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
