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 <j...@nanthrax.net>: > 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 > jbono...@apache.org > http://blog.nanthrax.net > Talend - http://www.talend.com >
