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243次。0x00 简介权限滥用漏洞一般归类于逻辑问题，是指服务端功能开放过多或权限限制不严格，导致攻击者可以通过直接或间接调用的方式达到攻击效果。随着物联网时代的到来，这种漏洞已经屡见不鲜，各种漏洞组合利用也是千奇百怪、五花八门，这里总结漏洞是为了更好地应对和预防，如有不妥之处还请业内人士多多指教。0x01 背景2014年4月，在比特币飞涨的时代某网站曾经..._使用物联网漏洞的使用者

Visual Odometry and Depth Calculation--Epipolar Geometry--Direct Method--PnP_normalized plane coordinates-程序员宅基地

文章浏览阅读786次。A. Epipolar geometry and triangulationThe epipolar geometry mainly adopts the feature point method, such as SIFT, SURF and ORB, etc. to obtain the feature points corresponding to two frames of images. As shown in Figure 1, let the first image be and th_normalized plane coordinates

开放信息抽取(OIE)系统（三）-- 第二代开放信息抽取系统(人工规则, rule-based, 先抽取关系)_语义角色增强的关系抽取-程序员宅基地

文章浏览阅读708次，点赞2次，收藏3次。开放信息抽取(OIE)系统（三）-- 第二代开放信息抽取系统(人工规则, rule-based, 先关系再实体)一.第二代开放信息抽取系统背景第一代开放信息抽取系统(Open Information Extraction， OIE， learning-based, 自学习, 先抽取实体)通常抽取大量冗余信息，为了消除这些冗余信息，诞生了第二代开放信息抽取系统。二.第二代开放信息抽取系统历史第二代开放信息抽取系统着眼于解决第一代系统的三大问题: 大量非信息性提取（即省略关键信息的提取）、_语义角色增强的关系抽取