[
https://issues.apache.org/jira/browse/SPARK-30393?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Stephen updated SPARK-30393:
----------------------------
Description:
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:
{quote}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:
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$retryOrTimeout$2.apply(KinesisBackedBlockRDD.scala:288)
bq. at scala.Option.getOrElse(Option.scala:121)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:282)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getKinesisIterator(KinesisBackedBlockRDD.scala:246)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getRecords(KinesisBackedBlockRDD.scala:206)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:162)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:133)
bq. at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
bq. at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
bq. at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462)
bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
bq. at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:187)
bq. at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
bq. at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
bq. at org.apache.spark.scheduler.Task.run(Task.scala:121)
bq. at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
bq. at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
bq. at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
bq. at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
bq. at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
bq. at java.lang.Thread.run(Thread.java:748)
bq. 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)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.handleErrorResponse(AmazonHttpClient.java:1712)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1367)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1113)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:770)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:744)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:726)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:686)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:668)
bq. at
com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:532)
bq. at
com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:512)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.doInvoke(AmazonKinesisClient.java:2782)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2749)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2738)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.executeGetShardIterator(AmazonKinesisClient.java:1383)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.getShardIterator(AmazonKinesisClient.java:1355)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:269)
bq. ... 20 more{quote}
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 beyond spark version 2.
Could this be related to my hour long sliding window I kept in memory? 3600
seconds X 200reconds/second = 720K record, if the recovery process try to load
all of them into memory from kinesis, it will exceed my limit of 12
shards*2000record/sec/shard = 24K records/second? If so, wouldn't this be a
flaw as I don't need 360 shards for this app just for checkpointing purpose.
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?
was:
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:
{quote}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:
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$retryOrTimeout$2.apply(KinesisBackedBlockRDD.scala:288)
bq. at scala.Option.getOrElse(Option.scala:121)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:282)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getKinesisIterator(KinesisBackedBlockRDD.scala:246)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getRecords(KinesisBackedBlockRDD.scala:206)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:162)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:133)
bq. at org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
bq. at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
bq. at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462)
bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
bq. at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:187)
bq. at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
bq. at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
bq. at org.apache.spark.scheduler.Task.run(Task.scala:121)
bq. at
org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
bq. at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
bq. at
org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
bq. at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
bq. at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
bq. at java.lang.Thread.run(Thread.java:748)
bq. 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)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.handleErrorResponse(AmazonHttpClient.java:1712)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1367)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1113)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:770)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:744)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:726)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:686)
bq. at
com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:668)
bq. at
com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:532)
bq. at
com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:512)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.doInvoke(AmazonKinesisClient.java:2782)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2749)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2738)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.executeGetShardIterator(AmazonKinesisClient.java:1383)
bq. at
com.amazonaws.services.kinesis.AmazonKinesisClient.getShardIterator(AmazonKinesisClient.java:1355)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247)
bq. at
org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:269)
bq. ... 20 more{quote}
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 beyond spark version 2.
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?
> 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
> Priority: Major
> Attachments: kinesisusagewhilecheckpointrecoveryerror.png,
> sparkuiwhilecheckpointrecoveryerror.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:
> {quote}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:
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$retryOrTimeout$2.apply(KinesisBackedBlockRDD.scala:288)
> bq. at scala.Option.getOrElse(Option.scala:121)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:282)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getKinesisIterator(KinesisBackedBlockRDD.scala:246)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getRecords(KinesisBackedBlockRDD.scala:206)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:162)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.getNext(KinesisBackedBlockRDD.scala:133)
> bq. at
> org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:73)
> bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
> bq. at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:439)
> bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
> bq. at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:462)
> bq. at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
> bq. at
> org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:187)
> bq. at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
> bq. at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
> bq. at org.apache.spark.scheduler.Task.run(Task.scala:121)
> bq. at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
> bq. at
> org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
> bq. at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
> bq. at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> bq. at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> bq. at java.lang.Thread.run(Thread.java:748)
> bq. 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)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutor.handleErrorResponse(AmazonHttpClient.java:1712)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeOneRequest(AmazonHttpClient.java:1367)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeHelper(AmazonHttpClient.java:1113)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutor.doExecute(AmazonHttpClient.java:770)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutor.executeWithTimer(AmazonHttpClient.java:744)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutor.execute(AmazonHttpClient.java:726)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutor.access$500(AmazonHttpClient.java:686)
> bq. at
> com.amazonaws.http.AmazonHttpClient$RequestExecutionBuilderImpl.execute(AmazonHttpClient.java:668)
> bq. at
> com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:532)
> bq. at
> com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:512)
> bq. at
> com.amazonaws.services.kinesis.AmazonKinesisClient.doInvoke(AmazonKinesisClient.java:2782)
> bq. at
> com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2749)
> bq. at
> com.amazonaws.services.kinesis.AmazonKinesisClient.invoke(AmazonKinesisClient.java:2738)
> bq. at
> com.amazonaws.services.kinesis.AmazonKinesisClient.executeGetShardIterator(AmazonKinesisClient.java:1383)
> bq. at
> com.amazonaws.services.kinesis.AmazonKinesisClient.getShardIterator(AmazonKinesisClient.java:1355)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator$$anonfun$3.apply(KinesisBackedBlockRDD.scala:247)
> bq. at
> org.apache.spark.streaming.kinesis.KinesisSequenceRangeIterator.retryOrTimeout(KinesisBackedBlockRDD.scala:269)
> bq. ... 20 more{quote}
> 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 beyond spark version 2.
> Could this be related to my hour long sliding window I kept in memory? 3600
> seconds X 200reconds/second = 720K record, if the recovery process try to
> load all of them into memory from kinesis, it will exceed my limit of 12
> shards*2000record/sec/shard = 24K records/second? If so, wouldn't this be a
> flaw as I don't need 360 shards for this app just for checkpointing purpose.
> 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?
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