Thanks all:
Patrick selected rev.* and I.* cleared the confusion. The Item actually
brought 4 rows hence the final result set had 4 rows.
Regards,
Meena
On Sun, Oct 22, 2023 at 10:13 AM Bjørn Jørgensen
wrote:
> alos remove the space in rev. scode
>
> søn. 22. okt. 2023 kl. 19:08 skrev Sadha Ch
alos remove the space in rev. scode
søn. 22. okt. 2023 kl. 19:08 skrev Sadha Chilukoori :
> Hi Meena,
>
> I'm asking to clarify, are the *on *& *and* keywords optional in the join
> conditions?
>
> Please try this snippet, and see if it helps
>
> select rev.* from rev
> inner join customer c
> on
Hi Meena,
I'm asking to clarify, are the *on *& *and* keywords optional in the join
conditions?
Please try this snippet, and see if it helps
select rev.* from rev
inner join customer c
on rev.custumer_id =c.id
inner join product p
on rev.sys = p.sys
and rev.prin = p.prin
and rev.scode= p.bcode
Hi Meena,
It's not impossible, but it's unlikely that there's a bug in Spark SQL
randomly duplicating rows. The most likely explanation is there are more
records in the item table that match your sys/custumer_id/scode criteria
than you expect.
In your original query, try changing select rev.* to
Hello all:
I am using spark sql to join two tables. To my surprise I am
getting redundant rows. What could be the cause.
select rev.* from rev
inner join customer c
on rev.custumer_id =c.id
inner join product p
rev.sys = p.sys
rev.prin = p.prin
rev.scode= p.bcode
left join item I
on rev.sys = i
