop-hadoop-namenode-zyr-Aspire-V5-551G.out
-rw-rw-r-- 1 hadoop hadoop 28631 3月 3 12:58 hadoop-hadoop-secondarynamenode-zyr-Aspire-V5-551G.log
-rw-rw-r-- 1 hadoop hadoop 718 3月 3 12:56 hadoop-hadoop-secondarynamenode-zyr-Aspire-V5-551G.out
-rw-rw-r-- 1 hadoop hadoop 0 3月 3 12:56 SecurityAuth-hadoop.audit hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop/logs$ cat hadoop-hadoop-datanode-zyr-Aspire-V5-551G.log
2018-03-03 12:56:23,450 INFO org.apache.hadoop.hdfs.server.datanode.DataNode: STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting DataNode
……
在这个地方我们需要注意，如果使用root权限开启应用，需要将root也加入到ssh的认证主机中去，不然会一直提示输入密码错误，登录失败之类的信息。
至此我们可以访问 Web 界面http://localhost:50070 查看 NameNode 和 Datanode 信息，还可以在线查看 HDFS 中的文件。
让我们再一次运行样例程序，首先创建hdfs文件系统中的文件夹/user/hadoop，我们可以在网页中看到。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ll
total 176
drwxr-xr-x 13 hadoop zyr 4096 3月 3 12:56 ./
drwxr-xr-x 11 root root 4096 3月 3 11:07 ../
drwxr-xr-x 2 hadoop zyr 4096 11月 14 07:28 bin/
drwxr-xr-x 3 hadoop zyr 4096 11月 14 07:28 etc/
drwxr-xr-x 2 hadoop zyr 4096 11月 14 07:28 include/
drwxrwxr-x 2 hadoop hadoop 4096 3月 3 11:18 input/
drwxr-xr-x 3 hadoop zyr 4096 11月 14 07:28 lib/
drwxr-xr-x 2 hadoop zyr 4096 11月 14 07:28 libexec/
-rw-r--r-- 1 hadoop zyr 106210 11月 14 07:28 LICENSE.txt
drwxrwxr-x 2 hadoop hadoop 4096 3月 3 12:56 logs/
-rw-r--r-- 1 hadoop zyr 15915 11月 14 07:28 NOTICE.txt
drwxrwxr-x 2 hadoop hadoop 4096 3月 3 12:23 output/
-rw-r--r-- 1 hadoop zyr 1366 11月 14 07:28 README.txt
drwxr-xr-x 3 hadoop zyr 4096 11月 14 07:28 sbin/
drwxr-xr-x 4 hadoop zyr 4096 11月 14 07:28 share/
drwxrwxr-x 3 hadoop hadoop 4096 3月 3 12:48 tmp/ hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -mkdir -p /user/hadoop
该文件夹是虚拟的，在真实的文件系统中不存在：
我们查看自己的位置：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ pwd
/usr/local/hadoop
然后在虚拟的hdfs中，我们创建输入文件夹，并且从真实的文件系统中将文件通过hdfs的put命令放入该新加的文件夹中！
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -mkdir -p input
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -put ./etc/hadoop/*.xml input
我们还可以查看hdfs上的文件，通过网页查看来比较。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -ls input
Found 8 items
-rw-r--r-- 1 hadoop supergroup 7861 2018-03-03 13:21 input/capacity-scheduler.xml
-rw-r--r-- 1 hadoop supergroup 1117 2018-03-03 13:21 input/core-site.xml
-rw-r--r-- 1 hadoop supergroup 10206 2018-03-03 13:21 input/hadoop-policy.xml
-rw-r--r-- 1 hadoop supergroup 1187 2018-03-03 13:21 input/hdfs-site.xml
-rw-r--r-- 1 hadoop supergroup 620 2018-03-03 13:21 input/httpfs-site.xml
-rw-r--r-- 1 hadoop supergroup 3518 2018-03-03 13:21 input/kms-acls.xml
-rw-r--r-- 1 hadoop supergroup 5939 2018-03-03 13:21 input/kms-site.xml
-rw-r--r-- 1 hadoop supergroup 690 2018-03-03 13:21 input/yarn-site.xml
之后我们执行和以前同样的命令，观察结果，发现使用第一个命令是没有结果的，原因是从本地文件系统中查找，我已经删除了这个文件夹，肯定找不到的，第二个是通过hdfs来查找，这次真的找到了结果，因为配置文件做出了改变，所以结果稍微有所变化。从侧面证明了，我们的系统是在hdfs上运行的。
./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep ./input ./output 'dfs[a-z.]+' hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ cat ./output/* hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -cat output/*
1 dfsadmin
1 dfs.replication
1 dfs.namenode.name.dir
1 dfs.datanode.data.dir
我将本地文件系统中的input和output都删除，但是从网站上依旧可以看到结果，更加证明了是在hdfs上运行的。
那hdfs到底给我们提供了多少命令呢，让我们使用help来查看：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -help
Usage: hadoop fs [generic options]
[-appendToFile <localsrc> ... <dst>]
[-cat [-ignoreCrc] <src> ...]
