This is something of a wild guess, but I find that when executors start
disappearingfor no obvious reason, this is usually because the yarn
node-managers have decided that the containers are using too much memory and
then terminate the executors.
Unfortunately, to see evidence of this, one needs to carefully review the yarn
node-manager logson the workers -- it doesn't seem to show up in the UI.
What I generally do is some combination of: 1) increasing executors memory
(often also decreasing number of executors) 2) decreasing the number of cores
per executor 3) increase the executor memory overhead.
Good luck.
-Mike
From: Sandy Ryza <[email protected]>
To: Umesh Kacha <[email protected]>
Cc: "[email protected]" <[email protected]>
Sent: Thursday, August 20, 2015 5:21 PM
Subject: Re: How to avoid executor time out on yarn spark while dealing with
large shuffle skewed data?
GC wouldn't necessarily result in errors - it could just be slowing down your
job and causing the executor JVMs to stall. If you click on a stage in the UI,
you should end up on a page with all the metrics concerning the tasks that ran
in that stage. "GC Time" is one of these task metrics.
-Sandy
On Thu, Aug 20, 2015 at 8:54 AM, Umesh Kacha <[email protected]> wrote:
Hi where do I see GC time in UI? I have set spark.yarn.executor.memoryOverhead
as 3500 which seems to be good enough I believe. So you mean only GC could be
the reason behind timeout I checked Yarn logs I did not see any GC error there.
Please guide. Thanks much.
On Thu, Aug 20, 2015 at 8:14 PM, Sandy Ryza <[email protected]> wrote:
Moving this back onto user@
Regarding GC, can you look in the web UI and see whether the "GC time" metric
dominates the amount of time spent on each task (or at least the tasks that
aren't completing)?
Also, have you tried bumping your spark.yarn.executor.memoryOverhead? YARN may
be killing your executors for using too much off-heap space. You can see
whether this is happening by looking in the Spark AM or YARN NodeManager logs.
-Sandy
On Thu, Aug 20, 2015 at 7:39 AM, Umesh Kacha <[email protected]> wrote:
Hi thanks much for the response. Yes I tried default settings too 0.2 it was
also going into timeout if it is spending time in GC then why it is not
throwing GC error I don't see any such error. Yarn logs are not helpful at all.
What is tungsten how do I use it? Spark is doing great I believe my job runs
successfully and 60% tasks completes only after first executor gets lost things
are messing. On Aug 20, 2015 7:59 PM, "Sandy Ryza" <[email protected]>
wrote:
What sounds most likely is that you're hitting heavy garbage collection. Did
you hit issues when the shuffle memory fraction was at its default of 0.2? A
potential danger with setting the shuffle storage to 0.7 is that it allows
shuffle objects to get into the GC old generation, which triggers more
stop-the-world garbage collections.
Have you tried enabling Tungsten / unsafe?
Unfortunately, Spark is still not that great at dealing with heavily-skewed
shuffle data, because its reduce-side aggregation still operates on Java
objects instead of binary data.
-Sandy
On Thu, Aug 20, 2015 at 7:21 AM, Umesh Kacha <[email protected]> wrote:
Hi Sandy thanks much for the response. I am using Spark 1.4.1 and I have set
spark.shuffle.storage as 0.7 as my spark job involves 4 groupby queries
executed using hiveContext.sql my data set is skewed so will be more shuffling
I believe I don't know what's wrong spark job runs fine for almost an hour and
when shuffle read shuffle write column in UI starts to show more than 10 gb
executor starts to getting lost because of timeout and slowly other executor
starts getting lost. Please guide. On Aug 20, 2015 7:38 PM, "Sandy Ryza"
<[email protected]> wrote:
What version of Spark are you using? Have you set any shuffle configs?
On Wed, Aug 19, 2015 at 11:46 AM, unk1102 <[email protected]> wrote:
I have one Spark job which seems to run fine but after one hour or so
executor start getting lost because of time out something like the following
error
cluster.yarnScheduler : Removing an executor 14 650000 timeout exceeds
600000 seconds
and because of above error couple of chained errors starts to come like
FetchFailedException, Rpc client disassociated, Connection reset by peer,
IOException etc
Please see the following UI page I have noticed when shuffle read/write
starts to increase more than 10 GB executors starts getting lost because of
timeout. How do I clear this stacked memory of 10 GB in shuffle read/write
section I dont cache anything why Spark is not clearing those memory. Please
guide.
IMG_20150819_231418358.jpg
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