沉淀,再出发——在Ubuntu Kylin15.04中配置Hadoop单机/伪分布式系统经验分享

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 [email protected]
cat ./id_rsa.pub >> ./authorized_keys

?    [email protected]:~$ 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:

[email protected]:~$ 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
[email protected]:~$ 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

[email protected]:~$ echo $JAVA_HOME
/usr/lib/jvm/java-7-openjdk-amd64/

[email protected]:~$ 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)

[email protected]:~$ $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

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。

[email protected]:~$ sudo tar -zxf ~/Downloads/hadoop-2.9.0.tar.gz -C /usr/local
[sudo] password for hadoop:
[email protected]:~$ cd /usr/local/
[email protected]:/usr/local$ sudo mv ./hadoop-2.9.0/ ./hadoop   
[email protected]:/usr/local$ sudo chown -R hadoop ./hadoop
[email protected]:/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。

[email protected]:/usr/local$ cd hadoop/
[email protected]:/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

[email protected]:/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。

[email protected]:/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:

[email protected]:/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。

  1 [email protected]:/usr/local/hadoop$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep ./input ./output ‘dfs[a-z.]+‘
  2 18/03/03 11:20:28 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
  3 18/03/03 11:20:28 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
  4 18/03/03 11:20:28 INFO input.FileInputFormat: Total input files to process : 8
  5 18/03/03 11:20:28 INFO mapreduce.JobSubmitter: number of splits:8
  6 18/03/03 11:20:30 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local325822439_0001
  7 18/03/03 11:20:31 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
  8 18/03/03 11:20:31 INFO mapreduce.Job: Running job: job_local325822439_0001
  9 18/03/03 11:20:31 INFO mapred.LocalJobRunner: OutputCommitter set in config null
 10 18/03/03 11:20:31 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
 11 18/03/03 11:20:31 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
 12 18/03/03 11:20:31 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
 13 18/03/03 11:20:31 INFO mapred.LocalJobRunner: Waiting for map tasks
 14 18/03/03 11:20:31 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000000_0
 15 18/03/03 11:20:31 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
 16 18/03/03 11:20:31 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
 17 18/03/03 11:20:31 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
 18 18/03/03 11:20:31 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/hadoop-policy.xml:0+10206
 19 18/03/03 11:20:31 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
 20 18/03/03 11:20:31 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
 21 18/03/03 11:20:31 INFO mapred.MapTask: soft limit at 83886080
 22 18/03/03 11:20:31 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
 23 18/03/03 11:20:31 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
 24 18/03/03 11:20:31 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
 25 18/03/03 11:20:31 INFO mapred.LocalJobRunner:
 26 18/03/03 11:20:31 INFO mapred.MapTask: Starting flush of map output
 27 18/03/03 11:20:31 INFO mapred.MapTask: Spilling map output
 28 18/03/03 11:20:31 INFO mapred.MapTask: bufstart = 0; bufend = 17; bufvoid = 104857600
 29 18/03/03 11:20:31 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214396(104857584); length = 1/6553600
 30 18/03/03 11:20:32 INFO mapred.MapTask: Finished spill 0
 31 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
 32 18/03/03 11:20:32 INFO mapred.LocalJobRunner: map
 33 18/03/03 11:20:32 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000000_0‘ done.
 34 18/03/03 11:20:32 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000000_0
 35 18/03/03 11:20:32 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000001_0
 36 18/03/03 11:20:32 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
 37 18/03/03 11:20:32 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
 38 18/03/03 11:20:32 INFO mapreduce.Job: Job job_local325822439_0001 running in uber mode : false
 39 18/03/03 11:20:32 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
 40 18/03/03 11:20:32 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/capacity-scheduler.xml:0+7861
 41 18/03/03 11:20:32 INFO mapreduce.Job:  map 100% reduce 0%
 42 18/03/03 11:20:32 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
 43 18/03/03 11:20:32 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
 44 18/03/03 11:20:32 INFO mapred.MapTask: soft limit at 83886080
 45 18/03/03 11:20:32 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
 46 18/03/03 11:20:32 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
 47 18/03/03 11:20:32 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
 48 18/03/03 11:20:32 INFO mapred.LocalJobRunner:
 49 18/03/03 11:20:32 INFO mapred.MapTask: Starting flush of map output
 50 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
 51 18/03/03 11:20:32 INFO mapred.LocalJobRunner: map
