[
https://issues.apache.org/jira/browse/SPARK-20050?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Sasaki Toru updated SPARK-20050:
--------------------------------
Description:
I use Kafka 0.10 DirectStream with properties 'enable.auto.commit=false' and
call 'DirectKafkaInputDStream#commitAsync' finally in each batches, such below
{code}
val kafkaStream = KafkaUtils.createDirectStream[String, String](...)
kafkaStream.map { input =>
"key: " + input.key.toString + " value: " + input.value.toString + " offset:
" + input.offset.toString
}.foreachRDD { rdd =>
rdd.foreach { input =>
println(input)
}
}
kafkaStream.foreachRDD { rdd =>
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
kafkaStream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
}
{\code}
Some records which processed in the last batch before Streaming graceful
shutdown reprocess in the first batch after Spark Streaming restart, such below
* output first run of this application
{code}
key: null value: 1 offset: 101452472
key: null value: 2 offset: 101452473
key: null value: 3 offset: 101452474
key: null value: 4 offset: 101452475
key: null value: 5 offset: 101452476
key: null value: 6 offset: 101452477
key: null value: 7 offset: 101452478
key: null value: 8 offset: 101452479
key: null value: 9 offset: 101452480 // this is a last record before shutdown
Spark Streaming gracefully
{\code}
* output re-run of this application
{code}
key: null value: 7 offset: 101452478 // duplication
key: null value: 8 offset: 101452479 // duplication
key: null value: 9 offset: 101452480 // duplication
key: null value: 10 offset: 101452481
{\code}
It may cause offsets specified in commitAsync will commit in the head of next
batch.
was:
I use Kafka 0.10 DirectStream with properties 'enable.auto.commit=false' and
call 'DirectKafkaInputDStream#commitAsync' finally in each batches, such below
{code}
val kafkaStream = KafkaUtils.createDirectStream[String, String](...)
kafkaStream.map { input =>
"key: " + input.key.toString + " value: " + input.value.toString + " offset:
" + input.offset.toString
}.foreachRDD { rdd =>
rdd.foreach { input =>
println(input)
}
}
kafkaStream.foreachRDD { rdd =>
val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
kafkaStream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
}
{\code}
Some records which processed in the last batch before Streaming graceful
shutdown reprocess in the first batch after Spark Streaming restart, such below
* output first run of this application
{code}
key: null value: 1 offset: 101452472
key: null value: 2 offset: 101452473
key: null value: 3 offset: 101452474
key: null value: 4 offset: 101452475
key: null value: 5 offset: 101452476
key: null value: 6 offset: 101452477
key: null value: 7 offset: 101452478
key: null value: 8 offset: 101452479
key: null value: 9 offset: 101452480
{\code}
* output re-run of this application
{code}
key: null value: 7 offset: 101452478 // duplication
key: null value: 8 offset: 101452479 // duplication
key: null value: 9 offset: 101452480 // duplication
key: null value: 10 offset: 101452481
{\code}
It may cause offsets specified in commitAsync will commit in the head of next
batch.
> Kafka 0.10 DirectStream doesn't commit last processed batch's offset when
> graceful shutdown
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-20050
> URL: https://issues.apache.org/jira/browse/SPARK-20050
> Project: Spark
> Issue Type: Bug
> Components: DStreams
> Affects Versions: 2.2.0
> Reporter: Sasaki Toru
>
> I use Kafka 0.10 DirectStream with properties 'enable.auto.commit=false' and
> call 'DirectKafkaInputDStream#commitAsync' finally in each batches, such
> below
> {code}
> val kafkaStream = KafkaUtils.createDirectStream[String, String](...)
> kafkaStream.map { input =>
> "key: " + input.key.toString + " value: " + input.value.toString + "
> offset: " + input.offset.toString
> }.foreachRDD { rdd =>
> rdd.foreach { input =>
> println(input)
> }
> }
> kafkaStream.foreachRDD { rdd =>
> val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
> kafkaStream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
> }
> {\code}
> Some records which processed in the last batch before Streaming graceful
> shutdown reprocess in the first batch after Spark Streaming restart, such
> below
> * output first run of this application
> {code}
> key: null value: 1 offset: 101452472
> key: null value: 2 offset: 101452473
> key: null value: 3 offset: 101452474
> key: null value: 4 offset: 101452475
> key: null value: 5 offset: 101452476
> key: null value: 6 offset: 101452477
> key: null value: 7 offset: 101452478
> key: null value: 8 offset: 101452479
> key: null value: 9 offset: 101452480 // this is a last record before
> shutdown Spark Streaming gracefully
> {\code}
> * output re-run of this application
> {code}
> key: null value: 7 offset: 101452478 // duplication
> key: null value: 8 offset: 101452479 // duplication
> key: null value: 9 offset: 101452480 // duplication
> key: null value: 10 offset: 101452481
> {\code}
> It may cause offsets specified in commitAsync will commit in the head of next
> batch.
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