Re: Task not serializable: java.io.NotSerializableException: org.apache.spark.SparkContext
Which code do you used, do you caused by your own code or something in
spark itself?
On Tue, Jul 22, 2014 at 8:50 AM, hsy...@gmail.com hsy...@gmail.com wrote:
I have the same problem
On Sat, Jul 19, 2014 at 12:31 AM, lihu lihu...@gmail.com wrote:
Hi,
Everyone. I have a piece of following code. When I run it,
it occurred the error just like below, it seem that the SparkContext is not
serializable, but i do not try to use the SparkContext except the broadcast.
[In fact, this code is in the MLLib, I just try to broadcast the
centerArrays ]
it can success in the redeceBykey operation, but failed at the
collect operation, this confused me.
INFO DAGScheduler: Failed to run collect at KMeans.scala:235
[error] (run-main-0) org.apache.spark.SparkException: Job aborted: Task
not serializable: java.io.NotSerializableException:
org.apache.spark.SparkContext
org.apache.spark.SparkException: Job aborted: Task not serializable:
java.io.NotSerializableException: org.apache.spark.SparkContext
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
private def initKMeansParallel(data: RDD[Array[Double]]):
Array[ClusterCenters] = {
@transient val sc = data.sparkContext // I try to add the
transient
annotation here, but it doesn't work
// Initialize each run's center to a random point
val seed = new XORShiftRandom().nextInt()
val sample = data.takeSample(true, runs, seed).toSeq
val centers = Array.tabulate(runs)(r = ArrayBuffer(sample(r)))
// On each step, sample 2 * k points on average for each run with
probability proportional
// to their squared distance from that run's current centers
for (step - 0 until initializationSteps) {
val centerArrays = sc.broadcast(centers.map(_.toArray))
val sumCosts = data.flatMap { point =
for (r - 0 until runs) yield (r,
KMeans.pointCost(centerArrays.value(r), point))
}.reduceByKey(_ + _).collectAsMap()
//can pass at this point
val chosen = data.mapPartitionsWithIndex { (index, points) =
val rand = new XORShiftRandom(seed ^ (step 16) ^ index)
for {
p - points
r - 0 until runs
if rand.nextDouble() KMeans.pointCost(centerArrays.value(r),
p) * 2 * k / sumCosts(r)
} yield (r, p)
}.collect()
// failed at this
point.
for ((r, p) - chosen) {
centers(r) += p
}
}
--
*Best Wishes!*
*Li Hu(李浒) | Graduate Student*
*Institute for Interdisciplinary Information Sciences(IIIS
http://iiis.tsinghua.edu.cn/) *
*Tsinghua University, China*
*Email: lihu...@gmail.com lihu...@gmail.com*
*Tel : +86 15120081920*
*Homepage: http://iiis.tsinghua.edu.cn/zh/lihu/
http://iiis.tsinghua.edu.cn/zh/lihu/*
Re: Task not serializable: java.io.NotSerializableException: org.apache.spark.SparkContext
You can set the Java option -Dsun.io.serialization.extendedDebugInfo=true to
have more information about the object be printed. It will help you trace
down the how the SparkContext is getting included in some kind of closure.
TD
On Thu, Jul 24, 2014 at 9:48 AM, lihu lihu...@gmail.com wrote:
Which code do you used, do you caused by your own code or something in
spark itself?
On Tue, Jul 22, 2014 at 8:50 AM, hsy...@gmail.com hsy...@gmail.com
wrote:
I have the same problem
On Sat, Jul 19, 2014 at 12:31 AM, lihu lihu...@gmail.com wrote:
Hi,
Everyone. I have a piece of following code. When I run it,
it occurred the error just like below, it seem that the SparkContext is not
serializable, but i do not try to use the SparkContext except the broadcast.
[In fact, this code is in the MLLib, I just try to broadcast the
centerArrays ]
it can success in the redeceBykey operation, but failed at the
collect operation, this confused me.
