Hi everyone, Gen Luo, Yun Gao and I had a long offline discussion about the implementation of the recovery part. The key point was should we store the retry state and do the recovery after the job restart?
We reached a consensus not to store the retry state for now, which is the clearest for users and does not require any new changes to the current recovery behavior. We have discussed three possible options, the behavior of these three options is identical in normal processing, the only difference lies in what retry state is recorded when do checkpointing, and what is the strategy when recovering. More details are updated into the FLIP[1], and the PoC[2] is also updated. [1] https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=211883963 [2] https://github.com/lincoln-lil/flink/tree/async-retry-poc Best, Lincoln Lee Lincoln Lee <lincoln.8...@gmail.com> 于2022年5月24日周二 12:23写道： > Hi Gen Luo, > > You're right, the total cost time include the failover-restart time. So > when the failover time exceeds the retry timeout set by the user, in fact, > all the data to be retry after recovery will have no additional retry > opportunities, which is equivalent to normal data. In such circumstances, > the retry state takes no effect. But not all jobs' restart is slow and in > flink it is becoming more and more fast due the continuously improvements. > Hope this can help explaining your question. > > Best, > Lincoln Lee > > > Gen Luo <luogen...@gmail.com> 于2022年5月24日周二 11:50写道： > >> Hi Lincoln, >> >> Thanks for the explanation. I understand your thought, but I'm a little >> confused by the additional detail. >> Is the startTime when the record is processed for the first time? And the >> cost time is counted based on it even after a job recovers from a failover >> or is restarted? For the failover case, the records may be processed >> successfully when normally running, but after some time (probably longer >> than the timeout) the job fails and restores, the records in the retry >> state will be timeout and discarded immediately. There's also same >> situation for the restarting case. I suppose in many cases the timeout >> will >> be less then the time a job may cost to restart, so in these cases the >> stored in-flight retry attempts will timeout immediately after the >> restarting, making the retry state meaningless. Please let me know if I >> mistake somthing. >> >> Lincoln Lee <lincoln.8...@gmail.com> 于 2022年5月24日周二 10:20写道： >> >> > Thanks Gen Luo! >> > >> > Agree with you that prefer the simpler design. >> > >> > I’d like to share my thoughts on this choice: whether store the retry >> state >> > or not only affect the recovery logic, not the per-record processing, >> so I >> > just compare the two: >> > 1. w/ retry state: simple recovery but lost precision >> > 2. w/o retry state: one more state and little complexly but precise for >> > users >> > I prefer the second one for the user perspective, the additional >> complexity >> > is manageable. >> > >> > One detail that not mentioned in the FLIP: we will check if any time >> left >> > (now() - startTime > timeout) for next attempt, so the real total >> attempts >> > will always less than or equal to maxAttempts and the total cost time <= >> > timeout (one special case is job failover takes too long) >> > >> > For the api, I've updated the FLIP[1] >> > >> > [1]: >> > >> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=211883963 >> > >> > Best, >> > Lincoln Lee >> > >> > >> > Gen Luo <luogen...@gmail.com> 于2022年5月23日周一 16:54写道： >> > >> > > Hi Lincoln, >> > > >> > > Thanks for the quick reply. >> > > >> > > >> > > >> > > 1. I understand when restarting a job with a savepoint, the retry >> state >> > can >> > > ensure the total retry attempts and delay is expected. However, when >> > > failover happens while a job is running, the remaining attempts >> recorded >> > in >> > > the state are actually redid, and of course the total attempts are >> more >> > > than expected. The delay is indeed one of the concerns, but I'm >> wondering >> > > whether the retry state kept here is really important to users or >> not. In >> > > my opinion its benefit is limited but it makes the change much more >> > > complex. I would prefer a simpler solution, in which the retry state >> is >> > > still possible to add if the need really arises in the future, but I >> > > respect your decision. >> > > >> > > >> > > >> > > 2. I think adding a currentAttempts parameter to the method is good >> > enough. >> > > >> > > Lincoln Lee <lincoln.8...