Using the DSL, this sounds about right.I am not worried about the complexity -- KS can handle it, and it's not uncommon to end up with such topologies.
You might be able to cut down on complexity by not using the DSL, but the Processor API. It gives you more control, and thus you might be able to optimize the overall topology.
Maybe inspect the details of `TopologyDescription` to spot inefficiencies of the DSL generated Topology that might give you an idea how much you could optimize using Processor API (to estimate if it would be worth the effort).
It's hard to tell w/o knowing the details. It could also be just an inherently complex problem, and the DSL program is already as efficient as it gets...
Of course, there might also be ways to play with configs to cut down on latency to some extend, if e2e latency is your main concern. Again, I don't know the use case: for many case, sub-second latency is actually sufficient.
HTH. -Matthias On 2/7/24 7:41 AM, Karsten Stöckmann wrote:
Sorry for being late with the response - I've been quite busy working on our Streams application lately. That leads me back to my initial question. The Folder class contains multiple fields with FK pointing to the Person table, all of them with different semantics (customer, billing address, etc). So in order to find _all_ folders related to a particular person regardless of its role, I guess I need to a) re-key the folder table on each person FK independently and then b) outer join the result tables. The entire topology is insanely complex, I've got around 10 tables with different levels of nesting (e.g. folder -- 1:n --> dependency a -- 1:n --> dependency b) that all need to be aggregated and in the end re-keyed to person IDs in order to build an aggregate person. There are 14 sub topologies... - measuring the e2e latency shows values around 600ms which seems rather high to me. Does that sound crazy? ;) Best wishes Karsten Am Do., 1. Feb. 2024 um 19:02 Uhr schrieb Matthias J. Sax <mj...@apache.org>:I see. You need to ensure that you get _all_ Person. For this case, I guess you are right. You would need to first aggregate the folder per person: KTable allPersonFolders = folder.groupBy((...) -> (folder.customerId, ...)) .aggregate(...) And in a second step, do a left join: result = personTable.leftJoin(allPersonFolders,...)Reading the topic as a table directly did not work out as that crashed the application; apparently reading the topic as a KTable and then using that for three independent re-key-operations is not allowed.Not sure if I can follow. What do you mean by "crashed". -- For tables, there is no `selectKey()` nor a `repartition()` as explained in my previous reply. However, doing a `table.groupBy(...)` will set a new key and repartition the data to your needs. -Matthias On 2/1/24 1:12 AM, Karsten Stöckmann wrote:Thanks so much for taking a look. An FK-table-table join is an inner join which implies there would be no Person entites without associated Folders. Unfortunately, that's not the case. That lead me to an attempt of re-keying the Folder topic by each of the three possible foreign keys in order to be able to left join Persons against each of the three re-keyed KTables to build an eventual Person aggregation containing all possible Folders associated in any way. Reading the topic as a table directly did not work out as that crashed the application; apparently reading the topic as a KTable and then using that for three independent re-key-operations is not allowed. Best wishes, Karsten Am Do., 1. Feb. 2024 um 02:16 Uhr schrieb Matthias J. Sax <mj...@apache.org>:Thanks for the details. This does make sense. So it seems you can read all topic as table (ie, builder.table("topic") -- no need to so `builder.stream().toTable()`). And you can use the built-in FK-table-table join, and aggregate the result: KTable result = folderTable .join(personTable, (folderId, folder) -> folder.customerId, ...) .groupBy((...) -> (personId, ...)) .aggregate(...); result.toStream().to("resultTopic"); Note the fk-extractor `(folderId, folder) -> folder.customerId` that tells the join to use `customerId` from the `folderTable` to lookup the person from personTable. Think of `folderTable` as fact-table and `personTable` as dimension table. KS will take care of everything else under the hood automatically. -Matthias On 1/30/24 11:25 AM, Karsten Stöckmann wrote:Matthias, thanks for getting back on this. I'll try to illustrate my intent with an example as I'm not yet fully familiar with Kafka (Streams) and