hey,
Thanks. Now it worked.. :)
On Wed, Jun 15, 2016 at 6:59 PM, Jeff Zhang <zjf...@gmail.com> wrote:
> 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
>
