Re: Cases when to clear the checkpoint directories.

[Tathagata Das]

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)  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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Cases when to clear the checkpoint directories.

[John, Vishal (Agoda)]

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



This message is confidential and is for the sole use of the intended 
recipient(s). It may also be privileged or otherwise protected by copyright or 
other legal rules. If you have received it by mistake please let us know by 
reply email and delete it from your system. It is prohibited to copy this 
message or disclose its content to anyone. Any confidentiality or privilege is 
not waived or lost by any mistaken delivery or unauthorized disclosure of the 
message. All messages sent to and from Agoda may be monitored to ensure 
compliance with company policies, to protect the company's interests and to 
remove potential malware. Electronic messages may be intercepted, amended, lost 
or deleted, or contain viruses.