 52 18/03/03 11:20:32 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000001_0‘ done.
 53 18/03/03 11:20:32 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000001_0
 54 18/03/03 11:20:32 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000002_0
 55 18/03/03 11:20:32 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
 56 18/03/03 11:20:32 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
 57 18/03/03 11:20:32 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
 58 18/03/03 11:20:32 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/kms-site.xml:0+5939
 59 18/03/03 11:20:32 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
 60 18/03/03 11:20:32 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
 61 18/03/03 11:20:32 INFO mapred.MapTask: soft limit at 83886080
 62 18/03/03 11:20:32 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
 63 18/03/03 11:20:32 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
 64 18/03/03 11:20:32 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
 65 18/03/03 11:20:32 INFO mapred.LocalJobRunner:
 66 18/03/03 11:20:32 INFO mapred.MapTask: Starting flush of map output
 67 18/03/03 11:20:33 INFO mapred.Task: Task:attempt_local325822439_0001_m_000002_0 is done. And is in the process of committing
 68 18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
 69 18/03/03 11:20:33 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000002_0‘ done.
 70 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000002_0
 71 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000003_0
 72 18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
 73 18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
 74 18/03/03 11:20:33 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
 75 18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/kms-acls.xml:0+3518
 76 18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
 77 18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
 78 18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
 79 18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
 80 18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
 81 18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
 82 18/03/03 11:20:33 INFO mapred.LocalJobRunner:
 83 18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
 84 18/03/03 11:20:33 INFO mapreduce.Job:  map 38% reduce 0%
 85 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
 86 18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
 87 18/03/03 11:20:33 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000003_0‘ done.
 88 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000003_0
 89 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000004_0
 90 18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
 91 18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
 92 18/03/03 11:20:33 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
 93 18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/hdfs-site.xml:0+775
 94 18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
 95 18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
 96 18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
 97 18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
 98 18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
 99 18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
100 18/03/03 11:20:33 INFO mapred.LocalJobRunner:
101 18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
102 18/03/03 11:20:33 INFO mapred.Task: Task:attempt_local325822439_0001_m_000004_0 is done. And is in the process of committing
103 18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
104 18/03/03 11:20:33 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000004_0‘ done.
105 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000004_0
106 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000005_0
107 18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
108 18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
109 18/03/03 11:20:33 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
110 18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/core-site.xml:0+774
111 18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
112 18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
113 18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
114 18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
115 18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
116 18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
117 18/03/03 11:20:33 INFO mapred.LocalJobRunner:
118 18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
119 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
120 18/03/03 11:20:33 INFO mapred.LocalJobRunner: map
121 18/03/03 11:20:33 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000005_0‘ done.
122 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000005_0
123 18/03/03 11:20:33 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000006_0
124 18/03/03 11:20:33 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
125 18/03/03 11:20:33 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
126 18/03/03 11:20:33 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
127 18/03/03 11:20:33 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/yarn-site.xml:0+690
