Hi -- Notice the additional "y" in red (as Mich mentioned)

pyspark --conf queue=default --conf executory-memory=24G

On Thu, May 20, 2021 at 12:02 PM Clay McDonald <stuart.mcdonald@bateswhite.com> wrote:

How so?


From: Mich Talebzadeh <mich.talebzadeh@gmail.com>
Sent: Wednesday, May 19, 2021 5:45 PM
To: Clay McDonald <stuart.mcdonald@bateswhite.com>
Cc: user@spark.apache.org
Subject: Re: PySpark Write File Container exited with a non-zero exit code 143


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Hi Clay,


Those parameters you are passing are not valid


pyspark --conf queue=default --conf executory-memory=24G


Python 3.7.3 (default, Apr  3 2021, 20:42:31)

[GCC 4.8.5 20150623 (Red Hat 4.8.5-39)] on linux

Type "help", "copyright", "credits" or "license" for more information.

Warning: Ignoring non-Spark config property: executory-memory

Warning: Ignoring non-Spark config property: queue

2021-05-19 22:28:20,521 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

Setting default log level to "WARN".

To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).

Welcome to

      ____              __

     / __/__  ___ _____/ /__

    _\ \/ _ \/ _ `/ __/  '_/

   /__ / .__/\_,_/_/ /_/\_\   version 3.1.1



Using Python version 3.7.3 (default, Apr  3 2021 20:42:31)

Spark context Web UI available at http://rhes75:4040

Spark context available as 'sc' (master = local[*], app id = local-1621459701490).

SparkSession available as 'spark'.




pyspark dynamic_ARRAY_generator_parquet.py


Running python applications through 'pyspark' is not supported as of Spark 2.0.

Use ./bin/spark-submit <python file>



This works


$SPARK_HOME/bin/spark-submit --master local[4] dynamic_ARRAY_generator_parquet.py









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On Wed, 19 May 2021 at 20:10, Clay McDonald <stuart.mcdonald@bateswhite.com> wrote:

Hello all,


I’m hoping someone can give me some direction for troubleshooting this issue, I’m trying to write from Spark on an HortonWorks(Cloudera) HDP cluster. I ssh directly to the first datanode and run PySpark with the following command; however, it is always failing no matter what size I set memory in Yarn Containers and Yarn Queues. Any suggestions?




pyspark --conf queue=default --conf executory-memory=24G





#HDFS_OUT="/ HDFS/Data/Test/Processed/Convert_parquet/Output"





'Test _2003.txt'


from  pyspark.sql.functions import regexp_replace,col

for f in fileList1:



                df = spark.read.option("delimiter","|").option("encoding",ENCODING).option("multiLine",True).option('wholeFile',"true").csv('{}/{}'.format(HDFS_RAW,fname), header=True)


                print('showing {}'.format(fname))

                if ('\r' in lastcol):


                                df=df.withColumn(lastcol, regexp_replace(col("{}\r".format(lastcol)), "[\r]", "")).drop('{}\r'.format(lastcol))





Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 4, DataNode01.mydomain.com, executor 5): ExecutorLostFailure (executor 5 exited caused by one of the running tasks) Reason: Container marked as failed: container_e331_1621375512548_0021_01_000006 on host: DataNode01.mydomain.com. Exit status: 143. Diagnostics: [2021-05-19 18:09:06.392]Container killed on request. Exit code is 143
[2021-05-19 18:09:06.413]Container exited with a non-zero exit code 143.
[2021-05-19 18:09:06.414]Killed by external signal





Best Regards,
Ayan Guha