peay created SPARK-27039:
----------------------------
Summary: toPandas with Avro swallows maxResultSize errors
Key: SPARK-27039
URL: https://issues.apache.org/jira/browse/SPARK-27039
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 2.4.0
Reporter: peay
I am running the following simple `toPandas` with {{maxResultSize}} set to 1mb:
{code:java}
import pyspark.sql.functions as F
df = spark.range(1000 * 1000)
df_pd = df.withColumn("test", F.lit("this is a long string that should make the
resulting dataframe too large for maxResult which is 1m")).toPandas()
{code}
With {{spark.sql.execution.arrow.enabled}} set to {{true}}, this returns an
empty Pandas dataframe without any error:
{code:python}
df_pd.info()
# <class 'pandas.core.frame.DataFrame'>
# Index: 0 entries
# Data columns (total 2 columns):
# id 0 non-null object
# test 0 non-null object
# dtypes: object(2)
# memory usage: 0.0+ bytes
{code}
The driver stderr does have an error, and so does the Spark UI:
{code:java}
ERROR TaskSetManager: Total size of serialized results of 1 tasks (52.8 MB) is
bigger than spark.driver.maxResultSize (1024.0 KB)
ERROR TaskSetManager: Total size of serialized results of 2 tasks (105.7 MB) is
bigger than spark.driver.maxResultSize (1024.0 KB)
Exception in thread "serve-Arrow" org.apache.spark.SparkException: Job aborted
due to stage failure: Total size of serialized results of 1 tasks (52.8 MB) is
bigger than spark.driver.maxResultSize (1024.0 KB)
at
org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2039)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2027)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2026)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2026)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
at scala.Option.foreach(Option.scala:257)
at
org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2260)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2209)
at
org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2198)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at
org.apache.spark.sql.Dataset$$anonfun$collectAsArrowToPython$1$$anonfun$apply$17.apply(Dataset.scala:3313)
at
org.apache.spark.sql.Dataset$$anonfun$collectAsArrowToPython$1$$anonfun$apply$17.apply(Dataset.scala:3282)
at
org.apache.spark.api.python.PythonRDD$$anonfun$6$$anonfun$apply$1.apply$mcV$sp(PythonRDD.scala:435)
at
org.apache.spark.api.python.PythonRDD$$anonfun$6$$anonfun$apply$1.apply(PythonRDD.scala:435)
at
org.apache.spark.api.python.PythonRDD$$anonfun$6$$anonfun$apply$1.apply(PythonRDD.scala:435)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.api.python.PythonRDD$$anonfun$6.apply(PythonRDD.scala:436)
at org.apache.spark.api.python.PythonRDD$$anonfun$6.apply(PythonRDD.scala:432)
at org.apache.spark.api.python.PythonServer$$anon$1.run(PythonRDD.scala:862)
{code}
With {{spark.sql.execution.arrow.enabled}} set to {{false}}, the Python call to
{{toPandas}} does fail as expected.
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