Thanks again for the replies, and the time to suggest alternate solution. I
know your time is valuable.
The reason for the List in the aggregate, which I missed trying to keep it
simple, is that the order of records added to topic is not guaranteed to be
chronological. Keeping the list allows to traverse and apply more complex
business rules to determine the output values.
Topic payload {txnId,lane,txnDate} Notice lane 'c' is duplicated 3 times.
. . .
{'03','c','11/07/2016 04:00:00'} // first entry for lane 'c'; no duplicate
. . .
{'11','c','11/08/2016 15:30:00'} // second entry for lane 'c'; no duplicate
. . .
{'09','c','11/07/2016 04:00:01'} // third entry for lane 'c' - added to
topic out of chronological order - is duplicate of txnId 03 even though time is
slightly different
. . .
Resulting materialized view.
'c':{('03','11/07/2016 04:00:00') ,
('11','11/08/2016 15:30:00') ,
('09','11/07/2016 04:00:01')}
When the kstream is joined to lookup ktable defined above, txnId '03' should be
identified as duplicate to ‘09’. So, output topic like...
Topic payload {txnId,lane,txnDate,duplicateTxnId}
. . .
{'03','c','11/07/2016 04:00:00',''}
. .
{'11','c','11/08/2016 15:30:00',''}
. . .
{'09','c','11/07/2016 04:00:01','03'} // note the duplicate value ‘03’ is
not from the most recent
. . .
Note both ksteam and ktable source are from same topic. When executed (see
code below), the joined ktable is 'behind' the kstream so lookup will sometimes
fail. I think the ktable is just slower to process because it is doing more
processing than kstream.
I need to synchronize such that a record must be added to the ktable before the
join operation occurs for the same record in kstream.
(1) Please give hint if any options to resolve?
Also, from below comments...
> About infinitely growing KTable: It seems you are extending each lane
> with a list of all txnId -- so your view needs infinite memory as you
> expend your values... A quick fix might be, to delete older txnID for
> this list, each time you update the list (as you mentioned you only
> need data for the last two weeks -- you might need to add a timestamp
> for each txnID in the list to do the pruning each time you append or
> lookup the list).
(2) Once I determine a key can be deleted, can I tombstone record
programmatically in the same process? I am not clear on how. My input topic
is created by a Kafka Connect configuration (no code).
If interested here is the main part of code so far…
KStream<GenericRecord, GenericRecord> txnStream =
builder.stream("LANE_TRANSACTIONS");
// Create new ktable from original.
KTable<GenericRecord, Long> countTableStream = txnStream
.map((dummy, value) -> {
return new KeyValue<>(value, value);
})
.countByKey(valueAvroSerde, "CountTable");
// aggregate ktable
KTable<String, ArrayList<GenericRecord>> lookup = countTableStream
.groupBy((key, value) -> {
return new KeyValue(key.get("LANE_ID").toString(), key);
},
stringSerdeKey,
valueAvroSerde)
.aggregate(
() -> { return new ArrayList<>(); },
(key, record, list) ->
{
list.add(record);
return list;
},
(key, record, list) ->
{
// list.remove(record); // this is always executed,
not sure why
return list;
},
new ArrayListSerde<>(keyAvroSerde,valueAvroSerde),
"TxnMapByTagPlaza");
ArrayListSerde<GenericRecord> arrayListSerde = new
ArrayListSerde<>(keyAvroSerde,valueAvroSerde);
lookup.through(stringSerdeKey,arrayListSerde,"DUPLICATE_LOOKUPS_STREAM");
// Create new stream from original.
KStream<String, GenericRecord> txnStreamFull =
txnStream.map((key, record) -> {
return new KeyValue<>(record.get("LANE_ID").toString(),record);
})
.through(stringSerdeKey,valueAvroSerde,"RekeyedIntermediateTopic19")
;
// Join stream to lookup ktable.
// Right now just evaluating results.
KStream<String, GenericRecord> duplicatesStream =
txnStreamFull.leftJoin(lookup,(vTxnStream, vLookupTable) -> {
// this is where vLookupTable list will be
traversed to determine any duplicates that match vTxnStream value.
// from inspecting JOINED objects I can see
vLookupTable values are late compared to txnSteamFull stream values.
return vTxnStream;
});
Know that I have spent time reading the docs / google searches, but still just
not all clear to me, and perhaps there are other docs I am not finding.
(3) Just wanted to get opinion also, is this the type of processing I should be
doing with kafka streams and ktables or is this something that might be better
solved using a traditional database?
Thanks again for any insight,
John
-----Original Message-----
From: R Krishna [mailto:krishna...@gmail.com]
Sent: Tuesday, November 08, 2016 1:44 AM
To: users@kafka.apache.org<mailto:users@kafka.apache.org>
Subject: Re: sliding ktable?
There is a problem with tombstoning old entries based on a new entry, that, the
keys which have no new entries will remain there forever.
On Mon, Nov 7, 2016 at 9:38 AM, Matthias J. Sax
<matth...@confluent.io<mailto:matth...@confluent.io>>
wrote:
> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA512
>
> John,
>
> your thinking is on the right track!
>
> About infinitely growing KTable: It seems you are extending each lane
> with a list of all txnId -- so your view needs infinite memory as you
> expend your values... A quick fix might be, to delete older txnID for
> this list, each time you update the list (as you mentioned you only
> need data for the last two weeks -- you might need to add a timestamp
> for each txnID in the list to do the pruning each time you append or
> lookup the list).
>
> > 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?
>
> There is no Kafka support for this. You would need to go with the
> suggest as describe above. The only "delete" mechanism Kafka offers is
> for compacted topics via tombstone message (ie, message with
> <key:null> format; value == null). However, tombstones do delete the
> whole record with this key, thus I doubt they are useful for your case.
>
> However, reading through your email, I am wondering why you do need
> the all old txnIds. You mentioned that you want to get the previous
> txnId for each duplicate (and you example results verifies this).
> Thus, it would be sufficient to only store the latest tnxId for each
> "lane" IMHO. Furhtermore, for this deduplication it seems sufficient
> to only use a KTable without a join.
>
> The ideas would the as follows: You consumer you streams as a
> changelog (ie, KTable). For each record, you check if there is an
> entry in the view. If not, just put the record itself as result
> because there is no duplicate. If you do find an entry, the current
> record is a duplicate of the record found. The record found, does
> contain it's txnId, you so can use this as "previous txnId". As
> result, you store the current record. You data format would be like
> <lane:(txnId,txnDate)> (for input) and
> <lane:(txnId,txnDate,previoudTxnId)> (for output.
>
> You stream and view would be like:
>
> {'c',('03','11/07/2016')} plus state: EMPTY
>
> => {'c',('03','11/07/2016',''} // this is output and state update
> at the same time
>
>
>
> {'c',('09','11/07/2016')} plus state: {'c',('03','11/07/2016',null}
>
> => {'c',('09','11/07/2016','03')} // this is output and state
> update at the same time
>
>
>
> {'c',('11','11/08/2016')} plus state: {'c',('09','11/07/2016','03')}
>
> => {'c',('11','11/08/2016','09')} // this is output and state
> update at the same time
>
>
> - -Matthias
>
> On 11/7/16 8:22 AM, John Hayles wrote:
> > 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<mailto:users@kafka.apache.org%3cmailto:users@kafka.apache.org>>
> >
> > Subject: Re: sliding ktable?
> >
> >
> >
> > 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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--
Radha Krishna, Proddaturi
253-234-5657
