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本篇内容介绍了“如何用MapReduce求各个部门的总工资”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！
EMPNO ENAME JOB MGR HIREDATE SAL COMM DEPTNO
7369 SMITH CLERK 7902 17-12月-80 800 20 7499 ALLEN SALESMAN 7698 20-2月 -81 1600 300 30 7521 WARD SALESMAN 7698 22-2月 -81 1250 500 30 7566 JONES MANAGER 7839 02-4月 -81 2975 20 7654 MARTIN SALESMAN 7698 28-9月 -81 1250 1400 30 7698 BLAKE MANAGER 7839 01-5月 -81 2850 30 7782 CLARK MANAGER 7839 09-6月 -81 2450 10 7839 KING PRESIDENT 17-11月-81 5000 10 7844 TURNER SALESMAN 7698 08-9月 -81 1500 0 30 7900 JAMES CLERK 7698 03-12月-81 950 30 7902 FORD ANALYST 7566 03-12月-81 3000 20 7934 MILLER CLERK 7782 23-1月 -82 1300 10
package cn.kissoft.hadoop.week07;
import java.io.IOException;
import java.text.DateFormat;
import java.text.SimpleDateFormat;
import java.util.Date;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import cn.kissoft.hadoop.util.HdfsUtil;
/**
* Homework-01:求各个部门的总工资
*
* @author wukong(jinsong.sun@139.com)
*/
public class TotalSalaryByDeptMR extends Configured implements Tool {
public static class M extends Mapper<LongWritable, Text, Text, IntWritable> {
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
String deptno = line.substring(79).trim();
String sal = line.substring(57, 68).trim();
int salary = Integer.valueOf(sal);
context.write(new Text(deptno), new IntWritable(salary));
}
}
public static class R extends Reducer<Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
context.write(key, new IntWritable(sum));
}
}
@Override
public int run(String[] args) throws Exception {
Configuration conf = getConf();
Job job = new Job(conf, "Job-TotalSalaryByDeptMR");
// job.setJarByClass(this.getClass());
job.setMapperClass(M.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setReducerClass(R.class);
job.setOutputFormatClass(TextOutputFormat.class);
// job.setOutputKeyClass(NullWritable.class); // 指定输出的KEY的格式
job.setOutputKeyClass(Text.class); // 指定输出的KEY的格式
job.setOutputValueClass(IntWritable.class); // 指定输出的VALUE的格式
FileInputFormat.addInputPath(job, new Path(args[0])); // 输入路径
FileOutputFormat.setOutputPath(job, new Path(args[1])); // 输出路径
return job.waitForCompletion(true) ? 0 : 1;
// job.waitForCompletion(true);
// return job.isSuccessful() ? 0 : 1;
}
/**
*
* @param args hdfs://bd11:9000/user/wukong/w07/emp.txt hdfs://bd11:9000/user/wukong/w07/out01/
* @throws Exception
*/
public static void main(String[] args) throws Exception {
checkArgs(args);
HdfsUtil.rm(args[1], true);
Date start = new Date();
int res = ToolRunner.run(new Configuration(), new TotalSalaryByDeptMR(), args);
printExcuteTime(start, new Date());
System.exit(res);
}
/**
* 判断参数个数是否正确，如果无参数运行则显示以作程序说明。
*
* @param args
*/
private static void checkArgs(String[] args) {
if (args.length != 2) {
System.err.println("");
System.err.println("Usage: Test_1 < input path > < output path > ");
System.err
.println("Example: hadoop jar ~/Test_1.jar hdfs://localhost:9000/home/james/Test_1 hdfs://localhost:9000/home/james/output");
System.err.println("Counter:");
System.err.println("\t" + "LINESKIP" + "\t"
+ "Lines which are too short");
System.exit(-1);
}
}
/**
* 打印程序运行时间
*
* @param start
* @param end
*/
private static void printExcuteTime(Date start, Date end) {
DateFormat formatter = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
float time = (float) ((end.getTime() - start.getTime()) / 60000.0);
System.out.println("任务开始：" + formatter.format(start));
System.out.println("任务结束：" + formatter.format(end));
System.out.println("任务耗时：" + String.valueOf(time) + " 分钟");
}
}10 8750 20 6775 30 9400
