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From Michal Borowiecki <michal.borowie...@openbet.com>
Subject Re: How to chain increasing window operations one after another
Date Tue, 09 May 2017 07:55:40 GMT
Hi Garrett,

I'm glad this helped.

You're absolutely right, only the "oneMinuteWindowed" KTable has a 
Windowed key - apologies again for getting it wrong the first time.

I admit I used window().end() arbitrarily. If window().start() matches 
your semantics better, use that. Further on that note (I didn't propose 
this originally simply because it is more error-prone for me to write) 
you can instead of Tuple2 actually use the Windowed class as the wrapper 
and then have both start and end time if you have use for them 
downstream. Then e.g. fiveMinuteWindowed becomes (notice Windowed is 
used intentionally this time!):

KTable<Windowed<String>, CountSumMinMaxAvgObj> fiveMinuteWindowed = oneMinuteWindowed
   .groupBy( (windowedKey, value) ->
     new KeyValue<>(
       new Windowed<>(
         windowedKey.key(),
         new Window<>(
           windowedKey.window().start() /1000/60/5 *1000*60*5,
           windowedKey.window().start() /1000/60/5 *1000*60*5 + 1000*60*5
         )
       ),
       value,new SessionKeySerde<>(Serdes.String()), valSerde)

     .reduce(/*your adder*/, /*your subtractor*/, "store5m");

In the above, regardless if you choose to use start() or end(), you need 
to use it consistently both times, as in the time-windowed aggregation 
on your original stream the 1-minute window start and end won't be 
aligned to full minutes (I assume), hence they could end up in two 
different 5-minute windows after rounding.

As to the generic types, in my experience getting well-typed Serdes gets 
the type parameters inferred correctly throughout the rest of the 
expression and they should only be needed on the KTable variable 
declaration.
And if you use Windowed keys as in the variant above, you can (sneakily) 
re-use the SessionKeySerde as a wrapper around the actual serde for your 
key, like this:
new SessionKeySerde<>(Serdes.String())
Again this should get type parameters inferred nicely and 
SessionKeySerde serializes both start and end time of the window.
*Caveat*: SessionKeySerde comes from the 
org.apache.kafka.streams.kstream.*internals* package, so it's not as 
safe to use as public APIs, but it's a small class you can fork if you wish.

This is so much fun ;-)

