map-reduce代码

mbzjlibv  于 2021-06-03  发布在  Hadoop
关注(0)|答案(1)|浏览(402)

我有一个场景，我想计算一个值的递减。
我的输入文件是一个csv格式：key，value，timestamp

1,600,2014-01-20 10:20:00
1,1200,2014-01-20 10:30:00
...
2,2400,2014-01-30 11:20:00
2,3600,2014-01-30 11:30:00
...

可以有多个键，每个键可以有多个值和一个时间戳来记录它。
我需要计算每个键的值在一段时间内的衰减。

Decline = (V2-V1) / (t2-t1)

这里，时间t的单位是秒。
我期望的结果是，

1,1
...
2,2
...

我写的mr代码是这样的，

import java.io.IOException;
import java.util.*;
import java.text.SimpleDateFormat;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class TestMR 
{    
    public static class Map extends Mapper<LongWritable,Text,Text,Text>
    { 
        public void map(LongWritable key, Text line, Context context) throws IOException, InterruptedException
        {
            String [] split = line.toString().split(",");

            long t1 = 0;
            SimpleDateFormat df = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
            try 
            {
                t1 = df.parse(split[2]).getTime() / 1000;
            }
            catch (java.text.ParseException e) 
            {
                System.out.println("Unable to parse date string: " + split[2]);
            }

            StringBuffer sb = new StringBuffer(split[1]+","+t1);

            context.write(new Text(split[0]), new Text(sb.toString()));
        }           
    }

    public static class Reduce extends Reducer<Text,Text,Text,Text>
    {
        public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException
        {
            Iterator iter = values.iterator();
            while(iter.hasNext())
            {
                String [] tmpBuf_1 = iter.next().toString().split(",");
                if(tmpBuf_1.length != 2)   
                    continue;
                String v1 = tmpBuf_1[0];
                double t1 = Double.parseDouble(tmpBuf_1[1]);

                if(!iter.hasNext())   
                    break;  

                String [] tmpBuf_2 = iter.next().toString().split(",");       
                if(tmpBuf_2.length != 2)   
                    continue;
                String v2 = tmpBuf_2[0];
                double t2 = Double.parseDouble(tmpBuf_2[1]);

                double vDiff = Double.parseDouble(v2) - Double.parseDouble(v1);    
                double tDiff = t2 - t1;

                if(tDiff == 0)
                    continue;

                double declineV = vDiff / tDiff;

                context.write(key, new Text(String.valueOf(declineV)));
            }
        }
    }

    public static int main(String[] args) throws Exception
    {
        // Get the default configuration object 
        Configuration conf = new Configuration();

        // Add resources 
        conf.addResource("hdfs-default.xml");
        conf.addResource("hdfs-site.xml");
        conf.addResource("mapred-default.xml");
        conf.addResource("mapred-site.xml");
        conf.set("mapred.job.tracker", "local");

        Job job = new Job(conf);
        job.setJobName("TestMR");
        job.setJarByClass(TestMR.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        job.setMapperClass(Map.class);
        job.setCombinerClass(Reduce.class);
        job.setReducerClass(Reduce.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        TextInputFormat.setInputPaths(job, new Path(args[0]));
        TextOutputFormat.setOutputPath(job, new Path(args[1]));  

        // Set the jar file to run 
        job.setJarByClass(Example.class);

        // Submit the job 
        Date startTime = new Date();
        System.out.println("Job started: " + startTime);    
        int exitCode = job.waitForCompletion(true) ? 0 : 1;

        if( exitCode == 0) 
        {            
            Date end_time = new Date();
            System.out.println("Job ended: " + end_time);
            System.out.println("The job took " + (end_time.getTime() - startTime.getTime()) / 1000 + " seconds.");                       
        } 
        else {
            System.out.println("Job Failed!!!");
        }

