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From Fabian Hueske <fhue...@gmail.com>
Subject Re: State Maintenance
Date Thu, 07 Sep 2017 07:44:03 GMT
Hi Navneeth,

there's a lower level state interface that should address your
requirements: OperatorStateStore.getUnionListState()

This union list state is similar to the regular operator list state, but
instead of splitting the list for recovery and giving out splits to
operator instance, it restores the complete list on each operator instance.
So it basically does a broadcast restore. If all operator have the same
state, only one instance checkpoints its state and this state is restored
to all other instances in case of a failure. This should also work with
rescaling.
The operator instance to checkpoint can be identified by
(RuntimeContext.getIndexOfThisSubtask == 0).

The OperatorStateStore is a bit hidden. You have to implement the
CheckpointedFunction interface. When
CheckpointedFunction.initializeState(FunctionInitializationContext context)
is called context has a method getOperatorStateStore().

I'd recommend to have a look at the detailed JavaDocs of all involved
classes and methods.

Hope this helps,
Fabian


2017-09-05 19:35 GMT+02:00 Navneeth Krishnan <reachnavneeth2@gmail.com>:

> Thanks Gordon for your response. I have around 80 parallel flatmap
> operator instances and each instance requires 3 states. Out of which one is
> user state in which each operator will have unique user's data and I need
> this data to be queryable. The other two states are kind of static states
> which are only modified when there an update message in config stream. This
> static data could easily be around 2GB and in my previous approach I used
> operator state where the data is retrieved inside open method across all
> operator instances whereas checkpointed only inside one of the operator
> instance.
>
> One of the issue that I have is if I change the operator parallelism how
> would it affect the internal state?
>
>
> On Tue, Sep 5, 2017 at 5:36 AM, Tzu-Li (Gordon) Tai <tzulitai@apache.org>
> wrote:
>
>> Hi Navneeth,
>>
>> Answering your three questions separately:
>>
>> 1. Yes. Your MapState will be backed by RocksDB, so when removing an entry
>> from the map state, the state will be removed from the local RocksDB as
>> well.
>>
>> 2. If state classes are not POJOs, they will be serialized by Kryo,
>> unless a
>> custom serializer is specifically specified otherwise. You can take a look
>> at this document on how to do that [1].
>>
>> 3. I might need to know more information to be able to suggest properly
>> for
>> this one. How are you using the "huge state values"? From what you
>> described, it seems like you only need it on one of the parallel
>> instances,
>> so I'm a bit curious on what they are actually used for. Are they needed
>> when processing your records?
>>
>> Cheers,
>> Gordon
>>
>> [1]
>> https://ci.apache.org/projects/flink/flink-docs-release-1.3/
>> dev/stream/state.html#custom-serialization-for-managed-state
>>
>>
>>
>> --
>> Sent from: http://apache-flink-user-mailing-list-archive.2336050.n4.
>> nabble.com/
>>
>
>

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