Hey Nick, Thank you for the kind words and the feedback! I'll definitely add an option to configure the transactional mechanism in Stores factory method via an argument as John previously suggested and might add the in-memory option via RocksDB Indexed Batches if I figure why their creation via rocksdb jni fails with `UnsatisfiedLinkException`.
Best, Alex On Wed, Jul 27, 2022 at 11:46 AM Alexander Sorokoumov < asorokou...@confluent.io> wrote: > Hey Guozhang, > > 1) About the param passed into the `recover()` function: it seems to me >> that the semantics of "recover(offset)" is: recover this state to a >> transaction boundary which is at least the passed-in offset. And the only >> possibility that the returned offset is different than the passed-in >> offset >> is that if the previous failure happens after we've done all the commit >> procedures except writing the new checkpoint, in which case the returned >> offset would be larger than the passed-in offset. Otherwise it should >> always be equal to the passed-in offset, is that right? > > > Right now, the only case when `recover` returns an offset different from > the passed one is when the failure happens *during* commit. > > > If the failure happens after commit but before the checkpoint, `recover` > might return either a passed or newer committed offset, depending on the > implementation. The `recover` implementation in the prototype returns a > passed offset because it deletes the commit marker that holds that offset > after the commit is done. In that case, the store will replay the last > commit from the changelog. I think it is fine as the changelog replay is > idempotent. > > 2) It seems the only use for the "transactional()" function is to determine >> if we can update the checkpoint file while in EOS. > > > Right now, there are 2 other uses for `transactional()`: > 1. To determine what to do during initialization if the checkpoint is gone > (see [1]). If the state store is transactional, we don't have to wipe the > existing data. Thinking about it now, we do not really need this check > whether the store is `transactional` because if it is not, we'd not have > written the checkpoint in the first place. I am going to remove that check. > 2. To determine if the persistent kv store in KStreamImplJoin should be > transactional (see [2], [3]). > > I am not sure if we can get rid of the checks in point 2. If so, I'd be > happy to encapsulate `transactional()` logic in `commit/recover`. > > Best, > Alex > > 1. > https://github.com/apache/kafka/pull/12393/files#diff-971d9ef7ea8aefffff687fc7ee131bd166ced94445f4ab55aa83007541dccfdaL256-R281 > 2. > https://github.com/apache/kafka/pull/12393/files#diff-9ce43046fdef1233ab762e728abd1d3d44d7c270b28dcf6b63aa31a93a30af07R266-R278 > 3. > https://github.com/apache/kafka/pull/12393/files#diff-9ce43046fdef1233ab762e728abd1d3d44d7c270b28dcf6b63aa31a93a30af07R348-R354 > > On Tue, Jul 26, 2022 at 6:39 PM Nick Telford <nick.telf...@gmail.com> > wrote: > >> Hi Alex, >> >> Excellent proposal, I'm very keen to see this land! >> >> Would it be useful to permit configuring the type of store used for >> uncommitted offsets on a store-by-store basis? This way, users could >> choose >> whether to use, e.g. an in-memory store or RocksDB, potentially reducing >> the overheads associated with RocksDb for smaller stores, but without the >> memory pressure issues? >> >> I suspect that in most cases, the number of uncommitted records will be >> very small, because the default commit interval is 100ms. >> >> Regards, >> >> Nick >> >> On Tue, 26 Jul 2022 at 01:36, Guozhang Wang <wangg...@gmail.com> wrote: >> >> > Hello Alex, >> > >> > Thanks for the updated KIP, I looked over it and browsed the WIP and >> just >> > have a couple meta thoughts: >> > >> > 1) About the param passed into the `recover()` function: it seems to me >> > that the semantics of "recover(offset)" is: recover this state to a >> > transaction boundary which is at least the passed-in offset. And the >> only >> > possibility that the returned offset is different than the passed-in >> offset >> > is that if the previous failure happens after we've done all the commit >> > procedures except writing the new checkpoint, in which case the returned >> > offset would be larger than the passed-in offset. Otherwise it should >> > always be equal to the passed-in offset, is that right? >> > >> > 2) It seems the only use for the "transactional()" function is to >> determine >> > if we can update the checkpoint file while in EOS. But the purpose of >> the >> > checkpoint file's offsets is just to tell "the local state's current >> > snapshot's progress is at least the indicated offsets" anyways, and with >> > this KIP maybe we would just do: >> > >> > a) when in ALOS, upon failover: we set the starting offset as >> > checkpointed-offset, then restore() from changelog till the end-offset. >> > This way we may restore some records twice. >> > b) when in EOS, upon failover: we first call >> recover(checkpointed-offset), >> > then set the starting offset as the returned offset (which may be larger >> > than checkpointed-offset), then restore until the end-offset. >> > >> > So why not also: >> > c) we let the `commit()` function to also return an offset, which >> indicates >> > "checkpointable offsets". >> > d) for existing non-transactional stores, we just have a default >> > implementation of "commit()" which is simply a flush, and returns a >> > sentinel value like -1. Then later if we get checkpointable offsets -1, >> we >> > do not write the checkpoint. Upon clean shutting down we can just >> > checkpoint regardless of the returned value from "commit". >> > e) for existing non-transactional stores, we just have a default >> > implementation of "recover()" which is to wipe out the local store and >> > return offset 0 if the passed in offset is -1, otherwise if not -1 then >> it >> > indicates a clean shutdown in the last run, can this function is just a >> > no-op. >> > >> > In that case, we would not need the "transactional()" function anymore, >> > since for non-transactional stores their behaviors are still wrapped in >> the >> > `commit / recover` function pairs. >> > >> > I have not completed the thorough pass on your WIP PR, so maybe I could >> > come up with some more feedback later, but just let me know if my >> > understanding above is correct or not? >> > >> > >> > Guozhang >> > >> > >> > >> > >> > On Thu, Jul 14, 2022 at 7:01 AM Alexander Sorokoumov >> > <asorokou...@confluent.io.invalid> wrote: >> > >> > > Hi, >> > > >> > > I updated the KIP with the following changes: >> > > * Replaced in-memory batches with the secondary-store approach as the >> > > default implementation to address the feedback about memory pressure >> as >> > > suggested by Sagar and Bruno. >> > > * Introduced StateStore#commit and StateStore#recover methods as an >> > > extension of the rollback idea. @Guozhang, please see the comment >> below >> > on >> > > why I took a slightly different approach than you suggested. >> > > * Removed mentions of changes to IQv1 and IQv2. Transactional state >> > stores >> > > enable reading committed in IQ, but it is really an independent >> feature >> > > that deserves its own KIP. Conflating them unnecessarily increases the >> > > scope for discussion, implementation, and testing in a single unit of >> > work. >> > > >> > > I also published a prototype - >> > https://github.com/apache/kafka/pull/12393 >> > > that implements changes described in the proposal. >> > > >> > > Regarding explicit rollback, I think it is a powerful idea that allows >> > > other StateStore implementations to take a different path to the >> > > transactional behavior rather than keep 2 state stores. Instead of >> > > introducing a new commit token, I suggest using a changelog offset >> that >> > > already 1:1 corresponds to the materialized state. This works nicely >> > > because Kafka Stream first commits an AK transaction and only then >> > > checkpoints the state store, so we can use the changelog offset to >> commit >> > > the state store transaction. >> > > >> > > I called the method StateStore#recover rather than StateStore#rollback >> > > because a state store might either roll back or forward depending on >> the >> > > specific point of the crash failure.Consider the write algorithm in >> Kafka >> > > Streams is: >> > > 1. write stuff to the state store >> > > 2. producer.sendOffsetsToTransaction(token); >> > producer.commitTransaction(); >> > > 3. flush >> > > 4. checkpoint >> > > >> > > Let's consider 3 cases: >> > > 1. If the crash failure happens between #2 and #3, the state store >> rolls >> > > back and replays the uncommitted transaction from the changelog. >> > > 2. If the crash failure happens during #3, the state store can roll >> > forward >> > > and finish the flush/commit. >> > > 3. If the crash failure happens between #3 and #4, the state store >> should >> > > do nothing during recovery and just proceed with the checkpoint. >> > > >> > > Looking forward to your feedback, >> > > Alexander >> > > >> > > On Wed, Jun 8, 2022 at 12:16 AM Alexander Sorokoumov < >> > > asorokou...@confluent.io> wrote: >> > > >> > > > Hi, >> > > > >> > > > As a status update, I did the following changes to the KIP: >> > > > * replaced configuration via the top-level config with configuration >> > via >> > > > Stores factory and StoreSuppliers, >> > > > * added IQv2 and elaborated how readCommitted will work when the >> store >> > is >> > > > not transactional, >> > > > * removed claims about ALOS. >> > > > >> > > > I am going to be OOO in the next couple of weeks and will resume >> > working >> > > > on the proposal and responding to the discussion in this thread >> > starting >> > > > June 27. My next top priorities are: >> > > > 1. Prototype the rollback approach as suggested by Guozhang. >> > > > 2. Replace in-memory batches with the secondary-store approach as >> the >> > > > default implementation to address the feedback about memory >> pressure as >> > > > suggested by Sagar and Bruno. >> > > > 3. Adjust Stores methods to make transactional implementations >> > pluggable. >> > > > 4. Publish the POC for the first review. >> > > > >> > > > Best regards, >> > > > Alex >> > > > >> > > > On Wed, Jun 1, 2022 at 2:52 PM Guozhang Wang <wangg...@gmail.com> >> > wrote: >> > > > >> > > >> Alex, >> > > >> >> > > >> Thanks for your replies! That is very helpful. >> > > >> >> > > >> Just to broaden our discussions a bit here, I think there are some >> > other >> > > >> approaches in parallel to the idea of "enforce to only persist upon >> > > >> explicit flush" and I'd like to throw one here -- not really >> > advocating >> > > >> it, >> > > >> but just for us to compare the pros and cons: >> > > >> >> > > >> 1) We let the StateStore's `flush` function to return a token >> instead >> > of >> > > >> returning `void`. >> > > >> 2) We add another `rollback(token)` interface of StateStore which >> > would >> > > >> effectively rollback the state as indicated by the token to the >> > snapshot >> > > >> when the corresponding `flush` is called. >> > > >> 3) We encode the token and commit as part of >> > > >> `producer#sendOffsetsToTransaction`. >> > > >> >> > > >> Users could optionally implement the new functions, or they can >> just >> > not >> > > >> return the token at all and not implement the second function. >> Again, >> > > the >> > > >> APIs are just for the sake of illustration, not feeling they are >> the >> > > most >> > > >> natural :) >> > > >> >> > > >> Then the procedure would be: >> > > >> >> > > >> 1. the previous checkpointed offset is 100 >> > > >> ... >> > > >> 3. flush store, make sure all writes are persisted; get the >> returned >> > > token >> > > >> that indicates the snapshot of 200. >> > > >> 4. producer.sendOffsetsToTransaction(token); >> > > producer.commitTransaction(); >> > > >> 5. Update the checkpoint file (say, the new value is 200). >> > > >> >> > > >> Then if there's a failure, say between 3/4, we would get the token >> > from >> > > >> the >> > > >> last committed txn, and first we would do the restoration (which >> may >> > get >> > > >> the state to somewhere between 100 and 200), then call >> > > >> `store.rollback(token)` to rollback to the snapshot of offset 100. >> > > >> >> > > >> The pros is that we would then not need to enforce the state >> stores to >> > > not >> > > >> persist any data during the txn: for stores that may not be able to >> > > >> implement the `rollback` function, they can still reduce its impl >> to >> > > "not >> > > >> persisting any data" via this API, but for stores that can indeed >> > > support >> > > >> the rollback, their implementation may be more efficient. The cons >> > > though, >> > > >> on top of my head are 1) more complicated logic differentiating >> > between >> > > >> EOS >> > > >> with and without store rollback support, and ALOS, 2) encoding the >> > token >> > > >> as >> > > >> part of the commit offset is not ideal if it is big, 3) the >> recovery >> > > logic >> > > >> including the state store is also a bit more complicated. >> > > >> >> > > >> >> > > >> Guozhang >> > > >> >> > > >> >> > > >> >> > > >> >> > > >> >> > > >> On Wed, Jun 1, 2022 at 1:29 PM Alexander Sorokoumov >> > > >> <asorokou...@confluent.io.invalid> wrote: >> > > >> >> > > >> > Hi Guozhang, >> > > >> > >> > > >> > But I'm still trying to clarify how it guarantees EOS, and it >> seems >> > > >> that we >> > > >> > > would achieve it by enforcing to not persist any data written >> > within >> > > >> this >> > > >> > > transaction until step 4. Is that correct? >> > > >> > >> > > >> > >> > > >> > This is correct. Both alternatives - in-memory >> WriteBatchWithIndex >> > and >> > > >> > transactionality via the secondary store guarantee EOS by not >> > > persisting >> > > >> > data in the "main" state store until it is committed in the >> > changelog >> > > >> > topic. >> > > >> > >> > > >> > Oh what I meant is not what KStream code does, but that >> StateStore >> > > impl >> > > >> > > classes themselves could potentially flush data to become >> > persisted >> > > >> > > asynchronously >> > > >> > >> > > >> > >> > > >> > Thank you for elaborating! You are correct, the underlying state >> > store >> > > >> > should not persist data until the streams app calls >> > StateStore#flush. >> > > >> There >> > > >> > are 2 options how a State Store implementation can guarantee >> that - >> > > >> either >> > > >> > keep uncommitted writes in memory or be able to roll back the >> > changes >> > > >> that >> > > >> > were not committed during recovery. RocksDB's >> WriteBatchWithIndex is >> > > an >> > > >> > implementation of the first option. A considered alternative, >> > > >> Transactions >> > > >> > via Secondary State Store for Uncommitted Changes, is the way to >> > > >> implement >> > > >> > the second option. >> > > >> > >> > > >> > As everyone correctly pointed out, keeping uncommitted data in >> > memory >> > > >> > introduces a very real risk of OOM that we will need to handle. >> The >> > > >> more I >> > > >> > think about it, the more I lean towards going with the >> Transactions >> > > via >> > > >> > Secondary Store as the way to implement transactionality as it >> does >> > > not >> > > >> > have that issue. >> > > >> > >> > > >> > Best, >> > > >> > Alex >> > > >> > >> > > >> > >> > > >> > On Wed, Jun 1, 2022 at 12:59 PM Guozhang Wang < >> wangg...@gmail.com> >> > > >> wrote: >> > > >> > >> > > >> > > Hello Alex, >> > > >> > > >> > > >> > > > we flush the cache, but not the underlying state store. >> > > >> > > >> > > >> > > You're right. The ordering I mentioned above is actually: >> > > >> > > >> > > >> > > ... >> > > >> > > 3. producer.sendOffsetsToTransaction(); >> > > producer.commitTransaction(); >> > > >> > > 4. flush store, make sure all writes are persisted. >> > > >> > > 5. Update the checkpoint file to 200. >> > > >> > > >> > > >> > > But I'm still trying to clarify how it guarantees EOS, and it >> > seems >> > > >> that >> > > >> > we >> > > >> > > would achieve it by enforcing to not persist any data written >> > within >> > > >> this >> > > >> > > transaction until step 4. Is that correct? >> > > >> > > >> > > >> > > > Can you please point me to the place in the codebase where we >> > > >> trigger >> > > >> > > async flush before the commit? >> > > >> > > >> > > >> > > Oh what I meant is not what KStream code does, but that >> StateStore >> > > >> impl >> > > >> > > classes themselves could potentially flush data to become >> > persisted >> > > >> > > asynchronously, e.g. RocksDB does that naturally out of the >> > control >> > > of >> > > >> > > KStream code. I think it is related to my previous question: >> if we >> > > >> think >> > > >> > by >> > > >> > > guaranteeing