        return exitCode;
    }
}

我运行mr作业时没有输出！以下是命令跟踪：

Job started: Sat Feb 08 16:36:07 PST 2014
14/02/08 16:36:07 WARN conf.Configuration: session.id is deprecated. Instead, use dfs.metrics.session-id
14/02/08 16:36:07 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
14/02/08 16:36:07 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
14/02/08 16:36:07 INFO input.FileInputFormat: Total input paths to process : 1
14/02/08 16:36:08 INFO mapred.JobClient: Running job: job_local2110196638_0001
14/02/08 16:36:08 INFO mapred.LocalJobRunner: OutputCommitter set in config null
14/02/08 16:36:08 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
14/02/08 16:36:08 INFO mapred.LocalJobRunner: Waiting for map tasks
14/02/08 16:36:08 INFO mapred.LocalJobRunner: Starting task: attempt_local2110196638_0001_m_000000_0
14/02/08 16:36:08 WARN mapreduce.Counters: Group org.apache.hadoop.mapred.Task$Counter is deprecated. Use org.apache.hadoop.mapreduce.TaskCounter instead
14/02/08 16:36:08 INFO util.ProcessTree: setsid exited with exit code 0
14/02/08 16:36:08 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@524c71d2
14/02/08 16:36:08 INFO mapred.MapTask: Processing split: hdfs://localhost.localdomain:8020/user/cloudera/input.csv:0+33554432
14/02/08 16:36:08 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
14/02/08 16:36:08 INFO mapred.MapTask: io.sort.mb = 50
14/02/08 16:36:08 INFO mapred.MapTask: data buffer = 39845888/49807360
14/02/08 16:36:08 INFO mapred.MapTask: record buffer = 131072/163840
14/02/08 16:36:09 INFO mapred.JobClient:  map 0% reduce 0%
In MAP!!
245,1334603716
14/02/08 16:36:14 INFO mapred.LocalJobRunner: 
14/02/08 16:36:15 INFO mapred.JobClient:  map 9% reduce 0%
14/02/08 16:36:16 INFO mapred.MapTask: Spilling map output: record full = true
14/02/08 16:36:16 INFO mapred.MapTask: bufstart = 0; bufend = 2620494; bufvoid = 49807360
14/02/08 16:36:16 INFO mapred.MapTask: kvstart = 0; kvend = 131072; length = 163840
14/02/08 16:36:16 INFO compress.CodecPool: Got brand-new compressor [.snappy]
In REDUCE!!
14/02/08 16:36:17 INFO mapred.LocalJobRunner: 
14/02/08 16:36:17 INFO mapred.LocalJobRunner: 
14/02/08 16:36:17 INFO mapred.MapTask: Starting flush of map output
14/02/08 16:36:18 INFO mapred.JobClient:  map 49% reduce 0%
14/02/08 16:36:18 INFO mapred.MapTask: Finished spill 0
14/02/08 16:36:18 INFO mapred.MapTask: Finished spill 1
14/02/08 16:36:18 INFO mapred.Merger: Merging 2 sorted segments
14/02/08 16:36:18 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
14/02/08 16:36:18 INFO compress.CodecPool: Got brand-new decompressor [.snappy]
14/02/08 16:36:18 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 339763 bytes
14/02/08 16:36:19 INFO mapred.Task: Task:attempt_local2110196638_0001_m_000000_0 is done. And is in the process of commiting
14/02/08 16:36:19 INFO mapred.LocalJobRunner: 
14/02/08 16:36:19 INFO mapred.Task: Task 'attempt_local2110196638_0001_m_000000_0' done.
14/02/08 16:36:19 INFO mapred.LocalJobRunner: Finishing task: attempt_local2110196638_0001_m_000000_0
14/02/08 16:36:19 INFO mapred.LocalJobRunner: Starting task: attempt_local2110196638_0001_m_000001_0
14/02/08 16:36:19 WARN mapreduce.Counters: Group org.apache.hadoop.mapred.Task$Counter is deprecated. Use org.apache.hadoop.mapreduce.TaskCounter instead
14/02/08 16:36:19 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@56a6cbf7
14/02/08 16:36:19 INFO mapred.MapTask: Processing split: hdfs://localhost.localdomain:8020/user/cloudera/input.csv:33554432+13261402
14/02/08 16:36:19 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
14/02/08 16:36:19 INFO mapred.MapTask: io.sort.mb = 50
14/02/08 16:36:19 INFO mapred.MapTask: data buffer = 39845888/49807360
14/02/08 16:36:19 INFO mapred.MapTask: record buffer = 131072/163840
14/02/08 16:36:20 INFO mapred.JobClient:  map 50% reduce 0%
14/02/08 16:36:20 INFO mapred.LocalJobRunner: 
14/02/08 16:36:20 INFO mapred.MapTask: Starting flush of map output
14/02/08 16:36:20 INFO mapred.MapTask: Finished spill 0
14/02/08 16:36:20 INFO mapred.Task: Task:attempt_local2110196638_0001_m_000001_0 is done. And is in the process of commiting
