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
>>>
>>
>>
>
