There is #3 which is use mapPartitions and init one jodatime obj per partition, which is less overhead for large objects— Sent from Mailbox for iPhone
On Sat, Mar 8, 2014 at 2:54 AM, Mayur Rustagi <mayur.rust...@gmail.com> wrote: > So the whole function closure you want to apply on your RDD needs to be > serializable so that it can be "serialized" & sent to workers to operate on > RDD. So objects of jodatime cannot be serialized & sent hence jodatime is > out of work. 2 bad answers > 1. initialize jodatime for each row & complete work & destroy them, that > way they are only intialized when job is running & need not be sent across. > 2. Write your own parser & hope jodatime guys get their act together. > Regards > Mayur Rustagi > Ph: +1 (760) 203 3257 > http://www.sigmoidanalytics.com > @mayur_rustagi <https://twitter.com/mayur_rustagi> > On Fri, Mar 7, 2014 at 12:56 PM, Ognen Duzlevski > <og...@nengoiksvelzud.com>wrote: >> Mayur, have not thought of that. Yes, I use jodatime. What is the scope >> that this serialization issue applies to? Only the method making a call >> into / using such a library? The whole class the method using such a >> library belongs to? Sorry if it is a dumb question :) >> >> Ognen >> >> >> On 3/7/14, 1:29 PM, Mayur Rustagi wrote: >> >> Mostly the job you are executing is not serializable, this typically >> happens when you have a library that is not serializable.. are you using >> any library like jodatime etc ? >> >> Mayur Rustagi >> Ph: +1 (760) 203 3257 >> http://www.sigmoidanalytics.com >> @mayur_rustagi <https://twitter.com/mayur_rustagi> >> >> >> >> On Thu, Mar 6, 2014 at 9:50 PM, Ognen Duzlevski < >> og...@plainvanillagames.com> wrote: >> >>> It looks like the problem is in the filter task - is there anything >>> special about filter()? >>> >>> I have removed the filter line from the loops just to see if things will >>> work and they do. >>> >>> Anyone has any ideas? >>> >>> Thanks! >>> Ognen >>> >>> >>> On 3/6/14, 9:39 PM, Ognen Duzlevski wrote: >>> >>>> Hello, >>>> >>>> What is the general approach people take when trying to do analysis >>>> across multiple large files where the data to be extracted from a >>>> successive file depends on the data extracted from a previous file or set >>>> of files? >>>> >>>> For example: >>>> I have the following: a group of HDFS files each 20+GB in size. I need >>>> to extract event1 on day 1 from first file and extract event2 from all >>>> remaining files in a period of successive dates, then do a calculation on >>>> the two events. >>>> I then need to move on to day2, extract event1 (with certain >>>> properties), take all following days, extract event2 and run a calculation >>>> against previous day for all days in period. So on and so on. >>>> >>>> I have verified that the following (very naive approach doesn't work): >>>> >>>> def >>>> calcSimpleRetention(start:String,end:String,event1:String,event2:String):Map[String,List[Double]] >>>> = { >>>> val epd = new PipelineDate(end) >>>> val result = for { >>>> dt1 <- PipelineDate.getPeriod(new PipelineDate(start), epd) >>>> val f1 = sc.textFile(dt1.toJsonHdfsFileName) >>>> val e1 = f1.filter(_.split(",")(0).split(":")(1).replace("\"","") >>>> == event1).map(line => >>>> (line.split(",")(2).split(":")(1).replace("\"",""),0)).cache >>>> val c = e1.count.toDouble >>>> >>>> val intres = for { >>>> dt2 <- PipelineDate.getPeriod(dt1+1,epd) >>>> val f2 = sc.textFile(dt2.toJsonHdfsFileName) >>>> val e2 = >>>> f2.filter(_.split(",")(0).split(":")(1).replace("\"","") == >>>> event2).map(line => (line.split(",")(2).split(":")(1).replace("\"",""),1)) >>>> val e1e2 = e1.union(e2) >>>> val r = e1e2.groupByKey().filter(e => e._2.length > 1 && >>>> e._2.filter(_==0).length>0).count.toDouble >>>> } yield (c/r) // get the retention rate >>>> } yield (dt1.toString->intres) >>>> Map(result:_*) >>>> } >>>> >>>> I am getting the following errors: >>>> 14/03/07 03:22:25 INFO SparkContext: Starting job: count at >>>> CountActor.scala:33 >>>> 14/03/07 03:22:25 INFO DAGScheduler: Got job 0 (count at >>>> CountActor.scala:33) with 140 output partitions (allowLocal=false) >>>> 14/03/07 03:22:25 INFO DAGScheduler: Final stage: Stage 0 (count at >>>> CountActor.scala:33) >>>> 14/03/07 03:22:25 INFO DAGScheduler: Parents of final stage: List() >>>> 14/03/07 03:22:25 INFO DAGScheduler: Missing parents: List() >>>> 14/03/07 03:22:25 INFO DAGScheduler: Submitting Stage 0 (MappedRDD[3] at >>>> map at CountActor.scala:32), which has no missing parents >>>> 14/03/07 03:22:25 INFO DAGScheduler: Failed to run count at >>>> CountActor.scala:33 >>>> 14/03/07 03:22:25 ERROR OneForOneStrategy: Job aborted: Task not >>>> serializable: java.io.NotSerializableException: >>>> com.github.ognenpv.pipeline.CountActor >>>> org.apache.spark.SparkException: Job aborted: Task not serializable: >>>> java.io.NotSerializableException: com.github.ognenpv.pipeline.CountActor >>>> at >>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026) >>>> at >>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>>> at org.apache.spark.scheduler.DAGScheduler.org >>>> $apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026) >>>> at org.apache.spark.scheduler.DAGScheduler.org >>>> $apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:794) >>>> at org.apache.spark.scheduler.DAGScheduler.org >>>> $apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:737) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:569) >>>> at >>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207) >>>> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) >>>> at akka.actor.ActorCell.invoke(ActorCell.scala:456) >>>> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) >>>> at akka.dispatch.Mailbox.run(Mailbox.scala:219) >>>> at >>>> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) >>>> at >>>> scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) >>>> at >>>> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) >>>> at >>>> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) >>>> at >>>> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) >>>> >>>> I should mention that this code is fired off from an Akka actor (which >>>> is controlled by a Scalatra servlet). >>>> >>>> Any ideas, recommendations etc.? I am fairly new to Scala and M/R >>>> principles in general, it is fair to say that at this point I am still >>>> thinking from a point of view of an imperative programmer trying to fit a >>>> square peg through a round hole ;) >>>> Ognen >>>> >>> >>> -- >>> Some people, when confronted with a problem, think "I know, I'll use >>> regular expressions." Now they have two problems. >>> -- Jamie Zawinski >>> >>> >> >>
