Hi Piotr, Thanks for your comments! Please see my reply inline.
On Wed, Jul 5, 2023 at 11:44 PM Piotr Nowojski <piotr.nowoj...@gmail.com> wrote: > Hi Dong, > > I have a couple of questions. > > Could you explain why those properties > > @Nullable private Boolean isOutputOnEOF = null; > @Nullable private Boolean isOutputOnCheckpoint = null; > @Nullable private Boolean isInternalSorterSupported = null; > > must be `@Nullable`, instead of having the default value set to `false`? > By initializing these private variables in OperatorAttributesBuilder as null, we can implement `OperatorAttributesBuilder#build()` in such a way that it can print DEBUG level logging to say "isOutputOnCheckpoint is not explicitly set". This can help user/SRE debug performance issues (or lack of the expected optimization) due to operators not explicitly setting the right operator attribute. For example, we might want a job to always use the longer checkpointing interval (i.e. execution.checkpointing.interval-during-backlog) if all running operators have isOutputOnCheckpoint==false, and use the short checkpointing interval otherwise. If a user has explicitly configured the execution.checkpointing.interval-during-backlog but the two-phase commit sink library has not been upgraded to set isOutputOnCheckpoint=true, then the job will end up using the long checkpointing interval, and it will be useful to figure out what is going wrong in this case by checking the log. Note that the default value of these fields of the OperatorAttributes instance built by OperatorAttributesBuilder will still be false. The following is mentioned in the Java doc of `OperatorAttributesBuilder#build()`: /** * If any operator attribute is null, we will log it at DEBUG level and use the following * default values. * - isOutputOnEOF defaults to false * - isOutputOnCheckpoint defaults to false * - isInternalSorterSupported defaults to false */ > > Second question, have you thought about cases where someone is > either bootstrapping from a streaming source like Kafka > or simply trying to catch up after a long period of downtime in a purely > streaming job? Generally speaking a cases where > user doesn't care about latency in the catch up phase, regardless if the > source is bounded or unbounded, but wants to process > the data as fast as possible, and then switch dynamically to real time > processing? > Yes, I have thought about this. We should allow this job to effectively run in batch mode when the job is in the catch-up phase. FLIP-327 is actually an important step toward addressing this use-case. In order to address the above use-case, all we need is a way for source operator (e.g. Kafka) to tell Flink runtime (via IsProcessingBacklog) whether it is in the catch-up phase. Since every Kafka message has event-timestamp, we can allow users to specify a job-level config such as backlog-watermark-lag-threshold, and consider a Kafka Source to have IsProcessingBacklog=true if system_time - watermark > backlog-watermark-lag-threshold. This effectively allows us to determine whether Kafka is in the catch up phase. Once we have this capability (I plan to work on this in FLIP-328), we can directly use the features proposed in FLIP-325 and FLIP-327 to optimize the above use-case. What do you think? Best, Dong > > Best, > Piotrek > > niedz., 2 lip 2023 o 16:15 Dong Lin <lindon...@gmail.com> napisał(a): > > > Hi all, > > > > I am opening this thread to discuss FLIP-327: Support stream-batch > unified > > operator to improve job throughput when processing backlog data. The > design > > doc can be found at > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-327%3A+Support+stream-batch+unified+operator+to+improve+job+throughput+when+processing+backlog+data > > . > > > > This FLIP enables a Flink job to initially operate in batch mode, > achieving > > high throughput while processing records that do not require low > processing > > latency. Subsequently, the job can seamlessly transition to stream mode > for > > processing real-time records with low latency. Importantly, the same > state > > can be utilized before and after this mode switch, making it particularly > > valuable when users wish to bootstrap the job's state using historical > > data. > > > > We would greatly appreciate any comments or feedback you may have on this > > proposal. > > > > Cheers, > > Dong > > >
