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From Michal Borowiecki <michal.borowie...@openbet.com>
Subject Re: Kafka Streams Usage Patterns
Date Sat, 27 May 2017 19:47:37 GMT
Hi all,

I've updated the wiki page with a draft pattern for consecutively 
growing time-windowed aggregations which was discussed some time ago on 
this mailing list.

I'm yet to add the part that cleans up the stores using punctuations. 
Stay tuned.

On a somewhat similar subject, I've been working to implement the 
following requirements:

* transaction sums per customer session (simple, just extract 
non-expired session-windowed aggregates from a SessionStore using 
interactive queries)

* global transaction sums for all _/currently active/_ customer sessions

The second bit proved non-trivial, because session-windowed KTables (or 
any windowed KTables for that matter) don't notify downstream when a 
window expires. And I can't use punctuate until KIP-138 is implemented 
because stream time punctuation is no good in this case (records can 
stop coming), reliable system time punctuation would be needed.

Below is how I implemented this, I'm yet to test it thoroughly.

I wonder if anyone knows of an easier way of achieving the same.

If so, I'm looking forward to suggestions. If not, I'll add that to the 
patterns wiki page too, in case someone else finds it useful.

   .stream(/*key serde*/, /*transaction serde*/,"transaciton-topic")

   .groupByKey(/*key serde*/, /*transaction serde*/)

     () -> /*empty aggregate*/,
     /* aggregate serde */,
     txPerCustomerSumStore()// this store can be queried for per customer session data )


   .filter(((key, value) -> value !=null))// tombstones only come when a session is merged
into a bigger session, 
so ignore them

// the below map/groupByKey/reduce operations are to only propagate 
updates to the _latest_ session per customer to downstream

   .map((windowedCustomerId, agg) ->// this moves timestamp from the windowed key into
the value // so that 
we can group by customerId only and reduce to the later value new KeyValue<>(
       windowedCustomerId.key(),// just customerId new WindowedAggsImpl(// this is just like
a tuple2 but with nicely named accessors: 
timestamp() and aggs()
   .groupByKey( /*key serde*/, /*windowed aggs serde*/ )// key is just customerId .reduce(//
take later session value and ignore any older - downstream only cares 
about _current_ sessions (val, agg) -> val.timestamp() > agg.timestamp() ? val : agg,
     "latest-session-windowed" )

   .groupBy((windowedCustomerId, timeAndAggs) ->// calculate totals with maximum granularity,
which is per-partition new KeyValue<>(
       new Windowed<>(
         windowedCustomerId.key().hashCode() %PARTITION_COUNT_FOR_TOTALS,// KIP-159 would
come in handy here, to access partition number instead
         windowedCustomerId.window()// will use this in the interactive queries to pick the
not-yet-expired window
     new SessionKeySerde<>(Serdes.Integer()),
/* aggregate serde */

     (val, agg) -> agg.add(val),
     (val, agg) -> agg.subtract(val),
     txTotalsStore()// this store can be queried to get totals per partition for all active

sessions );

   new SessionKeySerde<>(Serdes.Integer()),
   /* aggregate serde */,
// this global table puts per partition totals on every node, so that 
they can be easily summed for global totals, picking the oldest 
not-yet-expired window

TODO: put in StreamParitioners (with KTable.through variants added in 
KAFKA-5045) to avoid re-partitioning where I know it's unnecessary.

The idea behind the % PARTITION_COUNT_FOR_TOTALS bit is that I want to 
first do summation with max parallelism and minimize the work needed 
downstream. So I calculate a per-partition sum first to limit the 
updates that the totals topic will receive and the summing work done by 
the interactive queries on the global store. Is this a good way of going 
about it?



On 09/05/17 18:31, Matthias J. Sax wrote:
> Hi,
> I started a new Wiki page to collect some common usage patterns for
> Kafka Streams.
> Right now, it contains a quick example on "how to compute average". Hope
> we can collect more example like this!
> https://cwiki.apache.org/confluence/display/KAFKA/Kafka+Stream+Usage+Patterns
> -Matthias

<http://www.openbet.com/> 	Michal Borowiecki
Senior Software Engineer L4
	T: 	+44 208 742 1600

	+44 203 249 8448

	E: 	michal.borowiecki@openbet.com
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