Good luck,
Michał

On 08/05/17 23:05, Garrett Barton wrote:
> Michael,
>    This is slick!  I am still writing unit tests to verify it.  My code
> looks something like:
>
> KTable<Windowed<String>, CountSumMinMaxAvgObj> oneMinuteWindowed =
> srcStream    // my val object isnt really called that, just wanted to show
> a sample set of calculations the value can do!
>      .groupByKey(Serdes.String(), Serdes.Double())
>      .aggregate(/*initializer */, /* aggregator */, TimeWindows.of(60*1000,
> 60*1000), "store1m");
>
>     // i used an aggregate here so I could have a non-primitive value object
> that does the calculations on each aggregator, pojo has an .add(Double) in
> it.
> KTable<Tuple2<String, Long>, CountSumMinMaxAvgObj> fiveMinuteWindowed =
> oneMinuteWindowed    // I made my own Tuple2, will move window calc into it
>      .groupBy( (windowedKey, value) -> new KeyValue<>(new Tuple2<String,
> Long>(windowedKey.key(), windowedKey.window().end() /1000/60/5 *1000*60*5),
> value, keySerde, valSerde)
>
>          // the above rounds time down to a timestamp divisible by 5 minutes
>
>      .reduce(/*your adder*/, /*your subtractor*/, "store5m");
>
>          // where your subtractor can be as simple as (val, agg) -> agg - val
> for primitive types or as complex as you need,
>
>          // just make sure you get the order right (lesson hard learnt ;) ),
> subtraction is not commutative!
>
>          // again my val object has an .add(Obj) and a .sub() to handle
> this, so nice!
>
>
> KTable<Tuple2<String, Long>, CountSumMinMaxAvgObj> fifteenMinuteWindowed
=
> fiveMinuteWindowed
>
>      .groupBy( (keyPair, value) -> new KeyValue<>(new Tuple2(keyPair._1,
> keyPair._2 /1000/60/15 *1000*60*15), value, keySerde, valSerde)
>
>          // the above rounds time down to a timestamp divisible by 15 minutes
>
>      .reduce(/*your adder*/, /*your subtractor*/, "store15m");
>
>
> KTable<Tuple2<String, Long>, CountSumMinMaxAvgObj> sixtyMinuteWindowed =
> fifteeenMinuteWindowed
>
>      .groupBy( (keyPair, value) -> new KeyValue<>(new Tuple2(keyPairair._1,
> pair._2 /1000/60/60 *1000*60*60), value, keySerde, valSerde)
>
>          // the above rounds time down to a timestamp divisible by 60 minutes
>
>      .reduce(/*your adder*/, /*your subtractor*/, "store60m");
>
>
> Notes thus far:
>    Doesn't look like I need to start the 5min with a windowed KTable return
> object, it starts with the regular KTable<Tuple2<String,Long>> in this case.
>    I thinking about using windowedKey.window().start() instead of end() as I
> believe that is more consistent with what the windows themselves put out.
> They go into the stores bound by their start time I believe.
>    Serdes gets nuts as well as the Generic typing on some of these classes
> (yea you KeyValueMapper), makes for long code!  I had to specify them
> everywhere since the key/val's changed.
>
>
> I didn't get enough time to mess with it today, I will wrap up the unit
> tests and run it to see how it performs against my real data as well
> tomorrow.  I expect a huge reduction in resources (both streams and kafka
> storage) by moving to this.
> Thank you!
>
>
>
> On Mon, May 8, 2017 at 5:26 PM, Matthias J. Sax <matthias@confluent.io>
> wrote:
>
>> Michal,
>>
>> that's an interesting idea. In an ideal world, Kafka Streams should have
>> an optimizer that is able to to this automatically under the hood. Too
>> bad we are not there yet.
>>
>> @Garret: did you try this out?
>>
>> This seems to be a question that might affect many users, and it might
>> we worth to document it somewhere as a recommended pattern.
>>
>>
>> -Matthias
>>
>>
>> On 5/8/17 1:43 AM, Michal Borowiecki wrote:
>>> Apologies,
>>>
>>> In the code snippet of course only oneMinuteWindowed KTable will have a
>>> Windowed key (KTable<Windowed<Key>, Value>), all others would be
just
>>> KTable<Tuple2<Key, Long>, Value>.
>>>
>>> Michał
>>>
>>> On 07/05/17 16:09, Michal Borowiecki wrote:
>>>> Hi Garrett,
>>>>
>>>> I've encountered a similar challenge in a project I'm working on (it's
>>>> still work in progress, so please take my suggestions with a grain of
>>>> salt).
>>>>
>>>> Yes, I believe KTable.groupBy lets you accomplish what you are aiming
>>>> for with something like the following (same snippet attached as txt
>> file):
>>>>
>>>> KTable<Windowed<Key>, Value> oneMinuteWindowed = yourStream 
  //
>>>> where Key and Value stand for your actual key and value types
>>>>
>>>>      .groupByKey()
>>>>
>>>>      .reduce(/*your adder*/, TimeWindows.of(60*1000, 60*1000),
>> "store1m");
>>>>          //where your adder can be as simple as (val, agg) -> agg + val
>>>>
>>>>          //for primitive types or as complex as you need
>>>>
>>>>
>>>> KTable<Windowed<Tuple2<Key, Long>>, Value> fiveMinuteWindowed
=
>>>> oneMinuteWindowed    // Tuple2 for this example as defined by
>>>> javaslang library
>>>>
>>>>      .groupBy( (windowedKey, value) -> new KeyValue<>(new
>>>> Tuple2<>(windowedKey.key(), windowedKey.window().end() /1000/60/5
>>>> *1000*60*5), value)
>>>>
>>>>          // the above rounds time down to a timestamp divisible by 5
>>>> minutes
>>>>
>>>>      .reduce(/*your adder*/, /*your subtractor*/, "store5m");
>>>>
>>>>          // where your subtractor can be as simple as (val, agg) -> agg
>>>> - valfor primitive types or as complex as you need,
>>>>
>>>>          // just make sure you get the order right (lesson hard learnt
>>>> ;) ), subtraction is not commutative!
>>>>
>>>>