14/02/08 16:36:20 INFO mapred.LocalJobRunner: 
14/02/08 16:36:20 INFO mapred.Task: Task 'attempt_local2110196638_0001_m_000001_0' done.
14/02/08 16:36:20 INFO mapred.LocalJobRunner: Finishing task: attempt_local2110196638_0001_m_000001_0
14/02/08 16:36:20 INFO mapred.LocalJobRunner: Map task executor complete.
14/02/08 16:36:20 WARN mapreduce.Counters: Group org.apache.hadoop.mapred.Task$Counter is deprecated. Use org.apache.hadoop.mapreduce.TaskCounter instead
14/02/08 16:36:20 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@64c5e2cf
14/02/08 16:36:20 INFO mapred.LocalJobRunner: 
14/02/08 16:36:20 INFO mapred.Merger: Merging 2 sorted segments
14/02/08 16:36:20 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 496064 bytes
14/02/08 16:36:20 INFO mapred.LocalJobRunner: 
14/02/08 16:36:21 INFO mapred.Task: Task:attempt_local2110196638_0001_r_000000_0 is done. And is in the process of commiting
14/02/08 16:36:21 INFO mapred.LocalJobRunner: 
14/02/08 16:36:21 INFO mapred.Task: Task attempt_local2110196638_0001_r_000000_0 is allowed to commit now
14/02/08 16:36:21 INFO output.FileOutputCommitter: Saved output of task 'attempt_local2110196638_0001_r_000000_0' to /user/cloudera/output
14/02/08 16:36:21 INFO mapred.LocalJobRunner: reduce > reduce
14/02/08 16:36:21 INFO mapred.Task: Task 'attempt_local2110196638_0001_r_000000_0' done.
14/02/08 16:36:21 INFO mapred.JobClient:  map 100% reduce 100%
14/02/08 16:36:21 INFO mapred.JobClient: Job complete: job_local2110196638_0001
14/02/08 16:36:21 INFO mapred.JobClient: Counters: 25
14/02/08 16:36:21 INFO mapred.JobClient:   File System Counters
14/02/08 16:36:21 INFO mapred.JobClient:     FILE: Number of bytes read=1541573
14/02/08 16:36:21 INFO mapred.JobClient:     FILE: Number of bytes written=2668157
14/02/08 16:36:21 INFO mapred.JobClient:     FILE: Number of read operations=0
14/02/08 16:36:21 INFO mapred.JobClient:     FILE: Number of large read operations=0
14/02/08 16:36:21 INFO mapred.JobClient:     FILE: Number of write operations=0
14/02/08 16:36:21 INFO mapred.JobClient:     HDFS: Number of bytes read=127382708
14/02/08 16:36:21 INFO mapred.JobClient:     HDFS: Number of bytes written=0
14/02/08 16:36:21 INFO mapred.JobClient:     HDFS: Number of read operations=17
14/02/08 16:36:21 INFO mapred.JobClient:     HDFS: Number of large read operations=0
14/02/08 16:36:21 INFO mapred.JobClient:     HDFS: Number of write operations=4
14/02/08 16:36:21 INFO mapred.JobClient:   Map-Reduce Framework
14/02/08 16:36:21 INFO mapred.JobClient:     Map input records=419661
14/02/08 16:36:21 INFO mapred.JobClient:     Map output records=202114
14/02/08 16:36:21 INFO mapred.JobClient:     Map output bytes=4041067
14/02/08 16:36:21 INFO mapred.JobClient:     Input split bytes=292
14/02/08 16:36:21 INFO mapred.JobClient:     Combine input records=202114
14/02/08 16:36:21 INFO mapred.JobClient:     Combine output records=95846
14/02/08 16:36:21 INFO mapred.JobClient:     Reduce input groups=43
14/02/08 16:36:21 INFO mapred.JobClient:     Reduce shuffle bytes=0
14/02/08 16:36:21 INFO mapred.JobClient:     Reduce input records=95846
14/02/08 16:36:21 INFO mapred.JobClient:     Reduce output records=0
14/02/08 16:36:21 INFO mapred.JobClient:     Spilled Records=259510
14/02/08 16:36:21 INFO mapred.JobClient:     CPU time spent (ms)=0
14/02/08 16:36:21 INFO mapred.JobClient:     Physical memory (bytes) snapshot=0
14/02/08 16:36:21 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=0
14/02/08 16:36:21 INFO mapred.JobClient:     Total committed heap usage (bytes)=376516608
Job ended: Sat Feb 08 16:36:21 PST 2014
The job took 13 seconds.

我能看到的一件事是，reduce是在map作业完成之前发生的。
你认为这会引起问题吗？？
如果是，有没有办法说reduce要先等Map完成？
如果没有，上面的代码会出什么问题？

Javahadoopmapreducereducecloudera

1条答案

按热度按时间
ncecgwcz

ncecgwcz1#

（编辑：删除之前不正确的解释）
你正在申请 Reduce 作为合路器和减速器。合并器位工作，但输出会直接反馈到同一个类中，在该类中，所有内容都没有2列，因此每行都被跳过。你不能把它当作组合器。
此代码还依赖于按时间顺序查看事件，但似乎没有任何关于如何构造事件的信息可以保证这一点。
（这里有一些小的奇怪的东西，比如一个毫无意义的 StringBuffer （应该是 StringBuilder 无论如何），在异常后继续错误，不导入 ParseException ，并将一个long解析为一个double）

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