Question

我有一个mapreduce程序，有两个作业。第二份工作的关键和价值来自第一份工作。但我认为第二张地图没有得到第一份工作的结果。换句话说，我认为我的第二份工作没有读到我第一份工作的结果......我该怎么办？

这是代码：

public class dewpoint extends Configured implements Tool
{
  private static final Logger logger = LoggerFactory.getLogger(dewpoint.class);

static final String KEYSPACE = "weather";
static final String COLUMN_FAMILY = "user";
private static final String OUTPUT_PATH1 = "/tmp/intermediate1";
private static final String OUTPUT_PATH2 = "/tmp/intermediate2";
private static final String OUTPUT_PATH3 = "/tmp/intermediate3";
private static final String INPUT_PATH1 = "/tmp/intermediate1";

public static void main(String[] args) throws Exception
{

    ToolRunner.run(new Configuration(), new dewpoint(), args);
    System.exit(0);
}

///////////////////////////////////////////////////////////

public static class dpmap1 extends Mapper<Map<String, ByteBuffer>, Map<FloatWritable, ByteBuffer>, Text, DoubleWritable>
{
    DoubleWritable val1 = new DoubleWritable();
    Text word = new Text();
    String date;
    float temp;
    public void map(Map<String, ByteBuffer> keys, Map<FloatWritable, ByteBuffer> columns, Context context) throws IOException, InterruptedException
    {

         for (Entry<String, ByteBuffer> key : keys.entrySet())
         {
             //System.out.println(key.getKey());
             if (!"date".equals(key.getKey()))
                 continue;
             date = ByteBufferUtil.string(key.getValue());
             word.set(date);
         }


        for (Entry<FloatWritable, ByteBuffer> column : columns.entrySet())
        {
            if (!"temprature".equals(column.getKey()))
                continue;
            temp = ByteBufferUtil.toFloat(column.getValue());
            val1.set(temp);
            //System.out.println(temp);
       }
        context.write(word, val1);
    }
}

///////////////////////////////////////////////////////////

public static class dpred1 extends Reducer<Text, DoubleWritable, Text, DoubleWritable>
{
   public void reduce(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException
    {
        double beta = 17.62;
        double landa = 243.12;
        DoubleWritable result1 = new DoubleWritable();
        DoubleWritable result2 = new DoubleWritable();
         for (DoubleWritable val : values){
         //  System.out.println(val.get());
           beta *= val.get();
           landa+=val.get();
           }
         result1.set(beta);
         result2.set(landa);

         context.write(key, result1);
         context.write(key, result2);
     }
}
///////////////////////////////////////////////////////////

public static class dpmap2 extends Mapper <Text, DoubleWritable, Text, DoubleWritable>{

    Text key2 = new Text();
    double temp1, temp2 =0;

    public void map(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException {
        String[] sp = values.toString().split("\t");
        for (int i=0; i< sp.length; i+=4)
            //key2.set(sp[i]);
        System.out.println(sp[i]);
            for(int j=1;j< sp.length; j+=4)
                temp1 = Double.valueOf(sp[j]);
                for (int k=3;k< sp.length; k+=4)
                    temp2 = Double.valueOf(sp[k]);
        context.write(key2, new DoubleWritable(temp2/temp1));

    }
}       

///////////////////////////////////////////////////////////


public static class dpred2 extends Reducer<Text, DoubleWritable, Text, DoubleWritable>
{
   public void reduce(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException
    {

       double alpha = 6.112;
        double tmp = 0;
        DoubleWritable result3 = new DoubleWritable();
         for (DoubleWritable val : values){
             System.out.println(val.get()); 
             tmp = alpha*(Math.pow(Math.E, val.get()));

         }
         result3.set(tmp);
         context.write(key, result3);


  }
}


///////////////////////////////////////////////////////////


public int run(String[] args) throws Exception
{

     Job job1 = new Job(getConf(), "DewPoint");
     job1.setJarByClass(dewpoint.class);
     job1.setMapperClass(dpmap1.class);
     job1.setOutputFormatClass(SequenceFileOutputFormat.class);
     job1.setCombinerClass(dpred1.class);
     job1.setReducerClass(dpred1.class);
     job1.setMapOutputKeyClass(Text.class);
     job1.setMapOutputValueClass(DoubleWritable.class);
     job1.setOutputKeyClass(Text.class);
     job1.setOutputValueClass(DoubleWritable.class);
     FileOutputFormat.setOutputPath(job1, new Path(OUTPUT_PATH1));


     job1.setInputFormatClass(CqlPagingInputFormat.class);

