How to duplicate IDs with Hive QL Impala Python

Question

I need help deduplicating a list of users (20 million+) across a different set of IDs.

Here's how it looks like :
- We have 3 kinds of userIDs : ID1, ID2 and ID3. - At least 2 of them are always sent together : ID1 with ID2 or ID2 with ID3. ID3 is never sent with ID1.
- Users can have several ID1, ID2 or ID3.
- So sometimes, in my table, I will have several lines with lots of different IDs, but it's possible that all of those can describe one single user.

An example :

Problem is : I know how to do this on SQL Server through the CURSOR / OPEN / FETCH / NEXT commands, but I only have Hive QL, Impala and Python available on my environment.

Answer 1

Based on your example, supposing id2 always exist, you can aggregate rows, group by id2:

select max(id1) id1,  id2, max(id3) id3 from
( --your dataset as in example
 select 'A'  as id1, 1 as id2,  null   as id3 union all
 select null as id1, 1 as id2, 'Alpha' as id3 union all
 select null as id1, 2 as id2, 'Beta'  as id3 union all
 select 'A'  as id1, 2 as id2,  null   as id3
 )s
 group by id2;

OK
A       1       Alpha
A       2       Beta
Time taken: 58.739 seconds, Fetched: 2 row(s)

And now I'm trying to implement your logic as you described:

select --pass2
 id1, id2, id3,
 case when lag(id2) over (order by id2, GroupId) = id2 then lag(GroupId) over (order by id2, GroupId) else GroupId end GroupId2
 from
 (
 select        --pass1
 id1, id2, id3,
 case when 
 lag(id1) over(order by id1, NVL(ID1,ID3)) =id1 then lag(NVL(ID1,ID3))  over(order by id1, NVL(ID1,ID3)) else NVL(ID1,ID3) end GroupId
 from
( --your dataset as in example
 select 'A'  as id1, 1 as id2,  null   as id3 union all
 select null as id1, 1 as id2, 'Alpha' as id3 union all
 select null as id1, 2 as id2, 'Beta'  as id3 union all
 select 'A'  as id1, 2 as id2,  null   as id3
 )s
 )s --pass1
;


OK
id1     id2     id3     groupid2
A       1       NULL    A
NULL    1       Alpha   A
A       2       NULL    A
NULL    2       Beta    A
Time taken: 106.944 seconds, Fetched: 4 row(s)