You should also be able to simply add a Bounded Read from the backup data source to your pipeline and flatten it with your Pubsub topic. Because all of the elements produced by both the bounded and unbounded sources will have consistent timestamps, when you run the pipeline the watermark will be held until all of the data is read from the bounded sources. Once this is done, your pipeline can continue processing only elements from the PubSub source. If you don't want the backlog and the current processing to occur in the same pipeline, running the same pipeline but just reading from the archival data should be sufficient (all of the processing would be identical, just the source would need to change).
If you read from both the "live" and "archival" sources within the same pipeline, you will need to use additional machines so the backlog can be processed promptly if you use a watermark based trigger; watermarks will be held until the bounded source is fully processed. On Mon, May 1, 2017 at 9:29 AM, Lars BK <[email protected]> wrote: > I did not see Lukasz reply before I posted, and I will have to read it a > bit later! > > man. 1. mai 2017 kl. 18.28 skrev Lars BK <[email protected]>: > >> Yes, precisely. >> >> I think that could work, yes. What you are suggesting sounds like idea 2) >> in my original question. >> >> My main concern is that I would have to allow a great deal of lateness >> and that old windows would consume too much memory. Whether it works in my >> case or not I don't know yet as I haven't tested it. >> >> What if I had to process even older data? Could I handle any "oldness" of >> data by increasing the allowed lateness and throwing machines at the >> problem to hold all the old windows in memory while the backlog is >> processed? If so, great! But I would have to dial the allowed lateness back >> down when the processing has caught up with the present. >> >> Is there some intended way of handling reprocessing like this? Maybe not? >> Perhaps it is more of a Pubsub and Dataflow question than a Beam question >> when it comes down to it. >> >> >> man. 1. mai 2017 kl. 17.25 skrev Jean-Baptiste Onofré <[email protected]>: >> >>> OK, so the messages are "re-publish" on the topic, with the same >>> timestamp as >>> the original and consume again by the pipeline. >>> >>> Maybe, you can play with the allowed lateness and late firings ? >>> >>> Something like: >>> >>> Window.into(FixedWindows.of(Duration.minutes(xx))) >>> .triggering(AfterWatermark.pastEndOfWindow() >>> .withEarlyFirings(AfterProcessingTime. >>> pastFirstElementInPane() >>> .plusDelayOf(FIVE_MINUTES)) >>> .withLateFirings(AfterProcessingTime. >>> pastFirstElementInPane() >>> .plusDelayOf(TEN_MINUTES))) >>> .withAllowedLateness(Duration.minutes() >>> .accumulatingFiredPanes()) >>> >>> Thoughts ? >>> >>> Regards >>> JB >>> >>> On 05/01/2017 05:12 PM, Lars BK wrote: >>> > Hi Jean-Baptiste, >>> > >>> > I think the key point in my case is that I have to process or >>> reprocess "old" >>> > messages. That is, messages that are late because they are streamed >>> from an >>> > archive file and are older than the allowed lateness in the pipeline. >>> > >>> > In the case I described the messages had already been processed once >>> and no >>> > longer in the topic, so they had to be sent and processed again. But >>> it might as >>> > well have been that I had received a backfill of data that absolutely >>> needs to >>> > be processed regardless of it being later than the allowed lateness >>> with respect >>> > to present time. >>> > >>> > So when I write this now it really sounds like I either need to allow >>> more >>> > lateness or somehow rewind the watermark! >>> > >>> > Lars >>> > >>> > man. 1. mai 2017 kl. 16.34 skrev Jean-Baptiste Onofré <[email protected] >>> > <mailto:[email protected]>>: >>> > >>> > Hi Lars, >>> > >>> > interesting use case indeed ;) >>> > >>> > Just to understand: if possible, you don't want to re-consume the >>> messages from >>> > the PubSub topic right ? So, you want to "hold" the PCollections >>> for