Do you mean the input data size as 10M or the task result size ? >>> But my way is to setup a forever loop to handle continued income data. Not sure if it is the right way to use spark Not sure what this mean, do you use spark-streaming, for doing batch job in the forever loop ?
On Wed, Apr 20, 2016 at 3:55 PM, 李明伟 <kramer2...@126.com> wrote: > Hi Jeff > > The total size of my data is less than 10M. I already set the driver > memory to 4GB. > > > > > > > > 在 2016-04-20 13:42:25，"Jeff Zhang" <zjf...@gmail.com> 写道： > > Seems it is OOM in driver side when fetching task result. > > You can try to increase spark.driver.memory and spark.driver.maxResultSize > > On Tue, Apr 19, 2016 at 4:06 PM, 李明伟 <kramer2...@126.com> wrote: > >> Hi Zhan Zhang >> >> >> Please see the exception trace below. It is saying some GC overhead limit >> error >> I am not a java or scala developer so it is hard for me to understand >> these infor. >> Also reading coredump is too difficult to me.. >> >> I am not sure if the way I am using spark is correct. I understand that >> spark can do batch or stream calculation. But my way is to setup a forever >> loop to handle continued income data. >> Not sure if it is the right way to use spark >> >> >> 16/04/19 15:54:55 ERROR Utils: Uncaught exception in thread >> task-result-getter-2 >> java.lang.OutOfMemoryError: GC overhead limit exceeded >> at >> scala.collection.immutable.HashMap$HashTrieMap.updated0(HashMap.scala:328) >> at scala.collection.immutable.HashMap.updated(HashMap.scala:54) >> at >> scala.collection.immutable.HashMap$SerializationProxy.readObject(HashMap.scala:516) >> at sun.reflect.GeneratedMethodAccessor21.invoke(Unknown Source) >> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >> at java.lang.reflect.Method.invoke(Method.java:606) >> at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) >> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990) >> at java.io.ObjectInputStream.defaultReadObject(ObjectInputStream.java:500) >> at >> org.apache.spark.executor.TaskMetrics$$anonfun$readObject$1.apply$mcV$sp(TaskMetrics.scala:220) >> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1204) >> at org.apache.spark.executor.TaskMetrics.readObject(TaskMetrics.scala:219) >> at sun.reflect.GeneratedMethodAccessor19.invoke(Unknown Source) >> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >> at java.lang.reflect.Method.invoke(Method.java:606) >> at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) >> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >> at >> org.apache.spark.scheduler.DirectTaskResult$$anonfun$readExternal$1.apply$mcV$sp(TaskResult.scala:79) >> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1204) >> at >> org.apache.spark.scheduler.DirectTaskResult.readExternal(TaskResult.scala:62) >> at java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1837) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) >> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >> at >> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:76) >> at >> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:109) >> Exception in thread "task-result-getter-2" java.lang.OutOfMemoryError: GC >> overhead limit exceeded >> at >> scala.collection.immutable.HashMap$HashTrieMap.updated0(HashMap.scala:328) >> at scala.collection.immutable.HashMap.updated(HashMap.scala:54) >> at >> scala.collection.immutable.HashMap$SerializationProxy.readObject(HashMap.scala:516) >> at sun.reflect.GeneratedMethodAccessor21.invoke(Unknown Source) >> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >> at java.lang.reflect.Method.invoke(Method.java:606) >> at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) >> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at >> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1990) >> at java.io.ObjectInputStream.defaultReadObject(ObjectInputStream.java:500) >> at >> org.apache.spark.executor.TaskMetrics$$anonfun$readObject$1.apply$mcV$sp(TaskMetrics.scala:220) >> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1204) >> at org.apache.spark.executor.TaskMetrics.readObject(TaskMetrics.scala:219) >> at sun.reflect.GeneratedMethodAccessor19.invoke(Unknown Source) >> at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >> at java.lang.reflect.Method.invoke(Method.java:606) >> at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1017) >> at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1893) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1798) >> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >> at >> org.apache.spark.scheduler.DirectTaskResult$$anonfun$readExternal$1.apply$mcV$sp(TaskResult.scala:79) >> at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1204) >> at >> org.apache.spark.scheduler.DirectTaskResult.readExternal(TaskResult.scala:62) >> at java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1837) >> at >> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) >> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1350) >> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >> at >> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:76) >> at >> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:109) >> >> >> >> >> >> At 2016-04-19 13:10:20, "Zhan Zhang" <zzh...