>>> 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
