spark_data_array here has about 35k rows with 4k columns. I have 4 nodes in
the cluster and gave 48g to executors. also tried kyro serialization.
traceback (most recent call last):
File "/mohit/./m.py", line 58, in <module>
spark_data = sc.parallelize(spark_data_array)
File "/mohit/spark/python/pyspark/context.py", line 265, in parallelize
jrdd = readRDDFromFile(self._jsc, tempFile.name, numSlices)
File "/mohit/spark/python/lib/py4j-0.8.1-src.zip/py4j/java_gateway.py",
line 537, in __call__
File "/mohit/spark/python/lib/py4j-0.8.1-src.zip/py4j/protocol.py", line
300, in get_return_value
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:279)
at org.apache.spark.api.python.PythonRDD.readRDDFromFile(PythonRDD.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)
