Writing a Custom InputFormat

You can use a custom Java InputFormat together with a Python RecordReader: the java RecordReader supplied by the InputFormat will be overridden by the Python one.

Consider the following simple modification of Hadoop’s built-in TextInputFormat:

package it.crs4.pydoop.mapreduce;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;


public class TextInputFormat extends FileInputFormat<LongWritable, Text> {

    @Override
    public RecordReader<LongWritable, Text> createRecordReader(
        InputSplit split, TaskAttemptContext context) {
      return new LineRecordReader();
    }

    @Override
    protected boolean isSplitable(JobContext context, Path file) {
      return context.getConfiguration().getBoolean(
          "pydoop.input.issplitable", true);
    }
}

With respect to the default one, this InputFormat adds a configurable boolean parameter (pydoop.input.issplitable) that, if set to false, makes input files non-splitable (i.e., you can’t get more input splits than the number of input files).

For details on how to compile the above code into a jar and use it with Pydoop, see examples/input_format.