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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 04:50:20 GMT
Aah, I might have misinterpreted. The groupBy + window solution would give
the max time for each train over 24 hours (non-overlapping window) of event
data (timestamped by activity_timestamp). So the output would be like.

Train     Dest   Window(activity_timestamp)    max(Time)
1         HK     Aug28-00:00 to Aug29-00:00    10:00    <- updating
currently through aug29
1         HK    Aug27-00:00 to Aug28-00:00     09:00    <- not updating as
no new updates coming in with activity_timestamp in this range.

The drawback of this approach is that as soon as Aug28 starts, you have
wait for new event about a train to get a new max(time). You may rather
want a rolling 24 hour period, that is, the max time known over events in
the last 24 hours.
Then maintaining our own custom state using
mapGroupsWithState/flatMapGroupsWithState()
is the best and most flexible option.
It is available in Spark 2.2 in Scala, Java.

Here is an example that tracks sessions based on events.
Scala -
https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredSessionization.scala

You will have to create a custom per-train state which keeps track of last
24 hours of trains history, and use that state to calculate the max time
for each train.


def updateHistoryAndGetMax(train: String, events: Iterator[TrainEvents],
state: GroupState[TrainHistory]): Long = {
    // for every event, update history (i.e. last 24 hours of events) and
return the max time from the history
}

trainTimesDataset     // Dataset[TrainEvents]
  .groupByKey(_.train)
  .mapGroupsWithState(updateHistoryAndGetMax)

Hope this helps.


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
>>>>>
>>>>>
>>>>
>>>
>>
>

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