Hi Patrick, One approach, I would try, is to use Flink state and sync it with database in initializeState and CheckpointListener.notifyCheckpointComplete. Basically issue only idempotent updates to database but only when the last checkpoint is securely taken and records before it are not processed again. This has though a caveat that database might have stale data between checkpoints. Once the current state is synced with database, depending on your App, it might be even cleared from Flink state.
I also cc Piotr and Kostas, maybe, they have more ideas. Best, Andrey On Tue, Mar 19, 2019 at 10:09 AM Patrick Fial <patrick.f...@id1.de> wrote: > Hello, > > I am working on a streaming application with apache flink, which shall > provide end-to-end exactly-once delivery guarantees. The application is > roughly built like this: > > environment.addSource(consumer) > .map(… idempotent transformations ...) > .map(new DatabaseFunction) > .map(… idempotent transformations ...) > .addSink(producer) > > Both source and sink are kafka connectors, and thus support exactly-once > delivery guarantees. > > The tricky part comes with the .map() containing the DatabaseFunction. Its > job is to: > 1) look up the incoming message in some oracle database > 2a) insert it if it is not already stored in the database and publish the > incoming message > 2b) otherwise combine the incoming update with previous contents from the > database, and store back the combined update in the database > 3) output the result of 2) to the next operator > > This logic leads to inconsistent data beeing published to the sink in case > of a failure where the DatabaseFunction was already executed, but the > message is not yet published to the sink. > > My understanding is, that in such a scenario all operator states would be > reverted to the last checkpoint. Since the .map() operator is stateless, > nothing is done here, so only the consumer and producer states are > reverted. This leads to the message beeing reprocessed from the beginning > (source), and thus beeing processed *again* by the DatabaseFunction. > However, the DatabaseFunction is not idempotent (because of 1)-3) as > explained above), and thus leads to a different output than in the first > run. > > The question is, how I can assure transaction-safety in this application? > > Basically, I would need to use database transactions within the > DatabaseFunction, and commit those only if the messages are also commited > to the kafka sink. However, I don’t know how to achieve this. > > I read about the two phase commit protocol in flink ( > https://flink.apache.org/features/2018/03/01/end-to-end-exactly-once-apache-flink.html), > but I fail to find examples of how to implement this in detail for stream > operators (NOT sinks). All documentation I find only refers to using the > two phase commit protocol for sinks. Should I, in this case, only implement > the CheckpointedFunction and hook on the initializeState/snapshotState to > rollback/commit by database transactions? Would this already make things > work? I am a bit confused because there seem to be no hooks for the > pre-commit/commit/abort signals. > > Anyway, I am also afraid that this might also introduce scaling issues, > because depending on the message throughput, committing database actions > only with every checkpoint interval might blow the temp tablespace in the > oracle database. > > Thanks in advance for any help. > > best regards > Patrick Fial > > -- > > *Patrick Fial* > > Client Platform Entwickler > > Information Design One AG > > > Phone +49 69 244 502 38 > > Web www.id1.de > > > > Information Design One AG, Baseler Straße 10, 60329 Frankfurt am Main > > Registereintrag: Amtsgericht Frankfurt am Main, HRB 52596 > > Vorstand: Robert Peters, Benjamin Walther, Aufsichtsrat: Christian Hecht > (Vorsitz) >