@gmail.com> 于 2022年5月23日周一 14:52写道： >> > > >> > > > Hi Gen Luo, >> > > > Thanks a lot for your feedback! >> > > > >> > > > 1. About the retry state: >> > > > I considered dropping the retry state which really simplifies state >> > > changes >> > > > and avoids compatibility handling. The only reason I changed my mind >> > was >> > > > that it might be lossy to the user. Elements that has been tried >> > several >> > > > times but not exhausted its retry opportunities will reset the retry >> > > state >> > > > after a job failover-restart and start the retry process again (if >> the >> > > > retry condition persists true), which may cause a greater delay for >> the >> > > > retried elements, actually retrying more times and for longer than >> > > > expected. (Although in the PoC may also have a special case when >> > > > recovering: if the remaining timeout is exhausted for the >> > recalculation, >> > > it >> > > > will execute immediately but will have to register a timeout timer >> for >> > > the >> > > > async, here using an extra backoffTimeMillis) >> > > > For example, '60s fixed-delay retry if empty result, max-attempts: >> 5, >> > > > timeout 300s' >> > > > When checkpointing, some data has been retry 2 times, then suppose >> the >> > > job >> > > > is restarted and it takes 2min when the restart succeeds, if we drop >> > the >> > > > retry state, the worst case will take more 240s(60s * 2 + 2min) >> delay >> > for >> > > > users to finish retry. >> > > > >> > > > For my understanding(please correct me if I missed something), if a >> job >> > > is >> > > > resumed from a previous state and the retry strategy is changed, the >> > > > elements that need to be recovered in the retry state just needs the >> > new >> > > > strategy to take over the current attempts and time that has been >> used, >> > > or >> > > > give up retry if no retry strategy was set. >> > > > > and can be more compatible when the user restart a job with a >> changed >> > > > retry strategy. >> > > > >> > > > 2. About the interface, do you think it would be helpful if add the >> > > > currentAttempts into getBackoffTimeMillis()? e.g., long >> > > > getBackoffTimeMillis(int currentAttempts) >> > > > The existing RetryStrategy and RestartBackoffTimeStrategy were in my >> > > > candidate list but not exactly match, and I want to avoid creating >> the >> > > new >> > > > instances for every attempt in RetryStrategy. >> > > > >> > > > WDYT? >> > > > >> > > > Best, >> > > > Lincoln Lee >> > > > >> > > > >> > > > Gen Luo <luogen...@gmail.com> 于2022年5月23日周一 11:37写道： >> > > > >> > > > > Thank Lincoln for the proposal! >> > > > > >> > > > > The FLIP looks good to me. I'm in favor of the timer based >> > > > implementation, >> > > > > and I'd like to share some thoughts. >> > > > > >> > > > > I'm thinking if we have to store the retry status in the state. I >> > > suppose >> > > > > the retrying requests can just submit as the first attempt when >> the >> > job >> > > > > restores from a checkpoint, since in fact the side effect of the >> > > retries >> > > > > can not draw back by the restoring. This makes the state simpler >> and >> > > > makes >> > > > > it unnecessary to do the state migration, and can be more >> compatible >> > > when >> > > > > the user restart a job with a changed retry strategy. >> > > > > >> > > > > Besides, I find it hard to implement a flexible backoff strategy >> with >> > > the >> > > > > current AsyncRetryStrategy interface, for example an >> > > > > ExponentialBackoffRetryStrategy. Maybe we can add a parameter of >> the >> > > > > attempt or just use the >> > org.apache.flink.util.concurrent.RetryStrategy >> > > to >> > > > > take the place of the retry strategy part in the >> AsyncRetryStrategy? >> > > > > >> > > > > Lincoln Lee <lincoln.8...@gmail.com> 于 2022年5月20日周五 14:24写道： >> > > > > >> > > > > > Hi everyone, >> > > > > > >> > > > > > By comparing the two internal implementations of delayed >> > retries, >> > > we >> > > > > > prefer the timer-based solution, which obtains precise delay >> > control >> > > > > > through simple logic and only needs to pay (what we consider to >> be >> > > > > > acceptable) timer instance cost for the retry element. The >> FLIP[1] >> > > doc >> > > > > has >> > > > > > been updated. >> > > > > > >> > > > > > [1]: >> > > > > > >> > > > > >> > > > >> > > >> > >> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=211883963 >> > > > > > >> > > > > > Best, >> > > > > > Lincoln Lee >> > > > > > >> > > > > > >> > > > > > Lincoln Lee <lincoln.8...