If you are changing the SparkConf, that mean you have recreate the
SparkContext, isnt it? So you have to stop the previous SparkCotnext which
deletes all the information about stages that have been run. So the better
approach is to indeed save the data of the last stage explicitly and then
try reru
I meant a restart by the user, as ayan said.
I was thinking of a case where e.g. a Spark conf setting wrong and the job
failed in Stage 1, in my example .. and we want to rerun the job with the
right conf without rerunning Stage 0. Having this "re-start" capability may
cause some chaos if it would
Okay, may I am confused on the word "would be useful to *restart* from the
output of stage 0" ... did the OP mean restart by the user or restart
automatically by the system?
On Tue, Jul 28, 2015 at 3:43 PM, ayan guha wrote:
> Hi
>
> I do not think op asks about attempt failure but stage failure
Hi
I do not think op asks about attempt failure but stage failure and finally
leading to job failure. In that case, rdd info from last run is gone even
if from cache, isn't it?
Ayan
On 29 Jul 2015 07:01, "Tathagata Das" wrote:
> If you are using the same RDDs in the both the attempts to run the
If you are using the same RDDs in the both the attempts to run the job, the
previous stage outputs generated in the previous job will indeed be reused.
This applies to core though. For dataframes, depending on what you do, the
physical plan may get generated again leading to new RDDs which may caus
Is it possible to restart the job from the last successful stage instead of
from the beginning?
For example, if your job has stages 0, 1 and 2 .. and stage 0 takes a long
time and is successful, but the job fails on stage 1, it would be useful to
be able to restart from the output of stage 0 inste
