I most of the things you are asking for are already there: you can configure 
checkpoint interval + externalized cp, the backend, and the location for 
savepoints and externalized checkpoints. You can restart from savepoints and 
externalized checkpoints from the CLI. One point I am not entirely sure about 
are automatic CP or SP when a job is shut down. IIRC, this is either already 
available, or in the making.

Resolving the last external checkpoint is as easy as listing the configured 
directory, especially if you only retain the last one. Otherwise the timestamp 
gives the required information. It is true that there could also be an CLI 
option to automatically does the work to pick the latest.

And there is a command line parameter switch to supply savepoints and 
externalized checkpoints for restarts. I think that makes more sense than a 
general configuration of automatic restart behaviour because the user might 
also intend to start a new, clean run for the job.

 
> Am 10.08.2017 um 15:45 schrieb Henri Heiskanen <henri.heiska...@gmail.com>:
> 
> 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 
> <mailto: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 
>> <mailto: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 <mailto: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 
>>> <mailto: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.
>> 
>> 
> 
> 

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