Hey Josh, The way we replay historical data is we have a second Flink job that listens to the same live stream, and stores every single event in Google Cloud Storage.
When the main Flink job that is processing the live stream gets a request for a specific data set that it has not been processing yet, it sends a request to the historical flink job for the old data. The live job then starts storing relevant events from the live stream in state. It continues storing the live events until all the events form the historical job have been processed, then it processes the stored events, and finally starts processing the live stream again. As long as it's properly keyed (we key on the specific data set) then it doesn't block anything, keeps everything ordered, and eventually catches up. It also allows us to completely blow away state and rebuild it from scratch. So in you case it looks like what you could do is send a request to the "historical" job whenever you get a item that you don't yet have the current state of. The potential problems you may have are that it may not be possible to store every single historical event, and that you need to make sure there is enough memory to handle the ever increasing state size while the historical events are being replayed (and make sure to clear the state when it is done). It's a little complicated, and pretty expensive, but it works. Let me know if something doesn't make sense. On Thu, Jul 28, 2016 at 1:14 PM, Josh <jof...@gmail.com> wrote: > Hi all, > > I was wondering what approaches people usually take with reprocessing data > with Flink - specifically the case where you want to upgrade a Flink job, > and make it reprocess historical data before continuing to process a live > stream. > > I'm wondering if we can do something similar to the 'simple rewind' or > 'parallel rewind' which Samza uses to solve this problem, discussed here: > https://samza.apache.org/learn/documentation/0.10/jobs/reprocessing.html > > Having used Flink over the past couple of months, the main issue I've had > involves Flink's internal state - from my experience it seems it is easy to > break the state when upgrading a job, or when changing the parallelism of > operators, plus there's no easy way to view/access an internal key-value > state from outside Flink. > > For an example of what I mean, consider a Flink job which consumes a > stream of 'updates' to items, and maintains a key-value store of items > within Flink's internal state (e.g. in RocksDB). The job also writes the > updated items to a Kafka topic: > > http://oi64.tinypic.com/34q5opf.jpg > > My worry with this is that the state in RocksDB could be lost or become > incompatible with an updated version of the job. If this happens, we need > to be able to rebuild Flink's internal key-value store in RocksDB. So I'd > like to be able to do something like this (which I believe is the Samza > solution): > > http://oi67.tinypic.com/219ri95.jpg > > Has anyone done something like this already with Flink? If so are there > any examples of how to do this replay & switchover (rebuild state by > consuming from a historical log, then switch over to processing the live > stream)? > > Thanks for any insights, > Josh > > -- *Jason Brelloch* | Product Developer 3405 Piedmont Rd. NE, Suite 325, Atlanta, GA 30305 <http://www.bettercloud.com/> Subscribe to the BetterCloud Monitor <https://www.bettercloud.com/monitor?utm_source=bettercloud_email&utm_medium=email_signature&utm_campaign=monitor_launch> - Get IT delivered to your inbox
