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From Jianshi Huang <jianshi.hu...@gmail.com>
Subject Re: Specifying different version of pyspark.zip and py4j files on worker nodes with Spark pre-installed
Date Thu, 04 Oct 2018 17:22:40 GMT
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/

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