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From Tathagata Das <tathagata.das1...@gmail.com>
Subject Re: Cases when to clear the checkpoint directories.
Date Mon, 09 Oct 2017 20:06:51 GMT
Any changes in the Java code (to be specific, the generated bytecode) in
the functions you pass to Spark (i.e., map function, reduce function, as
well as it closure dependencies) counts as "application code change", and
will break the recovery from checkpoints.

On Sat, Oct 7, 2017 at 11:53 AM, John, Vishal (Agoda) <Vishal.John@agoda.com
> wrote:

>
>
> Hello TD,
>
> You had replied to one of the questions about checkpointing –
>
> This is an unfortunate design on my part when I was building DStreams :)
>
> Fortunately, we learnt from our mistakes and built Structured Streaming
> the correct way. Checkpointing in Structured Streaming stores only the
> progress information (offsets, etc.), and the user can change their
> application code (within certain constraints, of course) and still restart
> from checkpoints (unlike DStreams). If you are just building out your
> streaming applications, then I highly recommend you to try out Structured
> Streaming instead of DStreams (which is effectively in maintenance mode).
>
> Can you please elaborate on what you mean by application code change in
> DStream applications?
>
> If I add a couple of println statements in my application code will that
> become an application code change? or do you mean, changing method
> signatures or adding new methods etc.
> Could you please point to relevant source code in Spark, which does this
> type of code validation/de-serialisation in case of DStreams?
>
> We are using mapWithState in our application and it builds its state from
> checkpointed RDDs.  I would like understand the cases where we can avoid
> clearing the checkpoint directories.
>
>
> thanks in advance,
> Vishal
>
>
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