java - Hadoop Mapreduce コードで parseDouble を使用すると、あるシステムで例外が発生しますが、他のシステムでは正常に動作しますか?

Question

Java で Hadoop Mapreduce コードを実行しています。私のシステムでは問題なく動作しますが、同じプログラムを他の人のシステム (実行する必要がある最終システム) で実行しようとすると、次のエラーが発生します。エラーはおそらくline 81Double.parseDouble() コマンドがある場所にあります。私のシステムでは完全に動作します。何が問題なのですか？

13/06/25 12:07:05 INFO input.FileInputFormat: Total input paths to process : 2
13/06/25 12:07:05 INFO util.NativeCodeLoader: Loaded the native-hadoop library
13/06/25 12:07:05 WARN snappy.LoadSnappy: Snappy native library not loaded
13/06/25 12:07:06 INFO mapred.JobClient: Running job: job_201306101543_0158
13/06/25 12:07:07 INFO mapred.JobClient:  map 0% reduce 0%
13/06/25 12:07:11 INFO mapred.JobClient:  map 100% reduce 0%
13/06/25 12:07:18 INFO mapred.JobClient:  map 100% reduce 33%
13/06/25 12:07:20 INFO mapred.JobClient: Task Id :     attempt_201306101543_0158_r_000000_0, Status : FAILED
java.lang.NullPointerException
    at sun.misc.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:1008)
    at java.lang.Double.parseDouble(Double.java:540)
    at Transpose$Reduce.reduce(Transpose.java:89)
    at Transpose$Reduce.reduce(Transpose.java:61)
    at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:176)
    at org.apache.hadoop.mapred.ReduceTask.runNewReducer(ReduceTask.java:650)
    at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:418)
    at org.apache.hadoop.mapred.Child$4.run(Child.java:255)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
    at     org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1149)
        at org.apache.hadoop.mapred.Child.main(Child.java:249)

attempt_201306101543_0158_r_000000_0: log4j:WARN No appenders could be found for logger (org.apache.hadoop.hdfs.DFSClient).
attempt_201306101543_0158_r_000000_0: log4j:WARN Please initialize the log4j system properly.
13/06/25 12:07:21 INFO mapred.JobClient:  map 100% reduce 0%
13/06/25 12:07:29 INFO mapred.JobClient:  map 100% reduce 33%
13/06/25 12:07:31 INFO mapred.JobClient: Task Id :     attempt_201306101543_0158_r_000000_1, Status : FAILED
java.lang.NullPointerException
    at sun.misc.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:1008)
    at java.lang.Double.parseDouble(Double.java:540)
    at Transpose$Reduce.reduce(Transpose.java:89)
    at Transpose$Reduce.reduce(Transpose.java:61)
    at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:176)
    at org.apache.hadoop.mapred.ReduceTask.runNewReducer(ReduceTask.java:650)
    at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:418)
    at org.apache.hadoop.mapred.Child$4.run(Child.java:255)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
    at     org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1149)
    at org.apache.hadoop.mapred.Child.main(Child.java:249)

関連コード (完全な Reduce 関数)

 public static class Reduce extends Reducer<Text, Text, Text, Text> {
        private Text mult = new Text();

    public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {

            HashMap<Integer, String> mMap = new HashMap<Integer, String>();
            HashMap<Integer, String> mMap1 = new HashMap<Integer, String>();

            for(Text value : values){
                String[] line = value.toString().split(",", 3);

            Integer row = Integer.valueOf(line[1]);

            if(line[0].equals("M")){
                mMap.put(row, line[2]);
            }
            else if(line[0].equals("Mt")){
                mMap1.put(row, line[2]);
            }       
        }

        double sum=0.0;

            for(Integer i=1; i<=mMap.size(); i++){
    String val1 = mMap.get(i);
    String val2 = mMap1.get(i);                

            double mij = Double.parseDouble(val1);
    double mjk = Double.parseDouble(val2);

    sum += mij*mjk;
            }
        String str = Double.toString(sum);
        mult.set(str);    
            context.write(key, mult);  
        }
    }