You need to supply a rowencoder.
Regards,
Ramandeep Singh
On Thu, May 9, 2019, 11:33 SNEHASISH DUTTA wrote:
> Hi ,
>
> I am trying to write a generic method which will return custom type
> datasets as well as spark.sql.Row
>
> def read[T](params: Map[String, Any])(implicit encoder:
epts 'SELECT * FROM FOO', but it doesn't accept 'select * from
> foo'.
>
> But I can run the spark.sql("select * from foo") in the spark2-shell
> without any problem.
>
> Is there another 'layer' in the SPARK SQL to capitalize those 'tokens'
> before invoking the parser?
Explain extended or explain would list the plan along with the tables. Not
aware of any statements that explicitly list dependencies or tables
directly.
Regards,
Ramandeep Singh
On Wed, Jan 23, 2019, 11:05 Tomas Bartalos This might help:
>
> show tables;
>
> st 23. 1. 2019 o 10:43 napísal(a):
Basically, it is a zipping two flowables using the defined function[takes
two parameters and returns one, Hence the name BiFunction].
Obviously, you could avoid using RXJava and by using a TimerTask.
val a = Seq(1, 2, 3)
val b = a.zipWithIndex
b.foreach(b => new Timer().schedule(new TimerTask {
You can use analytical functions in spark sql.
Something like select * from (select id, row_number() over (partition by id
order by timestamp ) as rn from root) where rn=1
On Mon, Dec 17, 2018 at 4:03 PM Nikhil Goyal wrote:
> Hi guys,
>
> I have a dataframe of type Record (id: Long, timestamp:
HI,
I have some futures setup to operate in stages, where I expect one stage to
complete before another begins. I was hoping that dataframe write call is
blocking, whereas the behavior i see is that the call returns before data
is persisted. This can cause unintended consequences. I am also using