128 18/03/03 11:20:33 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
129 18/03/03 11:20:33 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
130 18/03/03 11:20:33 INFO mapred.MapTask: soft limit at 83886080
131 18/03/03 11:20:33 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
132 18/03/03 11:20:33 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
133 18/03/03 11:20:33 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
134 18/03/03 11:20:33 INFO mapred.LocalJobRunner:
135 18/03/03 11:20:33 INFO mapred.MapTask: Starting flush of map output
136 18/03/03 11:20:34 INFO mapred.Task: Task:attempt_local325822439_0001_m_000006_0 is done. And is in the process of committing
137 18/03/03 11:20:34 INFO mapred.LocalJobRunner: map
138 18/03/03 11:20:34 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000006_0‘ done.
139 18/03/03 11:20:34 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000006_0
140 18/03/03 11:20:34 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_m_000007_0
141 18/03/03 11:20:34 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
142 18/03/03 11:20:34 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
143 18/03/03 11:20:34 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
144 18/03/03 11:20:34 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/input/httpfs-site.xml:0+620
145 18/03/03 11:20:34 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
146 18/03/03 11:20:34 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
147 18/03/03 11:20:34 INFO mapred.MapTask: soft limit at 83886080
148 18/03/03 11:20:34 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
149 18/03/03 11:20:34 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
150 18/03/03 11:20:34 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
151 18/03/03 11:20:34 INFO mapred.LocalJobRunner:
152 18/03/03 11:20:34 INFO mapred.MapTask: Starting flush of map output
153 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
154 18/03/03 11:20:34 INFO mapred.LocalJobRunner: map
155 18/03/03 11:20:34 INFO mapred.Task: Task ‘attempt_local325822439_0001_m_000007_0‘ done.
156 18/03/03 11:20:34 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_m_000007_0
157 18/03/03 11:20:34 INFO mapred.LocalJobRunner: map task executor complete.
158 18/03/03 11:20:34 INFO mapred.LocalJobRunner: Waiting for reduce tasks
159 18/03/03 11:20:34 INFO mapred.LocalJobRunner: Starting task: attempt_local325822439_0001_r_000000_0
160 18/03/03 11:20:34 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
161 18/03/03 11:20:34 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
162 18/03/03 11:20:34 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
163 18/03/03 11:20:34 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: [email protected]
164 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=369937600, maxSingleShuffleLimit=92484400, mergeThreshold=244158832, ioSortFactor=10, memToMemMergeOutputsThreshold=10
165 18/03/03 11:20:34 INFO reduce.EventFetcher: attempt_local325822439_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
166 18/03/03 11:20:34 INFO mapreduce.Job:  map 100% reduce 0%
167 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
168 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000003_0
169 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->2
170 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
171 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 21 bytes from map-output for attempt_local325822439_0001_m_000000_0
172 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 21, inMemoryMapOutputs.size() -> 2, commitMemory -> 2, usedMemory ->23
173 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
174 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000006_0
175 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 3, commitMemory -> 23, usedMemory ->25
176 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
177 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000005_0
178 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 4, commitMemory -> 25, usedMemory ->27
179 18/03/03 11:20:34 WARN io.ReadaheadPool: Failed readahead on ifile
180 EBADF: Bad file descriptor
181     at org.apache.hadoop.io.nativeio.NativeIO$POSIX.posix_fadvise(Native Method)
182     at org.apache.hadoop.io.nativeio.NativeIO$POSIX.posixFadviseIfPossible(NativeIO.java:267)
183     at org.apache.hadoop.io.nativeio.NativeIO$POSIX$CacheManipulator.posixFadviseIfPossible(NativeIO.java:146)
184     at org.apache.hadoop.io.ReadaheadPool$ReadaheadRequestImpl.run(ReadaheadPool.java:208)
185     at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
186     at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
187     at java.lang.Thread.run(Thread.java:745)
188 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
189 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000001_0
190 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 5, commitMemory -> 27, usedMemory ->29
191 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
192 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000004_0
193 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 6, commitMemory -> 29, usedMemory ->31
194 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
195 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000007_0
