Some applications don’t want files to be split, as this allows a single mapper to process each input file in its entirety. For example, a simple way to check if all the records in a file are sorted is to go through the records in order, checking whether each record is not less than the preceding one. Implemented as a map task, this algorithm will work only if one map processes the whole file.

There are a couple of ways to ensure that an existing file is not split. The first dirty way is to increase the minimum split size to be larger than the largest file in your system. Setting it to its maximum value, Long.MAX_VALUE, has this effect. The second is to subclass the concrete subclass of FileInputFormat that you want to use, to override the isSplitable() method to return false. For example, here’s a nonsplittable TextInputFormat:

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;

public class NonSplittableTextInputFormat extends TextInputFormat {
@Override
protected boolean isSplitable(JobContext context, Path file) {
return false;
}}

A mapper processing a file input split can find information about the split by calling the getInputSplit() method on the Mapper’s Context object. When the input format derives from FileInputFormat, the InputSplit returned by this method can be cast to a FileSplit to access the file information.

Processing a whole file as a record

A related requirement that sometimes crops up is for mappers to have access to the full contents of a file. Not splitting the file gets you part of the way there, but you also need to have a RecordReader that delivers the file contents as the value of the record.

import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.NullWritable;
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;

public class CompleteFileInputFormat extends FileInputFormat<NullWritable, BytesWritable> {
@Override
protected boolean isSplitable(JobContext context, Path file) {
return false;
}

@Override
public RecordReader<NullWritable, BytesWritable> createRecordReader(InputSplit split, TaskAttemptContext context)
throws IOException, InterruptedException {
CompleteFileRecordReader reader = new CompleteFileRecordReader();
reader.initialize(split, context);
return reader;
}
}

CompleteFileRecordReader defines a format where the keys are not used, represented by NullWritable, and the values are the file contents, represented by BytesWritable instances. It defines two methods. First, the format is careful to specify that input files should never be split, by overriding isSplitable() to return false. Second, we implement createRecordReader() to return a custom implementation of RecordReader.

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.NullWritable;
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.FileSplit;

public class CompleteFileRecordReader extends RecordReader<NullWritable, BytesWritable> {

private FileSplit fileSplit;
private Configuration conf;
private BytesWritable value = new BytesWritable();
private boolean processed = false;

@Override
public void initialize(InputSplit split, TaskAttemptContext context) throws IOException, InterruptedException {
this.fileSplit = (FileSplit) split;
this.conf = context.getConfiguration();
}

@Override
public boolean nextKeyValue() throws IOException, InterruptedException {
if (!processed) {
byte[] contents = new byte[(int) fileSplit.getLength()];
Path file = fileSplit.getPath();
FileSystem fs = file.getFileSystem(conf);
FSDataInputStream in = null;
try {
in = fs.open(file);
IOUtils.readFully(in, contents, 0, contents.length);
value.set(contents, 0, contents.length);
} finally {
IOUtils.closeStream(in);
}
processed = true;
return true;
}
return false;
}

@Override
public NullWritable getCurrentKey() throws IOException, InterruptedException {
return NullWritable.get();
}

@Override
public BytesWritable getCurrentValue() throws IOException, InterruptedException {
return value;
}

@Override
public float getProgress() throws IOException {
return processed ? 1.0f : 0.0f;
}

@Override
public void close() throws IOException {
// do nothing
}

}

CompleteFileRecordReader is responsible for taking a FileSplit and converting it into a single record, with a null key and a value containing the bytes of the file. Because there is only a single record, WholeFileRecordReader has either processed it or not, so it maintains a Boolean called processed. If the file has not been processed when the nextKeyValue() method is called, then we open the file, create a byte array whose length is the length of the file, and use the Hadoop IOUtils class to slurp the file into the byte array. Then we set the array on the BytesWritable instance that was passed into the next() method, and return true to signal that a record has been read.

Mapper code to output filename and the content

Because the input format is a CompleteFileInputFormat, the mapper only has to find the filename for the input file split. It does this by casting the InputSplit from the context to a FileSplit, which has a method to retrieve the file path. The path is stored in a Text
object for the key.

@Override
protected void setup(Context context) throws IOException, InterruptedException {
InputSplit split = context.getInputSplit();
Path path = ((FileSplit) split).getPath();
filenameKey = new Text(path.toString());
}

@Override
protected void map(NullWritable key, BytesWritable value, Context context)
throws IOException, InterruptedException {
context.write(filenameKey, value);
}