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Posted to dev@spark.apache.org by Joseph Bradley <jo...@databricks.com> on 2016/04/04 20:33:12 UTC
Re: running lda in spark throws exception
It's possible this was caused by incorrect Graph creation, fixed in
[SPARK-13355].
Could you retry your dataset using the current master to see if the problem
is fixed? Thanks!
On Tue, Jan 19, 2016 at 5:31 AM, Li Li <fa...@gmail.com> wrote:
> I have modified my codes. I can get the total vocabulary size and
> index array and freq array from the jsonobject.
>
> JsonArray idxArr = jo.get("idxArr").getAsJsonArray();
>
> JsonArray freqArr=jo.get("freqArr").getAsJsonArray();
>
> int total=jo.get("vocabSize").getAsInt();
>
> String url = jo.get("webpageUrl").getAsString();
>
> int[] idx = new int[idxArr.size()];
>
> double[] freq=new double[freqArr.size()];
>
> for(int i=0;i<idxArr.size();i++){
>
> idx[i]=idxArr.get(i).getAsInt();
>
> freq[i]=freqArr.get(i).getAsDouble();
>
> }
>
>
>
> return new VectorUrl(Vectors.sparse(total, idx, freq), url);
>
> But when I run it, it throws exception Job aborted due to stage
> failure: Total size of serialized results of 22 tasks (3.1 GB) is
> bigger than spark.driver.maxResultSize (3.0 GB)
> I have set result to 3g but it still says not engouh.
> conf.set("spark.driver.maxResultSize", "3g");
> How much memory will it use?
> I use the following script to submit job to yarn cluster.
>
> bin/spark-submit --class xxx.yyyy.ReviewLDA \
> --master yarn-cluster \
> --num-executors 10 \
> --driver-memory 4g \
> --executor-memory 4g \
> --executor-cores 2
>
> On Fri, Jan 15, 2016 at 3:24 AM, Bryan Cutler <cu...@gmail.com> wrote:
> > What I mean is the input to LDA.run() is a RDD[(Long, Vector)] and the
> > Vector is a vector of counts of each term and should be the same size as
> the
> > vocabulary (so if the vocabulary, or dictionary has 10 words, each vector
> > should have a size of 10). This probably means that there will be some
> > elements with zero counts, and a sparse vector might be a good way to
> handle
> > that.
> >
> > On Wed, Jan 13, 2016 at 6:40 PM, Li Li <fa...@gmail.com> wrote:
> >>
> >> It looks like the problem is the vectors of term counts in the corpus
> >> are not always the vocabulary size.
> >> Do you mean some integers not occured in the corpus?
> >> for example, I have the dictionary is 0 - 9 (total 10 words).
> >> The docs are:
> >> 0 2 4 6 8
> >> 1 3 5 7 9
> >> Then it will be correct
> >> If the docs are:
> >> 0 2 4 6 9
> >> 1 3 5 6 7
> >> 8 is not occured in any document, Then it will wrong?
> >>
> >> So the workaround is to process the input to re-encode terms?
> >>
> >> On Thu, Jan 14, 2016 at 6:53 AM, Bryan Cutler <cu...@gmail.com>
> wrote:
> >> > I was now able to reproduce the exception using the master branch and
> >> > local
> >> > mode. It looks like the problem is the vectors of term counts in the
> >> > corpus
> >> > are not always the vocabulary size. Once I padded these with zero
> >> > counts to
> >> > the vocab size, it ran without the exception.
> >> >
> >> > Joseph, I also tried calling describeTopics and noticed that with the
> >> > improper vector size, it will not throw an exception but the term
> >> > indices
> >> > will start to be incorrect. For a small number of iterations, it is
> ok,
> >> > but
> >> > increasing iterations causes the indices to get larger also. Maybe
> that
> >> > is
> >> > what is going on in the JIRA you linked to?
> >> >
> >> > On Wed, Jan 13, 2016 at 1:17 AM, Li Li <fa...@gmail.com> wrote:
> >> >>
> >> >> I will try spark 1.6.0 to see it is the bug of 1.5.2.
> >> >>
> >> >> On Wed, Jan 13, 2016 at 3:58 PM, Li Li <fa...@gmail.com> wrote:
> >> >> > I have set up a stand alone spark cluster and use the same codes.
> it
> >> >> > still failed with the same exception
> >> >> > I also preprocessed the data to lines of integers and use the scala
> >> >> > codes of lda example. it still failed.