[-checksum <src> ...]
[-chgrp [-R] GROUP PATH...]
[-chmod [-R] <MODE[,MODE]... | OCTALMODE> PATH...]
[-chown [-R] [OWNER][:[GROUP]] PATH...]
[-copyFromLocal [-f] [-p] [-l] [-d] <localsrc> ... <dst>]
[-copyToLocal [-f] [-p] [-ignoreCrc] [-crc] <src> ... <localdst>]
[-count [-q] [-h] [-v] [-t [<storage type>]] [-u] [-x] <path> ...]
[-cp [-f] [-p | -p[topax]] [-d] <src> ... <dst>]
[-createSnapshot <snapshotDir> [<snapshotName>]]
[-deleteSnapshot <snapshotDir> <snapshotName>]
[-df [-h] [<path> ...]]
[-du [-s] [-h] [-x] <path> ...]
[-expunge]
[-find <path> ... <expression> ...]
[-get [-f] [-p] [-ignoreCrc] [-crc] <src> ... <localdst>]
[-getfacl [-R] <path>]
[-getfattr [-R] {-n name | -d} [-e en] <path>]
[-getmerge [-nl] [-skip-empty-file] <src> <localdst>]
[-help [cmd ...]]
[-ls [-C] [-d] [-h] [-q] [-R] [-t] [-S] [-r] [-u] [<path> ...]]
[-mkdir [-p] <path> ...]
[-moveFromLocal <localsrc> ... <dst>]
[-moveToLocal <src> <localdst>]
[-mv <src> ... <dst>]
[-put [-f] [-p] [-l] [-d] <localsrc> ... <dst>]
[-renameSnapshot <snapshotDir> <oldName> <newName>]
[-rm [-f] [-r|-R] [-skipTrash] [-safely] <src> ...]
[-rmdir [--ignore-fail-on-non-empty] <dir> ...]
[-setfacl [-R] [{-b|-k} {-m|-x <acl_spec>} <path>]|[--set <acl_spec> <path>]]
[-setfattr {-n name [-v value] | -x name} <path>]
[-setrep [-R] [-w] <rep> <path> ...]
[-stat [format] <path> ...]
[-tail [-f] <file>]
[-test -[defsz] <path>]
[-text [-ignoreCrc] <src> ...]
[-touchz <path> ...]
[-truncate [-w] <length> <path> ...]
[-usage [cmd ...]]
……
因此可以通过get将hdfs上的文件下载到本地：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -get output ./output
同样的，在hdfs上执行命令也需要注意文件夹不能一样，不然会报错：
这个时候我们可以通过如下命令来删除：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -rm -r output
Deleted output
然后再执行这样就可以了。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep input output 'dfs[a-z.]+'
最后我们需要知道停止服务的命令：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./sbin/stop-dfs.sh
Stopping namenodes on [localhost]
localhost: stopping namenode
localhost: stopping datanode
Stopping secondary namenodes [0.0.0.0]
0.0.0.0: stopping secondarynamenode
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./sbin/start-dfs.sh
Starting namenodes on [localhost]
localhost: starting namenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-namenode-zyr-Aspire-V5-551G.out
localhost: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-zyr-Aspire-V5-551G.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-secondarynamenode-zyr-Aspire-V5-551G.out
七、安装YARN
在完成了上面的操作，我们基本上算是进入了hadoop的大门了，但是我们也必须知道yarn这个资源管理器，因为这是MapReduce的下一个版本，安装方式很简单，只用修改几个文件即可，在单机/伪分布式系统中，我们不建议使用yarn，因为这会大大的拖慢运行速度，杀鸡焉用牛刀，真正的用处是在大型的分布式集群中才能发挥yarn的威力！
我们首先在配置文件中找到mapred-site.xml.template这个文件，非常重要，将其备份之后，重命名成mapred-site.xml，在对其进行修改，这样就完成了一大半工作了！
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ pwd
/usr/local/hadoop
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ cp ./etc/hadoop/mapred-site.xml.template ./etc/hadoop/mapred-site.xml.template.backup
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ mv ./etc/hadoop/mapred-site.xml.template ./etc/hadoop/mapred-site.xml
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ gedit ./etc/hadoop/mapred-site.xml
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ gedit ./etc/hadoop/yarn-site.xml
修改的mapred-site.xml方法为，加入如下配置：
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
之后我们对yarn-site.xml进行修改：
<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>
然后启动yarn，并用jps查看，可以看到多了三个进程。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./sbin/start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-resourcemanager-zyr-Aspire-V5-551G.out
localhost: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-zyr-Aspire-V5-551G.out
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./sbin/mr-jobhistory-daemon.sh start historyserver
starting historyserver, logging to /usr/local/hadoop/logs/mapred-hadoop-historyserver-zyr-Aspire-V5-551G.out
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ jps
14423 DataNode
14291 NameNode
15642 Jps
15570 JobHistoryServer
15220 NodeManager
15008 ResourceManager
