In fact you can implement own composite data types (like Tuple, Pojo) that
can deal with nullable fields as keys but you need custom serializers and
comparators for that. These types won't be as efficient as types that
cannot handle null fields.
Cheers, Fabian
2015-07-02 20:17 GMT+02:00 Flavio
Hi to all,
I'd like to join 2 datasets of POJO, let's say for example:
Person:
- name
- birthPlaceId
Place:
- id
- name
I'd like to do
people.coCoGroup(places).where(birthPlaceId).equalTo(id).with(...)
However, not all people have a birthPlaceId value in my use case..so I get
a
Hi Flavio!
Keys cannot be null in Flink, that is a contract deep in the system.
Filter out the null valued elements, or, if you want them in the result, I
would try to use a special value for null. That should do it.
BTW: In SQL, joining on null usually filters out elements, as key
operations
ok, thanks for the help Stephan!
On 2 Jul 2015 20:05, Stephan Ewen se...@apache.org wrote:
Hi Flavio!
Keys cannot be null in Flink, that is a contract deep in the system.
Filter out the null valued elements, or, if you want them in the result, I
would try to use a special value for null.
