Actually, we only need to ensure all records belonging to the same key will
be forwarded to the same operator instance(i), and we do not need to
guarantee that 'i' is the same with the 'i' in previous savepoints. When
the job is restarted, the rule 'same key's record will be in together' is
guaranteed and more slots will be surely useful, since each slot(operator
instance) will be responsible for less keys, leading to less records.

Tripathi,Vikash <vikash.tripa...@cerner.com> 于2021年2月18日周四 上午12:09写道:

> Hi there,
>
>
>
> I wanted to know how re-partitioning of keys per operator instance would
> happen when the current operator instances are scaled up or down and we are
> restarting our job from a previous savepoint which had a different number
> of parallel instances of the same operator.
>
>
>
> My main concern is whether the re-distribution would lead to mapping of
> same keys to same operator instances as was done earlier but if this
> happens then there would be no added advantage of adding new task slots for
> the same operator because they would remain less used or not used at all if
> all possible key values have been seen earlier and if we go by the other
> way around of evenly distributing out keys (based on the hash function) to
> the new parallel slots as well, won't this cause issues in terms of
> processing consistent results based on the state of operator as was
> provided by previous savepoint of application.
>
>
>
> Is there a guarantee given by the hash function as in attached snippet,
> that same keys which landed earlier on an operator instance will land back
> again to the same operator instance once the job is restarted with new set
> of parallelism configuration?
>
>
>
> Thanks,
>
> Vikash
>
>
>
>
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