The data ingestion is in outermost portion in foreachRDD block. Although now I close the statement of jdbc, the same exception happened again. It seems it is not related to the data ingestion part.
On Wed, Apr 29, 2015 at 8:35 PM, Cody Koeninger <c...@koeninger.org> wrote: > Use lsof to see what files are actually being held open. > > That stacktrace looks to me like it's from the driver, not executors. > Where in foreach is it being called? The outermost portion of foreachRDD > runs in the driver, the innermost portion runs in the executors. From the > docs: > > https://spark.apache.org/docs/latest/streaming-programming-guide.html > > dstream.foreachRDD { rdd => > val connection = createNewConnection() // executed at the driver > rdd.foreach { record => > connection.send(record) // executed at the worker > }} > > > @td I've specifically looked at kafka socket connections for the standard > 1.3 code vs my branch that has cached connections. The standard > non-caching code has very short lived connections. I've had jobs running > for a month at a time, including ones writing to mysql. Not saying it's > impossible, but I'd think we need some evidence before speculating this has > anything to do with it. > > > On Wed, Apr 29, 2015 at 6:50 PM, Bill Jay <bill.jaypeter...@gmail.com> > wrote: > >> This function is called in foreachRDD. I think it should be running in >> the executors. I add the statement.close() in the code and it is running. I >> will let you know if this fixes the issue. >> >> >> >> On Wed, Apr 29, 2015 at 4:06 PM, Tathagata Das <t...@databricks.com> >> wrote: >> >>> Is the function ingestToMysql running on the driver or on the executors? >>> Accordingly you can try debugging while running in a distributed manner, >>> with and without calling the function. >>> >>> If you dont get "too many open files" without calling ingestToMysql(), >>> the problem is likely to be in ingestToMysql(). >>> If you get the problem even without calling ingestToMysql(), then the >>> problem may be in Kafka. If the problem is occuring in the driver, then its >>> the DirecKafkaInputDStream code. If the problem is occurring in the >>> executor, then the problem is in KafkaRDD. >>> >>> TD >>> >>> On Wed, Apr 29, 2015 at 2:30 PM, Ted Yu <yuzhih...@gmail.com> wrote: >>> >>>> Maybe add statement.close() in finally block ? >>>> >>>> Streaming / Kafka experts may have better insight. >>>> >>>> On Wed, Apr 29, 2015 at 2:25 PM, Bill Jay <bill.jaypeter...@gmail.com> >>>> wrote: >>>> >>>>> Thanks for the suggestion. I ran the command and the limit is 1024. >>>>> >>>>> Based on my understanding, the connector to Kafka should not open so >>>>> many files. Do you think there is possible socket leakage? BTW, in every >>>>> batch which is 5 seconds, I output some results to mysql: >>>>> >>>>> def ingestToMysql(data: Array[String]) { >>>>> val url = >>>>> "jdbc:mysql://localhost:3306/realtime?user=root&password=123" >>>>> var sql = "insert into loggingserver1 values " >>>>> data.foreach(line => sql += line) >>>>> sql = sql.dropRight(1) >>>>> sql += ";" >>>>> logger.info(sql) >>>>> var conn: java.sql.Connection = null >>>>> try { >>>>> conn = DriverManager.getConnection(url) >>>>> val statement = conn.createStatement() >>>>> statement.executeUpdate(sql) >>>>> } catch { >>>>> case e: Exception => logger.error(e.getMessage()) >>>>> } finally { >>>>> if (conn != null) { >>>>> conn.close >>>>> } >>>>> } >>>>> } >>>>> >>>>> I am not sure whether the leakage originates from Kafka connector or >>>>> the sql connections. >>>>> >>>>> Bill >>>>> >>>>> On Wed, Apr 29, 2015 at 2:12 PM, Ted Yu <yuzhih...@gmail.com> wrote: >>>>> >>>>>> Can you run the command 'ulimit -n' to see the current limit ? >>>>>> >>>>>> To configure ulimit settings on Ubuntu, edit >>>>>> */etc/security/limits.conf* >>>>>> Cheers >>>>>> >>>>>> On Wed, Apr 29, 2015 at 2:07 PM, Bill Jay <bill.jaypeter...