EOS at the state store level, we would effectively >> > ask >> > > >> the >> > > >> > > impl classes that "you should not persist any data until >> `flush` >> > is >> > > >> > called >> > > >> > > explicitly", is the StateStore interface the right level to >> > enforce >> > > >> such >> > > >> > > mechanisms, or should we just do that on top of the >> StateStores, >> > > e.g. >> > > >> > > during the transaction we just keep all the writes in the cache >> > (of >> > > >> > course >> > > >> > > we need to consider how to work around memory pressure as >> > previously >> > > >> > > mentioned), and then upon committing, we just write the cached >> > > records >> > > >> > as a >> > > >> > > whole into the store and then call flush. >> > > >> > > >> > > >> > > >> > > >> > > Guozhang >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > >> > > On Tue, May 31, 2022 at 4:08 PM Alexander Sorokoumov >> > > >> > > <asorokou...@confluent.io.invalid> wrote: >> > > >> > > >> > > >> > > > Hey, >> > > >> > > > >> > > >> > > > Thank you for the wealth of great suggestions and questions! >> I >> > am >> > > >> going >> > > >> > > to >> > > >> > > > address the feedback in batches and update the proposal >> async, >> > as >> > > >> it is >> > > >> > > > probably going to be easier for everyone. I will also write a >> > > >> separate >> > > >> > > > message after making updates to the KIP. >> > > >> > > > >> > > >> > > > @John, >> > > >> > > > >> > > >> > > > > Did you consider instead just adding the option to the >> > > >> > > > > RocksDB*StoreSupplier classes and the factories in Stores ? >> > > >> > > > >> > > >> > > > Thank you for suggesting that. I think that this idea is >> better >> > > than >> > > >> > > what I >> > > >> > > > came up with and will update the KIP with configuring >> > > >> transactionality >> > > >> > > via >> > > >> > > > the suppliers and Stores. >> > > >> > > > >> > > >> > > > what is the advantage over just doing the same thing with the >> > > >> > RecordCache >> > > >> > > > > and not introducing the WriteBatch at all? >> > > >> > > > >> > > >> > > > Can you point me to RecordCache? I can't find it in the >> project. >> > > The >> > > >> > > > advantage would be that WriteBatch guarantees write >> atomicity. >> > As >> > > >> far >> > > >> > as >> > > >> > > I >> > > >> > > > understood the way RecordCache works, it might leave the >> system >> > in >> > > >> an >> > > >> > > > inconsistent state during crash failure on write. >> > > >> > > > >> > > >> > > > You mentioned that a transactional store can help reduce >> > > >> duplication in >> > > >> > > the >> > > >> > > > > case of ALOS >> > > >> > > > >> > > >> > > > I will remove claims about ALOS from the proposal. Thank you >> for >> > > >> > > > elaborating! >> > > >> > > > >> > > >> > > > As a reminder, we have a new IQv2 mechanism now. Should we >> > propose >> > > >> any >> > > >> > > > > changes to IQv1 to support this transactional mechanism, >> > versus >> > > >> just >> > > >> > > > > proposing it for IQv2? Certainly, it seems strange only to >> > > >> propose a >> > > >> > > > change >> > > >> > > > > for IQv1 and not v2. >> > > >> > > > >> > > >> > > > >> > > >> > > > I will update the proposal with complementary API changes >> for >> > > IQv2 >> > > >> > > > >> > > >> > > > What should IQ do if I request to readCommitted on a >> > > >> non-transactional >> > > >> > > > > store? >> > > >> > > > >> > > >> > > > We can assume that non-transactional stores commit on write, >> so >> > IQ >> > > >> > works >> > > >> > > in >> > > >> > > > the same way with non-transactional stores regardless of the >> > value >> > > >> of >> > > >> > > > readCommitted. >> > > >> > > > >> > > >> > > > >> > > >> > > > @Guozhang, >> > > >> > > > >> > > >> > > > * If we crash between line 3 and 4, then at that time the >> local >> > > >> > > persistent >> > > >> > > > > store image is representing as of offset 200, but upon >> > recovery >> > > >> all >> > > >> > > > > changelog records from 100 to log-end-offset would be >> > considered >> > > >> as >> > > >> > > > aborted >> > > >> > > > > and not be replayed and we would restart processing from >> > > position >> > > >> > 100. >> > > >> > > > > Restart processing will violate EOS.I'm not sure how e.g. >> > > >> RocksDB's >> > > >> > > > > WriteBatchWithIndex would make sure that the step 4 and >> step 5 >> > > >> could >> > > >> > be >> > > >> > > > > done atomically here. >> > > >> > > > >> > > >> > > > >> > > >> > > > Could you please point me to the place in the codebase where >> a >> > > task >> > > >> > > flushes >> > > >> > > > the store before committing the transaction? >> > > >> > > > Looking at TaskExecutor ( >> > > >> > > > >> > > >> > > > >> > > >> > > >> > > >> > >> > > >> >> > > >> > >> https://github.com/apache/kafka/blob/4c9eeef5b2dff9a4f0977fbc5ac7eaaf930d0d0e/streams/src/main/java/org/apache/kafka/streams/processor/internals/TaskExecutor.java#L144-L167 >> > > >> > > > ), >> > > >> > > > StreamTask#prepareCommit ( >> > > >> > > > >> > > >> > > > >> > > >> > > >> > > >> > >> > > >> >> > > >> > >> https://github.com/apache/kafka/blob/4c9eeef5b2dff9a4f0977fbc5ac7eaaf930d0d0e/streams/src/main/java/org/apache/kafka/streams/processor/internals/StreamTask.java#L398 >> > > >> > > > ), >> > > >> > > > and CachedStateStore ( >> > > >> > > > >> > > >> > > > >> > > >> > > >> > > >> > >> > > >> >> > > >> > >> https://github.com/apache/kafka/blob/4c9eeef5b2dff9a4f0977fbc5ac7eaaf930d0d0e/streams/src/main/java/org/apache/kafka/streams/state/internals/CachedStateStore.java#L29-L34 >> > > >> > > > ) >> > > >> > > > we