Question

我刚开始学习hadoop，并使用自定义分区程序和比较器运行hadoop map-reduce程序。我面临的问题是主要和次要排序没有在复合键上完成，更多 - 在一个部分复合键正在与其他compsite-key部分进行更改。

例如我在mapper中创建以下键

key1 -> tagA,1 
key2 -> tagA,1 
key3 -> tagA,1
key4 -> tagA,1 
key5 -> tagA,2 
key6 -> tagA,2
key7 -> tagB,1 
key8 -> tagB,1 
key9 -> tagB,1
key10 -> tagB,1 
key11 -> tagB,2 
key12 -> tagB,2

和分区器和组合器如下

    //Partitioner
public static class TaggedJoiningPartitioner implements Partitioner<Text, Text> {   
    @Override
    public int getPartition(Text key, Text value, int numPartitions) {
        String line = key.toString();
        String tokens[] = line.split(",");
        return (tokens[0].hashCode() & Integer.MAX_VALUE)% numPartitions;
    }
    @Override
    public void configure(JobConf arg0) {
        // TODO Auto-generated method stub //NOT OVERRIDING THIS METHOD
    }
}
//Comparator
public static class TaggedJoiningGroupingComparator extends WritableComparator {

    public TaggedJoiningGroupingComparator() {
        super(Text.class, true);
    }

    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        String taggedKey1[] = ((Text)a).toString().split(",");
        String taggedKey2[] = ((Text)b).toString().split(",");
        return taggedKey1[0].compareTo(taggedKey2[0]);
    }
}

在reducer中

这些键根据标签正确分组但未正确排序。 Reducer中键的顺序和内容如下：

//REDUCER 1
key1 -> tagA,1 
key2 -> tagA,1 
key3 -> tagA,1
key5 -> tagA,1 //2 changed by 1 here
key6 -> tagA,1 //2 changed by 1 here
key4 -> tagA,1 

//REDUCER 2
key7 ->  tagB,1 
key11 -> tagB,1 //2 changed by 1 here
key12 -> tagB,1 //2 changed by 1 here
key8 ->  tagB,1 
key9 ->  tagB,1
key10 -> tagB,1

尝试长时间解决但尚未成功，任何帮助都表示赞赏？

Answer 1

终于搞定了，实际上我改变了

conf.setOutputKeyComparatorClass(TaggedJoiningGroupingComparator.class);

到

conf.setOutputValueGroupingComparator(TaggedJoiningGroupingComparator.class);

同样来自hadoop API文档。 -

setOutputValueGroupingComparator(Class<? extends RawComparator> theClass)
Set the user defined RawComparator comparator for grouping keys in the input to the reduce.

复合键变了，Hadoop Map-Reduce？

1 个答案: