AccumulatorV2 used in RDD
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I have a question about using accumulators in RDD and how to use them reliably.
So imagine we have the following accumulator:
val sc: SparkContext = //...
val accum = sc.longAccumulator("Accumulator name")
val rdd = //some rdd
rdd foreach { _ =>
accum.add(1L)
}
As far as I understand it the data RDD represents are being splitted by partitions and each time we perform some action it tries to
computeOrReadCheckpoint(split: Partition, context: TaskContext)
for each partition.
So in case we already compute one partition fully we update the accumulator value on the driver side. But after that the executor containing this partition crashes, but the RDD was not checkpointed yet.
So the partition is recomputed from scratch. So I expect the accumulator is updated twice for these records.
Is such a scenario possible?
scala apache-spark
asked Nov 11 at 7:55
St.Antario
9,1961550135
add a comment |
up vote
1
down vote
favorite
I have a question about using accumulators in RDD and how to use them reliably.
So imagine we have the following accumulator:
val sc: SparkContext = //...
val accum = sc.longAccumulator("Accumulator name")
val rdd = //some rdd
rdd foreach { _ =>
accum.add(1L)
}
As far as I understand it the data RDD represents are being splitted by partitions and each time we perform some action it tries to
computeOrReadCheckpoint(split: Partition, context: TaskContext)
for each partition.
So in case we already compute one partition fully we update the accumulator value on the driver side. But after that the executor containing this partition crashes, but the RDD was not checkpointed yet.
So the partition is recomputed from scratch. So I expect the accumulator is updated twice for these records.
Is such a scenario possible?
scala apache-spark
asked Nov 11 at 7:55
St.Antario
9,1961550135
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have a question about using accumulators in RDD and how to use them reliably.
So imagine we have the following accumulator:
val sc: SparkContext = //...
val accum = sc.longAccumulator("Accumulator name")
val rdd = //some rdd
rdd foreach { _ =>
accum.add(1L)
}
As far as I understand it the data RDD represents are being splitted by partitions and each time we perform some action it tries to
computeOrReadCheckpoint(split: Partition, context: TaskContext)
for each partition.
So in case we already compute one partition fully we update the accumulator value on the driver side. But after that the executor containing this partition crashes, but the RDD was not checkpointed yet.
So the partition is recomputed from scratch. So I expect the accumulator is updated twice for these records.
Is such a scenario possible?
scala apache-spark
asked Nov 11 at 7:55
St.Antario
9,1961550135
I have a question about using accumulators in RDD and how to use them reliably.
So imagine we have the following accumulator:
val sc: SparkContext = //...
val accum = sc.longAccumulator("Accumulator name")
val rdd = //some rdd
rdd foreach { _ =>
accum.add(1L)
}
As far as I understand it the data RDD represents are being splitted by partitions and each time we perform some action it tries to
computeOrReadCheckpoint(split: Partition, context: TaskContext)
for each partition.
So in case we already compute one partition fully we update the accumulator value on the driver side. But after that the executor containing this partition crashes, but the RDD was not checkpointed yet.
So the partition is recomputed from scratch. So I expect the accumulator is updated twice for these records.
Is such a scenario possible?
scala apache-spark
scala apache-spark
asked Nov 11 at 7:55
St.Antario
9,1961550135
asked Nov 11 at 7:55
St.Antario
9,1961550135
asked Nov 11 at 7:55
St.Antario
9,1961550135
asked Nov 11 at 7:55
St.Antario
9,1961550135
asked Nov 11 at 7:55
St.Antario
9,1961550135
9,1961550135
add a comment |
add a comment |
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