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From Burak Yavuz <brk...@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 02:50:17 GMT
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.ayan@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 <kanth909@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 <brkyvz@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.das1565@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 <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
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
>
> --
> Best Regards,
> Ayan Guha
>

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