python - Unable to process the medium or large files using spark 1.3.1 when using stand alone cluster management -

i using spark 1.3.1 process files along spark cluster management having 1 worker attached it.

i able process small files , when try process medium or large files getting following error

py4jjavaerror: error occurred while calling z:org.apache.spark.api.python.pythonrdd.runjob. : org.apache.spark.sparkexception: job aborted due stage failure: task 0 in stage 19.0 failed 4 times, recent failure: lost task 0.3 in stage 19.0 (tid 54, 790-gykghs1.ad.mdp.com): java.net.socketexception: connection reset peer: socket write error     @ java.net.socketoutputstream.socketwrite0(native method)     @ java.net.socketoutputstream.socketwrite(unknown source)     @ java.net.socketoutputstream.write(unknown source)     @ java.io.bufferedoutputstream.flushbuffer(unknown source)     @ java.io.bufferedoutputstream.write(unknown source)     @ java.io.dataoutputstream.write(unknown source)     @ java.io.filteroutputstream.write(unknown source)     @ org.apache.spark.api.python.pythonrdd$.writeutf(pythonrdd.scala:591)     @ org.apache.spark.api.python.pythonrdd$.org$apache$spark$api$python$pythonrdd$$write$1(pythonrdd.scala:411)     @ org.apache.spark.api.python.pythonrdd$$anonfun$writeiteratortostream$1.apply(pythonrdd.scala:421)     @ org.apache.spark.api.python.pythonrdd$$anonfun$writeiteratortostream$1.apply(pythonrdd.scala:421)     @ scala.collection.iterator$class.foreach(iterator.scala:727)     @ scala.collection.abstractiterator.foreach(iterator.scala:1157)     @ org.apache.spark.api.python.pythonrdd$.writeiteratortostream(pythonrdd.scala:421)     @ org.apache.spark.api.python.pythonrdd$writerthread$$anonfun$run$1.apply(pythonrdd.scala:243)     @ org.apache.spark.util.utils$.loguncaughtexceptions(utils.scala:1618)     @ org.apache.spark.api.python.pythonrdd$writerthread.run(pythonrdd.scala:205)  driver stacktrace:     @ org.apache.spark.scheduler.dagscheduler.org$apache$spark$scheduler$dagscheduler$$failjobandindependentstages(dagscheduler.scala:1204)     @ org.apache.spark.scheduler.dagscheduler$$anonfun$abortstage$1.apply(dagscheduler.scala:1193)     @ org.apache.spark.scheduler.dagscheduler$$anonfun$abortstage$1.apply(dagscheduler.scala:1192)     @ scala.collection.mutable.resizablearray$class.foreach(resizablearray.scala:59)     @ scala.collection.mutable.arraybuffer.foreach(arraybuffer.scala:47)     @ org.apache.spark.scheduler.dagscheduler.abortstage(dagscheduler.scala:1192)     @ org.apache.spark.scheduler.dagscheduler$$anonfun$handletasksetfailed$1.apply(dagscheduler.scala:693)     @ org.apache.spark.scheduler.dagscheduler$$anonfun$handletasksetfailed$1.apply(dagscheduler.scala:693)     @ scala.option.foreach(option.scala:236)     @ org.apache.spark.scheduler.dagscheduler.handletasksetfailed(dagscheduler.scala:693)     @ org.apache.spark.scheduler.dagschedulereventprocessloop.onreceive(dagscheduler.scala:1393)     @ org.apache.spark.scheduler.dagschedulereventprocessloop.onreceive(dagscheduler.scala:1354)     @ org.apache.spark.util.eventloop$$anon$1.run(eventloop.scala:48) 

any regarding appreciated.