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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