How to transpose/pivot data in hive?

I know there's no direct way to transpose data in hive. I followed this question: Is there a way to transpose data in Hive? , but as there is no final answer there, could not get all the way.

This is the table I have:

 | ID   |   Code   |  Proc1   |   Proc2 | 
 | 1    |    A     |   p      |   e     | 
 | 2    |    B     |   q      |   f     |
 | 3    |    B     |   p      |   f     |
 | 3    |    B     |   q      |   h     |
 | 3    |    B     |   r      |   j     |
 | 3    |    C     |   t      |   k     |

Here Proc1 can have any number of values. ID, Code & Proc1 together form a unique key for this table. I want to Pivot/ transpose this table so that each unique value in Proc1 becomes a new column, and corresponding value from Proc2 is the value in that column for the corresponding row. In essense, I'm trying to get something like:

 | ID   |   Code   |  p   |   q |  r  |   t |
 | 1    |    A     |   e  |     |     |     |
 | 2    |    B     |      |   f |     |     |
 | 3    |    B     |   f  |   h |  j  |     |
 | 3    |    C     |      |     |     |  k  |

In the new transformed table, ID and code are the only primary key. From the ticket I mentioned above, I could get this far using the to_map UDAF. (Disclaimer - this may not be a step in the right direction, but just mentioning here, if it is)

 | ID   |   Code   |  Map_Aggregation   | 
 | 1    |    A     |   {p:e}            |
 | 2    |    B     |   {q:f}            |
 | 3    |    B     |   {p:f, q:h, r:j } |  
 | 3    |    C     |   {t:k}            |

But don't know how to get from this step to the pivot/transposed table I want. Any help on how to proceed will be great! Thanks.

Solution 1:

Here is the approach i used to solved this problem using hive's internal UDF function, "map":

select
    b.id,
    b.code,
    concat_ws('',b.p) as p,
    concat_ws('',b.q) as q,
    concat_ws('',b.r) as r,
    concat_ws('',b.t) as t
from 
    (
        select id, code,
        collect_list(a.group_map['p']) as p,
        collect_list(a.group_map['q']) as q,
        collect_list(a.group_map['r']) as r,
        collect_list(a.group_map['t']) as t
        from (
            select
              id,
              code,
              map(proc1,proc2) as group_map 
            from 
              test_sample
        ) a
        group by
            a.id,
            a.code
    ) b;

"concat_ws" and "map" are hive udf and "collect_list" is a hive udaf.

Solution 2:

Here is the solution I ended up using:

add jar brickhouse-0.7.0-SNAPSHOT.jar;
CREATE TEMPORARY FUNCTION collect AS 'brickhouse.udf.collect.CollectUDAF';

select 
    id, 
    code,
    group_map['p'] as p,
    group_map['q'] as q,
    group_map['r'] as r,
    group_map['t'] as t
    from ( select
        id, code,
        collect(proc1,proc2) as group_map 
        from test_sample 
        group by id, code
    ) gm;

The to_map UDF was used from the brickhouse repo: https://github.com/klout/brickhouse