@hortonworks.com> wrote: >> >> What kind of OOM? Driver or executor side? You can use coredump to find >> what cause the OOM. >> >> Thanks. >> >> Zhan Zhang >> >> On Apr 18, 2016, at 9:44 PM, 李明伟 <kramer2...@126.com> wrote: >> >> Hi Samaga >> >> Thanks very much for your reply and sorry for the delay reply. >> >> Cassandra or Hive is a good suggestion. >> However in my situation I am not sure if it will make sense. >> >> My requirements is that to get the recent 24 hour data to generate >> report. The frequency is 5 minute. >> So if use cassandra or hive, it means spark will have to read 24 hour >> data every 5 mintues. And among those data, a big part (like 23 hours or >> more ) will be repeatedly read. >> >> The window in spark is for stream computing. I did not use it but I will >> consider it >> >> >> Thanks again >> >> Regards >> Mingwei >> >> >> >> >> >> At 2016-04-11 19:09:48, "Lohith Samaga M" <lohith.sam...@mphasis.com> wrote: >> >Hi Kramer, >> > Some options: >> > 1. Store in Cassandra with TTL = 24 hours. When you read the full >> > table, you get the latest 24 hours data. >> > 2. Store in Hive as ORC file and use timestamp field to filter out the >> > old data. >> > 3. Try windowing in spark or flink (have not used either). >> > >> > >> >Best regards / Mit freundlichen Grüßen / Sincères salutations >> >M. Lohith Samaga >> > >> > >> >-----Original Message----- >> >From: kramer2...@126.com [mailto:kramer2...@126.com <kramer2...@126.com>] >> >Sent: Monday, April 11, 2016 16.18 >> >To: user@spark.apache.org >> >Subject: Why Spark having OutOfMemory Exception? >> > >> >I use spark to do some very simple calculation. The description is like >> >below (pseudo code): >> > >> > >> >While timestamp == 5 minutes >> > >> > df = read_hdf() # Read hdfs to get a dataframe every 5 minutes >> > >> > my_dict[timestamp] = df # Put the data frame into a dict >> > >> > delete_old_dataframe( my_dict ) # Delete old dataframe (timestamp is one >> >24 hour before) >> > >> > big_df = merge(my_dict) # Merge the recent 24 hours data frame >> > >> >To explain.. >> > >> >I have new files comes in every 5 minutes. But I need to generate report on >> >recent 24 hours data. >> >The concept of 24 hours means I need to delete the oldest data frame every >> >time I put a new one into it. >> >So I maintain a dict (my_dict in above code), the dict contains map like >> >timestamp: dataframe. Everytime I put dataframe into the dict, I will go >> >through the dict to delete those old data frame whose timestamp is 24 hour >> >ago. >> >After delete and input. I merge the data frames in the dict to a big one >> >and run SQL on it to get my report. >> > >> >* >> >I want to know if any thing wrong about this model? Because it is very slow >> >after started for a while and hit OutOfMemory. I know that my memory is >> >enough. Also size of file is very small for test purpose. So should not >> >have memory problem. >> > >> >I am wondering if there is lineage issue, but I am not sure. >> > >> >* >> > >> > >> > >> >-- >> >View this message in context: >> >http://apache-spark-user-list.1001560.n3.nabble.com/Why-Spark-having-OutOfMemory-Exception-tp26743.html >> >Sent from the Apache Spark User List mailing list archive at Nabble.com. >> > >> >--------------------------------------------------------------------- >> >To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional >> >commands, e-mail: user-h...@spark.apache.org >> > >> >Information transmitted by this e-mail is proprietary to Mphasis, its >> >associated companies and/ or its customers and is intended >> >for use only by the individual or entity to which it is addressed, and may >> >contain information that is privileged, confidential or >> >exempt from disclosure under applicable law. If you are not the intended >> >recipient or it appears that this mail has been forwarded >> >to you without proper authority, you are notified that any use or >> >dissemination of this information in any manner is strictly >> >prohibited. In such cases, please notify us immediately at >> >mailmas...@mphasis.com and delete this mail from your records. >> > >> >> >> >> >> >> >> >> >> >> > > > > -- > Best Regards > > Jeff Zhang > > > > > -- Best Regards Jeff Zhang
