Then the only solution is to increase your driver memory but still restricted by your machine's memory. "--driver-memory"
On Thu, Jun 16, 2016 at 9:53 AM, spR <data.smar...@gmail.com> wrote: > Hey, > > But I just have one machine. I am running everything on my laptop. Won't I > be able to do this processing in local mode then? > > Regards, > Tejaswini > > On Wed, Jun 15, 2016 at 6:32 PM, Jeff Zhang <zjf...@gmail.com> wrote: > >> You are using local mode, --executor-memory won't take effect for local >> mode, please use other cluster mode. >> >> On Thu, Jun 16, 2016 at 9:32 AM, Jeff Zhang <zjf...@gmail.com> wrote: >> >>> Specify --executor-memory in your spark-submit command. >>> >>> >>> >>> On Thu, Jun 16, 2016 at 9:01 AM, spR <data.smar...@gmail.com> wrote: >>> >>>> Thank you. Can you pls tell How to increase the executor memory? >>>> >>>> >>>> >>>> On Wed, Jun 15, 2016 at 5:59 PM, Jeff Zhang <zjf...@gmail.com> wrote: >>>> >>>>> >>> Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded >>>>> >>>>> >>>>> It is OOM on the executor. Please try to increase executor memory. >>>>> "--executor-memory" >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On Thu, Jun 16, 2016 at 8:54 AM, spR <data.smar...@gmail.com> wrote: >>>>> >>>>>> Hey, >>>>>> >>>>>> error trace - >>>>>> >>>>>> hey, >>>>>> >>>>>> >>>>>> error trace - >>>>>> >>>>>> >>>>>> ---------------------------------------------------------------------------Py4JJavaError >>>>>> Traceback (most recent call >>>>>> last)<ipython-input-22-925883e4d630> in <module>()----> 1 temp.take(2) >>>>>> >>>>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/pyspark/sql/dataframe.pyc >>>>>> in take(self, num) 304 with SCCallSiteSync(self._sc) as css: >>>>>> 305 port = >>>>>> self._sc._jvm.org.apache.spark.sql.execution.EvaluatePython.takeAndServe(--> >>>>>> 306 self._jdf, num) 307 return >>>>>> list(_load_from_socket(port, BatchedSerializer(PickleSerializer()))) >>>>>> 308 >>>>>> >>>>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py >>>>>> in __call__(self, *args) 811 answer = >>>>>> self.gateway_client.send_command(command) 812 return_value = >>>>>> get_return_value(--> 813 answer, self.gateway_client, >>>>>> self.target_id, self.name) 814 >>>>>> 815 for temp_arg in temp_args: >>>>>> >>>>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/pyspark/sql/utils.pyc >>>>>> in deco(*a, **kw) 43 def deco(*a, **kw): 44 >>>>>> try:---> 45 return f(*a, **kw) 46 except >>>>>> py4j.protocol.Py4JJavaError as e: 47 s = >>>>>> e.java_exception.toString() >>>>>> /Users/my/Documents/My_Study_folder/spark-1.6.1/python/lib/py4j-0.9-src.zip/py4j/protocol.py >>>>>> in get_return_value(answer, gateway_client, target_id, name) 306 >>>>>> raise Py4JJavaError( 307 "An error >>>>>> occurred while calling {0}{1}{2}.\n".--> 308 >>>>>> format(target_id, ".", name), value) 309 else: >>>>>> 310 raise Py4JError( >>>>>> Py4JJavaError: An error occurred while calling >>>>>> z:org.apache.spark.sql.execution.EvaluatePython.takeAndServe. >>>>>> : org.apache.spark.SparkException: Job aborted due to stage failure: >>>>>> Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 >>>>>> in stage 3.0 (TID 76, localhost): java.lang.OutOfMemoryError: GC >>>>>> overhead limit exceeded >>>>>> at com.mysql.jdbc.MysqlIO.nextRowFast(MysqlIO.java:2205) >>>>>> at com.mysql.jdbc.MysqlIO.nextRow(MysqlIO.java:1984) >>>>>> at com.mysql.jdbc.MysqlIO.readSingleRowSet(MysqlIO.java:3403) >>>>>> at com.mysql.jdbc.MysqlIO.getResultSet(MysqlIO.java:470) >>>>>> at com.mysql.jdbc.MysqlIO.readResultsForQueryOrUpdate(MysqlIO.java:3105) >>>>>> at com.mysql.jdbc.MysqlIO.readAllResults(MysqlIO.java:2336) >>>>>> at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2729) >>>>>> at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2549) >>>>>> at >>>>>> com.mysql.jdbc.PreparedStatement.executeInternal(PreparedStatement.java:1861) >>>>>> at >>>>>> com.mysql.jdbc.PreparedStatement.executeQuery(PreparedStatement.java:1962) >>>>>> at >>>>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anon$1.<init>(JDBCRDD.scala:363) >>>>>> at >>>>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD.compute(JDBCRDD.scala:339) >>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>>>> at >>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>>>> at >>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89) >>>>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>>>>> at java.lang.Thread.run(Thread.java:745) >>>>>> >>>>>> Driver stacktrace: >>>>>> at org.apache.spark.scheduler.DAGScheduler.org >>>>>> <http://org.apache.spark.scheduler.dagscheduler.org/>$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418) >>>>>> at >>>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799) >>>>>> at scala.Option.foreach(Option.scala:236) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588) >>>>>> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) >>>>>> at >>>>>> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620) >>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832) >>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1845) >>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1858) >>>>>> at >>>>>> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:212) >>>>>> at >>>>>> org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply$mcI$sp(python.scala:126) >>>>>> at >>>>>> org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply(python.scala:124) >>>>>> at >>>>>> org.apache.spark.sql.execution.EvaluatePython$$anonfun$takeAndServe$1.apply(python.scala:124) >>>>>> at >>>>>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56) >>>>>> at >>>>>> org.apache.spark.sql.DataFrame.withNewExecutionId(DataFrame.scala:2086) >>>>>> at >>>>>> org.apache.spark.sql.execution.EvaluatePython$.takeAndServe(python.scala:124) >>>>>> at >>>>>> org.apache.spark.sql.execution.EvaluatePython.takeAndServe(python.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:498) >>>>>> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231) >>>>>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381) >>>>>> at py4j.Gateway.invoke(Gateway.java:259) >>>>>> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133) >>>>>> at py4j.commands.CallCommand.execute(CallCommand.java:79) >>>>>> at py4j.GatewayConnection.run(GatewayConnection.java:209) >>>>>> at java.lang.Thread.run(Thread.java:745) >>>>>> Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded >>>>>> at com.mysql.jdbc.MysqlIO.nextRowFast(MysqlIO.java:2205) >>>>>> at com.mysql.jdbc.MysqlIO.nextRow(MysqlIO.java:1984) >>>>>> at com.mysql.jdbc.MysqlIO.readSingleRowSet(MysqlIO.java:3403) >>>>>> at com.mysql.jdbc.MysqlIO.getResultSet(MysqlIO.java:470) >>>>>> at com.mysql.jdbc.MysqlIO.readResultsForQueryOrUpdate(MysqlIO.java:3105) >>>>>> at com.mysql.jdbc.MysqlIO.readAllResults(MysqlIO.java:2336) >>>>>> at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2729) >>>>>> at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2549) >>>>>> at >>>>>> com.mysql.jdbc.PreparedStatement.executeInternal(PreparedStatement.java:1861) >>>>>> at >>>>>> com.mysql.jdbc.PreparedStatement.executeQuery(PreparedStatement.java:1962) >>>>>> at >>>>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$$anon$1.<init>(JDBCRDD.scala:363) >>>>>> at >>>>>> org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD.compute(JDBCRDD.scala:339) >>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>>>> at >>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>>>> at >>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) >>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) >>>>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) >>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89) >>>>>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>>>>> ... 1 more >>>>>> >>>>>> >>>>>> >>>>>> On Wed, Jun 15, 2016 at 5:39 PM, Jeff Zhang <zjf...@gmail.com> wrote: >>>>>> >>>>>>> Could you paste the full stacktrace ? >>>>>>> >>>>>>> On Thu, Jun 16, 2016 at 7:24 AM, spR <data.smar...@gmail.com> wrote: >>>>>>> >>>>>>>> Hi, >>>>>>>> I am getting this error while executing a query using sqlcontext.sql >>>>>>>> >>>>>>>> The table has around 2.5 gb of data to be scanned. >>>>>>>> >>>>>>>> First I get out of memory exception. But I have 16 gb of ram >>>>>>>> >>>>>>>> Then my notebook dies and I get below error >>>>>>>> >>>>>>>> Py4JNetworkError: An error occurred while trying to connect to the >>>>>>>> Java server >>>>>>>> >>>>>>>> >>>>>>>> Thank You >>>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Best Regards >>>>>>> >>>>>>> Jeff Zhang >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>>> -- >>>>> Best Regards >>>>> >>>>> Jeff Zhang >>>>> >>>> >>>> >>> >>> >>> -- >>> Best Regards >>> >>> Jeff Zhang >>> >> >> >> >> -- >> Best Regards >> >> Jeff Zhang >> > > -- Best Regards Jeff Zhang