@gmail.com> 于2022年5月16日周一 15:09写道： >> > > > > > >> > > > > > > Hi Jinsong, >> > > > > > > >> > > > > > > Good question! >> > > > > > > >> > > > > > > The delayQueue is very similar to incompleteElements in >> > > > > > > UnorderedStreamElementQueue, it only records the references of >> > > > > in-flight >> > > > > > > retry elements, the core value is for the ease of a fast scan >> > when >> > > > > force >> > > > > > > flush during endInput and less refactor for existing logic. >> > > > > > > >> > > > > > > Users needn't configure a new capacity for the delayQueue, >> just >> > > turn >> > > > > the >> > > > > > > original one up (if needed). >> > > > > > > And separately store the input data and retry state is mainly >> to >> > > > > > implement >> > > > > > > backwards compatibility. The first version of Poc, I used a >> > single >> > > > > > combined >> > > > > > > state in order to reduce state costs, but hard to keep >> > > compatibility, >> > > > > and >> > > > > > > changed into two via Yun Gao's concern about the >> compatibility. >> > > > > > > >> > > > > > > Best, >> > > > > > > Lincoln Lee >> > > > > > > >> > > > > > > >> > > > > > > Jingsong Li <jingsongl...@gmail.com> 于2022年5月16日周一 14:48写道： >> > > > > > > >> > > > > > >> Thanks Lincoln for your reply. >> > > > > > >> >> > > > > > >> I'm a little confused about the relationship between >> > > > Ordered/Unordered >> > > > > > >> Queue and DelayQueue. Why do we need to have a DelayQueue? >> > > > > > >> Can we remove the DelayQueue and put the state of the retry >> in >> > the >> > > > > > >> StreamRecordQueueEntry (seems like it's already in the FLIP) >> > > > > > >> The advantages of doing this are: >> > > > > > >> 1. twice less data is stored in state >> > > > > > >> 2. the concept is unified, the user only needs to configure >> one >> > > > queue >> > > > > > >> capacity >> > > > > > >> >> > > > > > >> Best, >> > > > > > >> Jingsong >> > > > > > >> >> > > > > > >> On Mon, May 16, 2022 at 12:10 PM Lincoln Lee < >> > > > lincoln.8...@gmail.com> >> > > > > > >> wrote: >> > > > > > >> >> > > > > > >> > Hi Jinsong, >> > > > > > >> > Thanks for your feedback! Let me try to answer the two >> > > questions: >> > > > > > >> > >> > > > > > >> > For q1: Motivation >> > > > > > >> > Yes, users can implement retries themselves based on the >> > > external >> > > > > > async >> > > > > > >> > client, but this requires each user to do similar things, >> and >> > if >> > > > we >> > > > > > can >> > > > > > >> > support retries uniformly, user code would become much >> > simpler. >> > > > > > >> > >> > > > > > >> > > The real external call should happen in the asynchronous >> > > thread. >> > > > > > >> > My question is: If the user makes a retry in this >> asynchronous >> > > > > thread >> > > > > > by >> > > > > > >> > themselves, is there a difference between this and the >> current >> > > > > FLIP's? >> > > > > > >> > >> > > > > > >> > >> > > > > > >> > For q2: Block Main Thread >> > > > > > >> > You're right, the queue data will be stored in the >> ListState >> > > which >> > > > > is >> > > > > > an >> > > > > > >> > OperateState, though in fact, for ListState storage, the >> > > > theoretical >> > > > > > >> upper >> > > > > > >> > limit is Integer.MAX_VALUE, but we can't increase the queue >> > > > capacity >> > > > > > too >> > > > > > >> > big in production because the risk of OOM increases when >> the >> > > queue >> > > > > > >> capacity >> > > > > > >> > grows, and increases the task parallelism maybe a more >> viable >> > > way >> > > > > when >> > > > > > >> > encounter too many retry items for a single task. >> > > > > > >> > We recommend using a proper estimate of queue capacity >> based >> > on >> > > > the >> > > > > > >> formula >> > > > > > >> > like this: 'inputRate * retryRate * avgRetryDuration', and >> > also >> > > > the >> > > > > > >> actual >> > > > > > >> > checkpoint duration in runtime. >> > > > > > >> > >> > > > > > >> > > If I understand correctly, the retry queue will be put >> into >> > > > > > ListState, >> > > > > > >> > this >> > > > > > >> > state is OperatorState? As far as I know, OperatorState >> does >> > not >> > > > > have >> > > > > > >> the >> > > > > > >> > ability to store a lot of data. >> > > > > > >> > So after we need to retry more data, we should need to >> block >> > the >> > > > > main >> > > > > > >> > thread? What is the maximum size of the default retry >> queue? >> > > > > > >> > >> > > > > > >> > >> > > > > > >> > >> > > > > > >> > Best, >> > > > > > >> > Lincoln Lee >> > > > > > >> > >> > > > > > >> > >> > > > > > >> > Jingsong Li <jingsongl...