Hi Xiangrui,
thank you a lot for the hint!
I just tried on another machine with a clean project and there it worked
like a charm. Will retry on the other machine tomorrow.
Regards,
Jeff
2015-03-17 19:57 GMT+01:00 Xiangrui Meng <men...@gmail.com>:
> Please check your classpath and make sure you don't have multiple
> Spark versions deployed. If the classpath looks correct, please create
> a JIRA for this issue. Thanks! -Xiangrui
>
> On Tue, Mar 17, 2015 at 2:03 AM, Jeffrey Jedele
> <jeffrey.jed...@gmail.com> wrote:
> > Hi all,
> > I'm trying to use the new LDA in mllib, but when trying to train the
> model,
> > I'm getting following error:
> >
> > java.lang.IllegalAccessError: tried to access class
> > org.apache.spark.util.collection.Sorter from class
> > org.apache.spark.graphx.impl.EdgePartitionBuilder
> > at
> >
> org.apache.spark.graphx.impl.EdgePartitionBuilder.toEdgePartition(EdgePartitionBuilder.scala:39)
> > at
> org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:109)
> >
> > Has anyone seen this yet and has an idea what might be the problem?
> > It happens both with the provided sample data and with my own corpus.
> >
> > Full code + more stack below.
> >
> > Thx and Regards,
> > Jeff
> >
> > Code:
> > ------------------
> > object LdaTest {
> >
> > def main(args: Array[String]) = {
> > val conf = new SparkConf().setAppName("LDA").setMaster("local[4]")
> > val sc = new SparkContext(conf)
> >
> > // val data =
> >
> scala.io.Source.fromFile("/home/jeff/nmf_compare/scikit_v.txt").getLines().toList
> > // val parsedData = data.map(s => Vectors.dense(s.trim().split("
> > ").map(_.toDouble)))
> > // val corpus = parsedData.zipWithIndex.map( t => (t._2.toLong, t._1)
> )
> >
> > //val data = sc.textFile("/home/jeff/nmf_compare/scikit_v.txt")
> > val data =
> >
> sc.textFile("/home/jeff/Downloads/spark-1.3.0-bin-hadoop2.4/data/mllib/sample_lda_data.txt")
> > val parsedData = data.map(s => Vectors.dense(s.trim().split("
> > ").map(_.toDouble)))
> > val corpus = parsedData.zipWithIndex.map(_.swap).cache()
> >
> > // val parCorpus = sc.parallelize(corpus)
> > // println(parCorpus)
> >
> > val ldaModel = new LDA().setK(10).run(corpus)
> >
> > println(ldaModel)
> > }
> >
> > }
> >
> > Stack:
> > ----------------
> > ...
> > 15/03/17 09:48:50 INFO spark.CacheManager: Partition rdd_8_0 not found,
> > computing it
> > 15/03/17 09:48:50 INFO spark.CacheManager: Partition rdd_8_1 not found,
> > computing it
> > 15/03/17 09:48:50 INFO spark.CacheManager: Another thread is loading
> > rdd_8_0, waiting for it to finish...
> > 15/03/17 09:48:50 INFO storage.BlockManager: Found block rdd_4_0 locally
> > 15/03/17 09:48:50 INFO spark.CacheManager: Partition rdd_4_1 not found,
> > computing it
> > 15/03/17 09:48:50 INFO spark.CacheManager: Another thread is loading
> > rdd_8_1, waiting for it to finish...
> > 15/03/17 09:48:50 INFO rdd.HadoopRDD: Input split:
> >
> file:/home/jeff/Downloads/spark-1.3.0-bin-hadoop2.4/data/mllib/sample_lda_data.txt:132+132
> > 15/03/17 09:48:50 INFO storage.MemoryStore: ensureFreeSpace(1048) called
> > with curMem=47264, maxMem=1965104824
> > 15/03/17 09:48:50 INFO spark.CacheManager: Finished waiting for rdd_8_0
> > 15/03/17 09:48:50 ERROR executor.Executor: Exception in task 0.0 in stage
> > 3.0 (TID 3)
> > java.lang.IllegalAccessError: tried to access class
> > org.apache.spark.util.collection.Sorter from class
> > org.apache.spark.graphx.impl.EdgePartitionBuilder
> > at
> >
> org.apache.spark.graphx.impl.EdgePartitionBuilder.toEdgePartition(EdgePartitionBuilder.scala:39)
> > at
> org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:109)
> > at
> org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:104)
> > at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:609)
> > at org.apache.spark.rdd.RDD$$anonfun$14.apply(RDD.scala:609)
> > at
> > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:61)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:227)
> > at
> > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> > at org.apache.spark.graphx.EdgeRDD.compute(EdgeRDD.scala:49)
> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> > at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262)
> > at org.apache.spark.rdd.RDD.iterator(RDD.scala:229)
> > at
> >
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> > at
> >
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> > at org.apache.spark.scheduler.Task.run(Task.scala:54)
> > at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:177)
> > at
> >
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> > at
> >
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> > at java.lang.Thread.run(Thread.java:745)
> > 15/03/17 09:48:50 INFO spark.CacheManager: Whoever was loading rdd_8_0
> > failed; we'll try it ourselves
> > 15/03/17 09:48:50 INFO storage.MemoryStore: Block rdd_4_1 stored as
> values
> > in memory (estimated size 1048.0 B, free 1874.0 MB)
> > 15/03/17 09:48:50 INFO spark.CacheManager: Partition rdd_8_0 not found,
> > computing it
> > 15/03/17 09:48:50 INFO storage.BlockManagerInfo: Added rdd_4_1 in memory
> on
> > 10.2.200.66:51465 (size: 1048.0 B, free: 1874.1 MB)
> > 15/03/17 09:48:50 INFO storage.BlockManager: Found block rdd_4_0 locally
> > 15/03/17 09:48:50 INFO storage.BlockManagerMaster: Updated info of block
> > rdd_4_1
> > 15/03/17 09:48:50 ERROR executor.Executor: Exception in task 0.0 in stage
> > 5.0 (TID 5)
> > java.lang.IllegalAccessError: org/apache/spark/util/collection/Sorter
> > at
> >
> org.apache.spark.graphx.impl.EdgePartitionBuilder.toEdgePartition(EdgePartitionBuilder.scala:39)
> > at
> org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:109)
> > at
> org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:104)
> > ...
>
