I ran 2 different spark 1.5 clusters that have been running for more than a day now. I do see jobs getting aborted due to task retry's maxing out (default 4) due to ConnectionException. It seems like the executors die and get restarted and I was unable to find the root cause (same app code and conf used on spark 1.4.1 I don't see ConnectionException).
Another question related to this, what happens to the kinesis records received when Job gets aborted? In Spark-1.5 and kinesis-asl-1.5 (which I am using) does the job gets resubmitted with the same received records? Or does the kinesis-asl library get those records again based on sequence numbers it tracks? It would good for me to understand the story around lossless processing of kinesis records in Spark-1.5 + kinesis-asl-1.5 when jobs are aborted. Any pointers or quick explanation would be very helpful. On Tue, Oct 13, 2015 at 4:04 PM, Tathagata Das <t...@databricks.com> wrote: > Is this happening too often? Is it slowing things down or blocking > progress. Failures once in a while is part of the norm, and the system > should take care of itself. > > On Tue, Oct 13, 2015 at 2:47 PM, Spark Newbie <sparknewbie1...@gmail.com> > wrote: > >> Hi Spark users, >> >> I'm seeing the below exception in my spark streaming application. It >> happens in the first stage where the kinesis receivers receive records and >> perform a flatMap operation on the unioned Dstream. A coalesce step also >> happens as a part of that stage for optimizing the performance. >> >> This is happening on my spark 1.5 instance using kinesis-asl-1.5. When I >> look at the executor logs I do not see any exceptions indicating the root >> cause of why there is no connectivity on xxx.xx.xx.xxx:36684 or when did >> that service go down. >> >> Any help debugging this problem will be helpful. >> >> 15/10/13 16:36:07 ERROR shuffle.RetryingBlockFetcher: Exception while >> beginning fetch of 1 outstanding blocks >> java.io.IOException: Failed to connect to >> ip-xxx-xx-xx-xxx.ec2.internal/xxx.xx.xx.xxx:36684 >> at >> org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:193) >> at >> org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:156) >> at >> org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:88) >> at >> org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140) >> at >> org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:120) >> at >> org.apache.spark.network.netty.NettyBlockTransferService.fetchBlocks(NettyBlockTransferService.scala:97) >> at >> org.apache.spark.network.BlockTransferService.fetchBlockSync(BlockTransferService.scala:89) >> at >> org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:595) >> at >> org.apache.spark.storage.BlockManager$$anonfun$doGetRemote$2.apply(BlockManager.scala:593) >> at >> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >> at >> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) >> at >> org.apache.spark.storage.BlockManager.doGetRemote(BlockManager.scala:593) >> at >> org.apache.spark.storage.BlockManager.getRemote(BlockManager.scala:579) >> at >> org.apache.spark.storage.BlockManager.get(BlockManager.scala:623) >> at >> org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:44) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:262) >> at >> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:139) >> at >> org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:135) >> at >> scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772) >> at scala.collection.immutable.List.foreach(List.scala:318) >> at >> scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771) >> at >> org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:135) >> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >> at >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >> at >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >> at >> org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:69) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:262) >> at >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >> at >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297) >> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264) >> at >> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73) >> at >> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41) >> at org.apache.spark.scheduler.Task.run(Task.scala:88) >> at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) >> at >> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) >> at >> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) >> at java.lang.Thread.run(Thread.java:745) >> Caused by: java.net.ConnectException: Connection refused: >> ip-xxx-xx-xx-xxx.ec2.internal/xxx.xx.xx.xxx:36684 >> >> Thanks, >> Bharath >> >> >