flush the cache, but not the underlying state store. >> Explicit >> > > >> > > > StateStore#flush happens in >> AbstractTask#maybeWriteCheckpoint ( >> > > >> > > > >> > > >> > > > >> > > >> > > >> > > >> > >> > > >> >> > > >> > >> https://github.com/apache/kafka/blob/4c9eeef5b2dff9a4f0977fbc5ac7eaaf930d0d0e/streams/src/main/java/org/apache/kafka/streams/processor/internals/AbstractTask.java#L91-L99 >> > > >> > > > ). >> > > >> > > > Is there something I am missing here? >> > > >> > > > >> > > >> > > > Today all cached data that have not been flushed are not >> > committed >> > > >> for >> > > >> > > > > sure, but even flushed data to the persistent underlying >> store >> > > may >> > > >> > also >> > > >> > > > be >> > > >> > > > > uncommitted since flushing can be triggered asynchronously >> > > before >> > > >> the >> > > >> > > > > commit. >> > > >> > > > >> > > >> > > > Can you please point me to the place in the codebase where we >> > > >> trigger >> > > >> > > async >> > > >> > > > flush before the commit? This would certainly be a reason to >> > > >> introduce >> > > >> > a >> > > >> > > > dedicated StateStore#commit method. >> > > >> > > > >> > > >> > > > Thanks again for the feedback. I am going to update the KIP >> and >> > > then >> > > >> > > > respond to the next batch of questions and suggestions. >> > > >> > > > >> > > >> > > > Best, >> > > >> > > > Alex >> > > >> > > > >> > > >> > > > On Mon, May 30, 2022 at 5:13 PM Suhas Satish >> > > >> > > <ssat...@confluent.io.invalid >> > > >> > > > > >> > > >> > > > wrote: >> > > >> > > > >> > > >> > > > > Thanks for the KIP proposal Alex. >> > > >> > > > > 1. Configuration default >> > > >> > > > > >> > > >> > > > > You mention applications using streams DSL with built-in >> > rocksDB >> > > >> > state >> > > >> > > > > store will get transactional state stores by default when >> EOS >> > is >> > > >> > > enabled, >> > > >> > > > > but the default implementation for apps using PAPI will >> > fallback >> > > >> to >> > > >> > > > > non-transactional behavior. >> > > >> > > > > Shouldn't we have the same default behavior for both types >> of >> > > >> apps - >> > > >> > > DSL >> > > >> > > > > and PAPI? >> > > >> > > > > >> > > >> > > > > On Mon, May 30, 2022 at 2:11 AM Bruno Cadonna < >> > > cado...@apache.org >> > > >> > >> > > >> > > > wrote: >> > > >> > > > > >> > > >> > > > > > Thanks for the PR, Alex! >> > > >> > > > > > >> > > >> > > > > > I am also glad to see this coming. >> > > >> > > > > > >> > > >> > > > > > >> > > >> > > > > > 1. Configuration >> > > >> > > > > > >> > > >> > > > > > I would also prefer to restrict the configuration of >> > > >> transactional >> > > >> > on >> > > >> > > > > > the state sore. Ideally, calling method transactional() >> on >> > the >> > > >> > state >> > > >> > > > > > store would be enough. An option on the store builder >> would >> > > >> make it >> > > >> > > > > > possible to turn transactionality on and off (as John >> > > proposed). >> > > >> > > > > > >> > > >> > > > > > >> > > >> > > > > > 2. Memory usage in RocksDB >> > > >> > > > > > >> > > >> > > > > > This seems to be a major issue. We do not have any >> guarantee >> > > >> that >> > > >> > > > > > uncommitted writes fit into memory and I guess we will >> never >> > > >> have. >> > > >> > > What >> > > >> > > > > > happens when the uncommitted writes do not fit into >> memory? >> > > Does >> > > >> > > > RocksDB >> > > >> > > > > > throw an exception? Can we handle such an exception >> without >> > > >> > crashing? >> > > >> > > > > > >> > > >> > > > > > Does the RocksDB behavior even need to be included in >> this >> > > KIP? >> > > >> In >> > > >> > > the >> > > >> > > > > > end it is an implementation detail. >> > > >> > > > > > >> > > >> > > > > > What we should consider - though - is a memory limit in >> some >> > > >> form. >> > > >> > > And >> > > >> > > > > > what we do when the memory limit is exceeded. >> > > >> > > > > > >> > > >> > > > > > >> > > >> > > > > > 3. PoC >> > > >> > > > > > >> > > >> > > > > > I agree with Guozhang that a PoC is a good idea to better >> > > >> > understand >> > > >> > > > the >> > > >> > > > > > devils in the details. >> > > >> > > > > > >> > > >> > > > > > >> > > >> > > > > > Best, >> > > >> > > > > > Bruno >> > > >> > > > > > >> > > >> > > > > > On 25.05.22 01:52, Guozhang Wang wrote: >> > > >> > > > > > > Hello Alex, >> > > >> > > > > > > >> > > >> > > > > > > Thanks for writing the proposal! Glad to see it >> coming. I >> > > >> think >> > > >> > > this >> > > >> > > > is >> > > >> > > > > > the >> > > >> > > > > > > kind of a KIP that since too many devils would be >> buried >> > in >> > > >> the >> > > >> > > > details >> > > >> > > > > > and >> > > >> > > > > > > it's better to start working on a POC, either in >> parallel, >> > > or >> > > >> > > before >> > > >> > > > we >> > > >> > > > > > > resume our discussion, rather than blocking any >> > > implementation >> > > >> > > until >> > > >> > > > we >> > > >> > > > > > are >> > > >> > > > > > > satisfied with the proposal. >> > > >> > > > > > > >> > > >> > > > > > > Just as a concrete example, I personally am still not >> 100% >> > > >> clear >> > > >> > > how >> > > >> > > > > the >> > > >> > > > > > > proposal would work to achieve EOS with the state >> stores. >> > > For >> > > >> > > > example, >> > > >> > > > > > the >> > > >> > > > > > > commit procedure today looks like this: >> > > >> > > > > > > >> > > >> > > > > > > 0: there's an existing checkpoint file indicating the >> > > >> changelog >> > > >> > > > offset >> > > >> > > > > of >> > > >> > > > > > > the