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Posted to issues@spark.apache.org by "Hiroki Takeda (JIRA)" <ji...@apache.org> on 2016/11/09 11:10:58 UTC
[jira] [Created] (SPARK-18380) CLONE - Kinesis receiver does not
snapshot when shard completes
Hiroki Takeda created SPARK-18380:
-------------------------------------
Summary: CLONE - Kinesis receiver does not snapshot when shard completes
Key: SPARK-18380
URL: https://issues.apache.org/jira/browse/SPARK-18380
Project: Spark
Issue Type: Bug
Components: DStreams
Affects Versions: 2.0.0
Reporter: Hiroki Takeda
Priority: Minor
When a kinesis shard is split or combined and the old shard ends, the Amazon Kinesis Client library [calls IRecordProcessor.shutdown|https://github.com/awslabs/amazon-kinesis-client/blob/v1.7.0/src/main/java/com/amazonaws/services/kinesis/clientlibrary/lib/worker/ShutdownTask.java#L100] and expects that {{IRecordProcessor.shutdown}} must checkpoint the sequence number {{ExtendedSequenceNumber.SHARD_END}} before returning. Unfortunately, spark’s [KinesisRecordProcessor|https://github.com/apache/spark/blob/v2.0.1/external/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala] sometimes does not checkpoint SHARD_END. This results in an error message, and spark is then blocked indefinitely from processing any items from the child shards.
This issue has also been raised on StackOverflow: [resharding while spark running on kinesis stream|http://stackoverflow.com/questions/38898691/resharding-while-spark-running-on-kinesis-stream]
Exception that is logged:
{code}
16/10/19 19:37:49 ERROR worker.ShutdownTask: Application exception.
java.lang.IllegalArgumentException: Application didn't checkpoint at end of shard shardId-000000000030
at com.amazonaws.services.kinesis.clientlibrary.lib.worker.ShutdownTask.call(ShutdownTask.java:106)
at com.amazonaws.services.kinesis.clientlibrary.lib.worker.MetricsCollectingTaskDecorator.call(MetricsCollectingTaskDecorator.java:49)
at com.amazonaws.services.kinesis.clientlibrary.lib.worker.MetricsCollectingTaskDecorator.call(MetricsCollectingTaskDecorator.java:24)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
Command used to split shard:
{code}
aws kinesis --region us-west-1 split-shard --stream-name my-stream --shard-to-split shardId-000000000030 --new-starting-hash-key 5316911983139663491615228241121378303
{code}
After the spark-streaming job has hung, examining the DynamoDB table indicates that the parent shard processor has not reached {{ExtendedSequenceNumber.SHARD_END}} and the child shards are still at {{ExtendedSequenceNumber.TRIM_HORIZON}} waiting for the parent to finish:
{code}
aws kinesis --region us-west-1 describe-stream --stream-name my-stream
{
"StreamDescription": {
"RetentionPeriodHours": 24,
"StreamName": "my-stream",
"Shards": [
{
"ShardId": "shardId-000000000030",
"HashKeyRange": {
"EndingHashKey": "10633823966279326983230456482242756606",
"StartingHashKey": "0"
},
...
},
{
"ShardId": "shardId-000000000062",
"HashKeyRange": {
"EndingHashKey": "5316911983139663491615228241121378302",
"StartingHashKey": "0"
},
"ParentShardId": "shardId-000000000030",
"SequenceNumberRange": {
"StartingSequenceNumber": "49566806087883755242230188435465744452396445937434624994"
}
},
{
"ShardId": "shardId-000000000063",
"HashKeyRange": {
"EndingHashKey": "10633823966279326983230456482242756606",
"StartingHashKey": "5316911983139663491615228241121378303"
},
"ParentShardId": "shardId-000000000030",
"SequenceNumberRange": {
"StartingSequenceNumber": "49566806087906055987428719058607280170669094298940605426"
}
},
...
],
"StreamStatus": "ACTIVE"
}
}
aws dynamodb --region us-west-1 scan --table-name my-processor
{
"Items": [
{
"leaseOwner": {
"S": "localhost:fd385c95-5d19-4678-926f-b6d5f5503cbe"
},
"leaseCounter": {
"N": "49318"
},
"ownerSwitchesSinceCheckpoint": {
"N": "62"
},
"checkpointSubSequenceNumber": {
"N": "0"
},
"checkpoint": {
"S": "49566573572821264975247582655142547856950135436343247330"
},
"parentShardId": {
"SS": [
"shardId-000000000014"
]
},
"leaseKey": {
"S": "shardId-000000000030"
}
},
{
"leaseOwner": {
"S": "localhost:ca44dc83-2580-4bf3-903f-e7ccc8a3ab02"
},
"leaseCounter": {
"N": "25439"
},
"ownerSwitchesSinceCheckpoint": {
"N": "69"
},
"checkpointSubSequenceNumber": {
"N": "0"
},
"checkpoint": {
"S": "TRIM_HORIZON"
},
"parentShardId": {
"SS": [
"shardId-000000000030"
]
},
"leaseKey": {
"S": "shardId-000000000062"
}
},
{
"leaseOwner": {
"S": "localhost:94bf603f-780b-4121-87a4-bdf501723f83"
},
"leaseCounter": {
"N": "25443"
},
"ownerSwitchesSinceCheckpoint": {
"N": "59"
},
"checkpointSubSequenceNumber": {
"N": "0"
},
"checkpoint": {
"S": "TRIM_HORIZON"
},
"parentShardId": {
"SS": [
"shardId-000000000030"
]
},
"leaseKey": {
"S": "shardId-000000000063"
}
},
...
]
}
{code}
Workaround: I manually edited the DynamoDB table to delete the checkpoints for the parent shards. The child shards were then able to begin processing. I’m not sure whether this resulted in a few items being lost though.
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