null - 程序员宅基地

运行 MapReduce 样例_hadoop-mapreduce-examples-*.jar-程序员宅基地

技术标签： Hadoop 样例 MapReduce

一 hadoop样例代码

1 样例程序路径

/opt/hadoop-2.7.4/share/hadoop/mapreduce

2 样例程序包

hadoop-mapreduce-examples-2.7.4.jar包含着数个可以直接运行的样例程序

3 如何查看样例程序

./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar

4 举例

[root@master hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.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.

二样例程序简介

三查看样例帮助

./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar wordcount

./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar pi

举例

[root@master hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar wordcount
Usage: wordcount <in> [<in>...] <out>
[root@master hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar pi
Usage: org.apache.hadoop.examples.QuasiMonteCarlo <nMaps> <nSamples>
Generic options supported are
-conf <configuration file>     specify an application configuration file
-D <property=value>            use value for given property
-fs <local|namenode:port>      specify a namenode
-jt <local|resourcemanager:port>    specify a ResourceManager
-files <comma separated list of files>    specify comma separated files to be copied to the map reduce cluster
-libjars <comma separated list of jars>    specify comma separated jar files to include in the classpath.
-archives <comma separated list of archives>    specify comma separated archives to be unarchived on the compute machines.
The general command line syntax is
bin/hadoop command [genericOptions] [commandOptions]

四运行wordcount样例

[root@master hadoop-2.7.4]# jps
4912 NameNode
9265 NodeManager
9155 ResourceManager
9561 Jps
5195 SecondaryNameNode
5038 DataNode
[root@master hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar wordcount /input /output2
17/12/17 16:28:33 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/12/17 16:28:35 INFO input.FileInputFormat: Total input paths to process : 1
17/12/17 16:28:35 INFO mapreduce.JobSubmitter: number of splits:1
17/12/17 16:28:35 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1513499297109_0001
17/12/17 16:28:36 INFO impl.YarnClientImpl: Submitted application application_1513499297109_0001
17/12/17 16:28:37 INFO mapreduce.Job: The url to track the job: http://centos:8088/proxy/application_1513499297109_0001/
17/12/17 16:28:37 INFO mapreduce.Job: Running job: job_1513499297109_0001
17/12/17 16:29:06 INFO mapreduce.Job: Job job_1513499297109_0001 running in uber mode : false
17/12/17 16:29:06 INFO mapreduce.Job:  map 0% reduce 0%
17/12/17 16:29:25 INFO mapreduce.Job:  map 100% reduce 0%
17/12/17 16:29:40 INFO mapreduce.Job:  map 100% reduce 100%
17/12/17 16:29:41 INFO mapreduce.Job: Job job_1513499297109_0001 completed successfully
17/12/17 16:29:42 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=339
        FILE: Number of bytes written=242217
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=267
        HDFS: Number of bytes written=217
        HDFS: Number of read operations=6
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters
        Launched map tasks=1
        Launched reduce tasks=1
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=16910
        Total time spent by all reduces in occupied slots (ms)=9673
        Total time spent by all map tasks (ms)=16910
        Total time spent by all reduce tasks (ms)=9673
        Total vcore-milliseconds taken by all map tasks=16910
        Total vcore-milliseconds taken by all reduce tasks=9673
        Total megabyte-milliseconds taken by all map tasks=17315840
        Total megabyte-milliseconds taken by all reduce tasks=9905152
    Map-Reduce Framework
        Map input records=4
        Map output records=31
        Map output bytes=295
        Map output materialized bytes=339
        Input split bytes=95
        Combine input records=31
        Combine output records=29
        Reduce input groups=29
        Reduce shuffle bytes=339
        Reduce input records=29
        Reduce output records=29
        Spilled Records=58
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=166
        CPU time spent (ms)=1380
        Physical memory (bytes) snapshot=279044096
        Virtual memory (bytes) snapshot=4160716800
        Total committed heap usage (bytes)=138969088
    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=172
    File Output Format Counters
        Bytes Written=217
[root@master hadoop-2.7.4]# ./bin/hdfs dfs -ls /output2/
Found 2 items
-rw-r--r--   1 root supergroup          0 2017-12-17 16:29 /output2/_SUCCESS
-rw-r--r--   1 root supergroup        217 2017-12-17 16:29 /output2/part-r-00000
[root@master hadoop-2.7.4]# ./bin/hdfs dfs -cat /output2/part-r-00000
78    1
ai    1
daokc    1
dfksdhlsd    1
dkhgf    1
docke    1
docker    1
erhejd    1
fdjk    1
fdskre    1
fjdk    1
fjdks    1
fjksl    1
fsd    1
go    1
haddop    1
hello    3
hi    1
hki    1
jfdk    1
scalw    1
sd    1
sdkf    1
sdkfj    1
sdl    1
sstem    1
woekd    1
yfdskt    1
yuihej    1

