Hi, But I still need to resolve the latest checkpoint and pass that as an argument. My question still is that why all this can not be handled by Flink core? Why not just have parameters enable savepoints, location of savepoints and state backend and system would then automatically do checkpoints / savepoints on exit and also start from the first available checkpoint?
Br, Henkka On Thu, Aug 10, 2017 at 3:15 PM, Stefan Richter <s.rich...@data-artisans.com > wrote: > Hi, > > but I think this is exactly the case for externalized checkpoints. > Periodic savepoints are problematic because, their lifecycle is meant to be > under the control of the user and Flink can not make any assumptions when > they can be dropped. So in the conservative scenario, savepoints would > quickly pile up. With externalized checkpoints, you can control the number > of retained checkpoints. if you set this number to one, that should be > exactly what you want. > > As for rescalability, this limitation is more of a future than a current > problem. Right now, you should be able to rescale from all externalized > checkpoints. But this might not hold in the future, because you can > optimize checkpoints in some cases if this is feature dropped. > > Right now, externalized checkpoints should offer all that you want. > > Best, > Stefan > > Am 10.08.2017 um 11:46 schrieb Henri Heiskanen <henri.heiska...@gmail.com > >: > > Hi, > > It would be super helpful if Flink would provide out of the box > functionality for writing automatic savepoints and then starting from the > latest savepoint. If external checkpoints would support rescaling then 1st > requirement is met, but one would still need to e.g. find the latest > checkpoint from some folder and pass that as argument. We are currently > writing our own functionality for this. Why not just tell Flink that this > job uses persistent states and default functionality is then to start from > the latest snapshot. > > Br, > Henri H > > On Thu, Aug 10, 2017 at 11:20 AM, Stefan Richter < > s.rich...@data-artisans.com> wrote: > >> Hi, >> >> I would explain the main conceptual difference as follows: >> >> - Checkpoints are periodically triggered by the system for fault >> tolerance. They are used to automatically recover from failures. Because of >> their automatic and periodical nature, they should be lightweight to >> produce and will restore the same job without any changes to the jobgraph, >> parallelism, etc. Checkpoints are usually dropped after the job was >> terminated by the user. >> >> - Savepoints are triggered by the user to store the state of the job for >> a manual resume and backup. Savepoints are usually not periodical but >> typically taken before some user actions to the job or the system. For >> example, this could be an update of your Flink version, changing your job >> graph, changing parallelism, forking a second job like for a red/blue >> deployment, and so on. Of course, savepoints must survive job termination. >> Conceptually, savepoints can be a bit more expensive to produce, because >> they should have a format that makes all those „changes to the job“ >> features possible. >> >> Besides this conceptual difference, the current implementations are >> basically using the same code and produce the same „format". However, there >> is currently one exception from this, but I would expect more differences >> in the future. This exception are incremental checkpoints with the RocksDB >> state backend. They are using some RocksDB internal format instead of >> Flink’s „savepoint format“. This makes them the first instance of a more >> lightweight checkpointing mechanism, compared to savepoints, at the cost of >> dropping support for certain features such as changing the parallelism. >> >> Furthermore, there also exists „externalized checkpoints“, which are >> somewhere in between checkpoints and savepoints. They are triggered by >> Flink, but can survive job termination and can then be used by the user to >> restart the job, similar to savepoints. They use the checkpointing code >> path, so there are for example externalized incremental checkpoints. >> However, exactly like a normal checkpoints, they might also lack certain >> features like rescalability. >> >> Best, >> Stefan >> >> Am 10.08.2017 um 05:32 schrieb Raja.Aravapalli < >> raja.aravapa...@target.com>: >> >> Hi, >> >> Can someone please help me understand the difference between Flink's >> Checkpoints & Savepoints. >> >> While I read the documentation, couldn't understand the difference! :s >> >> >> Thanks a lot. >> >> >> >> Regards, >> Raja. >> >> >> > >