     ConfigHelper.setInputRpcPort(job1.getConfiguration(), "9160");
     ConfigHelper.setInputInitialAddress(job1.getConfiguration(), "localhost");
     ConfigHelper.setInputColumnFamily(job1.getConfiguration(), KEYSPACE, COLUMN_FAMILY);
     ConfigHelper.setInputPartitioner(job1.getConfiguration(), "Murmur3Partitioner");

     CqlConfigHelper.setInputCQLPageRowSize(job1.getConfiguration(), "3");
     job1.waitForCompletion(true);

     /***************************************/

     if (job1.isSuccessful()){
     Job job2 = new Job(getConf(), "DewPoint");
     job2.setJarByClass(dewpoint.class);
     job2.setMapperClass(dpmap2.class);
     job2.setCombinerClass(dpred2.class);
     job2.setReducerClass(dpred2.class);
     job2.setMapOutputKeyClass(Text.class);
     job2.setMapOutputValueClass(DoubleWritable.class);
     job2.setOutputKeyClass(Text.class);
     job2.setOutputValueClass(DoubleWritable.class);
     job2.setOutputFormatClass(TextOutputFormat.class);
     job2.setInputFormatClass(SequenceFileInputFormat.class);
     FileInputFormat.addInputPath(job2, new Path(OUTPUT_PATH1));
     FileOutputFormat.setOutputPath(job2, new Path(OUTPUT_PATH2));
     job2.waitForCompletion(true);
     }
     ///////////////////////////////////////////////////

     return 0;
  }
}

例如在我的第二个地图阶段，当我执行System.out.println（键）时，它不打印任何东西，并且在reduce结果中值为'infinity'....

