kafka-users mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Matthias J. Sax" <matth...@confluent.io>
Subject Re: How to chain increasing window operations one after another
Date Mon, 08 May 2017 21:26:52 GMT
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>.
> 
> MichaƂ
> 
> 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
>>
>> 	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 have received this message in error, please immediately notify the
>> postmaster@openbet.com <mailto:postmaster@openbet.com> and delete it
>> from your system as well as any copies. The content of e-mails as well
>> as traffic data may be monitored by OpenBet for employment and
>> security purposes. To protect the environment please do not print this
>> e-mail unless necessary. OpenBet Ltd. Registered Office: Chiswick Park
>> Building 9, 566 Chiswick High Road, London, W4 5XT, United Kingdom. A
>> company registered in England and Wales. Registered no. 3134634. VAT
>> no. GB927523612
>>
> 
> -- 
> 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
> have received this message in error, please immediately notify the
> postmaster@openbet.com <mailto:postmaster@openbet.com> and delete it
> from your system as well as any copies. The content of e-mails as well
> as traffic data may be monitored by OpenBet for employment and security
> purposes. To protect the environment please do not print this e-mail
> unless necessary. OpenBet Ltd. Registered Office: Chiswick Park Building
> 9, 566 Chiswick High Road, London, W4 5XT, United Kingdom. A company
> registered in England and Wales. Registered no. 3134634. VAT no.
> GB927523612
> 


Mime
View raw message