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Posted to user@spark.apache.org by harelglik <ha...@gmail.com> on 2016/07/21 11:56:42 UTC

Using RDD.checkpoint to recover app failure

I am writing a Spark application that has many iterations.
I am planning to checkpoint on every Nth iteration to cut the graph of my
rdd and clear previous shuffle files.
I would also like to be able to restart my application completely using the
last checkpoint.

I understand that regular checkpoint will work inside the same app, but how
can I read the checkpointed rdd in case I launch the new app?

In Spark streaming there seems to be support for recreating the full context
from a checkpoint, but I can't figure out how to do it for non-streaming
Spark.

Many thanks,
Harel.



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Re: Using RDD.checkpoint to recover app failure

Posted by Jacek Laskowski <ja...@japila.pl>.
Hi,

You may want to evaluate Spark JobServer or Livy or something that
would keep your SparkContext alive.

Pozdrawiam,
Jacek Laskowski
----
https://medium.com/@jaceklaskowski/
Mastering Apache Spark http://bit.ly/mastering-apache-spark
Follow me at https://twitter.com/jaceklaskowski


On Thu, Jul 21, 2016 at 1:56 PM, harelglik <ha...@gmail.com> wrote:
> I am writing a Spark application that has many iterations.
> I am planning to checkpoint on every Nth iteration to cut the graph of my
> rdd and clear previous shuffle files.
> I would also like to be able to restart my application completely using the
> last checkpoint.
>
> I understand that regular checkpoint will work inside the same app, but how
> can I read the checkpointed rdd in case I launch the new app?
>
> In Spark streaming there seems to be support for recreating the full context
> from a checkpoint, but I can't figure out how to do it for non-streaming
> Spark.
>
> Many thanks,
> Harel.
>
>
>
> --
> View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Using-RDD-checkpoint-to-recover-app-failure-tp27383.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe e-mail: user-unsubscribe@spark.apache.org
>

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