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From Dominik Safaric <dominiksafa...@gmail.com>
Subject Re: Partitioning operator state
Date Thu, 08 Dec 2016 18:08:40 GMT
Dear Stefan, 

Thanks for the clarification. 

How is however the state recovered in the case of a task failure? Assuming there is a topology
of 10 workers and a worker dies. The state in this case, after restarting the entire execution,
will how exactly be distributed across the workers? 


> On 8 Dec 2016, at 10:04, Stefan Richter <s.richter@data-artisans.com> wrote:
> Hi Dominik,
> as Gordon’s response only covers keyed-state, I will briefly explain what happens for
non-keyed operator state. In contrast to Flink 1.1, Flink 1.2 checkpointing does not write
a single blackbox object (e.g. ONE object that is a set of all kafka offsets is emitted),
but a list of blackbox objects instead (e.g. think of all kafka offsets being emitted individually,
as MULTIPLE objects). While Flink 1.2 still has no knowledge about the emitted objects in
the list (thus they remain blackboxes), what the contract allows is that those objects can
be freely redistributed in case of scale-out or scale-in. Scaling is merely splitting or merging
of the checkpointed lists.
> Best,
> Stefan 
>> Am 08.12.2016 um 08:00 schrieb Tzu-Li (Gordon) Tai <tzulitai@apache.org <mailto:tzulitai@apache.org>>:
>> Hi Dominik,
>> Do you mean how Flink redistributes an operator’s state when the parallelism of
the operator is changed?
>> If so, you can take a look at [1] and [2].
>> Cheers,
>> Gordon
>> [1] https://issues.apache.org/jira/browse/FLINK-3755 <https://issues.apache.org/jira/browse/FLINK-3755>
>> [2] https://docs.google.com/document/d/1G1OS1z3xEBOrYD4wSu-LuBCyPUWyFd9l3T9WyssQ63w/edit#
>> On December 8, 2016 at 4:40:18 AM, Dominik Safaric (dominiksafaric@gmail.com <mailto:dominiksafaric@gmail.com>)
>>> Hi everyone, 
>>> In the case of scaling out a Flink cluster, how does Flink handle operator state
partitioning of a staged topology?  
>>> Regards, 
>>> Dominik 

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