Hi,
On Tue, Dec 9, 2014 at 4:39 AM, Sandy Ryza wrote:
>
> Can you try using the YARN Fair Scheduler and set
> yarn.scheduler.fair.continuous-scheduling-enabled to true?
>
I'm using Cloudera 5.2.0 and my configuration says
yarn.resourcemanager.scheduler.class =
org.apache.hadoop.yarn.server.re
Hey Tobias,
Can you try using the YARN Fair Scheduler and set
yarn.scheduler.fair.continuous-scheduling-enabled to true?
-Sandy
On Sun, Dec 7, 2014 at 5:39 PM, Tobias Pfeiffer wrote:
> Hi,
>
> thanks for your responses!
>
> On Sat, Dec 6, 2014 at 4:22 AM, Sandy Ryza
> wrote:
>>
>> What versio
Hi,
thanks for your responses!
On Sat, Dec 6, 2014 at 4:22 AM, Sandy Ryza wrote:
>
> What version are you using? In some recent versions, we had a couple of
> large hardcoded sleeps on the Spark side.
>
I am using Spark 1.1.1.
As Andrew mentioned, I guess most of the 10 seconds waiting time p
Great to hear!
-Sandy
On Fri, Dec 5, 2014 at 11:17 PM, Denny Lee wrote:
> Okay, my bad for not testing out the documented arguments - once i use the
> correct ones, the query shrinks completes in ~55s (I can probably make it
> faster). Thanks for the help, eh?!
>
>
>
> On Fri Dec 05 2014 at 1
Okay, my bad for not testing out the documented arguments - once i use the
correct ones, the query shrinks completes in ~55s (I can probably make it
faster). Thanks for the help, eh?!
On Fri Dec 05 2014 at 10:34:50 PM Denny Lee wrote:
> Sorry for the delay in my response - for my spark calls
Sorry for the delay in my response - for my spark calls for stand-alone and
YARN, I am using the --executor-memory and --total-executor-cores for the
submission. In standalone, my baseline query completes in ~40s while in
YARN, it completes in ~1800s. It does not appear from the RM web UI that
it
Hey Arun I've seen that behavior before. It happens when the cluster
doesn't have enough resources to offer and the RM hasn't given us our
containers yet. Can you check the RM Web UI at port 8088 to see whether
your application is requesting more resources than the cluster has to offer?
2014-12-05
Hey Arun,
The sleeps would only cause maximum like 5 second overhead. The idea was
to give executors some time to register. On more recent versions, they
were replaced with the spark.scheduler.minRegisteredResourcesRatio and
spark.scheduler.maxRegisteredResourcesWaitingTime. As of 1.1, by defau
Likely this not the case here yet one thing to point out with Yarn
parameters like --num-executors is that they should be specified *before*
app jar and app args on spark-submit command line otherwise the app only
gets the default number of containers which is 2.
On Dec 5, 2014 12:22 PM, "Sandy Ryz
Hey Sandy,
What are those sleeps for and do they still exist? We have seen about a
1min to 1:30 executor startup time, which is a large chunk for jobs that
run in ~10min.
Thanks,
Arun
On Fri, Dec 5, 2014 at 3:20 PM, Sandy Ryza wrote:
> Hi Denny,
>
> Those sleeps were only at startup, so if jo
Hi Denny,
Those sleeps were only at startup, so if jobs are taking significantly
longer on YARN, that should be a different problem. When you ran on YARN,
did you use the --executor-cores, --executor-memory, and --num-executors
arguments? When running against a standalone cluster, by default Spa
Just an FYI - I can submit the SparkPi app to YARN in cluster mode on a
1-node m3.xlarge EC2 instance instance and the app finishes running
successfully in about 40 seconds. I just figured the 30 - 40 sec run time
was normal b/c of the submitting overhead that Andrew mentioned.
Denny, you can mayb
My submissions of Spark on YARN (CDH 5.2) resulted in a few thousand steps.
If I was running this on standalone cluster mode the query finished in 55s
but on YARN, the query was still running 30min later. Would the hard coded
sleeps potentially be in play here?
On Fri, Dec 5, 2014 at 11:23 Sandy Ry
Hi Tobias,
What version are you using? In some recent versions, we had a couple of
large hardcoded sleeps on the Spark side.
-Sandy
On Fri, Dec 5, 2014 at 11:15 AM, Andrew Or wrote:
> Hey Tobias,
>
> As you suspect, the reason why it's slow is because the resource manager
> in YARN takes a wh
Hey Tobias,
As you suspect, the reason why it's slow is because the resource manager in
YARN takes a while to grant resources. This is because YARN needs to first
set up the application master container, and then this AM needs to request
more containers for Spark executors. I think this accounts f
Hi,
I am using spark-submit to submit my application to YARN in "yarn-cluster"
mode. I have both the Spark assembly jar file as well as my application jar
file put in HDFS and can see from the logging output that both files are
used from there. However, it still takes about 10 seconds for my
appli
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