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From Marcelo Vanzin <van...@cloudera.com.INVALID>
Subject Re: Specifying different version of pyspark.zip and py4j files on worker nodes with Spark pre-installed
Date Thu, 04 Oct 2018 17:25:47 GMT
Try "spark.executorEnv.SPARK_HOME=$PWD" (in quotes so it does not get
expanded by the shell).

But it's really weird to be setting SPARK_HOME in the environment of
your node managers. YARN shouldn't need to know about that.
On Thu, Oct 4, 2018 at 10:22 AM Jianshi Huang <jianshi.huang@gmail.com> wrote:
>
> https://github.com/apache/spark/blob/88e7e87bd5c052e10f52d4bb97a9d78f5b524128/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala#L31
>
> The code shows Spark will try to find the path if SPARK_HOME is specified. And on my
worker node, SPARK_HOME is specified in .bashrc , for the pre-installed 2.2.1 path.
>
> I don't want to make any changes to worker node configuration, so any way to override
the order?
>
> Jianshi
>
> On Fri, Oct 5, 2018 at 12:11 AM Marcelo Vanzin <vanzin@cloudera.com> wrote:
>>
>> Normally the version of Spark installed on the cluster does not
>> matter, since Spark is uploaded from your gateway machine to YARN by
>> default.
>>
>> You probably have some configuration (in spark-defaults.conf) that
>> tells YARN to use a cached copy. Get rid of that configuration, and
>> you can use whatever version you like.
>> On Thu, Oct 4, 2018 at 2:19 AM Jianshi Huang <jianshi.huang@gmail.com> wrote:
>> >
>> > Hi,
>> >
>> > I have a problem using multiple versions of Pyspark on YARN, the driver and
worker nodes are all preinstalled with Spark 2.2.1, for production tasks. And I want to use
2.3.2 for my personal EDA.
>> >
>> > I've tried both 'pyFiles=' option and sparkContext.addPyFiles(), however on
the worker node, the PYTHONPATH still uses the system SPARK_HOME.
>> >
>> > Anyone knows how to override the PYTHONPATH on worker nodes?
>> >
>> > Here's the error message,
>> >>
>> >>
>> >> Py4JJavaError: An error occurred while calling o75.collectToPython.
>> >> : org.apache.spark.SparkException: Job aborted due to stage failure: Task
0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, emr-worker-8.cluster-68492,
executor 2): org.apache.spark.SparkException:
>> >> Error from python worker:
>> >> Traceback (most recent call last):
>> >> File "/usr/local/Python-3.6.4/lib/python3.6/runpy.py", line 183, in _run_module_as_main
>> >> mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
>> >> File "/usr/local/Python-3.6.4/lib/python3.6/runpy.py", line 109, in _get_module_details
>> >> __import__(pkg_name)
>> >> File "/usr/lib/spark-current/python/lib/pyspark.zip/pyspark/__init__.py",
line 46, in <module>
>> >> File "/usr/lib/spark-current/python/lib/pyspark.zip/pyspark/context.py",
line 29, in <module>
>> >> ModuleNotFoundError: No module named 'py4j'
>> >> PYTHONPATH was:
>> >> /usr/lib/spark-current/python/lib/pyspark.zip:/usr/lib/spark-current/python/lib/py4j-0.10.7-src.zip:/mnt/disk1/yarn/usercache/jianshi.huang/filecache/130/__spark_libs__5227988272944669714.zip/spark-core_2.11-2.3.2.jar
>> >
>> >
>> > And here's how I started Pyspark session in Jupyter.
>> >>
>> >>
>> >> %env SPARK_HOME=/opt/apps/ecm/service/spark/2.3.2-bin-hadoop2.7
>> >> %env PYSPARK_PYTHON=/usr/bin/python3
>> >> import findspark
>> >> findspark.init()
>> >> import pyspark
>> >> sparkConf = pyspark.SparkConf()
>> >> sparkConf.setAll([
>> >>     ('spark.cores.max', '96')
>> >>     ,('spark.driver.memory', '2g')
>> >>     ,('spark.executor.cores', '4')
>> >>     ,('spark.executor.instances', '2')
>> >>     ,('spark.executor.memory', '4g')
>> >>     ,('spark.network.timeout', '800')
>> >>     ,('spark.scheduler.mode', 'FAIR')
>> >>     ,('spark.shuffle.service.enabled', 'true')
>> >>     ,('spark.dynamicAllocation.enabled', 'true')
>> >> ])
>> >> py_files = ['hdfs://emr-header-1.cluster-68492:9000/lib/py4j-0.10.7-src.zip']
>> >> sc = pyspark.SparkContext(appName="Jianshi", master="yarn-client", conf=sparkConf,
pyFiles=py_files)
>> >>
>> >
>> >
>> > Thanks,
>> > --
>> > Jianshi Huang
>> >
>>
>>
>> --
>> Marcelo
>
>
>
> --
> Jianshi Huang
>
> LinkedIn: jianshi
> Twitter: @jshuang
> Github & Blog: http://huangjs.github.com/



-- 
Marcelo

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