The issue is fixed.
Here's an explanation which interested people can read through:
For the earlier mail, the default resourceDirectory =>
core/src/main/resources didn't yield the expected result. By default all
the static resources placed under this directory are picked up and included
in the ja
Am going with the default java opts for emr-
-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps
-XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70
-XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled
-XX:OnOutOfMemoryError='kill -9 %p'
The data is not partitioned. Its 6Tb data of a
The median GC time is 1.3 mins for a median duration of 41 mins. What
parameters can I tune for controlling GC.
Other details, median Peak execution memory of 13 G and input records of
2.3 gigs.
180-200 executors launched.
- Thanks, via mobile, excuse brevity.
On May 21, 2016 10:59 AM, "Reynold
Yash:
Can you share the JVM parameters you used ?
How many partitions are there in your data set ?
Thanks
On Fri, May 20, 2016 at 5:59 PM, Reynold Xin wrote:
> It's probably due to GC.
>
> On Fri, May 20, 2016 at 5:54 PM, Yash Sharma wrote:
>
>> Hi All,
>> I am here to get some expert advice
It's probably due to GC.
On Fri, May 20, 2016 at 5:54 PM, Yash Sharma wrote:
> Hi All,
> I am here to get some expert advice on a use case I am working on.
>
> Cluster & job details below -
>
> Data - 6 Tb
> Cluster - EMR - 15 Nodes C3-8xLarge (shared by other MR apps)
>
> Parameters-
> --execut
Hi All,
I am here to get some expert advice on a use case I am working on.
Cluster & job details below -
Data - 6 Tb
Cluster - EMR - 15 Nodes C3-8xLarge (shared by other MR apps)
Parameters-
--executor-memory 10G \
--executor-cores 6 \
--conf spark.dynamicAllocation.enabled=true \
--conf spark.d
+1
Ricardo Almeida
On 20 May 2016 at 18:33, Mark Hamstra wrote:
> This is isn't yet a release candidate since, as Reynold mention in his
> opening post, preview releases are "not meant to be functional, i.e. they
> can and highly likely will contain critical bugs or documentation errors."
Ah, I think I see the issue. resourceManaged and core/src/resources
aren't included in the classpath; to achieve that, you need to scope
the setting to either "compile" or "test" (probably compile in your
case). So, the simplest way to add the extra settings would be
something like:
resourceGenera
This is isn't yet a release candidate since, as Reynold mention in his
opening post, preview releases are "not meant to be functional, i.e. they
can and highly likely will contain critical bugs or documentation errors."
Once we're at the point where we expect there not to be such bugs and
errors,
Some more info I’m still digging.
I’m just trying to do `spark.table(“db.table”).count`from a spark-shell
“db.table” is just a hive table.
At commit b67668b this worked just fine and it returned the number of rows in
db.table.
Starting at ca99171 "[SPARK-15073][SQL] Hide SparkSession constructo
On 20 May 2016, at 00:34, Shankar Venkataraman
mailto:shankarvenkataraman...@gmail.com>>
wrote:
Thanks Luciano. The case we are seeing is different - the yarn resource manager
is shutting down the container in which the executor is running since there
does not seem to be a response and it is
+1 Having an rc1 would help me get stable feedback on using my library with
Spark, compared to relying on 2.0.0-SNAPSHOT.
On Fri, 20 May 2016 at 05:57 Xiao Li wrote:
> Changed my vote to +1. Thanks!
>
> 2016-05-19 13:28 GMT-07:00 Xiao Li :
>
>> Will do. Thanks!
>>
>> 2016-05-19 13:26 GMT-07:00 R
Andres - this is great feedback. Let me think about it a little bit more
and reply later.
On Thu, May 19, 2016 at 11:12 AM, Andres Perez wrote:
> Hi all,
>
> We were in the process of porting an RDD program to one which uses
> Datasets. Most things were easy to transition, but one hole in
> fun
I filed https://issues.apache.org/jira/browse/SPARK-15441
On Thu, May 19, 2016 at 8:48 AM, Andres Perez wrote:
> Hi all, I'm getting some odd behavior when using the joinWith
> functionality for Datasets. Here is a small test case:
>
> val left = List(("a", 1), ("a", 2), ("b", 3), ("c", 4)).
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