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From John Omernik <j...@omernik.com>
Subject Re: Spark 1.3.1 On Mesos Issues.
Date Fri, 05 Jun 2015 21:38:41 GMT
Thanks all. The answers post is me too, I multi thread. That and Ted is
aware to and Mapr is helping me with it.  I shall report the answer of that
investigation when we have it.

As to reproduction, I've installed mapr file system, tired both version
4.0.2 and 4.1.0.  Have mesos running along side mapr, and then I use
standard methods for submitting spark jobs to mesos. I don't have my
configs now, on vacation :) but I can shar on Monday.

I appreciate the support I am getting from every one, mesos community,
spark community, and mapr.  Great to see folks solving problems and I will
be sure report back findings as they arise.



On Friday, June 5, 2015, Tim Chen <tim@mesosphere.io> wrote:

> It seems like there is another thread going on:
>
>
> http://answers.mapr.com/questions/163353/spark-from-apache-downloads-site-for-mapr.html
>
> I'm not particularly sure why, seems like the problem is that getting the
> current context class loader is returning null in this instance.
>
> Do you have some repro steps or config we can try this?
>
> Tim
>
> On Fri, Jun 5, 2015 at 3:40 AM, Steve Loughran <stevel@hortonworks.com
> <javascript:_e(%7B%7D,'cvml','stevel@hortonworks.com');>> wrote:
>
>>
>>  On 2 Jun 2015, at 00:14, Dean Wampler <deanwampler@gmail.com
>> <javascript:_e(%7B%7D,'cvml','deanwampler@gmail.com');>> wrote:
>>
>>  It would be nice to see the code for MapR FS Java API, but my google
>> foo failed me (assuming it's open source)...
>>
>>
>>  I know that MapRFS is closed source, don't know about the java JAR. Why
>> not ask Ted Dunning (cc'd)  nicely to see if he can track down the stack
>> trace for you.
>>
>>   So, shooting in the dark ;) there are a few things I would check, if
>> you haven't already:
>>
>>  1. Could there be 1.2 versions of some Spark jars that get picked up at
>> run time (but apparently not in local mode) on one or more nodes? (Side
>> question: Does your node experiment fail on all nodes?) Put another way,
>> are the classpaths good for all JVM tasks?
>> 2. Can you use just MapR and Spark 1.3.1 successfully, bypassing Mesos?
>>
>>  Incidentally, how are you combining Mesos and MapR? Are you running
>> Spark in Mesos, but accessing data in MapR-FS?
>>
>>  Perhaps the MapR "shim" library doesn't support Spark 1.3.1.
>>
>>  HTH,
>>
>>  dean
>>
>>  Dean Wampler, Ph.D.
>> Author: Programming Scala, 2nd Edition
>> <http://shop.oreilly.com/product/0636920033073.do> (O'Reilly)
>> Typesafe <http://typesafe.com/>
>> @deanwampler <http://twitter.com/deanwampler>
>> http://polyglotprogramming.com
>>
>> On Mon, Jun 1, 2015 at 2:49 PM, John Omernik <john@omernik.com
>> <javascript:_e(%7B%7D,'cvml','john@omernik.com');>> wrote:
>>
>>> All -
>>>
>>>  I am facing and odd issue and I am not really sure where to go for
>>> support at this point.  I am running MapR which complicates things as it
>>> relates to Mesos, however this HAS worked in the past with no issues so I
>>> am stumped here.
>>>
>>>  So for starters, here is what I am trying to run. This is a simple
>>> show tables using the Hive Context:
>>>
>>>  from pyspark import SparkContext, SparkConf
>>> from pyspark.sql import SQLContext, Row, HiveContext
>>> sparkhc = HiveContext(sc)
>>> test = sparkhc.sql("show tables")
>>> for r in test.collect():
>>>   print r
>>>
>>>  When I run it on 1.3.1 using ./bin/pyspark --master local  This works
>>> with no issues.
>>>
>>>  When I run it using Mesos with all the settings configured (as they
>>> had worked in the past) I get lost tasks and when I zoom in them, the error
>>> that is being reported is below.  Basically it's a NullPointerException on
>>> the com.mapr.fs.ShimLoader.  What's weird to me is is I took each instance
>>> and compared both together, the class path, everything is exactly the same.
>>> Yet running in local mode works, and running in mesos fails.  Also of note,
>>> when the task is scheduled to run on the same node as when I run locally,
>>> that fails too! (Baffling).
>>>
>>>  Ok, for comparison, how I configured Mesos was to download the mapr4
>>> package from spark.apache.org.  Using the exact same configuration file
>>> (except for changing the executor tgz from 1.2.0 to 1.3.1) from the 1.2.0.
>>> When I run this example with the mapr4 for 1.2.0 there is no issue in