196 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 7, commitMemory -> 31, usedMemory ->33
197 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
198 18/03/03 11:20:34 INFO reduce.InMemoryMapOutput: Read 2 bytes from map-output for attempt_local325822439_0001_m_000002_0
199 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 2, inMemoryMapOutputs.size() -> 8, commitMemory -> 33, usedMemory ->35
200 18/03/03 11:20:34 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
201 18/03/03 11:20:34 INFO mapred.LocalJobRunner: 8 / 8 copied.
202 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: finalMerge called with 8 in-memory map-outputs and 0 on-disk map-outputs
203 18/03/03 11:20:34 INFO mapred.Merger: Merging 8 sorted segments
204 18/03/03 11:20:34 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 10 bytes
205 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: Merged 8 segments, 35 bytes to disk to satisfy reduce memory limit
206 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: Merging 1 files, 25 bytes from disk
207 18/03/03 11:20:34 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
208 18/03/03 11:20:34 INFO mapred.Merger: Merging 1 sorted segments
209 18/03/03 11:20:34 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 10 bytes
210 18/03/03 11:20:34 INFO mapred.LocalJobRunner: 8 / 8 copied.
211 18/03/03 11:20:34 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
212 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
213 18/03/03 11:20:34 INFO mapred.LocalJobRunner: 8 / 8 copied.
214 18/03/03 11:20:34 INFO mapred.Task: Task attempt_local325822439_0001_r_000000_0 is allowed to commit now
215 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
216 18/03/03 11:20:34 INFO mapred.LocalJobRunner: reduce > reduce
217 18/03/03 11:20:34 INFO mapred.Task: Task ‘attempt_local325822439_0001_r_000000_0‘ done.
218 18/03/03 11:20:34 INFO mapred.LocalJobRunner: Finishing task: attempt_local325822439_0001_r_000000_0
219 18/03/03 11:20:34 INFO mapred.LocalJobRunner: reduce task executor complete.
220 18/03/03 11:20:35 INFO mapreduce.Job:  map 100% reduce 100%
221 18/03/03 11:20:35 INFO mapreduce.Job: Job job_local325822439_0001 completed successfully
222 18/03/03 11:20:35 INFO mapreduce.Job: Counters: 30
223     File System Counters
224         FILE: Number of bytes read=2993922
225         FILE: Number of bytes written=7026239
226         FILE: Number of read operations=0
227         FILE: Number of large read operations=0
228         FILE: Number of write operations=0
229     Map-Reduce Framework
230         Map input records=840
231         Map output records=1
232         Map output bytes=17
233         Map output materialized bytes=67
234         Input split bytes=869
235         Combine input records=1
236         Combine output records=1
237         Reduce input groups=1
238         Reduce shuffle bytes=67
239         Reduce input records=1
240         Reduce output records=1
241         Spilled Records=2
242         Shuffled Maps =8
243         Failed Shuffles=0
244         Merged Map outputs=8
245         GC time elapsed (ms)=120
246         Total committed heap usage (bytes)=3988258816
247     Shuffle Errors
248         BAD_ID=0
249         CONNECTION=0
250         IO_ERROR=0
251         WRONG_LENGTH=0
252         WRONG_MAP=0
253         WRONG_REDUCE=0
254     File Input Format Counters
255         Bytes Read=30383
256     File Output Format Counters
257         Bytes Written=123
258 18/03/03 11:20:35 INFO jvm.JvmMetrics: Cannot initialize JVM Metrics with processName=JobTracker, sessionId= - already initialized
259 18/03/03 11:20:35 INFO input.FileInputFormat: Total input files to process : 1
260 18/03/03 11:20:35 INFO mapreduce.JobSubmitter: number of splits:1
261 18/03/03 11:20:35 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local1695778912_0002
262 18/03/03 11:20:36 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
263 18/03/03 11:20:36 INFO mapreduce.Job: Running job: job_local1695778912_0002
264 18/03/03 11:20:36 INFO mapred.LocalJobRunner: OutputCommitter set in config null
265 18/03/03 11:20:36 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
266 18/03/03 11:20:36 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
267 18/03/03 11:20:36 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
268 18/03/03 11:20:36 INFO mapred.LocalJobRunner: Waiting for map tasks
269 18/03/03 11:20:36 INFO mapred.LocalJobRunner: Starting task: attempt_local1695778912_0002_m_000000_0
270 18/03/03 11:20:36 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
271 18/03/03 11:20:36 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
272 18/03/03 11:20:36 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
273 18/03/03 11:20:36 INFO mapred.MapTask: Processing split: file:/usr/local/hadoop/grep-temp-876870354/part-r-00000:0+111
274 18/03/03 11:20:36 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
275 18/03/03 11:20:36 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
276 18/03/03 11:20:36 INFO mapred.MapTask: soft limit at 83886080
277 18/03/03 11:20:36 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
278 18/03/03 11:20:36 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
279 18/03/03 11:20:36 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