五使用Web GUI监控实例

http://192.168.0.102:8088

六关于TearSort

七 TearSort的原理

八生成数据TearGen

简介：

./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar teragen <linenum> <output dir>

注意：teragen后的数值单位是行数，因为每行100个字节，所以如果要产生1T的数据，则这个值是1T/100=10000000000（10个0）

举例：

[root@master hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar teragen
teragen <num rows> <output dir>
[root@master hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar teragen 10000 /teragen
17/12/17 16:36:48 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/12/17 16:36:49 INFO terasort.TeraSort: Generating 10000 using 2
17/12/17 16:36:50 INFO mapreduce.JobSubmitter: number of splits:2
17/12/17 16:36:50 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1513499297109_0002
17/12/17 16:36:50 INFO impl.YarnClientImpl: Submitted application application_1513499297109_0002
17/12/17 16:36:50 INFO mapreduce.Job: The url to track the job: http://centos:8088/proxy/application_1513499297109_0002/
17/12/17 16:36:50 INFO mapreduce.Job: Running job: job_1513499297109_0002
17/12/17 16:37:01 INFO mapreduce.Job: Job job_1513499297109_0002 running in uber mode : false
17/12/17 16:37:01 INFO mapreduce.Job:  map 0% reduce 0%
17/12/17 16:37:19 INFO mapreduce.Job:  map 100% reduce 0%
17/12/17 16:37:21 INFO mapreduce.Job: Job job_1513499297109_0002 completed successfully
17/12/17 16:37:21 INFO mapreduce.Job: Counters: 31
    File System Counters
        FILE: Number of bytes read=0
        FILE: Number of bytes written=240922
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=164
        HDFS: Number of bytes written=1000000
        HDFS: Number of read operations=8
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=4
    Job Counters
        Launched map tasks=2
        Other local map tasks=2
        Total time spent by all maps in occupied slots (ms)=30146
        Total time spent by all reduces in occupied slots (ms)=0
        Total time spent by all map tasks (ms)=30146
        Total vcore-milliseconds taken by all map tasks=30146
        Total megabyte-milliseconds taken by all map tasks=30869504
    Map-Reduce Framework
        Map input records=10000
        Map output records=10000
        Input split bytes=164
        Spilled Records=0
        Failed Shuffles=0
        Merged Map outputs=0
        GC time elapsed (ms)=434
        CPU time spent (ms)=1400
        Physical memory (bytes) snapshot=161800192
        Virtual memory (bytes) snapshot=4156805120
        Total committed heap usage (bytes)=35074048
    org.apache.hadoop.examples.terasort.TeraGen$Counters
        CHECKSUM=21555350172850
    File Input Format Counters
        Bytes Read=0
    File Output Format Counters
        Bytes Written=1000000

九生成数据的格式

举例：

[root@master hadoop-2.7.4]# ./bin/hdfs dfs -ls /teragen
Found 3 items
-rw-r--r--   1 root supergroup          0 2017-12-17 16:37 /teragen/_SUCCESS
-rw-r--r--   1 root supergroup     500000 2017-12-17 16:37 /teragen/part-m-00000
-rw-r--r--   1 root supergroup     500000 2017-12-17 16:37 /teragen/part-m-00001

十运行TearSort

简介：

./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar terasort <input dir> <output dir>

