[ 
https://issues.apache.org/jira/browse/SPARK-23961?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Michel Lemay updated SPARK-23961:
---------------------------------
    Description: 
Given a dataframe and use toLocalIterator. If we do not consume all records, it 
will throw: 
{quote}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)
{quote}
 

To reproduce, here is a simple pyspark shell script that show the error:
{quote}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.
{quote}
 

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

 

  was:
Given a dataframe, take it's rdd and use toLocalIterator. If we do not consume 
all records, it will throw: 
{quote}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)
{quote}
 

To reproduce, here is a simple pyspark shell script that show the error:
{quote}import itertools
df = spark.read.parquet("large parquet folder")
cachedRDD = df.rdd.cache()
print(cachedRDD.count()) # materialize
b = cachedRDD.toLocalIterator()
print(len(list(itertools.islice(b, 20))))
b = None # Make the iterator goes out of scope.  Throws here.
{quote}
 

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

 


> pyspark toLocalIterator throws an exception
> -------------------------------------------
>
>                 Key: SPARK-23961
>                 URL: https://issues.apache.org/jira/browse/SPARK-23961
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark
>    Affects Versions: 2.2.1
>            Reporter: Michel Lemay
>            Priority: Minor
>              Labels: DataFrame, pyspark
>
> Given a dataframe and use toLocalIterator. If we do not consume all records, 
> it will throw: 
> {quote}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)
> {quote}
>  
> To reproduce, here is a simple pyspark shell script that show the error:
> {quote}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.
> {quote}
>  
> 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
>  



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