That failed since it defaulted the versions for yarn and hadoop 
I’ll give it a try with just 2.4.0 for both yarn and hadoop…

Thanks,
Ron

On Aug 4, 2014, at 9:44 AM, Patrick Wendell <pwend...@gmail.com> wrote:

> Can you try building without any of the special `hadoop.version` flags and 
> just building only with -Phadoop-2.4? In the past users have reported issues 
> trying to build random spot versions... I think HW is supposed to be 
> compatible with the normal 2.4.0 build.
> 
> 
> On Mon, Aug 4, 2014 at 8:35 AM, Ron's Yahoo! <zlgonza...@yahoo.com.invalid> 
> wrote:
> Thanks, I ensured that $SPARK_HOME/pom.xml had the HDP repository under the 
> repositories element. I also confirmed that if the build couldn’t find the 
> version, it would fail fast so it seems as if it’s able to get the versions 
> it needs to build the distribution.
> I ran the following (generated from make-distribution.sh), but it did not 
> address the problem, while building with an older version (2.4.0.2.1.2.0-402) 
> worked. Any other thing I can try?
> 
> mvn clean package -Phadoop-2.4 -Phive -Pyarn -Dyarn.version=2.4.0.2.1.2.0-563 
> -Dhadoop.version=2.4.0.2.1.3.0-563 -DskipTests
> 
> 
> Thanks,
> Ron
> 
> 
> On Aug 4, 2014, at 7:13 AM, Steve Nunez <snu...@hortonworks.com> wrote:
> 
>> Provided you¹ve got the HWX repo in your pom.xml, you can build with this
>> line:
>> 
>> mvn -Pyarn -Phive -Phadoop-2.4 -Dhadoop.version=2.4.0.2.1.1.0-385
>> -DskipTests clean package
>> 
>> I haven¹t tried building a distro, but it should be similar.
>> 
>> 
>>      - SteveN
>> 
>> On 8/4/14, 1:25, "Sean Owen" <so...@cloudera.com> wrote:
>> 
>>> For any Hadoop 2.4 distro, yes, set hadoop.version but also set
>>> -Phadoop-2.4. http://spark.apache.org/docs/latest/building-with-maven.html
>>> 
>>> On Mon, Aug 4, 2014 at 9:15 AM, Patrick Wendell <pwend...@gmail.com>
>>> wrote:
>>>> For hortonworks, I believe it should work to just link against the
>>>> corresponding upstream version. I.e. just set the Hadoop version to
>>>> "2.4.0"
>>>> 
>>>> Does that work?
>>>> 
>>>> - Patrick
>>>> 
>>>> 
>>>> On Mon, Aug 4, 2014 at 12:13 AM, Ron's Yahoo!
>>>> <zlgonza...@yahoo.com.invalid>
>>>> wrote:
>>>>> 
>>>>> Hi,
>>>>>  Not sure whose issue this is, but if I run make-distribution using
>>>>> HDP
>>>>> 2.4.0.2.1.3.0-563 as the hadoop version (replacing it in
>>>>> make-distribution.sh), I get a strange error with the exception below.
>>>>> If I
>>>>> use a slightly older version of HDP (2.4.0.2.1.2.0-402) with
>>>>> make-distribution, using the generated assembly all works fine for me.
>>>>> Either 1.0.0 or 1.0.1 will work fine.
>>>>> 
>>>>>  Should I file a JIRA or is this a known issue?
>>>>> 
>>>>> Thanks,
>>>>> Ron
>>>>> 
>>>>> Exception in thread "main" org.apache.spark.SparkException: Job aborted
>>>>> due to stage failure: Task 0.0:0 failed 1 times, most recent failure:
>>>>> Exception failure in TID 0 on host localhost:
>>>>> java.lang.IncompatibleClassChangeError: Found interface
>>>>> org.apache.hadoop.mapreduce.TaskAttemptContext, but class was expected
>>>>> 
>>>>> 
>>>>> org.apache.avro.mapreduce.AvroKeyInputFormat.createRecordReader(AvroKeyI
>>>>> nputFormat.java:47)
>>>>> 
>>>>> 
>>>>> org.apache.spark.rdd.NewHadoopRDD$$anon$1.<init>(NewHadoopRDD.scala:111)
>>>>> 
>>>>> org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:99)
>>>>> 
>>>>> org.apache.spark.rdd.NewHadoopRDD.compute(NewHadoopRDD.scala:61)
>>>>>        org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>>>>>        org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>>>>>        org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
>>>>>        org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>>>>> 
>>>>> org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:77)
>>>>>        org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
>>>>>        org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
>>>>>        org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>>>>>        org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>>>>> 
>>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:111)
>>>>>        org.apache.spark.scheduler.Task.run(Task.scala:51)
>>>>> 
>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187)
>>>>> 
>>>>> 
>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.jav
>>>>> a:1145)
>>>>> 
>>>>> 
>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.ja
>>>>> va:615)
>>>>>        java.lang.Thread.run(Thread.java:745)
>>>> 
>>>> 
>>> 
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