kevin fu created SPARK-23991: -------------------------------- Summary: data loss when allocateBlocksToBatch Key: SPARK-23991 URL: https://issues.apache.org/jira/browse/SPARK-23991 Project: Spark Issue Type: Bug Components: DStreams, Input/Output Affects Versions: 2.2.0 Environment: spark 2.11 Reporter: kevin fu
with checkpoint and WAL enabled, driver will write the allocation of blocks to batch into hdfs. however, if it fails as following, the blocks of this batch cannot be computed by the DAG. Because the blocks have been dequeued from the receivedBlockQueue and get lost. {quote}18/04/15 11:11:25 WARN ReceivedBlockTracker: Exception thrown while writing record: BatchAllocationEvent(1523765480000 ms,AllocatedBlocks(Map(0 -> ArrayBuffer()))) to the WriteAheadLog. org.apache.spark.SparkException: Exception thrown in awaitResult: at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:194) at org.apache.spark.streaming.util.BatchedWriteAheadLog.write(BatchedWriteAheadLog.scala:83) at org.apache.spark.streaming.scheduler.ReceivedBlockTracker.writeToLog(ReceivedBlockTracker.scala:234) at org.apache.spark.streaming.scheduler.ReceivedBlockTracker.allocateBlocksToBatch(ReceivedBlockTracker.scala:118) at org.apache.spark.streaming.scheduler.ReceiverTracker.allocateBlocksToBatch(ReceiverTracker.scala:213) at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:248) at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$3.apply(JobGenerator.scala:247) at scala.util.Try$.apply(Try.scala:192) at org.apache.spark.streaming.scheduler.JobGenerator.generateJobs(JobGenerator.scala:247) at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:183) at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:89) at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:88) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) Caused by: java.util.concurrent.TimeoutException: Futures timed out after [5000 milliseconds] at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219) at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:190) at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53) at scala.concurrent.Await$.result(package.scala:190) at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:190) ... 12 more{quote} the concerning codes are showed below: {quote} /** * Allocate all unallocated blocks to the given batch. * This event will get written to the write ahead log (if enabled). */ def allocateBlocksToBatch(batchTime: Time): Unit = synchronized { if (lastAllocatedBatchTime == null || batchTime > lastAllocatedBatchTime) { val streamIdToBlocks = streamIds.map { streamId => (streamId, getReceivedBlockQueue(streamId).dequeueAll(x => true)) }.toMap val allocatedBlocks = AllocatedBlocks(streamIdToBlocks) if (writeToLog(BatchAllocationEvent(batchTime, allocatedBlocks))) { timeToAllocatedBlocks.put(batchTime, allocatedBlocks) lastAllocatedBatchTime = batchTime } else { logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery") } } else { // This situation occurs when: // 1. WAL is ended with BatchAllocationEvent, but without BatchCleanupEvent, // possibly processed batch job or half-processed batch job need to be processed again, // so the batchTime will be equal to lastAllocatedBatchTime. // 2. Slow checkpointing makes recovered batch time older than WAL recovered // lastAllocatedBatchTime. // This situation will only occurs in recovery time. logInfo(s"Possibly processed batch $batchTime needs to be processed again in WAL recovery") } } {quote} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org