hey, I did this in my notebook. But still I get the same error. Is this the right way to do it?
from pyspark import SparkConf conf = (SparkConf() .setMaster("local[4]") .setAppName("My app") .set("spark.executor.memory", "12g")) sc.conf = conf 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 >