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
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?
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
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