Sorry for delay, by long-running I just meant if you were running an
iterative algorithm that was slowing down over time. We have observed this
in the spark-perf benchmark; as file system state builds up, the job can
slow down. Once the job finishes, however, it is cleaned up and should not
affect
On Mon, Feb 3, 2014 at 12:26 AM, Aaron Davidson wrote:
> Are you seeing any exceptions in between running apps? Does restarting the
> master/workers actually cause Spark to speed back up again? It's possible,
> for instance, that you run out of disk space, which should cause exceptions
> but not
Are you seeing any exceptions in between running apps? Does restarting the
master/workers actually cause Spark to speed back up again? It's possible,
for instance, that you run out of disk space, which should cause exceptions
but not go away by restarting the master/workers.
One thing to worry abo
Is your spark app an iterative one ? If so, your app is creating a big DAG
in every iteration. You should use checkpoint it periodically, say, 10
iterations one checkpoint.
2014-02-01 Aureliano Buendia :
> Hi,
>
> I've noticed my spark app (on ec2) gets slower and slower as I repeatedly
> execut
Hi,
I've noticed my spark app (on ec2) gets slower and slower as I repeatedly
execute it.
With a fresh ec2 cluster, it is snappy and takes about 15 mins to complete,
after running the same app 4 times it gets slower and takes to 40 mins and
more.
While the cluster gets slower, the monitoring met