@gmail.com >>>>>> > wrote: >>>>>> >>>>>>> Hi all, >>>>>>> >>>>>>> I am using the direct approach to receive real-time data from Kafka >>>>>>> in the following link: >>>>>>> >>>>>>> https://spark.apache.org/docs/1.3.0/streaming-kafka-integration.html >>>>>>> >>>>>>> >>>>>>> My code follows the word count direct example: >>>>>>> >>>>>>> >>>>>>> https://github.com/apache/spark/blob/master/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala >>>>>>> >>>>>>> >>>>>>> >>>>>>> After around 12 hours, I got the following error messages in Spark >>>>>>> log: >>>>>>> >>>>>>> 15/04/29 20:18:10 ERROR JobScheduler: Error generating jobs for time >>>>>>> 1430338690000 ms >>>>>>> org.apache.spark.SparkException: ArrayBuffer(java.io.IOException: >>>>>>> Too many open files, java.io.IOException: Too many open files, >>>>>>> java.io.IOException: Too many open files, java.io.IOException: Too many >>>>>>> open files, java.io.IOException: Too many open files) >>>>>>> at >>>>>>> org.apache.spark.streaming.kafka.DirectKafkaInputDStream.latestLeaderOffsets(DirectKafkaInputDStream.scala:94) >>>>>>> at >>>>>>> org.apache.spark.streaming.kafka.DirectKafkaInputDStream.compute(DirectKafkaInputDStream.scala:116) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> scala.util.DynamicVariable.withValue(DynamicVariable.scala:57) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:299) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287) >>>>>>> at scala.Option.orElse(Option.scala:257) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:35) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> scala.util.DynamicVariable.withValue(DynamicVariable.scala:57) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:299) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287) >>>>>>> at scala.Option.orElse(Option.scala:257) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:35) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> scala.util.DynamicVariable.withValue(DynamicVariable.scala:57) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:299) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287) >>>>>>> at scala.Option.orElse(Option.scala:257) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:35) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> scala.util.DynamicVariable.withValue(DynamicVariable.scala:57) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:299) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287) >>>>>>> at scala.Option.orElse(Option.scala:257) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.MappedDStream.compute(MappedDStream.scala:35) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1$$anonfun$1.apply(DStream.scala:300) >>>>>>> at >>>>>>> scala.util.DynamicVariable.withValue(DynamicVariable.scala:57) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:299) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287) >>>>>>> at scala.Option.orElse(Option.scala:257) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284) >>>>>>> at >>>>>>> org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:38) >>>>>>> at >>>>>>> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116) >>>>>>> at >>>>>>> org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116) >>>>>>> at >>>>>>> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251) >>>>>>> at >>>>>>> scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251) >>>>>>> at >>>>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>>>>> at >>>>>>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >>>>>>> at >>>>>>> scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251) >>>>>>> at >>>>>>> scala.collection.AbstractTraversable.flatMap(Traversable.scala:105) >>>>>>> at >>>>>>> org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116) >>>>>>> at >>>>>>> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$2.apply(JobGenerator.scala:239) >>>>>>> at >>>>>>> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$2.apply(JobGenerator.scala:237) >>>>>>> at scala.util.Try$.apply(Try.scala:161) >>>>>>> at >>>>>>> org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:237) >>>>>>> at org.apache.spark.streaming.scheduler.JobGenerator.org >>>>>>> $apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:174) >>>>>>> at >>>>>>> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1$$anonfun$receive$1.applyOrElse(JobGenerator.scala:85) >>>>>>> at akka.actor.Actor$class.aroundReceive(Actor.scala:465) >>>>>>> at >>>>>>> org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$start$1$$anon$1.aroundReceive(JobGenerator.scala:83) >>>>>>> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) >>>>>>> at akka.actor.ActorCell.invoke(ActorCell.scala:487) >>>>>>> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) >>>>>>> at akka.dispatch.Mailbox.run(Mailbox.scala:220) >>>>>>> at >>>>>>> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393) >>>>>>> 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) >>>>>>> >>>>>>> Thanks for the help. >>>>>>> >>>>>>> Bill >>>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> >