280 18/03/03 11:20:36 INFO mapred.LocalJobRunner:
281 18/03/03 11:20:36 INFO mapred.MapTask: Starting flush of map output
282 18/03/03 11:20:36 INFO mapred.MapTask: Spilling map output
283 18/03/03 11:20:36 INFO mapred.MapTask: bufstart = 0; bufend = 17; bufvoid = 104857600
284 18/03/03 11:20:36 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214396(104857584); length = 1/6553600
285 18/03/03 11:20:36 INFO mapred.MapTask: Finished spill 0
286 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
287 18/03/03 11:20:36 INFO mapred.LocalJobRunner: map
288 18/03/03 11:20:36 INFO mapred.Task: Task ‘attempt_local1695778912_0002_m_000000_0‘ done.
289 18/03/03 11:20:36 INFO mapred.LocalJobRunner: Finishing task: attempt_local1695778912_0002_m_000000_0
290 18/03/03 11:20:36 INFO mapred.LocalJobRunner: map task executor complete.
291 18/03/03 11:20:36 INFO mapred.LocalJobRunner: Waiting for reduce tasks
292 18/03/03 11:20:36 INFO mapred.LocalJobRunner: Starting task: attempt_local1695778912_0002_r_000000_0
293 18/03/03 11:20:36 INFO output.FileOutputCommitter: File Output Committer Algorithm version is 1
294 18/03/03 11:20:36 INFO output.FileOutputCommitter: FileOutputCommitter skip cleanup _temporary folders under output directory:false, ignore cleanup failures: false
295 18/03/03 11:20:36 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
296 18/03/03 11:20:36 INFO mapred.ReduceTask: Using ShuffleConsumerPlugin: [email protected]
297 18/03/03 11:20:36 INFO reduce.MergeManagerImpl: MergerManager: memoryLimit=370304608, maxSingleShuffleLimit=92576152, mergeThreshold=244401056, ioSortFactor=10, memToMemMergeOutputsThreshold=10
298 18/03/03 11:20:36 INFO reduce.EventFetcher: attempt_local1695778912_0002_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events
299 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
300 18/03/03 11:20:36 INFO reduce.InMemoryMapOutput: Read 21 bytes from map-output for attempt_local1695778912_0002_m_000000_0
301 18/03/03 11:20:36 INFO reduce.MergeManagerImpl: closeInMemoryFile -> map-output of size: 21, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->21
302 18/03/03 11:20:36 INFO reduce.EventFetcher: EventFetcher is interrupted.. Returning
303 18/03/03 11:20:36 INFO mapred.LocalJobRunner: 1 / 1 copied.
304 18/03/03 11:20:36 INFO reduce.MergeManagerImpl: finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs
305 18/03/03 11:20:36 INFO mapred.Merger: Merging 1 sorted segments
306 18/03/03 11:20:36 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 11 bytes
307 18/03/03 11:20:36 INFO reduce.MergeManagerImpl: Merged 1 segments, 21 bytes to disk to satisfy reduce memory limit
308 18/03/03 11:20:36 INFO reduce.MergeManagerImpl: Merging 1 files, 25 bytes from disk
309 18/03/03 11:20:36 INFO reduce.MergeManagerImpl: Merging 0 segments, 0 bytes from memory into reduce
310 18/03/03 11:20:36 INFO mapred.Merger: Merging 1 sorted segments
311 18/03/03 11:20:36 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 11 bytes
312 18/03/03 11:20:36 INFO mapred.LocalJobRunner: 1 / 1 copied.
313 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
314 18/03/03 11:20:36 INFO mapred.LocalJobRunner: 1 / 1 copied.
315 18/03/03 11:20:36 INFO mapred.Task: Task attempt_local1695778912_0002_r_000000_0 is allowed to commit now
316 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
317 18/03/03 11:20:36 INFO mapred.LocalJobRunner: reduce > reduce
318 18/03/03 11:20:36 INFO mapred.Task: Task ‘attempt_local1695778912_0002_r_000000_0‘ done.
319 18/03/03 11:20:36 INFO mapred.LocalJobRunner: Finishing task: attempt_local1695778912_0002_r_000000_0
320 18/03/03 11:20:36 INFO mapred.LocalJobRunner: reduce task executor complete.
321 18/03/03 11:20:37 INFO mapreduce.Job: Job job_local1695778912_0002 running in uber mode : false
322 18/03/03 11:20:37 INFO mapreduce.Job:  map 100% reduce 100%
323 18/03/03 11:20:37 INFO mapreduce.Job: Job job_local1695778912_0002 completed successfully
324 18/03/03 11:20:37 INFO mapreduce.Job: Counters: 30
325     File System Counters
326         FILE: Number of bytes read=1286912
327         FILE: Number of bytes written=3123146
328         FILE: Number of read operations=0
329         FILE: Number of large read operations=0
330         FILE: Number of write operations=0
331     Map-Reduce Framework
332         Map input records=1
333         Map output records=1
334         Map output bytes=17
335         Map output materialized bytes=25
336         Input split bytes=120
337         Combine input records=0
338         Combine output records=0
339         Reduce input groups=1
340         Reduce shuffle bytes=25
341         Reduce input records=1
342         Reduce output records=1
343         Spilled Records=2
344         Shuffled Maps =1
345         Failed Shuffles=0
346         Merged Map outputs=1
347         GC time elapsed (ms)=0
348         Total committed heap usage (bytes)=1058013184
349     Shuffle Errors
350         BAD_ID=0
351         CONNECTION=0
352         IO_ERROR=0
353         WRONG_LENGTH=0
354         WRONG_MAP=0
355         WRONG_REDUCE=0
356     File Input Format Counters
357         Bytes Read=123
358     File Output Format Counters
359         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。

