[SPARK-23961] pyspark toLocalIterator throws an exception

• Type: Bug
• Status: Resolved
• Priority: Minor
• Resolution: Fixed
• Affects Version/s: 2.0.2, 2.1.2, 2.2.1, 2.3.0
• Fix Version/s: 3.0.0
• Component/s: PySpark
• Labels: DataFrame pyspark
• Assignee: Bryan Cutler
• Reporter: Michel Lemay
• Created: 11/Apr/18 12:38
• Updated: 12/Dec/22 18:10
• Resolved: 07/May/19 21:48

Description

Given a dataframe and use toLocalIterator. If we do not consume all records, it will throw: 

ERROR PythonRDD: Error while sending iterator
java.net.SocketException: Connection reset by peer: socket write error
at java.net.SocketOutputStream.socketWrite0(Native Method)
at java.net.SocketOutputStream.socketWrite(SocketOutputStream.java:111)
at java.net.SocketOutputStream.write(SocketOutputStream.java:155)
at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122)
at java.io.DataOutputStream.write(DataOutputStream.java:107)
at java.io.FilterOutputStream.write(FilterOutputStream.java:97)
at org.apache.spark.api.python.PythonRDD$.org$apache$spark$api$python$PythonRDD$$write$1(PythonRDD.scala:497)
at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:509)
at org.apache.spark.api.python.PythonRDD$$anonfun$writeIteratorToStream$1.apply(PythonRDD.scala:509)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:509)
at org.apache.spark.api.python.PythonRDD$$anon$2$$anonfun$run$1.apply$mcV$sp(PythonRDD.scala:705)
at org.apache.spark.api.python.PythonRDD$$anon$2$$anonfun$run$1.apply(PythonRDD.scala:705)
at org.apache.spark.api.python.PythonRDD$$anon$2$$anonfun$run$1.apply(PythonRDD.scala:705)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1337)
at org.apache.spark.api.python.PythonRDD$$anon$2.run(PythonRDD.scala:706)

 

To reproduce, here is a simple pyspark shell script that show the error:

import itertools
df = spark.read.parquet("large parquet folder").cache()
print(df.count())
b = df.toLocalIterator()
print(len(list(itertools.islice(b, 20))))
b = None # Make the iterator goes out of scope.  Throws here.

 

Observations:

• Consuming all records do not throw.  Taking only a subset of the partitions create the error.
• In another experiment, doing the same on a regular RDD works if we cache/materialize it. If we do not cache the RDD, it throws similarly.
• It works in scala shell

 

Issue Links

• is duplicated by: SPARK-25733 The method toLocalIterator() with dataframe doesn't work
• relates to: SPARK-27548 PySpark toLocalIterator does not raise errors from worker
• relates to: SPARK-27659 Allow PySpark toLocalIterator to prefetch data
• links to: GitHub Pull Request #24070