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From Stephan Ewen <se...@apache.org>
Subject Re: Streaming - memory management
Date Wed, 31 Aug 2016 16:47:20 GMT
If you use RocksDB, you will not run into OutOfMemory errors.

On Wed, Aug 31, 2016 at 6:34 PM, Fabian Hueske <fhueske@gmail.com> wrote:

> Hi Vinaj,
>
> if you use user-defined state, you have to manually clear it.
> Otherwise, it will stay in the state backend (heap or RocksDB) until the
> job goes down (planned or due to an OOM error).
>
> This is esp. important to keep in mind, when using keyed state.
> If you have an unbounded, evolving key space you will likely run
> out-of-memory.
> The job will constantly add state for each new key but won't be able to
> clean up the state for "expired" keys.
>
> You could implement a clean-up mechanism this if you implement a custom
> stream operator.
> However this is a very low level interface and requires solid understanding
> of the internals like timestamps, watermarks and the checkpointing
> mechanism.
>
> The community is currently working on a state expiry feature (state will be
> discarded if not requested or updated for x minutes).
>
> Regarding the second question: Does state remain local after checkpointing?
> Yes, the local state is only copied to the remote FS (HDFS, S3, ...) but
> remains in the operator. So the state is not gone after a checkpoint is
> completed.
>
> Hope this helps,
> Fabian
>
> 2016-08-31 18:17 GMT+02:00 Vinay Patil <vinay18.patil@gmail.com>:
>
> > Hi Stephan,
> >
> > Just wanted to jump into this discussion regarding state.
> >
> > So do you mean that if we maintain user-defined state (for non-window
> > operators), then if we do  not clear it explicitly will the data for that
> > key remains in RocksDB.
> >
> > What happens in case of checkpoint ? I read in the documentation that
> after
> > the checkpoint happens the rocksDB data is pushed to the desired location
> > (hdfs or s3 or other fs), so for user-defined state does the data still
> > remain in RocksDB after checkpoint ?
> >
> > Correct me if I have misunderstood this concept
> >
> > For one of our use we were going for this, but since I read the above
> part
> > in documentation so we are going for Cassandra now (to store records and
> > query them for a special case)
> >
> >
> >
> >
> >
> > Regards,
> > Vinay Patil
> >
> > On Wed, Aug 31, 2016 at 4:51 AM, Stephan Ewen <sewen@apache.org> wrote:
> >
> > > In streaming, memory is mainly needed for state (key/value state). The
> > > exact representation depends on the chosen StateBackend.
> > >
> > > State is explicitly released: For windows, state is cleaned up
> > > automatically (firing / expiry), for user-defined state, keys have to
> be
> > > explicitly cleared (clear() method) or in the future will have the
> option
> > > to expire.
> > >
> > > The heavy work horse for streaming state is currently RocksDB, which
> > > internally uses native (off-heap) memory to keep the data.
> > >
> > > Does that help?
> > >
> > > Stephan
> > >
> > >
> > > On Tue, Aug 30, 2016 at 11:52 PM, Roshan Naik <roshan@hortonworks.com>
> > > wrote:
> > >
> > > > As per the docs, in Batch mode, dynamic memory allocation is avoided
> by
> > > > storing messages being processed in ByteBuffers via Unsafe methods.
> > > >
> > > > Couldn't find any docs  describing mem mgmt in Streamingn mode. So...
> > > >
> > > > - Am wondering if this is also the case with Streaming ?
> > > >
> > > > - If so, how does Flink detect that an object is no longer being used
> > and
> > > > can be reclaimed for reuse once again ?
> > > >
> > > > -roshan
> > > >
> > >
> >
>

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