local state store image is 100. Now a commit is >> > > triggered: >> > > >> > > > > > > 1. flush cache (since it contains partially processed >> > > >> records), >> > > >> > > make >> > > >> > > > > sure >> > > >> > > > > > > all records are written to the producer. >> > > >> > > > > > > 2. flush producer, making sure all changelog records >> have >> > > now >> > > >> > > acked. >> > > >> > > > // >> > > >> > > > > > > here we would get the new changelog position, say 200 >> > > >> > > > > > > 3. flush store, make sure all writes are persisted. >> > > >> > > > > > > 4. producer.sendOffsetsToTransaction(); >> > > >> > > producer.commitTransaction(); >> > > >> > > > > // >> > > >> > > > > > we >> > > >> > > > > > > would make the writes in changelog up to offset 200 >> > > committed >> > > >> > > > > > > 5. Update the checkpoint file to 200. >> > > >> > > > > > > >> > > >> > > > > > > The question about atomicity between those lines, for >> > > example: >> > > >> > > > > > > >> > > >> > > > > > > * If we crash between line 4 and line 5, the local >> > > checkpoint >> > > >> > file >> > > >> > > > > would >> > > >> > > > > > > stay as 100, and upon recovery we would replay the >> > changelog >> > > >> from >> > > >> > > 100 >> > > >> > > > > to >> > > >> > > > > > > 200. This is not ideal but does not violate EOS, since >> the >> > > >> > > changelogs >> > > >> > > > > are >> > > >> > > > > > > all overwrites anyways. >> > > >> > > > > > > * If we crash between line 3 and 4, then at that time >> the >> > > >> local >> > > >> > > > > > persistent >> > > >> > > > > > > store image is representing as of offset 200, but upon >> > > >> recovery >> > > >> > all >> > > >> > > > > > > changelog records from 100 to log-end-offset would be >> > > >> considered >> > > >> > as >> > > >> > > > > > aborted >> > > >> > > > > > > and not be replayed and we would restart processing >> from >> > > >> position >> > > >> > > > 100. >> > > >> > > > > > > Restart processing will violate EOS.I'm not sure how >> e.g. >> > > >> > RocksDB's >> > > >> > > > > > > WriteBatchWithIndex would make sure that the step 4 and >> > > step 5 >> > > >> > > could >> > > >> > > > be >> > > >> > > > > > > done atomically here. >> > > >> > > > > > > >> > > >> > > > > > > Originally what I was thinking when creating the JIRA >> > ticket >> > > >> is >> > > >> > > that >> > > >> > > > we >> > > >> > > > > > > need to let the state store to provide a transactional >> API >> > > >> like >> > > >> > > > "token >> > > >> > > > > > > commit()" used in step 4) above which returns a token, >> > that >> > > >> e.g. >> > > >> > in >> > > >> > > > our >> > > >> > > > > > > example above indicates offset 200, and that token >> would >> > be >> > > >> > written >> > > >> > > > as >> > > >> > > > > > part >> > > >> > > > > > > of the records in Kafka transaction in step 5). And >> upon >> > > >> recovery >> > > >> > > the >> > > >> > > > > > state >> > > >> > > > > > > store would have another API like "rollback(token)" >> where >> > > the >> > > >> > token >> > > >> > > > is >> > > >> > > > > > read >> > > >> > > > > > > from the latest committed txn, and be used to rollback >> the >> > > >> store >> > > >> > to >> > > >> > > > > that >> > > >> > > > > > > committed image. I think your proposal is different, >> and >> > it >> > > >> seems >> > > >> > > > like >> > > >> > > > > > > you're proposing we swap step 3) and 4) above, but the >> > > >> atomicity >> > > >> > > > issue >> > > >> > > > > > > still remains since now you may have the store image at >> > 100 >> > > >> but >> > > >> > the >> > > >> > > > > > > changelog is committed at 200. I'd like to learn more >> > about >> > > >> the >> > > >> > > > details >> > > >> > > > > > > on how it resolves such issues. >> > > >> > > > > > > >> > > >> > > > > > > Anyways, that's just an example to make the point that >> > there >> > > >> are >> > > >> > > lots >> > > >> > > > > of >> > > >> > > > > > > implementational details which would drive the public >> API >> > > >> design, >> > > >> > > and >> > > >> > > > > we >> > > >> > > > > > > should probably first do a POC, and come back to >> discuss >> > the >> > > >> KIP. >> > > >> > > Let >> > > >> > > > > me >> > > >> > > > > > > know what you think? >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > Guozhang >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > > On Tue, May 24, 2022 at 10:35 AM Sagar < >> > > >> > sagarmeansoc...@gmail.com> >> > > >> > > > > > wrote: >> > > >> > > > > > > >> > > >> > > > > > >> Hi Alexander, >> > > >> > > > > > >> >> > > >> > > > > > >> Thanks for the KIP! This seems like a great proposal. >> I >> > > have >> > > >> the >> > > >> > > > same >> > > >> > > > > > >> opinion as John on the Configuration part though. I >> think >> > > >> the 2 >> > > >> > > > level >> > > >> > > > > > >> config and its behaviour based on the >> setting/unsetting >> > of >> > > >> the >> > > >> > > flag >> > > >> > > > > > seems >> > > >> > > > > > >> confusing to me as well. Since the KIP seems >> specifically >> > > >> > centred >> > > >> > > > > around >> > > >> > > > > > >> RocksDB it might be better to add it at the Supplier >> > level >> > > as >> > > >> > John >> > > >> > > > > > >> suggested. >> > > >> > > > > > >> >> > > >> > > > > > >> On similar lines, this config name => >> > > >> > > > > > *statestore.transactional.mechanism >> > > >> > > > > > >> *may >> > > >> > > > > > >> also need rethinking as the value assigned to >> > > >> > > it(rocksdb_indexbatch) >> > > >> > > > > > >> implicitly seems to assume that rocksdb is the only >> > > >> statestore >> > > >> > > that >> > > >> > > > > > Kafka >> > > >> > > > > > >> Stream supports while that's not the case. >> > > >> > > > > > >> >> > > >> > > > > > >> Also, regarding the potential memory pressure that >> can be >> > > >> > > introduced >> > > >> > > > > by >> > > >> > > > > > >> WriteBatchIndex, do you think it might make more >> sense to >> > > >> > include >> > > >> > > > some >> > > >> > > > > > >> numbers/benchmarks on how much the memory consumption >> > might >> > > >> > > > increase? >> > > >> > > > > > >> >> > > >> > > > > > >> Lastly, the read_uncommitted flag's behaviour on IQ >> may >> > > need >> > > >> > more >> > > >> > > > > > >> elaboration. >> > > >> > > > > > >> >> > > >> > > > > > >> These points aside, as I said, this is a great >> proposal! >> > > >> > > > > > >> >> > > >> > > > > > >> Thanks! >> > > >> > > > > > >> Sagar. >> > > >> > > > > > >> >> > > >> > > > > > >> On Tue, May 24, 2022 at 10:35 PM John Roesler < >> > > >> > > vvcep...@apache.org> >> > > >> > > > > > wrote: >> > > >> > > > > > >> >> > > >> > > > > > >>> Thanks for the KIP, Alex! >> > > >> > > > > > >>> >> > > >> > > > > > >>> I'm really happy to see your proposal. This >> improvement >> > > >> fills a >> > > >> > > > > > >>> long-standing gap. >> > > >> > > > > > >>> >> > > >> > > > > > >>> I have a few questions: >> > > >> > > > > > >>> >> > > >> > > > > > >>> 1. Configuration >> > > >> > > > > > >>> The KIP only mentions RocksDB, but of course, Streams >> > also >> > > >> > ships >> > > >> > > > with >> > > >> > > > > > an >> > > >> > > > > > >>> InMemory store, and users also plug in their own >> custom >> > > >> state >> > > >> > > > stores. >> > > >> > > > > > It >> > > >> > > > > > >> is >> > > >> > > > > > >>> also common to use multiple types of state stores in >> the >> > > >> same >> > > >> > > > > > application >> > > >> > > > > > >>> for different purposes. >> > > >> > > > > > >>> >> > > >> > > > > > >>> Against this backdrop, the choice to configure >> > > >> transactionality >> > > >> > > as >> > > >> > > > a >> > > >> > > > > > >>> top-level config, as well as to configure the store >> > > >> transaction >> > > >> > > > > > mechanism >> > > >> > > > > > >>> as a top-level config, seems a bit off. >> > > >> > > > > > >>> >> > > >> > > > > > >>> Did you consider instead just adding the option to >> the >> > > >> > > > > > >>> RocksDB*StoreSupplier classes and the factories in >> > Stores >> > > ? >> > > >> It >> > > >> > > > seems >> > > >> > > > > > like >> > > >> > > > > > >>> the desire to enable the feature by default, but >> with a >> > > >> > > > feature-flag >> > > >> > > > > to >> > > >> > > > > > >>> disable it was a factor here. However, as you pointed >> > out, >> > > >> > there >> > > >> > > > are >> > > >> > > > > > some >> > > >> > > > > > >>> major considerations that users should be aware of, >> so >> > > >> opt-in >> > > >> > > > doesn't >> > > >> > > > > > >> seem >> > > >> > > > > > >>> like a bad choice, either. You could add an Enum >> > argument >> > > to >> > > >> > > those >> > > >> > > > > > >>> factories like `RocksDBTransactionalMechanism.{NONE, >> > > >> > > > > > >>> >> > > >> > > > > > >>> Some points in favor of this approach: >> > > >> > > > > > >>> * Avoid "stores that don't support transactions >> ignore >> > the >> > > >> > > config" >> > > >> > > > > > >>> complexity >> > > >> > > > > > >>> * Users can choose how to spend their memory budget, >> > > making >> > > >> > some >> > > >> > > > > stores >> > > >> > > > > > >>> transactional and others not >> > > >> > > > > > >>> * When we add transactional support to in-memory >> stores, >> > > we >> > > >> > don't >> > > >> > > > > have >> > > >> > > > > > to >> > > >> > > > > > >>> figure out what to do with the mechanism config >> (i.e., >> > > what >> > > >> do >> > > >> > > you >> > > >> > > > > set >> > > >> > > > > > >> the >> > > >> > > > > > >>> mechanism to when there are multiple kinds of >> > > transactional >> > > >> > > stores >> > > >> > > > in >> > > >> > > > > > the >> > > >> > > > > > >>> topology?) >> > > >> > > > > > >>> >> > > >> > > > > > >>> 2. caching/flushing/transactions >> > > >> > > > > > >>> The coupling between memory usage and flushing that >> you >> > > >> > mentioned >> > > >> > > > is >> > > >> > > > > a >> > > >> > > > > > >> bit >> > > >> > > > > > >>> troubling. It also occurs to me that there seems to >> be >> > > some >> > > >> > > > > > relationship >> > > >> > > > > > >>> with the existing record cache, which is also an >> > in-memory >> > > >> > > holding >> > > >> > > > > area >> > > >> > > > > > >> for >> > > >> > > > > > >>> records that are not yet written to the cache and/or >> > store >> > > >> > > (albeit >> > > >> > > > > with >> > > >> > > > > > >> no >> > > >> > > > > > >>> particular semantics). Have you considered how all >> these >> > > >> > > components >> > > >> > > > > > >> should >> > > >> > > > > > >>> relate? For example, should a "full" WriteBatch >> actually >> > > >> > trigger >> > > >> > > a >> > > >> > > > > > flush >> > > >> > > > > > >> so >> > > >> > > > > > >>> that we don't get OOMEs? If the proposed >> transactional >> > > >> > mechanism >> > > >> > > > > forces >> > > >> > > > > > >> all >> > > >> > > > > > >>> uncommitted writes to be buffered in memory, until a >> > > commit, >> > > >> > then >> > > >> > > > > what >> > > >> > > > > > is >> > > >> > > > > > >>> the advantage over just doing the same thing with the >> > > >> > RecordCache >> > > >> > > > and >> > > >> > > > > > not >> > > >> > > > > > >>> introducing the WriteBatch at