Michel Lemay created SPARK-23961:
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Summary: pyspark toLocalIterator throws an exception
Key: SPARK-23961
URL: https://issues.apache.org/jira/browse/SPARK-23961
Project: Spark
Issue Type: Improvement
Components: PySpark
Affects Versions: 2.2.1
Reporter: Michel Lemay
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
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