Hi all,
Another pattern I think is worth adding is a sliding-windowed message
reordering and de-duplicating processor.
The outline I have in mind is based on this (just the timestamp would
come from the record context - in this question the timestamp was in the
body of the message):
https://stackoverflow.com/a/44345374/7897191
Please let me know if you have a better design for this?
Cheers,
Michal
On 27/05/17 21:16, Jay Kreps wrote:
This is great!
-Jay
On Sat, May 27, 2017 at 12:47 PM, Michal Borowiecki
<michal.borowie...@openbet.com <mailto:michal.borowie...@openbet.com>>
wrote:
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.
builder
.stream(/*key serde*/, /*transaction serde*/,"transaciton-topic")
.groupByKey(/*key serde*/, /*transaction serde*/)
.aggregate(
() -> /*empty aggregate*/,
aggregator(),
merger(),
SessionWindows.with(SESSION_TIMEOUT_MS).until(SESSION_TIMEOUT_MS*2),
/* aggregate serde */,
txPerCustomerSumStore()// this store can be queried for per customer
session data )
.toStream()
.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()
windowedCustomerId.window().end(),
agg
)
)
)
.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,
TimeWindows.of(SESSION_TIMEOUT_MS).advanceBy(SESSION_TIMOUT_DELAY_TOLERANCE_MS),
"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 oldest
not-yet-expired window
),
timeAndAggs.aggs()
),
new SessionKeySerde<>(Serdes.Integer()),
/* aggregate serde */
)
.reduce(
(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 );
builder.globalTable(
new SessionKeySerde<>(Serdes.Integer()),
/* aggregate serde */,
changelogTopicForStore(TRANSACTION_TOTALS),"totals");
// 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?
Thanks,
Michał
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
<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 <tel:+44%2020%208742%201600>
+44 203 249 8448 <tel:+44%2020%203249%208448>
E: michal.borowie...@openbet.com
<mailto:michal.borowie...@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 postmas...@openbet.com <mailto:postmas...@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.borowie...@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
postmas...@openbet.com <mailto:postmas...@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