> >> >> > the codes:
> >> >> >
> >> >> > import org.apache.spark.mllib.clustering.{ LDA,
> DistributedLDAModel }
> >> >> >
> >> >> > import org.apache.spark.mllib.linalg.Vectors
> >> >> >
> >> >> > import org.apache.spark.SparkContext
> >> >> >
> >> >> > import org.apache.spark.SparkContext._
> >> >> >
> >> >> > import org.apache.spark.SparkConf
> >> >> >
> >> >> >
> >> >> > object TestLDA {
> >> >> >
> >> >> > def main(args: Array[String]) {
> >> >> >
> >> >> > if(args.length!=4){
> >> >> >
> >> >> > println("need 4 args inDir outDir topic iternum")
> >> >> >
> >> >> > System.exit(-1)
> >> >> >
> >> >> > }
> >> >> >
> >> >> > val conf = new SparkConf().setAppName("TestLDA")
> >> >> >
> >> >> > val sc = new SparkContext(conf)
> >> >> >
> >> >> > // Load and parse the data
> >> >> >
> >> >> > val data = sc.textFile(args(0))
> >> >> >
> >> >> > val parsedData = data.map(s => Vectors.dense(s.trim.split('
> >> >> > ').map(_.toDouble)))
> >> >> >
> >> >> > // Index documents with unique IDs
> >> >> >
> >> >> > val corpus = parsedData.zipWithIndex.map(_.swap).cache()
> >> >> >
> >> >> > val topicNum=Integer.valueOf(args(2))
> >> >> >
> >> >> > val iterNum=Integer.valueOf(args(1))
> >> >> >
> >> >> > // Cluster the documents into three topics using LDA
> >> >> >
> >> >> > val ldaModel = new
> >> >> > LDA().setK(topicNum).setMaxIterations(iterNum).run(corpus)
> >> >> >
> >> >> >
> >> >> > // Output topics. Each is a distribution over words (matching
> >> >> > word
> >> >> > count vectors)
> >> >> >
> >> >> > println("Learned topics (as distributions over vocab of " +
> >> >> > ldaModel.vocabSize + " words):")
> >> >> >
> >> >> > val topics = ldaModel.topicsMatrix
> >> >> >
> >> >> > for (topic <- Range(0, topicNum)) {
> >> >> >
> >> >> > print("Topic " + topic + ":")
> >> >> >
> >> >> > for (word <- Range(0, ldaModel.vocabSize)) { print(" " +
> >> >> > topics(word, topic)); }
> >> >> >
> >> >> > println()
> >> >> >
> >> >> > }
> >> >> >
> >> >> >
> >> >> > // Save and load model.
> >> >> >
> >> >> > ldaModel.save(sc, args(1))
> >> >> >
> >> >> > }
> >> >> >
> >> >> >
> >> >> > }
> >> >> >
> >> >> > scripts to submit:
> >> >> >
> >> >> > ~/spark-1.5.2-bin-hadoop2.6/bin/spark-submit --class
> >> >> > com.mobvoi.knowledgegraph.scala_test.TestLDA \
> >> >> >
> >> >> > --master spark://master:7077 \
> >> >> >
> >> >> > --num-executors 10 \
> >> >> >
> >> >> > --executor-memory 4g \
> >> >> >
> >> >> > --executor-cores 3 \
> >> >> >
> >> >> > scala_test-1.0-jar-with-dependencies.jar \
> >> >> >
> >> >> > /test.txt \
> >> >> >
> >> >> > 100 \
> >> >> >
> >> >> > 5 \
> >> >> >
> >> >> > /lda_model
> >> >> >
> >> >> > test.txt is in attachment
> >> >> >
> >> >> >
> >> >> > On Sat, Jan 9, 2016 at 6:21 AM, Bryan Cutler <cu...@gmail.com>
> >> >> > wrote:
> >> >> >> Hi Li,
> >> >> >>
> >> >> >> I tried out your code and sample data in both local mode and Spark
> >> >> >> Standalone and it ran correctly with output that looks good.
> Sorry,
> >> >> >> I
> >> >> >> don't
> >> >> >> have a YARN cluster setup right now, so maybe the error you are
> >> >> >> seeing
> >> >> >> is
> >> >> >> specific to that. Btw, I am running the latest Spark code from
> the
> >> >> >> master
> >> >> >> branch. Hope that helps some!
> >> >> >>
> >> >> >> Bryan
> >> >> >>
> >> >> >> On Mon, Jan 4, 2016 at 8:42 PM, Li Li <fa...@gmail.com>
> wrote:
> >> >> >>>
> >> >> >>> anyone could help? the problem is very easy to reproduce. What's
> >> >> >>> wrong?
