Linux鞏固記錄（5） hadoop 2.7.4下自己編譯代碼並運行MapReduce程序
阿新 • • 發佈：2017-09-02
parser mod pill self add let tokenize org cto 
程序代碼為 ~\hadoop-2.7.4\share\hadoop\mapreduce\sources\hadoop-mapreduce-examples-2.7.4-sources\org\apache\hadoop\examples\WordCount.java
第一次 刪除了package
import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; importView Codeorg.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.util.GenericOptionsParser; public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); publicvoid map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length < 2) { System.err.println("Usage: wordcount <in> [<in>...] <out>"); System.exit(2); } Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); for (int i = 0; i < otherArgs.length - 1; ++i) { FileInputFormat.addInputPath(job, new Path(otherArgs[i])); } FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
此程序需要下面三個jar包才能編譯通過
[[email protected] classes]# tree /home/jars/ /home/jars/ ├── commons-cli-1.4.jar ├── hadoop-common-2.7.4.jar └── hadoop-mapreduce-client-core-2.7.4.jar
執行過程及結果如下
[[email protected] classes]#
[[email protected] classes]# pwd
/home/classes
[[email protected] classes]# tree
.
0 directories, 0 files
[[email protected] classes]# tree /home/javaFile/
/home/javaFile/
└── WordCount.java
0 directories, 1 file
[[email protected] classes]# tree /home/jars/
/home/jars/
├── commons-cli-1.4.jar
├── hadoop-common-2.7.4.jar
└── hadoop-mapreduce-client-core-2.7.4.jar
0 directories, 3 files
[[email protected] classes]# javac -classpath .:/home/jars/* -d /home/classes/ /home/javaFile/WordCount.java
[[email protected] classes]# tree
.
├── WordCount.class
├── WordCount$IntSumReducer.class
└── WordCount$TokenizerMapper.class
0 directories, 3 files
[[email protected] classes]# jar -cvf wordc.jar ./*.class
added manifest
adding: WordCount.class(in = 1907) (out= 1040)(deflated 45%)
adding: WordCount$IntSumReducer.class(in = 1739) (out= 742)(deflated 57%)
adding: WordCount$TokenizerMapper.class(in = 1736) (out= 753)(deflated 56%)
[[email protected] classes]# tree
.
├── wordc.jar
├── WordCount.class
├── WordCount$IntSumReducer.class
└── WordCount$TokenizerMapper.class
0 directories, 4 files
[[email protected] classes]# /home/hadoop-2.7.4/bin/hadoop jar /home/classes/wordc.jar WordCount /hdfs-input.txt /result-self-compile
17/09/02 02:11:45 INFO client.RMProxy: Connecting to ResourceManager at master/192.168.0.80:8032
17/09/02 02:11:47 INFO input.FileInputFormat: Total input paths to process : 1
17/09/02 02:11:47 INFO mapreduce.JobSubmitter: number of splits:1
17/09/02 02:11:47 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1504320356950_0010
17/09/02 02:11:47 INFO impl.YarnClientImpl: Submitted application application_1504320356950_0010
17/09/02 02:11:47 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1504320356950_0010/
17/09/02 02:11:47 INFO mapreduce.Job: Running job: job_1504320356950_0010
17/09/02 02:11:56 INFO mapreduce.Job: Job job_1504320356950_0010 running in uber mode : false
17/09/02 02:11:56 INFO mapreduce.Job: map 0% reduce 0%
17/09/02 02:12:02 INFO mapreduce.Job: map 100% reduce 0%
17/09/02 02:12:09 INFO mapreduce.Job: map 100% reduce 100%
17/09/02 02:12:09 INFO mapreduce.Job: Job job_1504320356950_0010 completed successfully
17/09/02 02:12:10 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=118
FILE: Number of bytes written=241697
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=174
HDFS: Number of bytes written=76
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)=3745
Total time spent by all reduces in occupied slots (ms)=4081
Total time spent by all map tasks (ms)=3745
Total time spent by all reduce tasks (ms)=4081
Total vcore-milliseconds taken by all map tasks=3745
Total vcore-milliseconds taken by all reduce tasks=4081
Total megabyte-milliseconds taken by all map tasks=3834880
Total megabyte-milliseconds taken by all reduce tasks=4178944
Map-Reduce Framework
Map input records=6
Map output records=12
Map output bytes=118
Map output materialized bytes=118
Input split bytes=98
Combine input records=12
Combine output records=9
Reduce input groups=9
Reduce shuffle bytes=118
Reduce input records=9
Reduce output records=9
Spilled Records=18
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=155
CPU time spent (ms)=1430
Physical memory (bytes) snapshot=299466752
Virtual memory (bytes) snapshot=4159479808
Total committed heap usage (bytes)=141385728
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=76
File Output Format Counters
Bytes Written=76
[[email protected] classes]# /home/hadoop-2.7.4/bin/hadoop fs -ls /