14655 SecondaryNameNode
启动 YARN 之后，运行实例的方法还是一样的，仅仅是资源管理方式、任务调度不同。观察日志信息可以发现，不启用 YARN 时，是 “mapred.LocalJobRunner” 在跑任务，启用 YARN 之后，是 “mapred.YARNRunner” 在跑任务。启动 YARN 有个好处是可以通过 Web 界面查看任务的运行情况：http://localhost:8088/cluster，如下图所示。
不启动 YARN 需重命名 mapred-site.xml，如果不想启动 YARN，务必把配置文件 mapred-site.xml 重命名，改成 mapred-site.xml.template，需要用时改回来就行（这个时候不需要修改里面已经修改过的内容）。否则在该配置文件存在，而未开启 YARN 的情况下，运行程序会提示 “Retrying connect to server: 0.0.0.0/0.0.0.0:8032” 的错误，这也是为何该配置文件初始文件名为 mapred-site.xml.template。
再执行可以看到程序执行的非常缓慢，系统资源被大量占用，程序变得非常的卡顿，可以看到yarn的优缺点。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep input output_yarn 'dfs[a-z.]+'
执行的日志如下：
18/03/03 14:23:43 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/03/03 14:23:45 INFO input.FileInputFormat: Total input files to process : 8
18/03/03 14:23:45 INFO mapreduce.JobSubmitter: number of splits:8
18/03/03 14:23:45 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
18/03/03 14:23:47 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1520057034339_0002
18/03/03 14:23:48 INFO impl.YarnClientImpl: Submitted application application_1520057034339_0002
18/03/03 14:23:48 INFO mapreduce.Job: The url to track the job: http://zyr-Aspire-V5-551G:8088/proxy/application_1520057034339_0002/
18/03/03 14:23:48 INFO mapreduce.Job: Running job: job_1520057034339_0002
18/03/03 14:24:05 INFO mapreduce.Job: Job job_1520057034339_0002 running in uber mode : false
18/03/03 14:24:05 INFO mapreduce.Job: map 0% reduce 0%
18/03/03 14:24:33 INFO mapreduce.Job: map 13% reduce 0%
18/03/03 14:24:50 INFO mapreduce.Job: map 63% reduce 0%
18/03/03 14:24:51 INFO mapreduce.Job: map 75% reduce 0%
18/03/03 14:25:31 INFO mapreduce.Job: map 100% reduce 0%
18/03/03 14:25:33 INFO mapreduce.Job: map 100% reduce 100%
18/03/03 14:25:35 INFO mapreduce.Job: Job job_1520057034339_0002 completed successfully
18/03/03 14:25:35 INFO mapreduce.Job: Counters: 50
File System Counters
FILE: Number of bytes read=115
FILE: Number of bytes written=1819819
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=32095
HDFS: Number of bytes written=219
HDFS: Number of read operations=27
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Killed map tasks=2
Launched map tasks=10
Launched reduce tasks=1
Data-local map tasks=10
Total time spent by all maps in occupied slots (ms)=352992
Total time spent by all reduces in occupied slots (ms)=36370
Total time spent by all map tasks (ms)=352992
Total time spent by all reduce tasks (ms)=36370
Total vcore-milliseconds taken by all map tasks=352992
Total vcore-milliseconds taken by all reduce tasks=36370
Total megabyte-milliseconds taken by all map tasks=361463808
Total megabyte-milliseconds taken by all reduce tasks=37242880
Map-Reduce Framework
Map input records=861
Map output records=4
Map output bytes=101
Map output materialized bytes=157
Input split bytes=957
Combine input records=4
Combine output records=4
Reduce input groups=4
Reduce shuffle bytes=157
Reduce input records=4
Reduce output records=4
Spilled Records=8
Shuffled Maps =8
Failed Shuffles=0
Merged Map outputs=8
GC time elapsed (ms)=1582
CPU time spent (ms)=16070
Physical memory (bytes) snapshot=2409881600
Virtual memory (bytes) snapshot=7588835328
Total committed heap usage (bytes)=1692925952
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=31138
File Output Format Counters
Bytes Written=219
18/03/03 14:25:36 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/03/03 14:25:36 INFO input.FileInputFormat: Total input files to process : 1
18/03/03 14:25:37 INFO mapreduce.JobSubmitter: number of splits:1
18/03/03 14:25:37 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1520057034339_0003
18/03/03 14:25:38 INFO impl.YarnClientImpl: Submitted application application_1520057034339_0003
18/03/03 14:25:38 INFO mapreduce.Job: The url to track the job: http://zyr-Aspire-V5-551G:8088/proxy/application_1520057034339_0003/
18/03/03 14:25:38 INFO mapreduce.Job: Running job: job_1520057034339_0003
18/03/03 14:25:58 INFO mapreduce.Job: Job job_1520057034339_0003 running in uber mode : false
18/03/03 14:25:58 INFO mapreduce.Job: map 0% reduce 0%
