Could you save the data before ALS and try to reproduce the problem?
You might try reducing the number of partitions and not using Kryo
serialization, just to narrow down the issue. -Xiangrui
On Mon, Oct 27, 2014 at 1:29 PM, Ilya Ganelin <ilgan...@gmail.com> wrote:
> Hi Burak.
>
> I always see this error. I'm running the CDH 5.2 version of Spark 1.1.0. I
> load my data from HDFS. By the time it hits the recommender it had gone
> through many spark operations.
>
> On Oct 27, 2014 4:03 PM, "Burak Yavuz" <bya...@stanford.edu> wrote:
>>
>> Hi,
>>
>> I've come across this multiple times, but not in a consistent manner. I
>> found it hard to reproduce. I have a jira for it: SPARK-3080
>>
>> Do you observe this error every single time? Where do you load your data
>> from? Which version of Spark are you running?
>> Figuring out the similarities may help in pinpointing the bug.
>>
>> Thanks,
>> Burak
>>
>> ----- Original Message -----
>> From: "Ilya Ganelin" <ilgan...@gmail.com>
>> To: "user" <user@spark.apache.org>
>> Sent: Monday, October 27, 2014 11:36:46 AM
>> Subject: MLLib ALS ArrayIndexOutOfBoundsException with Scala Spark 1.1.0
>>
>> Hello all - I am attempting to run MLLib's ALS algorithm on a substantial
>> test vector - approx. 200 million records.
>>
>> I have resolved a few issues I've had with regards to garbage collection,
>> KryoSeralization, and memory usage.
>>
>> I have not been able to get around this issue I see below however:
>>
>>
>> > java.lang.
>> > ArrayIndexOutOfBoundsException: 6106
>> >
>> >
>> > org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateBlock$1.apply$mcVI$sp(ALS.
>> > scala:543)
>> > scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
>> > org.apache.spark.mllib.recommendation.ALS.org
>> > $apache$spark$mllib$recommendation$ALS$$updateBlock(ALS.scala:537)
>> >
>> >
>> > org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateFeatures$2.apply(ALS.scala:505)
>> >
>> >
>> > org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateFeatures$2.apply(ALS.scala:504)
>> >
>> >
>> > org.apache.spark.rdd.MappedValuesRDD$$anonfun$compute$1.apply(MappedValuesRDD.scala:31)
>> >
>> >
>> > org.apache.spark.rdd.MappedValuesRDD$$anonfun$compute$1.apply(MappedValuesRDD.scala:31)
>> > scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>> > scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>> >
>> >
>> > org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:144)
>> >
>> >
>> > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159)
>> >
>> >
>> > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158)
>> >
>> >
>> > scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772)
>> >
>> >
>> > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>> >
>> > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>> >
>> >
>> > scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771)
>> >
>> > org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158)
>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>> >
>> > org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31)
>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
>> > org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
>>
>>
>> I do not have any negative indices or indices that exceed Int-Max.
>>
>> I have partitioned the input data into 300 partitions and my Spark config
>> is below:
>>
>> .set("spark.executor.memory", "14g")
>> .set("spark.storage.memoryFraction", "0.8")
>> .set("spark.serializer",
>> "org.apache.spark.serializer.KryoSerializer")
>> .set("spark.kryo.registrator", "MyRegistrator")
>> .set("spark.core.connection.ack.wait.timeout","600")
>> .set("spark.akka.frameSize","50")
>> .set("spark.yarn.executor.memoryOverhead","1024")
>>
>> Does anyone have any suggestions as to why i'm seeing the above error or
>> how to get around it?
>> It may be possible to upgrade to the latest version of Spark but the
>> mechanism for doing so in our environment isn't obvious yet.
>>
>> -Ilya Ganelin
>>
>
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