>>> Mesos, everything runs as intended. Using the same package for 1.3.1 then
>>> it fails.
>>>
>>>  (Also of note, 1.2.1 gives a 404 error, 1.2.2 fails, and 1.3.0 fails
>>> as well).
>>>
>>>  So basically When I used 1.2.0 and followed a set of steps, it worked
>>> on Mesos and 1.3.1 fails.  Since this is a "current" version of Spark, MapR
>>> is supports 1.2.1 only.  (Still working on that).
>>>
>>>  I guess I am at a loss right now on why this would be happening, any
>>> pointers on where I could look or what I could tweak would be greatly
>>> appreciated. Additionally, if there is something I could specifically draw
>>> to the attention of MapR on this problem please let me know, I am perplexed
>>> on the change from 1.2.0 to 1.3.1.
>>>
>>>  Thank you,
>>>
>>>  John
>>>
>>>
>>>
>>>
>>>  Full Error on 1.3.1 on Mesos:
>>> 15/05/19 09:31:26 INFO MemoryStore: MemoryStore started with capacity
>>> 1060.3 MB java.lang.NullPointerException at
>>> com.mapr.fs.ShimLoader.getRootClassLoader(ShimLoader.java:96) at
>>> com.mapr.fs.ShimLoader.injectNativeLoader(ShimLoader.java:232) at
>>> com.mapr.fs.ShimLoader.load(ShimLoader.java:194) at
>>> org.apache.hadoop.conf.CoreDefaultProperties.(CoreDefaultProperties.java:60)
>>> at java.lang.Class.forName0(Native Method) at
>>> java.lang.Class.forName(Class.java:274) at
>>> org.apache.hadoop.conf.Configuration.getClassByNameOrNull(Configuration.java:1847)
>>> at
>>> org.apache.hadoop.conf.Configuration.getProperties(Configuration.java:2062)
>>> at
>>> org.apache.hadoop.conf.Configuration.loadResource(Configuration.java:2272)
>>> at
>>> org.apache.hadoop.conf.Configuration.loadResources(Configuration.java:2224)
>>> at org.apache.hadoop.conf.Configuration.getProps(Configuration.java:2141)
>>> at org.apache.hadoop.conf.Configuration.set(Configuration.java:992) at
>>> org.apache.hadoop.conf.Configuration.set(Configuration.java:966) at
>>> org.apache.spark.deploy.SparkHadoopUtil.newConfiguration(SparkHadoopUtil.scala:98)
>>> at org.apache.spark.deploy.SparkHadoopUtil.(SparkHadoopUtil.scala:43) at
>>> org.apache.spark.deploy.SparkHadoopUtil$.(SparkHadoopUtil.scala:220) at
>>> org.apache.spark.deploy.SparkHadoopUtil$.(SparkHadoopUtil.scala) at
>>> org.apache.spark.util.Utils$.getSparkOrYarnConfig(Utils.scala:1959) at
>>> org.apache.spark.storage.BlockManager.(BlockManager.scala:104) at
>>> org.apache.spark.storage.BlockManager.(BlockManager.scala:179) at
>>> org.apache.spark.SparkEnv$.create(SparkEnv.scala:310) at
>>> org.apache.spark.SparkEnv$.createExecutorEnv(SparkEnv.scala:186) at
>>> org.apache.spark.executor.MesosExecutorBackend.registered(MesosExecutorBackend.scala:70)
>>> java.lang.RuntimeException: Failure loading MapRClient. at
>>> com.mapr.fs.ShimLoader.injectNativeLoader(ShimLoader.java:283) at
>>> com.mapr.fs.ShimLoader.load(ShimLoader.java:194) at
>>> org.apache.hadoop.conf.CoreDefaultProperties.(CoreDefaultProperties.java:60)
>>> at java.lang.Class.forName0(Native Method) at
>>> java.lang.Class.forName(Class.java:274) at
>>> org.apache.hadoop.conf.Configuration.getClassByNameOrNull(Configuration.java:1847)
>>> at
>>> org.apache.hadoop.conf.Configuration.getProperties(Configuration.java:2062)
>>> at
>>> org.apache.hadoop.conf.Configuration.loadResource(Configuration.java:2272)
>>> at
>>> org.apache.hadoop.conf.Configuration.loadResources(Configuration.java:2224)
>>> at org.apache.hadoop.conf.Configuration.getProps(Configuration.java:2141)
>>> at org.apache.hadoop.conf.Configuration.set(Configuration.java:992) at
>>> org.apache.hadoop.conf.Configuration.set(Configuration.java:966) at
>>> org.apache.spark.deploy.SparkHadoopUtil.newConfiguration(SparkHadoopUtil.scala:98)
>>> at org.apache.spark.deploy.SparkHadoopUtil.(SparkHadoopUtil.scala:43) at
>>> org.apache.spark.deploy.SparkHadoopUtil$.(SparkHadoopUtil.scala:220) at
>>> org.apache.spark.deploy.SparkHadoopUtil$.(SparkHadoopUtil.scala) at
>>> org.apache.spark.util.Utils$.getSparkOrYarnConfig(Utils.scala:1959) at
>>> org.apache.spark.storage.BlockManager.(BlockManager.scala:104) at
>>> org.apache.spark.storage.BlockManager.(BlockManager.scala:179) at
>>> org.apache.spark.SparkEnv$.create(SparkEnv.scala:310) at
>>> org.apache.spark.SparkEnv$.createExecutorEnv(SparkEnv.scala:186) at
>>> org.apache.spark.executor.MesosExecutorBackend.registered(MesosExecutorBackend.scala:70)
>>>
>>>
>>>
>>
>>
>

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