Hi Chris,
I've encountered this error when running Spark’s ALS methods too. In my case,
it was because I set spark.local.dir improperly, and every time there was a
shuffle, it would spill many GB of data onto the local drive. What fixed it
was setting it to use the /mnt directory, where a network drive is mounted.
For example, setting an environmental variable:
export SPACE=$(mount | grep mnt | awk '{print $3"/spark/"}' | xargs | sed 's/
/,/g’)
Then adding -Dspark.local.dir=$SPACE or simply
-Dspark.local.dir=/mnt/spark/,/mnt2/spark/ when you run your driver application
Chris
On Jul 15, 2014, at 11:39 PM, Xiangrui Meng <men...@gmail.com> wrote:
> Check the number of inodes (df -i). The assembly build may create many
> small files. -Xiangrui
>
> On Tue, Jul 15, 2014 at 11:35 PM, Chris DuBois <chris.dub...@gmail.com> wrote:
>> Hi all,
>>
>> I am encountering the following error:
>>
>> INFO scheduler.TaskSetManager: Loss was due to java.io.IOException: No space
>> left on device [duplicate 4]
>>
>> For each slave, df -h looks roughtly like this, which makes the above error
>> surprising.
>>
>> Filesystem Size Used Avail Use% Mounted on
>> /dev/xvda1 7.9G 4.4G 3.5G 57% /
>> tmpfs 7.4G 4.0K 7.4G 1% /dev/shm
>> /dev/xvdb 37G 3.3G 32G 10% /mnt
>> /dev/xvdf 37G 2.0G 34G 6% /mnt2
>> /dev/xvdv 500G 33M 500G 1% /vol
>>
>> I'm on an EC2 cluster (c3.xlarge + 5 x m3) that I launched using the
>> spark-ec2 scripts and a clone of spark from today. The job I am running
>> closely resembles the collaborative filtering example. This issue happens
>> with the 1M version as well as the 10 million rating version of the
>> MovieLens dataset.
>>
>> I have seen previous questions, but they haven't helped yet. For example, I
>> tried setting the Spark tmp directory to the EBS volume at /vol/, both by
>> editing the spark conf file (and copy-dir'ing it to the slaves) as well as
>> through the SparkConf. Yet I still get the above error. Here is my current
>> Spark config below. Note that I'm launching via ~/spark/bin/spark-submit.
>>
>> conf = SparkConf()
>> conf.setAppName("RecommendALS").set("spark.local.dir",
>> "/vol/").set("spark.executor.memory", "7g").set("spark.akka.frameSize",
>> "100").setExecutorEnv("SPARK_JAVA_OPTS", " -Dspark.akka.frameSize=100")
>> sc = SparkContext(conf=conf)
>>
>> Thanks for any advice,
>> Chris
>>
