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
>
