You are viewing a plain text version of this content. The canonical link for it is here.
Posted to user@spark.apache.org by wdbaruni <wd...@gmail.com> on 2015/07/21 22:50:20 UTC
Spark spark.shuffle.memoryFraction has no affect
Hi
I am testing Spark on Amazon EMR using Python and the basic wordcount
example shipped with Spark.
After running the application, I realized that in Stage 0 reduceByKey(add),
around 2.5GB shuffle is spilled to memory and 4GB shuffle is spilled to
disk. Since in the wordcount example I am not caching or persisting any
data, so I thought I can increase the performance of this application by
giving more shuffle memoryFraction. So, in spark-defaults.conf, I added the
following:
spark.storage.memoryFraction 0.2
spark.shuffle.memoryFraction 0.6
However, I am still getting the same performance and the same amount of
shuffle data is being spilled to disk and memory. I validated that Spark is
reading these configurations using Spark UI/Environment and I can see my
changes. Moreover, I tried setting spark.shuffle.spill to false and I got
the performance I am looking for and all shuffle data was spilled to memory
only.
So, what am I getting wrong here and why not the extra shuffle memory
fraction is not utilized?
*My environment:*
Amazon EMR with Spark 1.3.1 running using -x argument
1 Master node: m3.xlarge
3 Core nodes: m3.xlarge
Application: wordcount.py
Input: 10 .gz files 90MB each (~350MB unarchived) stored in S3
*Submit command:*
/home/hadoop/spark/bin/spark-submit --deploy-mode client /mnt/wordcount.py
s3n://<input location>
*spark-defaults.conf:*
spark.eventLog.enabled false
spark.executor.extraJavaOptions -verbose:gc -XX:+PrintGCDetails
-XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC
-XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70
spark.driver.extraJavaOptions -Dspark.driver.log.level=INFO
spark.master yarn
spark.executor.instances 3
spark.executor.cores 4
spark.executor.memory 9404M
spark.default.parallelism 12
spark.eventLog.enabled true
spark.eventLog.dir hdfs:///spark-logs/
spark.storage.memoryFraction 0.2
spark.shuffle.memoryFraction 0.6
--
View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-spark-shuffle-memoryFraction-has-no-affect-tp23944.html
Sent from the Apache Spark User List mailing list archive at Nabble.com.
---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
For additional commands, e-mail: user-help@spark.apache.org
Re: Spark spark.shuffle.memoryFraction has no affect
Posted by Walid Baroni <wd...@gmail.com>.
Hi Andrew
I tried many different combinations, but still no change in the amount of shuffle bytes spilled to disk by checking the UI. I made sure the configuration have been applied by checking Spark UI/Environment. I only see changes in shuffle bytes spilled if I disable spark.shuffle.spill
> On Jul 22, 2015, at 3:15 AM, Andrew Or <an...@databricks.com> wrote:
>
> Hi,
>
> The setting of 0.2 / 0.6 looks reasonable to me. Since you are not using caching at all, have you tried trying something more extreme, like 0.1 / 0.9? Since disabling spark.shuffle.spill didn't cause an OOM this setting should be fine. Also, one thing you could do is to verify the shuffle bytes spilled on the UI before and after the change.
>
> Let me know if that helped.
> -Andrew
>
> 2015-07-21 13:50 GMT-07:00 wdbaruni <wdbaruni@gmail.com <ma...@gmail.com>>:
> Hi
> I am testing Spark on Amazon EMR using Python and the basic wordcount
> example shipped with Spark.
>
> After running the application, I realized that in Stage 0 reduceByKey(add),
> around 2.5GB shuffle is spilled to memory and 4GB shuffle is spilled to
> disk. Since in the wordcount example I am not caching or persisting any
> data, so I thought I can increase the performance of this application by
> giving more shuffle memoryFraction. So, in spark-defaults.conf, I added the
> following:
>
> spark.storage.memoryFraction 0.2
> spark.shuffle.memoryFraction 0.6
>
> However, I am still getting the same performance and the same amount of
> shuffle data is being spilled to disk and memory. I validated that Spark is
> reading these configurations using Spark UI/Environment and I can see my
> changes. Moreover, I tried setting spark.shuffle.spill to false and I got
> the performance I am looking for and all shuffle data was spilled to memory
> only.
