Pig Changing Schema to required type - hadoop

Question

I'm a new Pig user.
I have an existing schema which I want to modify. My source data is as follows with 6 columns:
Name Type Date Region Op Value
-----------------------------------------------------
john ab 20130106 D X 20
john ab 20130106 D C 19
jphn ab 20130106 D T 8
jphn ab 20130106 E C 854
jphn ab 20130106 E T 67
jphn ab 20130106 E X 98
and so on. Each Op value is always C, T or X.
I basically want to split my data in the following way into 7 columns:
Name Type Date Region OpX OpC OpT
----------------------------------------------------------
john ab 20130106 D 20 19 8
john ab 20130106 E 98 854 67
Basically split the Op column into 3 columns: each for one Op value. Each of these columns should contain appropriate value from column Value.
How can I do this in Pig?

Answer 1

One way to achieve the desired result:
IN = load 'data.txt' using PigStorage(',') as (name:chararray, type:chararray,
date:int, region:chararray, op:chararray, value:int);
A = order IN by op asc;
B = group A by (name, type, date, region);
C = foreach B {
bs = STRSPLIT(BagToString(A.value, ','),',',3);
generate flatten(group) as (name, type, date, region),
bs.$2 as OpX:chararray, bs.$0 as OpC:chararray, bs.$1 as OpT:chararray;
}
describe C;
C: {name: chararray,type: chararray,date: int,region: chararray,OpX:
chararray,OpC: chararray,OpT: chararray}
dump C;
(john,ab,20130106,D,20,19,8)
(john,ab,20130106,E,98,854,67)
Update:
If you want to skip order by which adds an additional reduce phase to the computation, you can prefix each value with its corresponding op in tuple v. Then sort the tuple fields by using a custom UDF to have the desired OpX, OpC, OpT order:
register 'myjar.jar';
A = load 'data.txt' using PigStorage(',') as (name:chararray, type:chararray,
date:int, region:chararray, op:chararray, value:int);
B = group A by (name, type, date, region);
C = foreach B {
v = foreach A generate CONCAT(op, (chararray)value);
bs = STRSPLIT(BagToString(v, ','),',',3);
generate flatten(group) as (name, type, date, region),
flatten(TupleArrange(bs)) as (OpX:chararray, OpC:chararray, OpT:chararray);
}
where TupleArrange in mjar.jar is something like this:
..
import org.apache.pig.EvalFunc;
import org.apache.pig.data.Tuple;
import org.apache.pig.data.TupleFactory;
import org.apache.pig.impl.logicalLayer.schema.Schema;
public class TupleArrange extends EvalFunc<Tuple> {
private static final TupleFactory tupleFactory = TupleFactory.getInstance();
#Override
public Tuple exec(Tuple input) throws IOException {
try {
Tuple result = tupleFactory.newTuple(3);
Tuple inputTuple = (Tuple) input.get(0);
String[] tupleArr = new String[] {
(String) inputTuple.get(0),
(String) inputTuple.get(1),
(String) inputTuple.get(2)
};
Arrays.sort(tupleArr); //ascending
result.set(0, tupleArr[2].substring(1));
result.set(1, tupleArr[0].substring(1));
result.set(2, tupleArr[1].substring(1));
return result;
}
catch (Exception e) {
throw new RuntimeException("TupleArrange error", e);
}
}
#Override
public Schema outputSchema(Schema input) {
return input;
}
}