这是日志：

13/10/25 11:33:37 INFO util.NativeCodeLoader: Loaded the native-hadoop library
13/10/25 11:33:37 WARN mapred.JobClient: No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
13/10/25 11:33:40 INFO mapred.JobClient: Running job: job_local1294015510_0001
13/10/25 11:33:41 INFO mapred.LocalJobRunner: Waiting for map tasks
13/10/25 11:33:41 INFO mapred.LocalJobRunner: Starting task: attempt_local1294015510_0001_m_000000_0
13/10/25 11:33:41 INFO util.ProcessTree: setsid exited with exit code 0
13/10/25 11:33:41 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@190a0d6
13/10/25 11:33:41 INFO mapred.MapTask: Processing split: ColumnFamilySplit((-9223372036854775808, '1684704676388456087] @[localhost])
13/10/25 11:33:41 INFO mapred.MapTask: io.sort.mb = 100
13/10/25 11:33:41 INFO mapred.JobClient:  map 0% reduce 0%
13/10/25 11:33:43 INFO mapred.MapTask: data buffer = 79691776/99614720
13/10/25 11:33:43 INFO mapred.MapTask: record buffer = 262144/327680
13/10/25 11:33:44 INFO mapred.MapTask: Starting flush of map output
13/10/25 11:33:44 INFO mapred.MapTask: Finished spill 0
13/10/25 11:33:44 INFO mapred.Task: Task:attempt_local1294015510_0001_m_000000_0 is done. And is in the process of commiting
13/10/25 11:33:44 INFO mapred.LocalJobRunner: 
13/10/25 11:33:44 INFO mapred.Task: Task 'attempt_local1294015510_0001_m_000000_0' done.
13/10/25 11:33:44 INFO mapred.LocalJobRunner: Finishing task: attempt_local1294015510_0001_m_000000_0
13/10/25 11:33:44 INFO mapred.LocalJobRunner: Starting task: attempt_local1294015510_0001_m_000001_0
13/10/25 11:33:44 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@9aba32
13/10/25 11:33:44 INFO mapred.MapTask: Processing split: ColumnFamilySplit((1684704676388456087, '-9223372036854775808] @[localhost])
13/10/25 11:33:44 INFO mapred.MapTask: io.sort.mb = 100
13/10/25 11:33:47 INFO mapred.JobClient:  map 50% reduce 0%
13/10/25 11:33:47 INFO mapred.MapTask: data buffer = 79691776/99614720
13/10/25 11:33:47 INFO mapred.MapTask: record buffer = 262144/327680
13/10/25 11:33:47 INFO mapred.MapTask: Starting flush of map output
13/10/25 11:33:47 INFO mapred.MapTask: Finished spill 0
13/10/25 11:33:47 INFO mapred.Task: Task:attempt_local1294015510_0001_m_000001_0 is done. And is in the process of commiting
13/10/25 11:33:47 INFO mapred.LocalJobRunner: 
13/10/25 11:33:47 INFO mapred.Task: Task 'attempt_local1294015510_0001_m_000001_0' done.
13/10/25 11:33:47 INFO mapred.LocalJobRunner: Finishing task:  attempt_local1294015510_0001_m_000001_0
13/10/25 11:33:47 INFO mapred.LocalJobRunner: Map task executor complete.
13/10/25 11:33:48 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@17f11fb
13/10/25 11:33:48 INFO mapred.LocalJobRunner: 
13/10/25 11:33:48 INFO mapred.Merger: Merging 2 sorted segments
13/10/25 11:33:48 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 204 bytes
13/10/25 11:33:48 INFO mapred.LocalJobRunner: 
13/10/25 11:33:48 INFO mapred.Task: Task:attempt_local1294015510_0001_r_000000_0 is done. And is in the process of commiting
13/10/25 11:33:48 INFO mapred.LocalJobRunner: 
13/10/25 11:33:48 INFO mapred.Task: Task attempt_local1294015510_0001_r_000000_0 is allowed to commit now
13/10/25 11:33:48 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1294015510_0001_r_000000_0' to /tmp/intermediate1
13/10/25 11:33:48 INFO mapred.LocalJobRunner: reduce > reduce
13/10/25 11:33:48 INFO mapred.Task: Task 'attempt_local1294015510_0001_r_000000_0' done.
13/10/25 11:33:48 INFO mapred.JobClient:  map 100% reduce 100%
13/10/25 11:33:48 INFO mapred.JobClient: Job complete: job_local1294015510_0001
13/10/25 11:33:48 INFO mapred.JobClient: Counters: 20
13/10/25 11:33:48 INFO mapred.JobClient:   File Output Format Counters 
13/10/25 11:33:48 INFO mapred.JobClient:     Bytes Written=324
13/10/25 11:33:48 INFO mapred.JobClient:   FileSystemCounters
13/10/25 11:33:48 INFO mapred.JobClient:     FILE_BYTES_READ=1503
13/10/25 11:33:48 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=161938
13/10/25 11:33:48 INFO mapred.JobClient:   File Input Format Counters 
13/10/25 11:33:48 INFO mapred.JobClient:     Bytes Read=0
13/10/25 11:33:48 INFO mapred.JobClient:   Map-Reduce Framework
13/10/25 11:33:48 INFO mapred.JobClient:     Map output materialized bytes=212
13/10/25 11:33:48 INFO mapred.JobClient:     Map input records=8
13/10/25 11:33:48 INFO mapred.JobClient:     Reduce shuffle bytes=0
13/10/25 11:33:48 INFO mapred.JobClient:     Spilled Records=24
13/10/25 11:33:48 INFO mapred.JobClient:     Map output bytes=120
13/10/25 11:33:48 INFO mapred.JobClient:     Total committed heap usage (bytes)=485359616
13/10/25 11:33:48 INFO mapred.JobClient:     CPU time spent (ms)=0
13/10/25 11:33:48 INFO mapred.JobClient:     SPLIT_RAW_BYTES=208
13/10/25 11:33:48 INFO mapred.JobClient:     Combine input records=8