> >> >> >>>
> >> >> >>> On Wed, Dec 30, 2015 at 8:59 PM, Li Li <fa...@gmail.com>
> wrote:
> >> >> >>> > I use a small data and reproduce the problem.
> >> >> >>> > But I don't know my codes are correct or not because I am not
> >> >> >>> > familiar
> >> >> >>> > with spark.
> >> >> >>> > So I first post my codes here. If it's correct, then I will
> post
> >> >> >>> > the
> >> >> >>> > data.
> >> >> >>> > one line of my data like:
> >> >> >>> >
> >> >> >>> > { "time":"08-09-17","cmtUrl":"2094361"
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> >> >> >>> > ,"rvId":"rev_10000020","webpageUrl":"
> http://www.dianping.com/shop/2094361","word_vec":[0,1,2,3,4,5,6,2,7,8,9
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> ,10,11,12,13,14,15,16,8,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,32,35,36,37,38,15,39,40,41,42,5,43,44,17,45,46,42,47,26,48,49]}
> >> >> >>> >
> >> >> >>> > it's a json file which contains webpageUrl and word_vec which
> is
> >> >> >>> > the
> >> >> >>> > encoded words.
> >> >> >>> > The first step is to prase the input rdd to a rdd of VectorUrl.
> >> >> >>> > BTW, if public VectorUrl call(String s) return null, is it ok?
> >> >> >>> > Then follow the example Index documents with unique IDs
> >> >> >>> > Then I create a rdd to map id to url so after lda training, I
> can
> >> >> >>> > find
> >> >> >>> > the url of the document. Then save this rdd to hdfs.
> >> >> >>> > Then create corpus rdd and train
> >> >> >>> >
> >> >> >>> > The exception stack is
> >> >> >>> >
> >> >> >>> > 15/12/30 20:45:42 ERROR yarn.ApplicationMaster: User class
> threw
> >> >> >>> > exception: java.lang.IndexOutOfBoundsException: (454,0) not in
> >> >> >>> > [-58,58) x [-100,100)
> >> >> >>> > java.lang.IndexOutOfBoundsException: (454,0) not in [-58,58) x
> >> >> >>> > [-100,100)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> breeze.linalg.DenseMatrix$mcD$sp.update$mcD$sp(DenseMatrix.scala:112)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:534)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:531)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix$lzycompute(LDAModel.scala:531)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix(LDAModel.scala:523)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> com.mobvoi.knowledgegraph.textmining.lda.ReviewLDA.main(ReviewLDA.java:89)
> >> >> >>> > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> >> >> >>> > at java.lang.reflect.Method.invoke(Method.java:606)
> >> >> >>> > at
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:525)
> >> >> >>> >
> >> >> >>> >
> >> >> >>> > ==========here is my codes==============
> >> >> >>> >
> >> >> >>> > SparkConf conf = new
> >> >> >>> > SparkConf().setAppName(ReviewLDA.class.getName());
> >> >> >>> >
> >> >> >>> > JavaSparkContext sc = new JavaSparkContext(conf);
> >> >> >>> >
> >> >> >>> >
> >> >> >>> > // Load and parse the data
> >> >> >>> >
> >> >> >>> > JavaRDD<String> data = sc.textFile(inputDir + "/*");
> >> >> >>> >
> >> >> >>> > JavaRDD<VectorUrl> parsedData = data.map(new
> Function<String,
> >> >> >>> > VectorUrl>() {
> >> >> >>> >
> >> >> >>> > public VectorUrl call(String s) {
> >> >> >>> >
> >> >> >>> > JsonParser parser = new JsonParser();
> >> >> >>> >
> >> >> >>> > JsonObject jo = parser.parse(s).getAsJsonObject();
> >> >> >>> >
> >> >> >>> > if (!jo.has("word_vec") || !jo.has("webpageUrl")) {
> >> >> >>> >
> >> >> >>> > return null;
> >> >> >>> >
> >> >> >>> > }
> >> >> >>> >
> >> >> >>> > JsonArray word_vec =
> jo.get("word_vec").getAsJsonArray();
