Sorry I didn't notice this mail, seems like a wrong cmdline problem, please
ignore my previous comment.

On Fri, Jan 29, 2016 at 11:58 AM, Nirav Patel <npa...@xactlycorp.com> wrote:

> Thanks Saisai. I saw following in yarn container logs. I think that killed
> sparkcontext.
>
> 16/01/28 17:38:29 INFO yarn.ApplicationMaster: Registered signal handlers for 
> [TERM, HUP, INT]*Unknown/unsupported param List*(--properties-file, 
> /tmp/hadoop-xactly/nm-local-dir/usercache/nir/appcache/application_1453752281504_3427/container_1453752281504_3427_01_000002/__spark_conf__/__spark_conf__.properties)
>
> Usage: org.apache.spark.deploy.yarn.ApplicationMaster [options]
> Options:
>   --jar JAR_PATH       Path to your application's JAR file
>   --class CLASS_NAME   Name of your application's main class
>   --primary-py-file    A main Python file
>   --py-files PY_FILES  Comma-separated list of .zip, .egg, or .py files to
>                        place on the PYTHONPATH for Python apps.
>   --args ARGS          Arguments to be passed to your application's main 
> class.
>                        Multiple invocations are possible, each will be passed 
> in order.
>   --num-executors NUM    Number of executors to start (Default: 2)
>   --executor-cores NUM   Number of cores for the executors (Default: 1)
>   --executor-memory MEM  Memory per executor (e.g. 1000M, 2G) (Default: 1G)
>
>
>
> But if you are saying creating sparkcontext manually in your application
> still works then I'll investigate more on my side. It just before I dig
> more I wanted to know if it was still supported.
>
> Nir
>
> On Thu, Jan 28, 2016 at 7:47 PM, Saisai Shao <sai.sai.s...@gmail.com>
> wrote:
>
>> I think I met this problem before, this problem might be due to some race
>> conditions in exit period. The way you mentioned is still valid, this
>> problem only occurs when stopping the application.
>>
>> Thanks
>> Saisai
>>
>> On Fri, Jan 29, 2016 at 10:22 AM, Nirav Patel <npa...@xactlycorp.com>
>> wrote:
>>
>>> Hi, we were using spark 1.3.1 and launching our spark jobs on
>>> yarn-client mode programmatically via creating a sparkConf and sparkContext
>>> object manually. It was inspired from spark self-contained application
>>> example here:
>>>
>>>
>>> https://spark.apache.org/docs/1.5.2/quick-start.html#self-contained-applications\
>>>
>>>
>>>
>>> Only additional configuration we would provide would be all related to
>>> yarn like executor instance, cores etc.
>>>
>>> However after upgrading to spark 1.5.2 above application breaks on a
>>> line `val sparkContext = new SparkContext(sparkConf)`
>>>
>>> 16/01/28 17:38:35 ERROR util.Utils: Uncaught exception in thread main
>>>
>>> java.lang.NullPointerException
>>>
>>> at
>>> org.apache.spark.network.netty.NettyBlockTransferService.close(NettyBlockTransferService.scala:152)
>>>
>>> at org.apache.spark.storage.BlockManager.stop(BlockManager.scala:1228)
>>>
>>> at org.apache.spark.SparkEnv.stop(SparkEnv.scala:100)
>>>
>>> at
>>> org.apache.spark.SparkContext$$anonfun$stop$12.apply$mcV$sp(SparkContext.scala:1749)
>>>
>>> at org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1185)
>>>
>>> at org.apache.spark.SparkContext.stop(SparkContext.scala:1748)
>>>
>>> at org.apache.spark.SparkContext.<init>(SparkContext.scala:593)
>>>
>>>
>>> So is this approach still supposed to work? Or do I must use
>>> SparkLauncher class with spark 1.5.2?
>>>
>>>
>>> Thanks
>>>
>>>
>>>
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>>>
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>>
>>
>>
>
>
>
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>
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