Ah, for #3, maybe this is what *rdd.checkpoint *does!
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.rdd.RDD
Thomas
On Mon, Jun 29, 2015 at 7:12 PM, Thomas Gerber thomas.ger...@radius.com
wrote:
Hello,
It is my understanding that shuffle are written on disk and
Thanks Silvio.
On Mon, Jun 29, 2015 at 7:41 PM, Silvio Fiorito
silvio.fior...@granturing.com wrote:
Regarding 1 and 2, yes shuffle output is stored on the worker local
disks and will be reused across jobs as long as they’re available. You can
identify when they’re used by seeing skipped
Regarding 1 and 2, yes shuffle output is stored on the worker local disks and
will be reused across jobs as long as they’re available. You can identify when
they’re used by seeing skipped stages in the job UI. They are periodically
cleaned up based on available space of the configured
Hi TD,
That little experiment helped a bit. This time we did not see any
exceptions for about 16 hours but eventually it did throw the same
exceptions as before. The cleaning of the shuffle files also stopped much
before these exceptions happened - about 7-1/2 hours after startup.
I am not quite
Thanks for the response, Conor. I tried with those settings and for a while
it seemed like it was cleaning up shuffle files after itself. However,
after exactly 5 hours later it started throwing exceptions and eventually
stopped working again. A sample stack trace is below. What is curious about
5
What was the state of your streaming application? Was it falling behind
with a large increasing scheduling delay?
TD
On Thu, Apr 23, 2015 at 11:31 AM, N B nb.nos...@gmail.com wrote:
Thanks for the response, Conor. I tried with those settings and for a
while it seemed like it was cleaning up
We already do have a cron job in place to clean just the shuffle files.
However, what I would really like to know is whether there is a proper
way of telling spark to clean up these files once its done with them?
Thanks
NB
On Mon, Apr 20, 2015 at 10:47 AM, Jeetendra Gangele gangele...@gmail.com
Write a crone job for this like below
12 * * * * find $SPARK_HOME/work -cmin +1440 -prune -exec rm -rf {} \+
32 * * * * find /tmp -type d -cmin +1440 -name spark-*-*-* -prune -exec
rm -rf {} \+
52 * * * * find $SPARK_LOCAL_DIR -mindepth 1 -maxdepth 1 -type d -cmin
+1440 -name spark-*-*-*
: Re: Shuffle files
- We set ulimit to 50. But I still get the same too many open files
warning.
- I tried setting consolidateFiles to True, but that did not help either.
I am using a Mesos cluster. Does Mesos have any limit on the number of
open files?
thanks
--
View
Cc: Sunny Khatri; Lisonbee, Todd; u...@spark.incubator.apache.org
Subject: Re: Shuffle files
My observation is opposite. When my job runs under default
spark.shuffle.manager, I don't see this exception. However, when it runs with
SORT based, I start seeing this error? How would that be possible
- We set ulimit to 50. But I still get the same too many open files
warning.
- I tried setting consolidateFiles to True, but that did not help either.
I am using a Mesos cluster. Does Mesos have any limit on the number of
open files?
thanks
--
View this message in context:
-on-reduceByKey-td2462.html
Thanks,
Todd
-Original Message-
From: SK [mailto:skrishna...@gmail.com]
Sent: Tuesday, October 7, 2014 2:12 PM
To: u...@spark.incubator.apache.org
Subject: Re: Shuffle files
- We set ulimit to 50. But I still get the same too many open files
warning.
- I tried
-Original Message-
From: SK [mailto:skrishna...@gmail.com]
Sent: Tuesday, October 7, 2014 2:12 PM
To: u...@spark.incubator.apache.org
Subject: Re: Shuffle files
- We set ulimit to 50. But I still get the same too many open files
warning.
- I tried setting consolidateFiles to True
Subject: Re: Shuffle files
- We set ulimit to 50. But I still get the same too many open files
warning.
- I tried setting consolidateFiles to True, but that did not help either.
I am using a Mesos cluster. Does Mesos have any limit on the number of
open files?
thanks
--
View
Hi SK,
For the problem with lots of shuffle files and the too many open files
exception there are a couple options:
1. The linux kernel has a limit on the number of open files at once. This
is set with ulimit -n, and can be set permanently in /etc/sysctl.conf or
/etc/sysctl.d/. Try increasing
From BlockManager code + ShuffleMapTask code, it writes under
spark.local.dir or java.io.tmpdir.
val diskBlockManager = new DiskBlockManager(shuffleBlockManager,
conf.get(spark.local.dir, System.getProperty(java.io.tmpdir)))
On Mon, Mar 3, 2014 at 10:45 PM, Usman Ghani us...@platfora.com
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