James Porritt created SPARK-20809:
-------------------------------------
Summary: PySpark: Java heap space issue despite apparently being
within memory limits
Key: SPARK-20809
URL: https://issues.apache.org/jira/browse/SPARK-20809
Project: Spark
Issue Type: Bug
Components: PySpark
Affects Versions: 2.1.1
Environment: Linux x86_64
Reporter: James Porritt
I have the following script:
{code}
import itertools
import loremipsum
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
conf = SparkConf().set("spark.cores.max", "16") \
.set("spark.driver.memory", "16g") \
.set("spark.executor.memory", "16g") \
.set("spark.executor.memory_overhead", "16g") \
.set("spark.driver.maxResultsSize", "0")
sc = SparkContext(appName="testRDD", conf=conf)
ss = SparkSession(sc)
j = itertools.cycle(range(8))
rows = [(i, j.next(), ' '.join(map(lambda x: x[2],
loremipsum.generate_sentences(600)))) for i in range(500)] * 100
rrd = sc.parallelize(rows, 128)
{code}
When I run it with:
{noformat}
<system path>/spark-2.1.1-bin-hadoop2.7/bin/spark-submit <home
directory>/writeTest.py
{noformat}
it fails with a 'Java heap space' error:
{noformat}
py4j.protocol.Py4JJavaError: An error occurred while calling
z:org.apache.spark.api.python.PythonRDD.readRDDFromFile.
: java.lang.OutOfMemoryError: Java heap space
at
org.apache.spark.api.python.PythonRDD$.readRDDFromFile(PythonRDD.scala:468)
at
org.apache.spark.api.python.PythonRDD.readRDDFromFile(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:745)
{noformat}
The data I create here approximates my actual data. The third element of each
tuple should be around 25k, and there are 50k tuples overall. I estimate that I
should have around 1.2G of data.
Why then does it fail? All parts of the system should have enough memory?
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