>>>> KTable<Windowed<Tuple2<Key, Long>>, Value> fifteenMinuteWindowed
=
>>>> fiveMinuteWindowed
>>>>
>>>>      .groupBy( (keyPair, value) -> new KeyValue<>(new
>>>> Tuple2(keyPair._1, keyPair._2/1000/60/15 *1000*60*15), value)
>>>>
>>>>          // the above rounds time down to a timestamp divisible by 15
>>>> minutes
>>>>
>>>>      .reduce(/*your adder*/, /*your subtractor*/, "store15m");
>>>>
>>>>
>>>> KTable<Windowed<Tuple2<Key, Long>>, Value> sixtyMinuteWindowed
=
>>>> fifteeenMinuteWindowed
>>>>
>>>>      .groupBy( (keyPair, value) -> new KeyValue<>(new
>>>> Tuple2(keyPairair._1, pair._2 /1000/60/60 *1000*60*60), value)
>>>>
>>>>          // the above rounds time down to a timestamp divisible by 5
>>>> minutes
>>>>
>>>>      .reduce(/*your adder*/, /*your subtractor*/, "store60m");
>>>>
>>>>
>>>> So, step by step:
>>>>
>>>>    * You use a windowed aggregation only once, from there on you use
>>>>      the KTable abstraction only (which doesn't have windowed
>>>>      aggregations).
>>>>    * In each subsequent groupBy you map the key to a pair of
>>>>      (your-real-key, timestamp) where the timestamp is rounded down
>>>>      with the precision of the size of the new window.
>>>>    * reduce() on a KGroupedTable takes an adder and a subtractor and it
>>>>      will correctly update the new aggregate by first subtracting the
>>>>      previous value of the upstream record before adding the new value
>>>>      (this way, just as you said, the downstream is aware of the
>>>>      statefulness of the upstream and correctly treats each record as
>>>>      an update)
>>>>    * If you want to reduce message volume further, you can break these
>>>>      into separate KafkaStreams instances and configure downstream ones
>>>>      with a higher commit.interval.ms (unfortunately you can't have
>>>>      different values of this setting in different places of the same
>>>>      topology I'm afraid)
>>>>    * TODO: Look into retention policies, I haven't investigated that in
>>>>      any detail.
>>>>
>>>> I haven't tested this exact code, so please excuse any typos.
>>>>
>>>> Also, if someone with more experience could chip in and check if I'm
>>>> not talking nonsense here, or if there's an easier way to this, that
>>>> would be great.
>>>>
>>>>
>>>> I don't know if the alternative approach is possible, where you
>>>> convert each resulting KTable back into a stream and just do a
>>>> windowed aggregation somehow. That would feel more natural, but I
>>>> haven't figured out how to correctly window over a changelog in the
>>>> KStream abstraction, feels impossible in the high-level DSL.
>>>>
>>>> Hope that helps,
>>>> Michal
>>>>
>>>> On 02/05/17 18:03, Garrett Barton wrote:
>>>>> Lets say I want to sum values over increasing window sizes of 1,5,15,60
>>>>> minutes.  Right now I have them running in parallel, meaning if I am
>>>>> producing 1k/sec records I am consuming 4k/sec to feed each
>> calculation.
>>>>> In reality I am calculating far more than sum, and in this pattern I'm
>>>>> looking at something like (producing rate)*(calculations)*(windows)
>> for a
>>>>> consumption rate.
>>>>>
>>>>>   So I had the idea, could I feed the 1 minute window into the 5
>> minute, and
>>>>> 5 into 15, and 15 into 60.  Theoretically I would consume a fraction
>> of the
>>>>> records, not have to scale as huge and be back to something like
>> (producing
>>>>> rate)*(calculations)+(updates).
>>>>>
>>>>>    Thinking this is an awesome idea I went to try and implement it and
>> got
>>>>> twisted around.  These are windowed grouping operations that produce
>>>>> KTables, which means instead of a raw stream I have an update stream.
>> To
>>>>> me this implies that downstream must be aware of this and consume
>> stateful
>>>>> information, knowing that each record is an update and not an in
>> addition
>>>>> to.  Does the high level api handle that construct and let me do
>> that?  For
>>>>> a simple sum it would have to hold each of the latest values for say
>> the 5
>>>>> 1 minute sum's in a given window, to perform the 5 minute sum.
>> Reading the
>>>>> docs which are awesome, I cannot determine if the KTable.groupby()
>> would
>>>>> work over a window, and would reduce or aggregate thus do what I need?
>>>>>
>>>>> Any ideas?
>>>>>
>>>> --
>>>> Signature
>>>> <http://www.openbet.com/>    Michal Borowiecki
>>>> Senior Software Engineer L4
>>>>       T:      +44 208 742 1600
>>>>
>>>>
>>>>       +44 203 249 8448
>>>>
>>>>
>>>>
>>>>       E:      michal.borowiecki@openbet.com
>>>>       W:      www.openbet.com <http://www.openbet.com/>
>>>>
>>>>
>>>>       OpenBet Ltd
>>>>
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>>>>
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>>>>
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>>>>
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>>> --
>>> Signature
>>> <http://www.openbet.com/>     Michal Borowiecki
>>> Senior Software Engineer L4
>>>        T:      +44 208 742 1600
>>>
>>>
>>>        +44 203 249 8448
>>>
>>>
>>>
>>>        E:      michal.borowiecki@openbet.com
>>>        W:      www.openbet.com <http://www.openbet.com/>
>>>
>>>
>>>        OpenBet Ltd
>>>
>>>        Chiswick Park Building 9
>>>
>>>        566 Chiswick High Rd
>>>
>>>        London
>>>
>>>        W4 5XT
>>>
>>>        UK
>>>
>>>
>>> <https://www.openbet.com/email_promo>
>>>
>>> This message is confidential and intended only for the addressee. If you
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