[email protected]:/usr/local/hadoop$ cat ./output/*
1    dfsadmin
[email protected]:/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

 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.xmlhdfs-site.xml,具体的修改代码如下:

[email protected]:~$ cd  /usr/local/hadoop/

[email protected]:/usr/local/hadoop$ cp ./etc/hadoop/core-site.xml  ./etc/hadoop/core-site.xml.backup
[email protected]:/usr/local/hadoop$ gedit ./etc/hadoop/core-site.xml

[email protected]:/usr/local/hadoop$ cp ./etc/hadoop/hdfs-site.xml  ./etc/hadoop/hdfs-site.xml.backup
[email protected]:/usr/local/hadoop$ gedit ./etc/hadoop/hdfs-site.xml

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>

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

[email protected]:/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:

[email protected]:/usr/local/hadoop$ ls
bin  include  lib      LICENSE.txt  output      sbin   tmp
etc  input    libexec  NOTICE.txt   README.txt  share

[email protected]:/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:

[email protected]:/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:

[email protected]:/usr/local/hadoop$ cd  /usr/local/hadoop/logs/
[email protected]:/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

[email protected]:/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
……

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。

[email protected]:/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/

[email protected]:/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:

[email protected]:/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!

[email protected]:/usr/local/hadoop$ ./bin/hdfs dfs -mkdir input
[email protected]:/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。

[email protected]:/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。

[email protected]:/usr/local/hadoop$ cat ./output/*

[email protected]:/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:

[email protected]:/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:

[email protected]:/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:

[email protected]:/usr/local/hadoop$ ./bin/hdfs dfs -rm -r output 
Deleted output

然后再执行这样就可以了。

[email protected]:/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:

[email protected]:/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
[email protected]:/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!

[email protected]:/usr/local/hadoop$ pwd
/usr/local/hadoop
[email protected]:/usr/local/hadoop$ cp ./etc/hadoop/mapred-site.xml.template  ./etc/hadoop/mapred-site.xml.template.backup
[email protected]:/usr/local/hadoop$ mv ./etc/hadoop/mapred-site.xml.template  ./etc/hadoop/mapred-site.xml
[email protected]:/usr/local/hadoop$ gedit ./etc/hadoop/mapred-site.xml
[email protected]:/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。

[email protected]:/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
[email protected]:/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
[email protected]:/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。

[email protected]:/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

[email protected]:/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

[email protected]:/usr/local/hadoop$ ./bin/hdfs dfs -cat ./output/*
1    dfsadmin
1    dfs.replication
1    dfs.namenode.name.dir
1    dfs.datanode.data.dir

[email protected]:/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:

[email protected]:/usr/local/hadoop$ jps
14423 DataNode
14291 NameNode
18221 Jps
15570 JobHistoryServer
15220 NodeManager
15008 ResourceManager
14655 SecondaryNameNode
[email protected]:/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
[email protected]:/usr/local/hadoop$ ./sbin/mr-jobhistory-daemon.sh stop historyserver
stopping historyserver
[email protected]:/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。

[email protected]:/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:

 1 18/03/03 14:42:20 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
 2 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)
 3 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,
 4 …...
 5 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)
 6 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.
 7
 8
 9 After modify the yarn file,the mapreduce program running well.
10 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~~~

原文地址:https://www.cnblogs.com/zyrblog/p/8503123.html

时间: 03-05

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