PIG UDF handle multi-lined tuple split into different mapper

Question

I have file where each tuple span multiple lines, for example:

START
name: Jim
phone: 2128789283
address: 56 2nd street, New York, USA
END
START
name: Tom
phone: 6308789283
address: 56 5th street, Chicago, 13611, USA
END
.
.
.

So above are 2 tuples in my file. I wrote my UDF that defined a getNext() function which check if it is START then I will initialize my tuple; if it is END then I will return the tuple (from string buffer); otherwise I will just add the string to string buffer.

It works well for file size is less than HDFS block size which is 64 MB (on Amazon EMR), whereas it will fail for the size larger than this. I try to google around, find this blog post. Raja's explaination is easy to understand and he provided a sample code. But the code is implementing the RecordReader part, instead of getNext() for pig LoadFunc. Just wondering if anyone has experience to handle multi-lined pig tuple split problem? Should I go ahead implement RecordReader in Pig? If so, how?

Thanks.

It will be simpler to pre-process your file to put all the information of a record in a single line - one line of awk — Guy, Dec 14 '12 at 09:04
I have no control to the input file if I do pre-procesising myself, then the overhead will overcome the benefit Hadoop provides. Except I use pre-processing in Hadoop. Now, I am still looking for solution to handle tuple across mapper boundary. — Simon Guo, Dec 14 '12 at 20:43

score 1 · Answer 1 · edited May 23 '17 at 12:25

You may preprocess your input as Guy mentioned or can apply other tricks described here.

I think the cleanest solution would be to implement a custom InputFormat (along with its RecordReader) which creates one record/START-END. The Pig's LoadFunc sits on the top of the Hadoop's InputFormat, so you can define which InputFormat your LoadFunc will use.
A raw, skeleton implementation of a custom LoadFunc would look like:

import java.io.IOException;
import org.apache.hadoop.mapreduce.InputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.pig.LoadFunc;
import org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigSplit;
import org.apache.pig.data.Tuple;
import org.apache.pig.data.TupleFactory;

public class CustomLoader extends LoadFunc {

    private RecordReader reader;
    private TupleFactory tupleFactory;

    public CustomLoader() {
        tupleFactory = TupleFactory.getInstance();
    }

    @Override
    public InputFormat getInputFormat() throws IOException {
        return new MyInputFormat(); //custom InputFormat
    }

    @Override
    public Tuple getNext() {
        Tuple result = null;
        try {
            if (!reader.nextKeyValue()) {
                return null;
            }
            //value can be a custom Writable containing your name/value 
            //field pairs for a given record
            Object value = reader.getCurrentValue();
            result = tupleFactory.newTuple();
            // ...
            //append fields to tuple
        }
        catch (Exception e) {
            // ...
        }
        return result;
    }

    @Override
    public void prepareToRead(RecordReader reader, PigSplit pigSplit) 
      throws IOException {
        this.reader = reader;
    }

    @Override
    public void setLocation(String location, Job job) throws IOException {
        FileInputFormat.setInputPaths(job, location);
    }
}

After the LoadFunc initializes the InputFormat and its RecordReader, it locates the input location of your data and begins to obtain the records from recordReader, creates the resulting tuples (getNext()) until the input has been fully read.

Some remarks on the custom InputFormat:

I'd create a custom InputFormat in which the RecordReader is a modified version of org.apache.hadoop.mapreduce.lib.input.LineRecordReader: Most of the methods would remain the same, except initialize(): it would call a custom LineReader (based on org.apache.hadoop.util.LineReader). The InputFormat's key would be the line offset (Long), the value would be a custom Writable. This would hold the fields of a record (i.e data between START-END) as a list of key-value pairs. Each time your RecordReader's nextKeyValue() is called the record is written to the custom Writable by the LineReader. The gist of the whole thing is how you implement LineReader.readLine().

Another, probably an easier approach would be to change the delimiter of TextInputFormat (It is configurable in Hadoop 0.23, see textinputformat.record.delimiter) to one that is appropriate for your data structure (if it is possible). In this case you'll end up having your data in Text from which you need to split and extract KV pairs and into tuples.

Just wondering if you can provide a skeleton implementation of the customized InpurFormat and RecordReader? Especially how to deal with the tuple across the mappers' boundary? Thanks. — Simon Guo, Dec 14 '12 at 20:39
MyInputFormat seems doesn't get used. I put print statement in side MyInputFormat and it doesn't get printed. — Simon Guo, Dec 18 '12 at 23:07

score 0 · Answer 2 · answered Feb 15 '17 at 14:21

If can take start as your delimiter, probably below code works without UDF

SET textinputformat.record.delimiter 'START';
a  =  load  '<input path>' as  (data:chararray);
dump a;

the output would look like:

    (
    name: Jim
    enter code here`phone: 2128789283
    address: 56 2nd street, New York, USA
    END
    )

    (
    name: Tom
    phone: 6308789283
    address: 56 5th street, Chicago, 13611, USA
    END
    )

Now both are separated into two tuples.

PIG UDF handle multi-lined tuple split into different mapper

2 Answers2

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