>
> So, what am I getting wrong here and why not the extra shuffle memory
> fraction is not utilized?
>
> *My environment:*
> Amazon EMR with Spark 1.3.1 running using -x argument
> 1 Master node: m3.xlarge
> 3 Core nodes: m3.xlarge
> Application: wordcount.py
> Input: 10 .gz files 90MB each (~350MB unarchived) stored in S3
>
> *Submit command:*
> /home/hadoop/spark/bin/spark-submit --deploy-mode client /mnt/wordcount.py
> s3n://<input location>
>
> *spark-defaults.conf:*
> spark.eventLog.enabled false
> spark.executor.extraJavaOptions -verbose:gc -XX:+PrintGCDetails
> -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC
> -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70
> spark.driver.extraJavaOptions -Dspark.driver.log.level=INFO
> spark.master yarn
> spark.executor.instances 3
> spark.executor.cores 4
> spark.executor.memory 9404M
> spark.default.parallelism 12
> spark.eventLog.enabled true
> spark.eventLog.dir hdfs:///spark-logs/
> spark.storage.memoryFraction 0.2
> spark.shuffle.memoryFraction 0.6
>
>
>
> --
> View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-spark-shuffle-memoryFraction-has-no-affect-tp23944.html <http://apache-spark-user-list.1001560.n3.nabble.com/Spark-spark-shuffle-memoryFraction-has-no-affect-tp23944.html>
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org <ma...@spark.apache.org>
> For additional commands, e-mail: user-help@spark.apache.org <ma...@spark.apache.org>
>
>
Re: Spark spark.shuffle.memoryFraction has no affect
Posted by Andrew Or <an...@databricks.com>.
Hi,
The setting of 0.2 / 0.6 looks reasonable to me. Since you are not using
caching at all, have you tried trying something more extreme, like 0.1 /
0.9? Since disabling spark.shuffle.spill didn't cause an OOM this setting
should be fine. Also, one thing you could do is to verify the shuffle bytes
spilled on the UI before and after the change.
Let me know if that helped.
-Andrew
2015-07-21 13:50 GMT-07:00 wdbaruni <wd...@gmail.com>:
> Hi
> I am testing Spark on Amazon EMR using Python and the basic wordcount
> example shipped with Spark.
>
> After running the application, I realized that in Stage 0 reduceByKey(add),
> around 2.5GB shuffle is spilled to memory and 4GB shuffle is spilled to
> disk. Since in the wordcount example I am not caching or persisting any
> data, so I thought I can increase the performance of this application by
> giving more shuffle memoryFraction. So, in spark-defaults.conf, I added the
> following:
>
> spark.storage.memoryFraction 0.2
> spark.shuffle.memoryFraction 0.6
>
> However, I am still getting the same performance and the same amount of
> shuffle data is being spilled to disk and memory. I validated that Spark is
> reading these configurations using Spark UI/Environment and I can see my
> changes. Moreover, I tried setting spark.shuffle.spill to false and I got
> the performance I am looking for and all shuffle data was spilled to memory
> only.
>
> So, what am I getting wrong here and why not the extra shuffle memory
> fraction is not utilized?
>
> *My environment:*
> Amazon EMR with Spark 1.3.1 running using -x argument
> 1 Master node: m3.xlarge
> 3 Core nodes: m3.xlarge
> Application: wordcount.py
> Input: 10 .gz files 90MB each (~350MB unarchived) stored in S3
>
> *Submit command:*
> /home/hadoop/spark/bin/spark-submit --deploy-mode client /mnt/wordcount.py
> s3n://<input location>
>
> *spark-defaults.conf:*
> spark.eventLog.enabled false
> spark.executor.extraJavaOptions -verbose:gc -XX:+PrintGCDetails
> -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC
> -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70
> spark.driver.extraJavaOptions -Dspark.driver.log.level=INFO
> spark.master yarn
> spark.executor.instances 3
> spark.executor.cores 4
> spark.executor.memory 9404M
> spark.default.parallelism 12
> spark.eventLog.enabled true
> spark.eventLog.dir hdfs:///spark-logs/
> spark.storage.memoryFraction 0.2
> spark.shuffle.memoryFraction 0.6
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/Spark-spark-shuffle-memoryFraction-has-no-affect-tp23944.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
> For additional commands, e-mail: user-help@spark.apache.org
>
>