hadoop - CombineFileInputFormat 始终只启动一个映射 Hadoop 1.2.1

Question

我正在尝试使用测试 CombineFileInputFormat 来处理每个 8 MB 的几个小文件(20 个文件)。我遵循了此 blog 中给出的示例.我能够实现和测试它。最终结果是正确的。但令我惊讶的是，它总是以一张 map 结束。我尝试将属性“mapred.max.split.size”设置为各种值，如 16MB、32MB 等(当然以字节为单位)但没有成功。还有什么我需要做的吗？或者这是正确的行为吗？

我正在运行一个默认复制为 2 的双节点集群。下面给出的是开发的代码。非常感谢任何帮助。

package inverika.test.retail;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
import org.apache.hadoop.mapreduce.Reducer;

public class CategoryCount {

    public static class CategoryMapper
        extends Mapper<LongWritable, Text, Text, IntWritable>    {

        private final static IntWritable one = new IntWritable(1);
        private String[] columns = new String[8];

        @Override
        public void map(LongWritable key, Text value, Context context)
                throws     IOException, InterruptedException {
            columns = value.toString().split(",");  
            context.write(new Text(columns[4]), one);
        }
    }

    public static class CategoryReducer
        extends Reducer< Text, IntWritable, Text, IntWritable>    {

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

                int sum = 0;

                for (IntWritable value :  values) {
                        sum += value.get();
                }
               context.write(key, new IntWritable(sum));
        }
    }

    public static void main(String args[]) throws Exception    {
        if (args.length != 2)  {
                System.err.println("Usage: CategoryCount <input Path> <output Path>");
                System.exit(-1);
        } 

        Configuration conf = new Configuration();
        conf.set("mapred.textoutputformat.separator", ",");
        conf.set("mapred.max.split.size", "16777216");   // 16 MB

        Job job = new Job(conf, "Retail Category Count");
        job.setJarByClass(CategoryCount.class);
        job.setMapperClass(CategoryMapper.class);
        job.setReducerClass(CategoryReducer.class);
        job.setInputFormatClass(CombinedInputFormat.class);
        //CombineFileInputFormat.setMaxInputSplitSize(job, 16777216);
        CombinedInputFormat.setMaxInputSplitSize(job, 16777216);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        FileInputFormat.addInputPath(job, new Path(args[0]) );
        FileOutputFormat.setOutputPath(job, new Path(args[1]) );
        //job.submit();
        //System.exit(job.waitForCompletion(false) ?  0 : 1);
        System.exit(job.waitForCompletion(true) ?  0 : 1);
    }
}

这是实现的 CombinedFileInputFormat

package inverika.test.retail;

import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.CombineFileRecordReader;
import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat;

public class CombinedInputFormat extends CombineFileInputFormat<LongWritable, Text> {

    @Override
    public RecordReader<LongWritable, Text>
            createRecordReader(InputSplit split, TaskAttemptContext context)
                    throws IOException {

        CombineFileRecordReader<LongWritable, Text> reader = 
                new CombineFileRecordReader<LongWritable, Text>(
                        (CombineFileSplit) split, context, myCombineFileRecordReader.class);        
        return reader;
    }

    public static class myCombineFileRecordReader extends RecordReader<LongWritable, Text> {
        private LineRecordReader lineRecordReader = new LineRecordReader();

        public myCombineFileRecordReader(CombineFileSplit split, 
                TaskAttemptContext context, Integer index) throws IOException {

            FileSplit fileSplit = new FileSplit(split.getPath(index), 
                                                split.getOffset(index),
                                                split.getLength(index), 
                                                split.getLocations());
            lineRecordReader.initialize(fileSplit, context);
        }

        @Override
        public void initialize(InputSplit inputSplit, TaskAttemptContext context)
                throws IOException, InterruptedException {
            //linerecordReader.initialize(inputSplit, context);
        }

        @Override
        public void close() throws IOException {
            lineRecordReader.close();
        }

        @Override
        public float getProgress() throws IOException {
            return lineRecordReader.getProgress();
        }

        @Override
        public LongWritable getCurrentKey() throws IOException,
                InterruptedException {
            return lineRecordReader.getCurrentKey();
        }

        @Override
        public Text getCurrentValue() throws IOException, InterruptedException {
            return lineRecordReader.getCurrentValue();
        }

        @Override
        public boolean nextKeyValue() throws IOException, InterruptedException {
            return lineRecordReader.nextKeyValue();
        }        
    }
}

Answer 1

当使用CombineFileInputFormat 作为输入格式类时，您需要设置最大拆分大小。或者，当所有 block 都来自同一个机架时，您可能会得到恰好 ONLY ONE 映射器。

您可以通过以下方式之一实现此目的:

• 调用CombineFileInputFormat.setMaxSplitSize()方法

• 设置 mapreduce.input.fileinputformat.split.maxsize 或 mapred.max.split.size(已弃用)配置参数
  例如，通过发出以下调用

  job.getConfiguration().setLong("mapreduce.input.fileinputformat.split.maxsize", (long)(256*1024*1024));
  

  您将最大拆分大小设置为 256MB。

引用:

• https://hadoop.apache.org/docs/r2.2.0/api/org/apache/hadoop/mapreduce/lib/input/CombineFileInputFormat.html
• http://mail-archives.apache.org/mod_mbox/hadoop-common-user/201004.mbox/%3C35374.30384.qm@web63402.mail.re1.yahoo.com%3E