Hi,
I'm sure that cluster deploy mode would solve it very well. It'd be a
cluster issue then to re-execute the driver then?
Pozdrawiam,
Jacek Laskowski
https://medium.com/@jaceklaskowski/
Mastering Apache Spark 2.0 http://bit.ly/mastering-apache-spark
Follow me at https://twitter.com/jacekla
Thanks Ted,
In this case we were using Standalone with Standalone master started on
another node.
The app was started on a node but not the master node. The master node was
not affected. The node in question was the edge (running spark-submit).
>From the link I was not sure this matter would hav
Have you read
https://spark.apache.org/docs/latest/spark-standalone.html#high-availability
?
FYI
On Thu, Aug 11, 2016 at 12:40 PM, Mich Talebzadeh wrote:
>
> Hi,
>
> Although Spark is fault tolerant when nodes go down like below:
>
> FROM tmp
> [Stage 1:===>
Hi,
Although Spark is fault tolerant when nodes go down like below:
FROM tmp
[Stage 1:===> (20 + 10) /
100]16/08/11 20:21:34 ERROR TaskSchedulerImpl: Lost executor 3 on
xx.xxx.197.216: worker lost
[Stage 1:>
