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https://issues.apache.org/jira/browse/SPARK-21935?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-21935:
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Labels: bulk-closed pyspark udf (was: pyspark udf)
> Pyspark UDF causing ExecutorLostFailure
> ----------------------------------------
>
> Key: SPARK-21935
> URL: https://issues.apache.org/jira/browse/SPARK-21935
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.1.0
> Reporter: Nikolaos Tsipas
> Priority: Major
> Labels: bulk-closed, pyspark, udf
> Attachments: Screen Shot 2017-09-06 at 11.30.28.png, Screen Shot
> 2017-09-06 at 11.31.13.png, Screen Shot 2017-09-06 at 11.31.31.png, cpu.png
>
>
> Hi,
> I'm using spark 2.1.0 on AWS EMR (Yarn) and trying to use a UDF in python as
> follows:
> {code}
> from pyspark.sql.functions import col, udf
> from pyspark.sql.types import StringType
> path = 's3://some/parquet/dir/myfile.parquet'
> df = spark.read.load(path)
> def _test_udf(useragent):
> return useragent.upper()
> test_udf = udf(_test_udf, StringType())
> df = df.withColumn('test_field', test_udf(col('std_useragent')))
> df.write.parquet('/output.parquet')
> {code}
> The following config is used in {{spark-defaults.conf}} (using
> {{maximizeResourceAllocation}} in EMR)
> {code}
> ...
> spark.executor.instances 4
> spark.executor.cores 8
> spark.driver.memory 8G
> spark.executor.memory 9658M
> spark.default.parallelism 64
> spark.driver.maxResultSize 3G
> ...
> {code}
> The cluster has 4 worker nodes (+1 master) with the following specs: 8 vCPU,
> 15 GiB memory, 160 SSD GB storage
> The above example fails every single time with errors like the following:
> {code}
> 17/09/06 09:58:08 WARN TaskSetManager: Lost task 26.1 in stage 1.0 (TID 50,
> ip-172-31-7-125.eu-west-1.compute.internal, executor 10): ExecutorLostFailure
> (executor 10 exited caused by one of the running tasks) Reason: Container
> killed by YARN for exceeding memory limits. 10.4 GB of 10.4 GB physical
> memory used. Consider boosting spark.yarn.executor.memoryOverhead.
> {code}
> I tried to increase the {{spark.yarn.executor.memoryOverhead}} to 3000 which
> delays the errors but eventually I get them before the end of the job. The
> job eventually fails.
> !Screen Shot 2017-09-06 at 11.31.31.png|width=800!
> If I run the above job in scala everything works as expected (without having
> to adjust the memoryOverhead)
> {code}
> import org.apache.spark.sql.functions.udf
> val upper: String => String = _.toUpperCase
> val df = spark.read.load("s3://some/parquet/dir/myfile.parquet")
> val upperUDF = udf(upper)
> val newdf = df.withColumn("test_field", upperUDF(col("std_useragent")))
> newdf.write.parquet("/output.parquet")
> {code}
> !Screen Shot 2017-09-06 at 11.31.13.png|width=800!
> Cpu utilisation is very bad with pyspark
> !cpu.png|width=800!
> Is this a known bug with pyspark and udfs or is it a matter of bad
> configuration?
> Looking forward to suggestions. Thanks!
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