its idioms... Assume classes Person and Folder: class Person { Long id; String firstname; String lastname; // some content } class Folder { Long id; String folderNumber; // some other content Long customerId; // FK, points to Person.id Long billingAddressId; // FK, also points to Person.id } Thus both foreign keys of Folder point to Person entities, yet with different semantics. They're not composite keys but act independently. Now assume I want to build an aggregate Person object containing Folder.folderNumber of all folders associated with a Person entity, regardless whether it acts as a customer or billing address. My (naive) idea was to build re-keyed KTables by Folder.customerId and Folder.billingAddressId and then joining / aggregating them with the Person KTable in order to build something like this: class AggregatedPerson { Long id; List<String> folderNumbers; // or even List<Folder> // ... } (The latter supposed to be written to an output topic in order to serve as input for Solr or ElasticSearch.) Does this even make sense?If you read the topic a KTable, you cannot repartition because it violates the contract. A KTable must be partitioned by it's primary key, ie, the ID field, and thus the DSL does not offer you a repartition option.So re-key means repartition? ATM the partition size of all input topics is 1 as per Kafka UI, as I've specified no extra configuration for them. Best wishes, Karsten Am Di., 30. Jan. 2024 um 20:03 Uhr schrieb Matthias J. Sax <mj...@apache.org>:Both fk1 and fk2 point to the PK of another entity (not shown for brevity, of no relevance to the question).It this two independent FK, or one two-column FK?Ingesting the topic into a Kafka Streams application, how can I re-key the resulting KTable<Long, A> by both fk1 and fk2?If you read the topic a KTable, you cannot repartition because it violates the contract. A KTable must be partitioned by it's primary key, ie, the ID field, and thus the DSL does not offer you a repartition option. You could read the topic as KStream though, and provide a custom `StreamPartitioner` for a `repartition()` operation. However, this is also "dangerous" because for a KStream it's also assumed that it's partitioned by it's key, and you might break downstream DSL operators with such a violation of the "contract". Looking into your solution:.toTable() .groupBy( (key, value) -> KeyValue.pair(value.fk1(), value), Grouped.with(...))This will set fk1 as key, what seems not to align with you previous comment about the key should stay the ID? (Same for f2k). Your last step seems to join fk1-fk2 -- is this on purpose? I guess it's unclear what you try to actually do to begin with? It sound like it's overall a self-join of the input topic on fk1 and fk2 ? -Matthias On 1/28/24 2:24 AM, Karsten Stöckmann wrote:Hi all, just stumbled upon another Kafka Streams issue that keeps me busy these days. Assume a (simplified) class A like this: class A { private Long id; private String someContent; private Long fk1; private Long fk2; // Getters and setters accordingly } Both fk1 and fk2 point to the PK of another entity (not shown for brevity, of no relevance to the question). Now assume a Kafka topic built from instances of class A, keyed by its id (see above). Ingesting the topic into a Kafka Streams application, how can I re-key the resulting KTable<Long, A> by both fk1 and fk2? Note that the resulting key should not be changed or turned into some kind of composite key as it is used in later join operations. My (naive) solution involves creating two KTables from the input stream, re-keying them by fk1 and fk2 accordingly and then outer joining both resulting (re-keyed) KTables. KStream<Long, A> in = streamsBuilder.stream(topic, Consumed.with(...)); KTable<Long, A> rekeyedByFk1 = in .toTable() .groupBy( (key, value) -> KeyValue.pair(value.fk1(), value), Grouped.with(...)) .aggregate( Aggregate::new, (key, value, aggregate) -> aggregate.add(value), (key, value, aggregate) -> aggregate.remove(value), Materialized.with(...)); KTable<Long, a> rekeyedByFk2 = in .toTable() .groupBy( (key, value) -> KeyValue.pair(value.fk2(), value), Grouped.with(...)) .aggregate( ... same as above ); KTable<Long, A> joined = rekeyedByFk1 .outerJoin( rekeyedByFk2, <value joiner>) .groupBy(KeyValue::pair, Grouped.with(...)) .aggregate(...); <value joiner> would integrate the (already pre-joined) Aggregates as to avoid duplicates. Does this seem like a viable solution, or are there better / simpler / more efficient implementations? Best wishes, Karsten
