spark1.1.1 + Hbase (CDH5.3.1). 20 nodes each with 4 cores and 32G memory. 3
cores and 16G memory were assigned to spark in each worker node. Standalone
mode. Data set is 3.8 T. wondering how to fix this. Thanks!

org.apache.spark.rdd.PairRDDFunctions.saveAsNewAPIHadoopDataset(PairRDDFunctions.scala:935)
org.apache.spark.api.python.PythonRDD$.saveAsHadoopDataset(PythonRDD.scala:691)
org.apache.spark.api.python.PythonRDD.saveAsHadoopDataset(PythonRDD.scala)
sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
java.lang.reflect.Method.invoke(Method.java:606)
py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
py4j.Gateway.invoke(Gateway.java:259)
py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
py4j.commands.CallCommand.execute(CallCommand.java:79)
py4j.GatewayConnection.run(GatewayConnection.java:207)
java.lang.Thread.run(Thread.java:745)



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