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From Tathagata Das <tathagata.das1...@gmail.com>
Subject Re: How to select the entire row that has max timestamp for every key in Spark Structured Streaming 2.1.1?
Date Wed, 30 Aug 2017 00:18:59 GMT
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 <kanth909@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.das1565@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 <kanth909@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
>>>
>>>
>>
>

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