Found 3 items
-rw-r--r-- 2 root supergroup 76 2017-09-02 00:57 /hdfs-input.txt
drwxr-xr-x - root supergroup 0 2017-09-02 02:12 /result-self-compile
drwx------ - root supergroup 0 2017-09-02 02:11 /tmp
[[email protected] classes]#
[[email protected] classes]#
第二次 沒有刪除package
package org.apache.hadoop.examples; import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length < 2) { System.err.println("Usage: wordcount <in> [<in>...] <out>"); System.exit(2); } Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); for (int i = 0; i < otherArgs.length - 1; ++i) { FileInputFormat.addInputPath(job, new Path(otherArgs[i])); } FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } }View Code
[[email protected] classes]# [[email protected] classes]# tree . 0 directories, 0 files [[email protected] classes]# javac -classpath .:/home/jars/* -d /home/classes/ /home/javaFile/WordCount.java [[email protected] classes]# tree . └── org └── apache └── hadoop └── examples ├── WordCount.class ├── WordCount$IntSumReducer.class └── WordCount$TokenizerMapper.class 4 directories, 3 files [[email protected] classes]# jar -cvf wordcount.jar ./* added manifest adding: org/(in = 0) (out= 0)(stored 0%) adding: org/apache/(in = 0) (out= 0)(stored 0%) adding: org/apache/hadoop/(in = 0) (out= 0)(stored 0%) adding: org/apache/hadoop/examples/(in = 0) (out= 0)(stored 0%) adding: org/apache/hadoop/examples/WordCount$TokenizerMapper.class(in = 1790) (out= 764)(deflated 57%) adding: org/apache/hadoop/examples/WordCount$IntSumReducer.class(in = 1793) (out= 749)(deflated 58%) adding: org/apache/hadoop/examples/WordCount.class(in = 1988) (out= 1050)(deflated 47%) [[email protected] classes]# /home/hadoop-2.7.4/bin/hadoop jar /home/classes/wordcount.jar org.apache.hadoop.examples.WordCount /hdfs-input.txt /result-package 17/09/02 02:20:41 INFO client.RMProxy: Connecting to ResourceManager at master/192.168.0.80:8032 17/09/02 02:20:43 INFO input.FileInputFormat: Total input paths to process : 1 17/09/02 02:20:43 INFO mapreduce.JobSubmitter: number of splits:1 17/09/02 02:20:43 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1504320356950_0011 17/09/02 02:20:43 INFO impl.YarnClientImpl: Submitted application application_1504320356950_0011 17/09/02 02:20:43 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1504320356950_0011/ 17/09/02 02:20:43 INFO mapreduce.Job: Running job: job_1504320356950_0011 17/09/02 02:20:51 INFO mapreduce.Job: Job job_1504320356950_0011 running in uber mode : false 17/09/02 02:20:51 INFO mapreduce.Job: map 0% reduce 0% 17/09/02 02:20:58 INFO mapreduce.Job: map 100% reduce 0% 17/09/02 02:21:05 INFO mapreduce.Job: map 100% reduce 100% 17/09/02 02:21:06 INFO mapreduce.Job: Job job_1504320356950_0011 completed successfully 17/09/02 02:21:06 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=118 FILE: Number of bytes written=241857 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=174 HDFS: Number of bytes written=76 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)=3828 Total time spent by all reduces in occupied slots (ms)=4312 Total time spent by all map tasks (ms)=3828 Total time spent by all reduce tasks (ms)=4312 Total vcore-milliseconds taken by all map tasks=3828 Total vcore-milliseconds taken by all reduce tasks=4312 Total megabyte-milliseconds taken by all map tasks=3919872 Total megabyte-milliseconds taken by all reduce tasks=4415488 Map-Reduce Framework Map input records=6 Map output records=12 Map output bytes=118 Map output materialized bytes=118 Input split bytes=98 Combine input records=12 Combine output records=9 Reduce input groups=9 Reduce shuffle bytes=118 Reduce input records=9 Reduce output records=9 Spilled Records=18 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=186 CPU time spent (ms)=1200 Physical memory (bytes) snapshot=297316352 Virtual memory (bytes) snapshot=4159815680 Total committed heap usage (bytes)=139595776 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=76 File Output Format Counters Bytes Written=76 [[email protected] classes]# /home/hadoop-2.7.4/bin/hadoop fs -ls / Found 4 items -rw-r--r-- 2 root supergroup 76 2017-09-02 00:57 /hdfs-input.txt drwxr-xr-x - root supergroup 0 2017-09-02 02:21 /result-package drwxr-xr-x - root supergroup 0 2017-09-02 02:12 /result-self-compile drwx------ - root supergroup 0 2017-09-02 02:11 /tmp [[email protected] classes]# [[email protected] classes]#
為啥要刪除package，就是因為有包路徑的時候 調用方式就要 xxx.xxxxx.xxx來執行，而且打包的時候就不能只打class了，目錄結構也要一並打進去
同理，自己寫的代碼也可按照這個方式執行
Linux鞏固記錄（5） hadoop 2.7.4下自己編譯代碼並運行MapReduce程序