hadoop - Partitioner 似乎不能在单个节点上工作？

Question

我已经编写了 map reduce 代码和自定义分区。自定义分区根据某些条件对键进行排序。我在驱动程序类中设置了 setNumReduceTasks=6。但是我正在我的单台机器上测试这段代码。我只得到一个 reducer 输出文件而不是 6 个 reducer 文件。分区器不能在单机上工作吗？是否需要多节点集群才能看到自定义分区器的效果？对此有任何见解将不胜感激。

Answer 1

当你将 reducer 的数量设置为大于 1 时，Partitioner 总是工作，即使它是一个单节点集群。

我已经在单节点集群上测试了以下代码，它按预期工作:

public final class SortMapReduce extends Configured implements Tool {

public static void main(final String[] args) throws Exception {
    int res = ToolRunner.run(new Configuration(), new SortMapReduce(), args);
    System.exit(res);
}

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

    Path inputPath = new Path(args[0]);
    Path outputPath = new Path(args[1]);

    Configuration conf = super.getConf();

    Job job = Job.getInstance(conf);

    job.setJarByClass(SortMapReduce.class);
    job.setMapperClass(Map.class);
    job.setReducerClass(Reduce.class);

    job.setInputFormatClass(KeyValueTextInputFormat.class);

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

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

    job.setPartitionerClass(PersonNamePartitioner.class);

    job.setNumReduceTasks(5);

    FileInputFormat.setInputPaths(job, inputPath);
    FileOutputFormat.setOutputPath(job, outputPath);

    if (job.waitForCompletion(true)) {
        return 0;
    }
    return 1;
}

public static class Map extends Mapper<Text, Text, Person, Text> {

    private Person outputKey = new Person();

    @Override
    protected void map(Text pointID, Text firstName, Context context) throws IOException, InterruptedException {
        outputKey.set(pointID.toString(), firstName.toString());
        context.write(outputKey, firstName);
    }
}

public static class Reduce extends Reducer<Person, Text, Text, Text> {

    Text pointID = new Text();

    @Override
    public void reduce(Person key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
        pointID.set(key.getpointID());
        for (Text firstName : values) {
            context.write(pointID, firstName);
        }
    }
}

分区器类:

public class PersonNamePartitioner extends Partitioner<Person, Text> {

@Override
public int getPartition(Person key, Text value, int numPartitions) {

    return Math.abs(key.getpointID().hashCode() * 127) % numPartitions;
}

运行命令:

hadoop jar /home/hdfs/SecondarySort.jar org.test.SortMapReduce /demo/data/Customer/acct.txt /demo/data/Customer/output2

谢谢，