14/08/31 23:01:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/08/31 23:01:01 WARN mapred.JobClient: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String). 14/08/31 23:01:01 INFO input.FileInputFormat: Total input paths to process : 1 14/08/31 23:01:02 WARN snappy.LoadSnappy: Snappy native library not loaded 14/08/31 23:01:02 INFO mapred.JobClient: Running job: job_local248108448_0001 14/08/31 23:01:02 INFO mapred.LocalJobRunner: Waiting for map tasks 14/08/31 23:01:02 INFO mapred.LocalJobRunner: Starting task: attempt_local248108448_0001_m_000000_0 14/08/31 23:01:02 INFO mapred.Task: Using ResourceCalculatorPlugin : null 14/08/31 23:01:02 INFO mapred.MapTask: Processing split: hdfs://bd11:9000/user/wukong/w07/emp.txt:0+1119 14/08/31 23:01:02 INFO mapred.MapTask: io.sort.mb = 100 14/08/31 23:01:02 INFO mapred.MapTask: data buffer = 79691776/99614720 14/08/31 23:01:02 INFO mapred.MapTask: record buffer = 262144/327680 14/08/31 23:01:02 INFO mapred.MapTask: Starting flush of map output 14/08/31 23:01:02 INFO mapred.MapTask: Finished spill 0 14/08/31 23:01:02 INFO mapred.Task: Task:attempt_local248108448_0001_m_000000_0 is done. And is in the process of commiting 14/08/31 23:01:02 INFO mapred.LocalJobRunner: 14/08/31 23:01:02 INFO mapred.Task: Task 'attempt_local248108448_0001_m_000000_0' done. 14/08/31 23:01:02 INFO mapred.LocalJobRunner: Finishing task: attempt_local248108448_0001_m_000000_0 14/08/31 23:01:02 INFO mapred.LocalJobRunner: Map task executor complete. 14/08/31 23:01:02 INFO mapred.Task: Using ResourceCalculatorPlugin : null 14/08/31 23:01:02 INFO mapred.LocalJobRunner: 14/08/31 23:01:02 INFO mapred.Merger: Merging 1 sorted segments 14/08/31 23:01:02 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 110 bytes 14/08/31 23:01:02 INFO mapred.LocalJobRunner: 14/08/31 23:01:02 INFO mapred.Task: Task:attempt_local248108448_0001_r_000000_0 is done. And is in the process of commiting 14/08/31 23:01:02 INFO mapred.LocalJobRunner: 14/08/31 23:01:02 INFO mapred.Task: Task attempt_local248108448_0001_r_000000_0 is allowed to commit now 14/08/31 23:01:02 INFO output.FileOutputCommitter: Saved output of task 'attempt_local248108448_0001_r_000000_0' to hdfs://bd11:9000/user/wukong/w07/out01 14/08/31 23:01:02 INFO mapred.LocalJobRunner: reduce > reduce 14/08/31 23:01:02 INFO mapred.Task: Task 'attempt_local248108448_0001_r_000000_0' done. 14/08/31 23:01:03 INFO mapred.JobClient: map 100% reduce 100% 14/08/31 23:01:03 INFO mapred.JobClient: Job complete: job_local248108448_0001 14/08/31 23:01:03 INFO mapred.JobClient: Counters: 19 14/08/31 23:01:03 INFO mapred.JobClient: File Output Format Counters 14/08/31 23:01:03 INFO mapred.JobClient: Bytes Written=24 14/08/31 23:01:03 INFO mapred.JobClient: File Input Format Counters 14/08/31 23:01:03 INFO mapred.JobClient: Bytes Read=1119 14/08/31 23:01:03 INFO mapred.JobClient: FileSystemCounters 14/08/31 23:01:03 INFO mapred.JobClient: FILE_BYTES_READ=426 14/08/31 23:01:03 INFO mapred.JobClient: HDFS_BYTES_READ=2238 14/08/31 23:01:03 INFO mapred.JobClient: FILE_BYTES_WRITTEN=138578 14/08/31 23:01:03 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=24 14/08/31 23:01:03 INFO mapred.JobClient: Map-Reduce Framework 14/08/31 23:01:03 INFO mapred.JobClient: Reduce input groups=3 14/08/31 23:01:03 INFO mapred.JobClient: Map output materialized bytes=114 14/08/31 23:01:03 INFO mapred.JobClient: Combine output records=0 14/08/31 23:01:03 INFO mapred.JobClient: Map input records=12 14/08/31 23:01:03 INFO mapred.JobClient: Reduce shuffle bytes=0 14/08/31 23:01:03 INFO mapred.JobClient: Reduce output records=3 14/08/31 23:01:03 INFO mapred.JobClient: Spilled Records=24 14/08/31 23:01:03 INFO mapred.JobClient: Map output bytes=84 14/08/31 23:01:03 INFO mapred.JobClient: Total committed heap usage (bytes)=326107136 14/08/31 23:01:03 INFO mapred.JobClient: SPLIT_RAW_BYTES=105 14/08/31 23:01:03 INFO mapred.JobClient: Map output records=12 14/08/31 23:01:03 INFO mapred.JobClient: Combine input records=0 14/08/31 23:01:03 INFO mapred.JobClient: Reduce input records=12 任务开始：2014-08-31 23:01:01 任务结束：2014-08-31 23:01:03 任务耗时：0.024416666 分钟
“如何用MapReduce求各个部门的总工资”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注亿速云网站，小编将为大家输出更多高质量的实用文章！
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