all? >> > > >> > > > > > >>> >> > > >> > > > > > >>> 3. ALOS >> > > >> > > > > > >>> You mentioned that a transactional store can help >> reduce >> > > >> > > > duplication >> > > >> > > > > in >> > > >> > > > > > >>> the case of ALOS. We might want to be careful about >> > claims >> > > >> like >> > > >> > > > that. >> > > >> > > > > > >>> Duplication isn't the way that repeated processing >> > > >> manifests in >> > > >> > > > state >> > > >> > > > > > >>> stores. Rather, it is in the form of dirty reads >> during >> > > >> > > > reprocessing. >> > > >> > > > > > >> This >> > > >> > > > > > >>> feature may reduce the incidence of dirty reads >> during >> > > >> > > > reprocessing, >> > > >> > > > > > but >> > > >> > > > > > >>> not in a predictable way. During regular processing >> > today, >> > > >> we >> > > >> > > will >> > > >> > > > > send >> > > >> > > > > > >>> some records through to the changelog in between >> commit >> > > >> > > intervals. >> > > >> > > > > > Under >> > > >> > > > > > >>> ALOS, if any of those dirty writes gets committed to >> the >> > > >> > > changelog >> > > >> > > > > > topic, >> > > >> > > > > > >>> then upon failure, we have to roll the store forward >> to >> > > them >> > > >> > > > anyway, >> > > >> > > > > > >>> regardless of this new transactional mechanism. >> That's a >> > > >> > fixable >> > > >> > > > > > problem, >> > > >> > > > > > >>> by the way, but this KIP doesn't seem to fix it. I >> > wonder >> > > >> if we >> > > >> > > > > should >> > > >> > > > > > >> make >> > > >> > > > > > >>> any claims about the relationship of this feature to >> > ALOS >> > > if >> > > >> > the >> > > >> > > > > > >> real-world >> > > >> > > > > > >>> behavior is so complex. >> > > >> > > > > > >>> >> > > >> > > > > > >>> 4. IQ >> > > >> > > > > > >>> As a reminder, we have a new IQv2 mechanism now. >> Should >> > we >> > > >> > > propose >> > > >> > > > > any >> > > >> > > > > > >>> changes to IQv1 to support this transactional >> mechanism, >> > > >> versus >> > > >> > > > just >> > > >> > > > > > >>> proposing it for IQv2? Certainly, it seems strange >> only >> > to >> > > >> > > propose >> > > >> > > > a >> > > >> > > > > > >> change >> > > >> > > > > > >>> for IQv1 and not v2. >> > > >> > > > > > >>> >> > > >> > > > > > >>> Regarding your proposal for IQv1, I'm unsure what the >> > > >> behavior >> > > >> > > > should >> > > >> > > > > > be >> > > >> > > > > > >>> for readCommitted, since the current behavior also >> reads >> > > >> out of >> > > >> > > the >> > > >> > > > > > >>> RecordCache. I guess if readCommitted==false, then we >> > will >> > > >> > > continue >> > > >> > > > > to >> > > >> > > > > > >> read >> > > >> > > > > > >>> from the cache first, then the Batch, then the store; >> > and >> > > if >> > > >> > > > > > >>> readCommitted==true, we would skip the cache and the >> > Batch >> > > >> and >> > > >> > > only >> > > >> > > > > > read >> > > >> > > > > > >>> from the persistent RocksDB store? >> > > >> > > > > > >>> >> > > >> > > > > > >>> What should IQ do if I request to readCommitted on a >> > > >> > > > > non-transactional >> > > >> > > > > > >>> store? >> > > >> > > > > > >>> >> > > >> > > > > > >>> Thanks again for proposing the KIP, and my apologies >> for >> > > the >> > > >> > long >> > > >> > > > > > reply; >> > > >> > > > > > >>> I'm hoping to air all my concerns in one "batch" to >> save >> > > >> time >> > > >> > for >> > > >> > > > > you. >> > > >> > > > > > >>> >> > > >> > > > > > >>> Thanks, >> > > >> > > > > > >>> -John >> > > >> > > > > > >>> >> > > >> > > > > > >>> On Tue, May 24, 2022, at 03:45, Alexander Sorokoumov >> > > wrote: >> > > >> > > > > > >>>> Hi all, >> > > >> > > > > > >>>> >> > > >> > > > > > >>>> I've written a KIP for making Kafka Streams state >> > stores >> > > >> > > > > transactional >> > > >> > > > > > >>> and >> > > >> > > > > > >>>> would like to start a discussion: >> > > >> > > > > > >>>> >> > > >> > > > > > >>>> >> > > >> > > > > > >>> >> > > >> > > > > > >> >> > > >> > > > > > >> > > >> > > > > >> > > >> > > > >> > > >> > > >> > > >> > >> > > >> >> > > >> > >> https://cwiki.apache.org/confluence/display/KAFKA/KIP-844%3A+Transactional+State+Stores >> > > >> > > > > > >>>> >> > > >> > > > > > >>>> Best, >> > > >> > > > > > >>>> Alex >> > > >> > > > > > >>> >> > > >> > > > > > >> >> > > >> > > > > > > >> > > >> > > > > > > >> > > >> > > > > > >> > > >> > > > > >> > > >> > > > > >> > > >> > > > > -- >> > > >> > > > > >> > > >> > > > > [image: Confluent] <https://www.confluent.io> >> > > >> > > > > Suhas Satish >> > > >> > > > > Engineering Manager >> > > >> > > > > Follow us: [image: Blog] >> > > >> > > > > < >> > > >> > > > > >> > > >> > > > >> > > >> > > >> > > >> > >> > > >> >> > > >> > >> https://www.confluent.io/blog?utm_source=footer&utm_medium=email&utm_campaign=ch.email-signature_type.community_content.blog >> > > >> > > > > >[image: >> > > >> > > > > Twitter] <https://twitter.com/ConfluentInc>[image: >> LinkedIn] >> > > >> > > > > <https://www.linkedin.com/company/confluent/> >> > > >> > > > > >> > > >> > > > > [image: Try Confluent Cloud for Free] >> > > >> > > > > < >> > > >> > > > > >> > > >> > > > >> > > >> > > >> > > >> > >> > > >> >> > > >> > >> https://www.confluent.io/get-started?utm_campaign=tm.fm-apac_cd.inbound&utm_source=gmail&utm_medium=organic >> > > >> > > > > > >> > > >> > > > > >> > > >> > > > >> > > >> > > >> > > >> > > >> > > >> > > -- >> > > >> > > -- Guozhang >> > > >> > > >> > > >> > >> > > >> >> > > >> >> > > >> -- >> > > >> -- Guozhang >> > > >> >> > > > >> > > >> > >> > >> > -- >> > -- Guozhang >> > >> >