late data >>> > processing ? >>> > >>> > Regards >>> > JB >>> > >>> > On 05/01/2017 04:15 PM, Lars BK wrote: >>> > > Hi, >>> > > >>> > > Is there a preferred way of approaching reprocessing historic >>> data with >>> > > streaming jobs? >>> > > >>> > > I want to pose this as a general question, but I'm working with >>> Pubsub and >>> > > Dataflow specifically. I am a fan of the idea of replaying/fast >>> forwarding >>> > > through historic data to reproduce results (as you perhaps would >>> with Kafka), >>> > > but I'm having a hard time unifying this way of thinking with >>> the concepts of >>> > > watermarks and late data in Beam. I'm not sure how to best mimic >>> this with the >>> > > tools I'm using, or if there is a better way. >>> > > >>> > > If there is a previous discussion about this I might have missed >>> (and I'm >>> > > guessing there is), please direct me to it! >>> > > >>> > > >>> > > The use case: >>> > > >>> > > Suppose I discover a bug in a streaming job with event time >>> windows and an >>> > > allowed lateness of 7 days, and that I subsequently have to >>> reprocess all the >>> > > data for the past month. Let us also assume that I have an >>> archive of my >>> > source >>> > > data (in my case in Google cloud storage) and that I can >>> republish it all >>> > to the >>> > > message queue I'm using. >>> > > >>> > > Some ideas that may or may not work I would love to get your >>> thoughts on: >>> > > >>> > > 1) Start a new instance of the job that reads from a separate >>> source to >>> > which I >>> > > republish all messages. This shouldn't work because 14 days of >>> my data is >>> > later >>> > > than the allowed limit, buy the remaining 7 days should be >>> reprocessed as >>> > intended. >>> > > >>> > > 2) The same as 1), but with allowed lateness of one month. When >>> the job is >>> > > caught up, the lateness can be adjusted back to 7 days. I am >>> afraid this >>> > > approach may consume too much memory since I'm letting a whole >>> month of >>> > windows >>> > > remain in memory. Also I wouldn't get the same triggering >>> behaviour as in the >>> > > original job since most or all of the data is late with respect >>> to the >>> > > watermark, which I assume is near real time when the historic >>> data enters the >>> > > pipeline. >>> > > >>> > > 3) The same as 1), but with the republishing first and only >>> starting the >>> > new job >>> > > when all messages are already waiting in the queue. The >>> watermark should then >>> > > start one month back in time and only catch up with the present >>> once all the >>> > > data is reprocessed, yielding no late data. (Experiments I've >>> done with this >>> > > approach produce somewhat unexpected results where early panes >>> that are older >>> > > than 7 days appear to be both the first and the last firing from >>> their >>> > > respective windows.) Early firings triggered by processing time >>> would probably >>> > > differ by the results should be the same? This approach also >>> feels a bit >>> > awkward >>> > > as it requires more orchestration. >>> > > >>> > > 4) Batch process the archived data instead and start a streaming >>> job in >>> > > parallel. Would this in a sense be a more honest approach since >>> I'm actually >>> > > reprocessing batches of archived data? The triggering behaviour >>> in the >>> > streaming >>> > > version of the job would not apply in batch, and I would want to >>> avoid >>> > stitching >>> > > together results from two jobs if I can. >>> > > >>> > > >>> > > These are the approaches I've thought of currently, and any >>> input is much >>> > > appreciated. Have any of you faced similar situations, and how >>> did you >>> > solve them? >>> > > >>> > > >>> > > Regards, >>> > > Lars >>> > > >>> > > >>> > >>> > -- >>> > Jean-Baptiste Onofré >>> > [email protected] <mailto:[email protected]> >>> > http://blog.nanthrax.net >>> > Talend - http://www.talend.com >>> > >>> >>> -- >>> Jean-Baptiste Onofré >>> [email protected] >>> http://blog.nanthrax.net >>> Talend - http://www.talend.com >>> >>