18/03/03 14:26:11 INFO mapreduce.Job: map 100% reduce 0%
18/03/03 14:26:22 INFO mapreduce.Job: map 100% reduce 100%
18/03/03 14:26:24 INFO mapreduce.Job: Job job_1520057034339_0003 completed successfully
18/03/03 14:26:24 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=115
FILE: Number of bytes written=403351
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=351
HDFS: Number of bytes written=77
HDFS: Number of read operations=7
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)=9271
Total time spent by all reduces in occupied slots (ms)=9646
Total time spent by all map tasks (ms)=9271
Total time spent by all reduce tasks (ms)=9646
Total vcore-milliseconds taken by all map tasks=9271
Total vcore-milliseconds taken by all reduce tasks=9646
Total megabyte-milliseconds taken by all map tasks=9493504
Total megabyte-milliseconds taken by all reduce tasks=9877504
Map-Reduce Framework
Map input records=4
Map output records=4
Map output bytes=101
Map output materialized bytes=115
Input split bytes=132
Combine input records=0
Combine output records=0
Reduce input groups=1
Reduce shuffle bytes=115
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)=178
CPU time spent (ms)=2890
Physical memory (bytes) snapshot=490590208
Virtual memory (bytes) snapshot=1719349248
Total committed heap usage (bytes)=298319872
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=219
File Output Format Counters
Bytes Written=77
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -ls
Found 3 items
drwxr-xr-x - hadoop supergroup 0 2018-03-03 13:21 input
drwxr-xr-x - hadoop supergroup 0 2018-03-03 13:47 output
drwxr-xr-x - hadoop supergroup 0 2018-03-03 14:26 output_yarn hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -cat ./output/*
1 dfsadmin
1 dfs.replication
1 dfs.namenode.name.dir
1 dfs.datanode.data.dir hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hdfs dfs -cat ./output_yarn/*
1 dfsadmin
1 dfs.replication
1 dfs.namenode.name.dir
1 dfs.datanode.data.dir
关闭 YARN 的脚本如下：
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ jps
14423 DataNode
14291 NameNode
18221 Jps
15570 JobHistoryServer
15220 NodeManager
15008 ResourceManager
14655 SecondaryNameNode
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./sbin/stop-yarn.sh
stopping yarn daemons
stopping resourcemanager
localhost: stopping nodemanager
localhost: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
no proxyserver to stop
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./sbin/mr-jobhistory-daemon.sh stop historyserver
stopping historyserver
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ jps
14423 DataNode
14291 NameNode
14655 SecondaryNameNode
18427 Jps
关闭之后再执行程序，发现不能运行这是在配置了yarn并关闭之后的必然结果。
hadoop@zyr-Aspire-V5-551G:/usr/local/hadoop$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep input output_yarn_close 'dfs[a-z.]+'
结果如下：
18/03/03 14:42:20 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/03/03 14:42:21 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 0 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)
18/03/03 14:42:22 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 1 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10,
…...
18/03/03 14:42:40 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:8032. Already tried 9 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)
18/03/03 14:42:40 INFO retry.RetryInvocationHandler: java.net.ConnectException: Your endpoint configuration is wrong; For more details see: http://wiki.apache.org/hadoop/UnsetHostnameOrPort, while invoking ApplicationClientProtocolPBClientImpl.getNewApplication over null after 1 failover attempts. Trying to failover after sleeping for 31517ms. After modify the yarn file,the mapreduce program running well.
The http://localhost:8088/cluster could not find.
八、项目小结
至此，我们已经从最开始的配置系统，到之后的配置ssh，java环境，安装hadoop，单机hadoop运行，伪分布式hadoop运行，以及最后的安装yarn，使用yarn运行，不知不觉的，我们对hadoop的基本主线有了本质性的把握，深入的了解了hdfs，知道了MapReduce的执行过程，了解了很多的命令，同时也锻炼了自己的查找问题，分析问题，解决问题的能力，在一番沉淀之后，我们将会搭建真正的集群，不积跬步无以至千里，细节决定成败，虚心，踏实，不断的积累，未来必将属于我们！静下心来，认真探索，深入研究，前方的风景无限美好~~~