启动m个mapper(取决于数据文件个数)和r个reduce（取决于设置项：mapred.reduce.tasks）

举例：

[root@centos hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar terasort /teragen /terasort
17/12/17 16:46:24 INFO terasort.TeraSort: starting
17/12/17 16:46:25 INFO input.FileInputFormat: Total input paths to process : 2
Spent 135ms computing base-splits.
Spent 3ms computing TeraScheduler splits.
Computing input splits took 139ms
Sampling 2 splits of 2
Making 1 from 10000 sampled records
Computing parititions took 384ms
Spent 530ms computing partitions.
17/12/17 16:46:26 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/12/17 16:46:27 INFO mapreduce.JobSubmitter: number of splits:2
17/12/17 16:46:27 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1513499297109_0003
17/12/17 16:46:28 INFO impl.YarnClientImpl: Submitted application application_1513499297109_0003
17/12/17 16:46:28 INFO mapreduce.Job: The url to track the job: http://centos:8088/proxy/application_1513499297109_0003/
17/12/17 16:46:28 INFO mapreduce.Job: Running job: job_1513499297109_0003
17/12/17 16:46:38 INFO mapreduce.Job: Job job_1513499297109_0003 running in uber mode : false
17/12/17 16:46:38 INFO mapreduce.Job:  map 0% reduce 0%
17/12/17 16:47:19 INFO mapreduce.Job:  map 100% reduce 0%
17/12/17 16:47:41 INFO mapreduce.Job:  map 100% reduce 100%
17/12/17 16:47:44 INFO mapreduce.Job: Job job_1513499297109_0003 completed successfully
17/12/17 16:47:45 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=1040006
        FILE: Number of bytes written=2445488
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=1000208
        HDFS: Number of bytes written=1000000
        HDFS: Number of read operations=9
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters
        Launched map tasks=2
        Launched reduce tasks=1
        Data-local map tasks=2
        Total time spent by all maps in occupied slots (ms)=87622
        Total time spent by all reduces in occupied slots (ms)=12795
        Total time spent by all map tasks (ms)=87622
        Total time spent by all reduce tasks (ms)=12795
        Total vcore-milliseconds taken by all map tasks=87622
        Total vcore-milliseconds taken by all reduce tasks=12795
        Total megabyte-milliseconds taken by all map tasks=89724928
        Total megabyte-milliseconds taken by all reduce tasks=13102080
    Map-Reduce Framework
        Map input records=10000
        Map output records=10000
        Map output bytes=1020000
        Map output materialized bytes=1040012
        Input split bytes=208
        Combine input records=0
        Combine output records=0
        Reduce input groups=10000
        Reduce shuffle bytes=1040012
        Reduce input records=10000
        Reduce output records=10000
        Spilled Records=20000
        Shuffled Maps =2
        Failed Shuffles=0
        Merged Map outputs=2
        GC time elapsed (ms)=3246
        CPU time spent (ms)=3580
        Physical memory (bytes) snapshot=400408576
        Virtual memory (bytes) snapshot=6236995584
        Total committed heap usage (bytes)=262987776
    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=1000000
    File Output Format Counters
        Bytes Written=1000000
17/12/17 16:47:45 INFO terasort.TeraSort: done
[root@centos hadoop-2.7.4]# ./bin/hdfs dfs -ls /terasort
Found 3 items
-rw-r--r--   1 root supergroup          0 2017-12-17 16:47 /terasort/_SUCCESS
-rw-r--r--  10 root supergroup          0 2017-12-17 16:46 /terasort/_partition.lst
-rw-r--r--   1 root supergroup    1000000 2017-12-17 16:47 /terasort/part-r-00000

十一结果校验

简介：

TearSort还自带一个校验程序，来检验排序结果是否有序的。

执行TearValidate的命令是

./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar tervalidate <terasort output dir> <teravalidete output dir>

举例：

[root@centos hadoop-2.7.4]# ./bin/yarn jar /opt/hadoop-2.7.4/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar teravalidate /terasort /report
17/12/17 17:03:46 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/12/17 17:03:48 INFO input.FileInputFormat: Total input paths to process : 1
Spent 56ms computing base-splits.
Spent 3ms computing TeraScheduler splits.
17/12/17 17:03:48 INFO mapreduce.JobSubmitter: number of splits:1
17/12/17 17:03:49 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1513499297109_0007
17/12/17 17:03:49 INFO impl.YarnClientImpl: Submitted application application_1513499297109_0007
17/12/17 17:03:49 INFO mapreduce.Job: The url to track the job: http://centos:8088/proxy/application_1513499297109_0007/
17/12/17 17:03:49 INFO mapreduce.Job: Running job: job_1513499297109_0007
17/12/17 17:04:00 INFO mapreduce.Job: Job job_1513499297109_0007 running in uber mode : false
17/12/17 17:04:00 INFO mapreduce.Job:  map 0% reduce 0%
17/12/17 17:04:08 INFO mapreduce.Job:  map 100% reduce 0%
17/12/17 17:04:19 INFO mapreduce.Job:  map 100% reduce 100%
17/12/17 17:04:20 INFO mapreduce.Job: Job job_1513499297109_0007 completed successfully
17/12/17 17:04:20 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=92
        FILE: Number of bytes written=241805
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=1000105
        HDFS: Number of bytes written=22
        HDFS: Number of read operations=6
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters
        Launched map tasks=1
        Launched reduce tasks=1
        Data-local map tasks=1
        Total time spent by all maps in occupied slots (ms)=4952
        Total time spent by all reduces in occupied slots (ms)=8032
        Total time spent by all map tasks (ms)=4952
        Total time spent by all reduce tasks (ms)=8032
        Total vcore-milliseconds taken by all map tasks=4952
        Total vcore-milliseconds taken by all reduce tasks=8032
        Total megabyte-milliseconds taken by all map tasks=5070848
        Total megabyte-milliseconds taken by all reduce tasks=8224768
    Map-Reduce Framework
        Map input records=10000
        Map output records=3
        Map output bytes=80
        Map output materialized bytes=92
        Input split bytes=105
        Combine input records=0
        Combine output records=0
        Reduce input groups=3
        Reduce shuffle bytes=92
        Reduce input records=3
        Reduce output records=1
        Spilled Records=6
        Shuffled Maps =1
        Failed Shuffles=0
        Merged Map outputs=1
        GC time elapsed (ms)=193
        CPU time spent (ms)=1250
        Physical memory (bytes) snapshot=281731072
        Virtual memory (bytes) snapshot=4160716800
        Total committed heap usage (bytes)=139284480
    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=1000000
    File Output Format Counters
        Bytes Written=22
[root@centos hadoop-2.7.4]# ./bin/hdfs dfs -ls /report
Found 2 items
-rw-r--r--   1 root supergroup          0 2017-12-17 17:04 /report/_SUCCESS
-rw-r--r--   1 root supergroup         22 2017-12-17 17:04 /report/part-r-00000
[root@centos hadoop-2.7.4]# ./bin/hdfs dfs -cat /report/part-r-00000
checksum    139abefd74b2

