I'm using spark 2.1.0 on AWS EMR (Yarn) and trying to use a UDF in python as follows:
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')
The following config is used in spark-defaults.conf (using maximizeResourceAllocation in EMR)
... spark.executor.instances 4 spark.executor.cores 8 spark.driver.memory 8G spark.executor.memory 9658M spark.default.parallelism 64 spark.driver.maxResultSize 3G ...
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:
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.
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.
If I run the above job in scala everything works as expected (without having to adjust the memoryOverhead)
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")
Cpu utilisation is very bad with pyspark
Is this a known bug with pyspark and udfs or is it a matter of bad configuration?
Looking forward to suggestions. Thanks!