Hadoop read multiple lines at a time

Question

I have a file in which a set of every four lines represents a record.

eg, first four lines represent record1, next four represent record 2 and so on..

How can I

Answer 1

A few approaches, some dirtier than others:

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The right way

You may have to define your own RecordReader, InputSplit, and InputFormat. Depending on exactly what you are trying to do, you will be able to reuse some of the already existing ones of the three above. You will likely have to write your own RecordReader to define the key/value pair and you will likely have to write your own InputSplit to help define the boundary.

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Another right way, which may not be possible

The above task is quite daunting. Do you have any control over your data set? Can you preprocess it in someway (either while it is coming in or at rest)? If so, you should strongly consider trying to transform your dataset int something that is easier to read out of the box in Hadoop.

Something like:

ALine1
ALine2            ALine1;Aline2;Aline3;Aline4
ALine3
ALine4        ->
BLine1
BLine2            BLine1;Bline2;Bline3;Bline4;
BLine3
BLine4

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Down and Dirty

Do you have any control over the file sizes of your data? If you manually split your data on the block boundary, you can force Hadoop to not care about records spanning splits. For example, if your block size is 64MB, write your files out in 60MB chunks.

Without worrying about input splits, you could do something dirty: In your map function, add your new key/value pair into a list object. If the list object has 4 items in it, do processing, emit something, then clean out the list. Otherwise, don't emit anything and move on without doing anything.

The reason why you have to manually split the data is that you are not going to be guaranteed that an entire 4-row record will be given to the same map task.

Answer 2

Another way (easy but may not be efficient in some cases) is to implement the FileInputFormat#isSplitable(). Then the input files are not split and are processed one per map.

import org.apache.hadoop.fs.*;
import org.apache.hadoop.mapred.TextInputFormat;
public class NonSplittableTextInputFormat extends TextInputFormat {
    @Override
    protected boolean isSplitable(FileSystem fs, Path file) {
        return false;
    }
}

And as orangeoctopus said

In your map function, add your new key/value pair into a list object. If the list object has 4 items in it, do processing, emit something, then clean out the list. Otherwise, don't emit anything and move on without doing anything.

This has some overhead for the following reasons

• Time to process the largest file drags the job completion time.
• A lot of data may be transferred between the data nodes.
• The cluster is not properly utilized, since # of maps = # of files.

** The above code is from Hadoop : The Definitive Guide