That just gives you the max time for each train. If I understood the question correctly, OP wants the whole row with the max time. That's generally solved through joins or subqueries, which would be hard to do in a streaming setting
On Aug 29, 2017 7:29 PM, "ayan guha" <guha.a...@gmail.com> wrote: > Why removing the destination from the window wont work? Like this: > > *trainTimesDataset* > * .withWatermark("**activity_timestamp", "5 days")* > * .groupBy(window(activity_timestamp, "24 hours", "24 hours"), "train")* > * .max("time")* > > On Wed, Aug 30, 2017 at 10:38 AM, kant kodali <kanth...@gmail.com> wrote: > >> @Burak so how would the transformation or query would look like for the >> above example? I don't see flatMapGroupsWithState in the DataSet API >> Spark 2.1.1. I may be able to upgrade to 2.2.0 if that makes life easier. >> >> >> >> On Tue, Aug 29, 2017 at 5:25 PM, Burak Yavuz <brk...@gmail.com> wrote: >> >>> Hey TD, >>> >>> If I understood the question correctly, your solution wouldn't return >>> the exact solution, since it also groups by on destination. I would say the >>> easiest solution would be to use flatMapGroupsWithState, where you: >>> .groupByKey(_.train) >>> >>> and keep in state the row with the maximum time. >>> >>> On Tue, Aug 29, 2017 at 5:18 PM, Tathagata Das < >>> tathagata.das1...@gmail.com> wrote: >>> >>>> Yes. And in that case, if you just care about only the last few days of >>>> max, then you should set watermark on the timestamp column. >>>> >>>> *trainTimesDataset* >>>> * .withWatermark("**activity_timestamp", "5 days")* >>>> * .groupBy(window(activity_timestamp, "24 hours", "24 hours"), >>>> "train", "dest")* >>>> * .max("time")* >>>> >>>> Any counts which are more than 5 days old will be dropped from the >>>> streaming state. >>>> >>>> On Tue, Aug 29, 2017 at 2:06 PM, kant kodali <kanth...@gmail.com> >>>> wrote: >>>> >>>>> Hi, >>>>> >>>>> Thanks for the response. Since this is a streaming based query and in >>>>> my case I need to hold state for 24 hours which I forgot to mention in my >>>>> previous email. can I do ? >>>>> >>>>> *trainTimesDataset.groupBy(window(activity_timestamp, "24 hours", >>>>> "24 hours"), "train", "dest").max("time")* >>>>> >>>>> >>>>> On Tue, Aug 29, 2017 at 1:38 PM, Tathagata Das < >>>>> tathagata.das1...@gmail.com> wrote: >>>>> >>>>>> 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 <kanth...@gmail.com> >>>>>> 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 >>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> > > > -- > Best Regards, > Ayan Guha >