Continuing to debug with Scala, I tried this on local with enough memory (10g) and it is able to count the dataset. With more memory(for executor and driver) in a cluster it still fails. The data is about 2Gbytes. It is 30k * 4k doubles.
On Sat, Jul 12, 2014 at 6:31 PM, Aaron Davidson <ilike...@gmail.com> wrote: > I think this is probably dying on the driver itself, as you are probably > materializing the whole dataset inside your python driver. How large is > spark_data_array compared to your driver memory? > > > On Fri, Jul 11, 2014 at 7:30 PM, Mohit Jaggi <mohitja...@gmail.com> wrote: > >> I put the same dataset into scala (using spark-shell) and it acts weird. >> I cannot do a count on it, the executors seem to hang. The WebUI shows 0/96 >> in the status bar, shows details about the worker nodes but there is no >> progress. >> sc.parallelize does finish (takes too long for the data size) in scala. >> >> >> On Fri, Jul 11, 2014 at 2:00 PM, Mohit Jaggi <mohitja...@gmail.com> >> wrote: >> >>> 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) >>> >> >> >