INFO DAGScheduler: Failed to run collect at KMeans.scala:235
[error] (run-main-0) org.apache.spark.SparkException: Job aborted: Task
not serializable: java.io.NotSerializableException:
org.apache.spark.SparkContext
org.apache.spark.SparkException: Job aborted: Task not serializable:
java.io.NotSerializableException: org.apache.spark.SparkContext
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
private def initKMeansParallel(data: RDD[Array[Double]]):
Array[ClusterCenters] = {
@transient val sc = data.sparkContext // I try to add the
transient
annotation here, but it doesn't work
// Initialize each run's center to a random point
val seed = new XORShiftRandom().nextInt()
val sample = data.takeSample(true, runs, seed).toSeq
val centers = Array.tabulate(runs)(r = ArrayBuffer(sample(r)))
// On each step, sample 2 * k points on average for each run with
probability proportional
// to their squared distance from that run's current centers
for (step - 0 until initializationSteps) {
val centerArrays = sc.broadcast(centers.map(_.toArray))
val sumCosts = data.flatMap { point =
for (r - 0 until runs) yield (r,
KMeans.pointCost(centerArrays.value(r), point))
}.reduceByKey(_ + _).collectAsMap()
//can pass at this point
val chosen = data.mapPartitionsWithIndex { (index, points) =
val rand = new XORShiftRandom(seed ^ (step 16) ^ index)
for {
p - points
r - 0 until runs
if rand.nextDouble() KMeans.pointCost(centerArrays.value(r),
p) * 2 * k / sumCosts(r)
} yield (r, p)
}.collect()
// failed at this
point.
for ((r, p) - chosen) {
centers(r) += p
}
}
--
*Best Wishes!*
*Li Hu(李浒) | Graduate Student*
*Institute for Interdisciplinary Information Sciences(IIIS
http://iiis.tsinghua.edu.cn/) *
*Tsinghua University, China*
*Email: lihu...@gmail.com lihu...@gmail.com*
*Tel : +86 15120081920 %2B86%2015120081920*
*Homepage: http://iiis.tsinghua.edu.cn/zh/lihu/
http://iiis.tsinghua.edu.cn/zh/lihu/*
Re: Task not serializable: java.io.NotSerializableException: org.apache.spark.SparkContext
I have the same problem
On Sat, Jul 19, 2014 at 12:31 AM, lihu lihu...@gmail.com wrote:
Hi,
Everyone. I have a piece of following code. When I run it,
it occurred the error just like below, it seem that the SparkContext is not
serializable, but i do not try to use the SparkContext except the broadcast.
[In fact, this code is in the MLLib, I just try to broadcast the
centerArrays ]
it can success in the redeceBykey operation, but failed at the collect
operation, this confused me.
INFO DAGScheduler: Failed to run collect at KMeans.scala:235
[error] (run-main-0) org.apache.spark.SparkException: Job aborted: Task
not serializable: java.io.NotSerializableException:
org.apache.spark.SparkContext
org.apache.spark.SparkException: Job aborted: Task not serializable:
java.io.NotSerializableException: org.apache.spark.SparkContext
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
at
org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
private def initKMeansParallel(data: RDD[Array[Double]]):
Array[ClusterCenters] = {
@transient val sc = data.sparkContext // I try to add the
transient
annotation here, but it doesn't work
// Initialize each run's center to a random point
val seed = new XORShiftRandom().nextInt()
val sample = data.takeSample(true, runs, seed).toSeq
val centers = Array.tabulate(runs)(r = ArrayBuffer(sample(r)))
// On each step, sample 2 * k points on average for each run with
probability proportional
// to their squared distance from that run's current centers
for (step - 0 until initializationSteps) {
val centerArrays = sc.broadcast(centers.map(_.toArray))
val sumCosts = data.flatMap { point =
for (r - 0 until runs) yield (r,
KMeans.pointCost(centerArrays.value(r), point))
}.reduceByKey(_ + _).collectAsMap()
//can pass at this point
val chosen = data.mapPartitionsWithIndex { (index, points) =
val rand = new XORShiftRandom(seed ^ (step 16) ^ index)
for {
p - points
r - 0 until runs
if rand.nextDouble() KMeans.pointCost(centerArrays.value(r),
p) * 2 * k / sumCosts(r)
} yield (r, p)
}.collect()
// failed at this
point.
for ((r, p) - chosen) {
centers(r) += p
}
}
