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.hu...@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 <van...@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.hu...@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 --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