@gmail.com> 于2022年5月16日周一 >> 10:31写道： >> > > > > > >> > >> > > > > > >> > > Thank Lincoln for the proposal. >> > > > > > >> > > >> > > > > > >> > > ## Motivation: >> > > > > > >> > > >> > > > > > >> > > > asyncInvoke and callback functions are executed >> > > synchronously >> > > > by >> > > > > > the >> > > > > > >> > main >> > > > > > >> > > thread, which is not suitable adding long time blocking >> > > > > operations, >> > > > > > >> and >> > > > > > >> > > introducing additional thread will bring extra complexity >> > for >> > > > > users >> > > > > > >> > > >> > > > > > >> > > According to the documentation of AsyncFunction: >> > > > > > >> > > >> > > > > > >> > > > For each #asyncInvoke, an async io operation can be >> > > triggered, >> > > > > and >> > > > > > >> once >> > > > > > >> > > it has been done, the result can be collected by calling >> > > {@link >> > > > > > >> > > ResultFuture#complete}. For each async operation, its >> > context >> > > is >> > > > > > >> stored >> > > > > > >> > in >> > > > > > >> > > the operator immediately after invoking #asyncInvoke, >> > avoiding >> > > > > > >> blocking >> > > > > > >> > for >> > > > > > >> > > each stream input as long as the internal buffer is not >> > full. >> > > > > > >> > > >> > > > > > >> > > The real external call should happen in the asynchronous >> > > thread. >> > > > > > >> > > >> > > > > > >> > > My question is: If the user makes a retry in this >> > asynchronous >> > > > > > thread >> > > > > > >> by >> > > > > > >> > > themselves, is there a difference between this and the >> > current >> > > > > > FLIP's? >> > > > > > >> > > >> > > > > > >> > > ## Block Main Thread >> > > > > > >> > > >> > > > > > >> > > If I understand correctly, the retry queue will be put >> into >> > > > > > ListState, >> > > > > > >> > this >> > > > > > >> > > state is OperatorState? As far as I know, OperatorState >> does >> > > not >> > > > > > have >> > > > > > >> the >> > > > > > >> > > ability to store a lot of data. >> > > > > > >> > > So after we need to retry more data, we should need to >> block >> > > the >> > > > > > main >> > > > > > >> > > thread? What is the maximum size of the default retry >> queue? >> > > > > > >> > > >> > > > > > >> > > Best, >> > > > > > >> > > Jingsong >> > > > > > >> > > >> > > > > > >> > > On Thu, May 12, 2022 at 8:56 PM Lincoln Lee < >> > > > > lincoln.8...@gmail.com >> > > > > > > >> > > > > > >> > > wrote: >> > > > > > >> > > >> > > > > > >> > > > Dear Flink developers, >> > > > > > >> > > > >> > > > > > >> > > > I would like to open a discussion on FLIP 232 [1], >> for an >> > > > > > >> extension of >> > > > > > >> > > > AsyncWaitOperator to support retry for user's >> > asyncFunction. >> > > > > > >> > > > >> > > > > > >> > > > To do so, new user interface will added to define the >> > > trigger >> > > > > > >> condition >> > > > > > >> > > for >> > > > > > >> > > > retry and when should retry. Internally, a delayed >> retry >> > > > > mechanism >> > > > > > >> will >> > > > > > >> > > be >> > > > > > >> > > > introduced. >> > > > > > >> > > > >> > > > > > >> > > > There's PoC for this FLIP [2][3], thanks Yun Gao for >> > offline >> > > > > > >> > discussions >> > > > > > >> > > > and valuable comments. >> > > > > > >> > > > The new feature is backwards compatible that can >> recover >> > > from >> > > > > > state >> > > > > > >> > which >> > > > > > >> > > > was generated by prior flink versions, and if no retry >> > > > strategy >> > > > > > >> enabled >> > > > > > >> > > the >> > > > > > >> > > > behavior is as before. >> > > > > > >> > > > >> > > > > > >> > > > [1] >> > > > > > >> > > > >> > > > > > >> > > >> > > > > > >> > >> > > > > > >> >> > > > > > >> > > > > >> > > > >> > > >> > >> https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=211883963 >> > > > > > >> > > > [2] based on timer trigger >> > > > > > >> > > > >> > > > https://github.com/lincoln-lil/flink/pull/new/async-retry-timer >> > > > > > >> > > > [3] based on DelayQueue with pull fashion >> > > > > > >> > > > >> > > https://github.com/lincoln-lil/flink/pull/new/async-op-retry >> > > > > > >> > > > >> > > > > > >> > > > >> > > > > > >> > > > Best, >> > > > > > >> > > > Lincoln Lee >> > > > > > >> > > > >> > > > > > >> > > >> > > > > > >> > >> > > > > > >> >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> >