13/10/25 11:33:48 INFO mapred.JobClient:     Reduce input records=12
13/10/25 11:33:48 INFO mapred.JobClient:     Reduce input groups=5
13/10/25 11:33:48 INFO mapred.JobClient:     Combine output records=12
13/10/25 11:33:48 INFO mapred.JobClient:     Physical memory (bytes) snapshot=0
13/10/25 11:33:48 INFO mapred.JobClient:     Reduce output records=10
13/10/25 11:33:48 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=0
13/10/25 11:33:48 INFO mapred.JobClient:     Map output records=8
13/10/25 11:33:49 WARN mapred.JobClient: No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
13/10/25 11:33:49 INFO input.FileInputFormat: Total input paths to process : 1
13/10/25 11:33:49 INFO mapred.JobClient: Running job: job_local600426365_0002
13/10/25 11:33:49 INFO mapred.LocalJobRunner: Waiting for map tasks
13/10/25 11:33:49 INFO mapred.LocalJobRunner: Starting task: attempt_local600426365_0002_m_000000_0
13/10/25 11:33:49 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@18d30fb
13/10/25 11:33:49 INFO mapred.MapTask: Processing split: file:/tmp/intermediate1/part-r-00000:0+312
13/10/25 11:33:49 INFO mapred.MapTask: io.sort.mb = 100
13/10/25 11:33:50 INFO mapred.MapTask: data buffer = 79691776/99614720
13/10/25 11:33:50 INFO mapred.MapTask: record buffer = 262144/327680
13/10/25 11:33:50 INFO mapred.MapTask: Starting flush of map output
13/10/25 11:33:50 INFO mapred.MapTask: Finished spill 0
13/10/25 11:33:50 INFO mapred.Task: Task:attempt_local600426365_0002_m_000000_0 is done. And is in the process of commiting
13/10/25 11:33:50 INFO mapred.LocalJobRunner: 
13/10/25 11:33:50 INFO mapred.Task: Task 'attempt_local600426365_0002_m_000000_0' done.
13/10/25 11:33:50 INFO mapred.LocalJobRunner: Finishing task: attempt_local600426365_0002_m_000000_0
13/10/25 11:33:50 INFO mapred.LocalJobRunner: Map task executor complete.
13/10/25 11:33:50 INFO mapred.Task:  Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@d75c47
13/10/25 11:33:50 INFO mapred.LocalJobRunner: 
13/10/25 11:33:50 INFO mapred.Merger: Merging 1 sorted segments
13/10/25 11:33:50 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 84 bytes
13/10/25 11:33:50 INFO mapred.LocalJobRunner: 
13/10/25 11:33:50 INFO mapred.Task: Task:attempt_local600426365_0002_r_000000_0 is done. And is in the process of commiting
13/10/25 11:33:50 INFO mapred.LocalJobRunner: 
13/10/25 11:33:50 INFO mapred.Task: Task attempt_local600426365_0002_r_000000_0 is allowed to commit now
13/10/25 11:33:50 INFO output.FileOutputCommitter: Saved output of task 'attempt_local600426365_0002_r_000000_0' to /tmp/intermediate2
13/10/25 11:33:50 INFO mapred.LocalJobRunner: reduce > reduce
13/10/25 11:33:50 INFO mapred.Task: Task 'attempt_local600426365_0002_r_000000_0' done.
13/10/25 11:33:50 INFO mapred.JobClient:  map 100% reduce 100%
13/10/25 11:33:50 INFO mapred.JobClient: Job complete: job_local600426365_0002
13/10/25 11:33:50 INFO mapred.JobClient: Counters: 20
13/10/25 11:33:50 INFO mapred.JobClient:   File Output Format Counters 
13/10/25 11:33:50 INFO mapred.JobClient:     Bytes Written=89
13/10/25 11:33:50 INFO mapred.JobClient:   File Input Format Counters 
13/10/25 11:33:50 INFO mapred.JobClient:     Bytes Read=324
13/10/25 11:33:50 INFO mapred.JobClient:   FileSystemCounters
13/10/25 11:33:50 INFO mapred.JobClient:     FILE_BYTES_READ=2486
13/10/25 11:33:50 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=213321
13/10/25 11:33:50 INFO mapred.JobClient:   Map-Reduce Framework
13/10/25 11:33:50 INFO mapred.JobClient:     Map output materialized bytes=88
13/10/25 11:33:50 INFO mapred.JobClient:     Map input records=10
13/10/25 11:33:50 INFO mapred.JobClient:     Reduce shuffle bytes=0
13/10/25 11:33:50 INFO mapred.JobClient:     Spilled Records=10
13/10/25 11:33:50 INFO mapred.JobClient:     Map output bytes=144
13/10/25 11:33:50 INFO mapred.JobClient:     CPU time spent (ms)=0
13/10/25 11:33:50 INFO mapred.JobClient:     Total committed heap usage (bytes)=538705920
13/10/25 11:33:50 INFO mapred.JobClient:     Combine input records=10
13/10/25 11:33:50 INFO mapred.JobClient:     SPLIT_RAW_BYTES=101
13/10/25 11:33:50 INFO mapred.JobClient:     Reduce input records=5
13/10/25 11:33:50 INFO mapred.JobClient:     Reduce input groups=5
13/10/25 11:33:50 INFO mapred.JobClient:     Combine output records=5
13/10/25 11:33:50 INFO mapred.JobClient:     Physical memory (bytes) snapshot=0
13/10/25 11:33:50 INFO mapred.JobClient:     Reduce output records=5
13/10/25 11:33:50 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=0
13/10/25 11:33:50 INFO mapred.JobClient:     Map output records=10

Answer 1

它与dpmap2中的for循环中缺少{}有什么关系吗？

编辑：我想我明白了问题所在。在你的第二个映射器中你会发出temp2 / temp1 ，因为你的最终结果中的无穷大意味着 temp1 = 0 我认为你需要打印sp.length我认为你会发现长度为1，这意味着temp1 = 0值永远不会改变。

map阶段不读取中间结果

1 个答案: