Say, *trainTimesDataset* is the streaming Dataset of schema *[train: Int,
dest: String, time: Timestamp] *
*Scala*: *trainTimesDataset.groupBy("train", "dest").max("time")*
*SQL*: *"select train, dest, max(time) from trainTimesView group by train,
dest"* // after calling
*trainTimesData.createOrReplaceTempView(trainTimesView)*
On Tue, Aug 29, 2017 at 12:59 PM, kant kodali <[email protected]> wrote:
> Hi All,
>
> I am wondering what is the easiest and concise way to express the
> computation below in Spark Structured streaming given that it supports both
> imperative and declarative styles?
> I am just trying to select rows that has max timestamp for each train?
> Instead of doing some sort of nested queries like we normally do in any
> relational database I am trying to see if I can leverage both imperative
> and declarative at the same time. If nested queries or join are not
> required then I would like to see how this can be possible? I am using
> spark 2.1.1.
>
> Dataset
>
> Train Dest Time1 HK 10:001 SH 12:001
> SZ 14:002 HK 13:002 SH 09:002 SZ
> 07:00
>
> The desired result should be:
>
> Train Dest Time1 SZ 14:002 HK 13:00
>
>
