运行 MapReduce 样例
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 [...]
[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
Generic options supported are
-conf specify an application configuration file
-D use value for given property
-fs specify a namenode
-jt specify a ResourceManager
-files specify comma separated files to be copied to the map reduce cluster
-libjars specify comma separated jar files to include in the classpath.
-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
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
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
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
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