@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
>>>>>
>>>>>
>>>>
>>>
>>
>
