Stephen created SPARK-30393: ------------------------------- Summary: Too much ProvisionedThroughputExceededException while recover from checkpoint Key: SPARK-30393 URL: https://issues.apache.org/jira/browse/SPARK-30393 Project: Spark Issue Type: Question Components: DStreams Affects Versions: 2.4.3 Environment: I am using EMR 5.23.0, Spark 2.4.3, spark-streaming-kinesis-asl 2.4.3 I have 6 r5.4xLarge in my cluster, plenty of memory. 6 kinesis shards, I even increased to 12 shards but still see the kinesis error Reporter: Stephen Attachments: kinesisusagewhilecheckpointrecoveryerror.png
I have a spark application which consume from Kinesis with 6 shards. Data was produced to Kinesis at at most 2000 records/second. At non peak time data only comes in at 200 records/second. Each record is 0.5K Bytes. So 6 shards is enough to handle that. I use reduceByKeyAndWindow and mapWithState in the program and the sliding window is one hour long. Recently I am trying to checkpoint the application to S3. I am testing this at nonpeak time so the data incoming rate is very low like 200 records/sec. I run the Spark application by creating new context, checkpoint is created at s3, but when I kill the app and restarts, it failed to recover from checkpoint, and the error message is the following and my SparkUI shows all the batches are stucked, and it takes a long time for the checkpoint recovery to complete, 15 minutes to over an hour. I found lots of error message in the log related to Kinesis exceeding read limit: {{19/12/24 00:15:21 WARN TaskSetManager: Lost task 571.0 in stage 33.0 (TID 4452, ip-172-17-32-11.ec2.internal, executor 9): org.apache.spark.SparkException: Gave up after 3 retries while getting shard iterator from sequence number 49601654074184110438492229476281538439036626028298502210, last exception: at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$retryOrTimeout$2.apply(KinesisBackedBlockRDD.scala:288) at scala.Option.getOrElse(Option.scala:121) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:282) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getKinesisIterator(KinesisBackedBlockRDD.scala:246) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getRecords(KinesisBackedBlockRDD.scala:206) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:162) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:133) at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) at org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:187) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55) at org.apache.spark.scheduler.Task.run(Task.scala:121) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Caused by: com.amazonaws.services.kinesis.model.ProvisionedThroughputExceededException: Rate exceeded for shard shardId-000000000004 in stream my-stream-name under account my-account-number. (Service: AmazonKinesis; Status Code: 400; Error Code: ProvisionedThroughputExceededException; Request ID: e368b876-c315-d0f0-b513-e2af2bd14525) at com.amazonaws.http.AmazonHttpClient$RequestExecutor.handleErrorResponse(AmazonHttpClient.java:1712) at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1367) at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1113) at com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:770) at com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:744) at com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:726) at com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:686) at com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:668) at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:532) at com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:512) at com.amazonaws.services.kinesis.AmazonKinesisClient.doInvoke(AmazonKinesisClient.java:2782) at com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2749) at com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2738) at com.amazonaws.services.kinesis.AmazonKinesisClient.executeGetShardIterator(AmazonKinesisClient.java:1383) at com.amazonaws.services.kinesis.AmazonKinesisClient.getShardIterator(AmazonKinesisClient.java:1355) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247) at org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:269) ... 20 more}} I see someone reported the similar problem https://issues.apache.org/jira/browse/SPARK-24970, not sure whether there is any fix for that. Since my batchinterval is 150 seconds, I have tried increase blockinterval to 1000ms (1 second) so that I have less number of partitions. But the problem still exists. I also tried enable WAL, spark.streaming.receiver.writeAheadLog.enable=true, but still the problem exists. I also read that enable WAL is no longer necessary from somewhere. I understand checkpoint recovery might be a lengthy process, but how do I eliminate the " ProvisionedThroughputExceededException" error, I think that is perhaps causing the slow checkpoint recovery. Thanks, can someone please help? -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org