Thanks for the reply.  I really appreciate the insight. Again newbie here.  I 
want to expand on what I am struggling with.  It may be that I just need to get 
my mind thinking more in a streaming mode.  Please let me know you thoughts.  
Just having problem ‘getting it’ on my own.



Below is a simple topic I want to identify where the 'lane' duplicates, and 
when it does get the 'txnId' of the duplicate record.  The txnId is distinct 
and will never be duplicate.  The lane will seldom have a duplicate.





Topic payload {txnId,lane,txnDate}  Notice lane 'c' is dulplicated 3 times.



{'01','wfasd','11/07/2016'}

{'02','bas','11/07/2016'}

{'03','c','11/07/2016'}

{'04','xxwq','11/07/2016'}

{'05','dasf','11/07/2016'}

{'06','drdd','11/07/2016'}

{'07','tasd','11/07/2016'}

{'08','ywq','11/07/2016'}

{'09','c','11/07/2016'}

{'10','jda','11/07/2016'}

{'11','c','11/08/2016'}

{'12','ozs','11/09/2016'}

. . .

Note txnId and lane keep getting more distinct values.





My thought is to join the data to itself,  one as kstream the other as ktable 
for lookups.



kstream as



  {lane:(txnId,txnDate)}



  so I visualize like ...



 ('wfasd':('01','11/07/2016')),

  ('bas'  :('02','11/07/2016')),

 ('c'    :('03','11/07/2016')), ...



The ktable (lookup table) is an aggregate view I built to hold historic data by 
lane:



 (lane:{(txnId1,txnDate1),

       (txnId2,txnDate2),

       . . .})



  I visualize the materialized view as below.

  'c' being the important key/value for this example...

  Also note this materialized view will keep growing without bound.

  There will always be new keys and txnIds.



  ('wfasd':{('01','11/07/2016')}),

  ('bas'  :{('02','11/07/2016')}),

  ('c'    :{('03','11/07/2016'),

          ('09','11/07/2016'),

          ('11','11/09/2016')})

  . . .



Now I can join a kstream to ktable on lane, and duplicates are easy to 
identify.  I can traverse list from value found in materialized view to get 
previous txnId I need.



So I can build resulting stream / topic like…



{txnId,lane,txnDate,duplicateTxnId}



note where c duplicates there is a duplicate txnId...



{'01','wfasd','11/07/2016',''}

{'02','bas','11/07/2016',''}

{'03','c','11/07/2016',''}

{'04','xxwq','11/07/2016',''}

{'05','dasf','11/07/2016',''}

{'06','drdd','11/07/2016',''}

{'07','tasd','11/07/2016',''}

{'08','ywq','11/07/2016',''}

{'09','c','11/07/2016','03'}

{'10','jda','11/07/2016',''}

{'11','c','11/08/2016','09'}

{'12','ozs','11/09/2016',''}





The issue is the materialized view of the ktable keeps growing without bound, 
however by business rule I only need past 2 weeks, so I think over time there 
is performance impact that is not needed regarding the materialized view, one, 
the size of materialized view keeps growing, and two, traversing ever 
increasing larger value lists.



Ideally if the topic is set to two weeks retention, then once an item is 
'popped off' I would like to do an aggregate subtraction for it's value.  But I 
don't think this is how kafka works.  Is this possible?  Any other 
feedback/suggestion?    Perhaps a better approach?



Thanks

John







-----Original Message-----

From: Matthias J. Sax [mailto:matth...@confluent.io]

Sent: Thursday, November 03, 2016 4:29 PM

To: users@kafka.apache.org<mailto:users@kafka.apache.org>

Subject: Re: sliding ktable?



-----BEGIN PGP SIGNED MESSAGE-----

Hash: SHA512



Hi John,



first of all, a KTable is a (changelog) stream; thus, by definition it is 
infinite.



However, I assume you are worried about the internal materialized view, of the 
changelog stream (ie, a table state). This view only contains the latest value 
for each key, ie, a single entry for each key. Thus, it's size is bound by the 
number of key and does not change as long as you number of distinct keys does 
not change.



> At any given time I need at least 2 weeks data in my ktable



There is no such think as "data of the last 2 weeks":



Using a KTable for a KStream-KTable join to do lookups, each lookup will be 
done on the current state if the KTable and thus only return a single value for 
each key. There is no old data in the materialized view with this regard. Of 
course, if a key does not get any update for a long time, you can consider the 
corresponding value as old, but it is still the latest (ie, current) value for 
the key.



> ktable.foreach



#foreach() is applied to the changelog stream and not the internally 
materialized view. Thus, it does not scan over the key space or is applied to 
each currently stored key in the view. It is rather called for each update 
record that is in the changelog stream.



> not sure keys can be removed this way



The only way to delete a key-value entry in the materialized view is to send a 
so-called tombstone record with format <key:null> (ie, value is null). By 
"send" I mean that this tombstone record must be in the input of the KTable.







- -Matthias





On 11/3/16 12:39 PM, John Hayles wrote:

> Newbie here, I am working with Kafka Streams with java 1.8.

>

>

>

> I want to use the ktable as a lookup table in a join to a kstream.

> I had no issue implementing this.  However, I do not want the ktable

> to grow without bounds, I want to limit the ktable to the past 2 weeks

> data, more of a 'sliding' window ktable.  At any given time I need at

> least 2 weeks data in my ktable, so I don’t think solution like

> tumbling table will work since it starts over every time it hops.

>

>

>

> A little simplified example. . .

>

>

>

>

>

> KStream<String, GenericRecord> txnStream = builder.stream("TXN_DATA");

>

>

>

> KStream<String, GenericRecord> txnStreamFull = txnStream

>

> .map((key, record) -> {

>

> return new KeyValue<>(record.get("TXN").toString(), record);

>

> })

>

> .through("RekeyedIntermediateTopic1")

>

> ;

>

>

>

> KTable<String,Long> countTableStream =  txnStream   //  do not want

> this table to grow indefinitely.

>

> .map((key, record) -> {

>

> return new KeyValue<>(record.get("TXN").toString(), record);

>

> })

>

> .through("RekeyedIntermediateTopic2")

>

> .countByKey(stringSerdeKey, "DupCountKTable10");

>

>

>

> KStream<String, GenericRecord> duplicatesStream =

>

> txnStreamFull.leftJoin(countTableStream,

> (vTxnStream,vCountTableStream) -> {

>

> vTxnStream.put("count",

> Long.toString(vCountTableStream.longValue()));

>

> return vTxnStream;});

>

>

>

> duplicatesStream.to("DUP_TXNS");

>

>

>

> I thought perhaps can schedule ktable.foreach to inspect and clean,

> but not sure keys can be removed this way.

>

>

>

> I may be missing a basic concept here.  I have spent some time

> searching but not finding good answer, thanks for any tips.

>

>

>

> Thanks,

>

> John

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

>

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