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
>>>
>>>
>>
>