十二应用场景

十三参考

http://www.jikexueyuan.com/course/2116.html

本文链接：https://blog.csdn.net/chengqiuming/article/details/78826143

原作者删帖不实内容删帖广告或垃圾文章投诉

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文章浏览阅读316次。路由的概念路由器它称之为网关设备。路由器就是用于连接不同网络的设备路由器是位于OSI模型的第三层。路由器通过路由决定数据的转发。网关的背景：当时每家计算机厂商，用于交换数据的通信程序（协议）和数据描述格式各不相同。因此，就把用于相互转换这些协议和格式的计算机称为网关。路由器与三层交换器的对比路由协议对比路由器的作用：1.路由寻址2.实现不同网络之间相连的功能3.通过路由决定数据的转发，转发策略称为路由选择。VLAN相关技术什么是VLAN？中文名称叫：虚拟局域网。虚_路由和vlan

设置div背景颜色透明度，内部元素不透明_div设置透明度,里面的内容不透明-程序员宅基地

文章浏览阅读2.8w次，点赞6次，收藏22次。设置div背景颜色透明度，内部元素不透明：.demo{ 　background-color:rgba(255,255,255,0.15) } 错误方式：.demo{ background-color:#5CACEE;opacity:0.75;} 这样会导致div里面的元素内容和背景颜色一起变透明只针对谷歌浏览器的测试_div设置透明度,里面的内容不透明

Discuz!代码大全-程序员宅基地

文章浏览阅读563次。1.[ u]文字：在文字的位置可以任意加入您需要的字符，显示为下划线效果。2.[ align=center]文字：在文字的位置可以任意加入您需要的字符，center位置center表示居中，left表示居左，right表示居右。5.[ color=red]文字：输入您的颜色代码，在标签的中间插入文字可以实现文字颜色改变。6.[ SIZE=数字]文字：输入您的字体大小，在标签的中间插入文..._discuzcode 大全

iOS NSTimer定时器-程序员宅基地

文章浏览阅读2.6k次。iOS中定时器有三种，分别是NSTimer、CADisplayLink、dispatch_source，下面就分别对这三种计时器进行说明。一、NSTimerNSTimer这种定时器用的比较多，但是特别需要注意释放问题，如果处理不好很容易引起循环引用问题，造成内存泄漏。1.1 NSTimer的创建NSTimer有两种创建方法。方法一：这种方法虽然创建了NSTimer，但是定时器却没有起作用。这种方式创建的NSTimer，需要加入到NSRunLoop中，有NSRunLoop的驱动才会让定时器跑起来。_ios nstimer

Linux常用命令_ls-lmore-程序员宅基地

文章浏览阅读4.8k次，点赞17次，收藏51次。Linux的命令有几百个，对程序员来说，常用的并不多，考虑各位是初学者，先学习本章节前15个命令就可以了，其它的命令以后用到的时候再学习。1、开机物理机服务器，按下电源开关，就像windows开机一样。在VMware中点击“开启此虚拟机”。2、登录启动完成后，输入用户名和密码，一般情况下，不要用root用户..._ls-lmore

MySQL基础命令_mysql -u user-程序员宅基地

文章浏览阅读4.1k次。1.登录MYSQL系统命令打开DOS命令框shengfen，以管理员的身份运行命令1：mysql -u usernae -p password命令2：mysql -u username -p password -h 需要连接的mysql主机名(localhost本地主机名)或是mysql的ip地址（默认为：127.0.0.1）-P 端口号（默认：3306端口）使用其中任意一个就OK,输入命令后DOS命令框得到mysql>就说明已经进入了mysql系统2. 查看mysql当中的._mysql -u user