> >> >> >>> >
> >> >> >>> > String url = jo.get("webpageUrl").getAsString();
> >> >> >>> >
> >> >> >>> > double[] values = new double[word_vec.size()];
> >> >> >>> >
> >> >> >>> > for (int i = 0; i < values.length; i++)
> >> >> >>> >
> >> >> >>> > values[i] = word_vec.get(i).getAsInt();
> >> >> >>> >
> >> >> >>> > return new VectorUrl(Vectors.dense(values), url);
> >> >> >>> >
> >> >> >>> > }
> >> >> >>> >
> >> >> >>> > });
> >> >> >>> >
> >> >> >>> >
> >> >> >>> >
> >> >> >>> > // Index documents with unique IDs
> >> >> >>> >
> >> >> >>> > JavaPairRDD<Long, VectorUrl> id2doc =
> >> >> >>> > JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map(
> >> >> >>> >
> >> >> >>> > new Function<Tuple2<VectorUrl, Long>, Tuple2<Long,
> >> >> >>> > VectorUrl>>()
> >> >> >>> > {
> >> >> >>> >
> >> >> >>> > public Tuple2<Long, VectorUrl> call(Tuple2<VectorUrl,
> >> >> >>> > Long>
> >> >> >>> > doc_id) {
> >> >> >>> >
> >> >> >>> > return doc_id.swap();
> >> >> >>> >
> >> >> >>> > }
> >> >> >>> >
> >> >> >>> > }));
> >> >> >>> >
> >> >> >>> > JavaPairRDD<Long, String> id2Url =
> >> >> >>> > JavaPairRDD.fromJavaRDD(id2doc
> >> >> >>> >
> >> >> >>> > .map(new Function<Tuple2<Long, VectorUrl>, Tuple2<Long,
> >> >> >>> > String>>() {
> >> >> >>> >
> >> >> >>> > @Override
> >> >> >>> >
> >> >> >>> > public Tuple2<Long, String> call(Tuple2<Long,
> >> >> >>> > VectorUrl>
> >> >> >>> > id2doc) throws Exception {
> >> >> >>> >
> >> >> >>> > return new Tuple2(id2doc._1, id2doc._2.url);
> >> >> >>> >
> >> >> >>> > }
> >> >> >>> >
> >> >> >>> > }));
> >> >> >>> >
> >> >> >>> > id2Url.saveAsTextFile(id2UrlPath);
> >> >> >>> >
> >> >> >>> > JavaPairRDD<Long, Vector> corpus =
> >> >> >>> > JavaPairRDD.fromJavaRDD(id2doc
> >> >> >>> >
> >> >> >>> > .map(new Function<Tuple2<Long, VectorUrl>, Tuple2<Long,
> >> >> >>> > Vector>>() {
> >> >> >>> >
> >> >> >>> > @Override
> >> >> >>> >
> >> >> >>> > public Tuple2<Long, Vector> call(Tuple2<Long,
> >> >> >>> > VectorUrl>
> >> >> >>> > id2doc) throws Exception {
> >> >> >>> >
> >> >> >>> > return new Tuple2(id2doc._1, id2doc._2.vec);
> >> >> >>> >
> >> >> >>> > }
> >> >> >>> >
> >> >> >>> > }));
> >> >> >>> >
> >> >> >>> > corpus.cache();
> >> >> >>> >
> >> >> >>> >
> >> >> >>> > // Cluster the documents into three topics using LDA
> >> >> >>> >
> >> >> >>> > DistributedLDAModel ldaModel = (DistributedLDAModel) new
> >> >> >>> > LDA().setMaxIterations(iterNumber)
> >> >> >>> >
> >> >> >>> > .setK(topicNumber).run(corpus);
> >> >> >>> >
> >> >> >>> > On Wed, Dec 30, 2015 at 3:34 PM, Li Li <fa...@gmail.com>
> >> >> >>> > wrote:
> >> >> >>> >> I will use a portion of data and try. will the hdfs block
> affect
> >> >> >>> >> spark?(if so, it's hard to reproduce)
> >> >> >>> >>
> >> >> >>> >> On Wed, Dec 30, 2015 at 3:22 AM, Joseph Bradley
> >> >> >>> >> <jo...@databricks.com>
> >> >> >>> >> wrote:
> >> >> >>> >>> Hi Li,
> >> >> >>> >>>
> >> >> >>> >>> I'm wondering if you're running into the same bug reported
> >> >> >>> >>> here:
> >> >> >>> >>> https://issues.apache.org/jira/browse/SPARK-12488
> >> >> >>> >>>
> >> >> >>> >>> I haven't figured out yet what is causing it. Do you have a
> >> >> >>> >>> small
> >> >> >>> >>> corpus
> >> >> >>> >>> which reproduces this error, and which you can share on the
> >> >> >>> >>> JIRA?
> >> >> >>> >>> If
> >> >> >>> >>> so,
> >> >> >>> >>> that would help a lot in debugging this failure.
> >> >> >>> >>>
> >> >> >>> >>> Thanks!
> >> >> >>> >>> Joseph
> >> >> >>> >>>
> >> >> >>> >>> On Sun, Dec 27, 2015 at 7:26 PM, Li Li <fa...@gmail.com>
> >> >> >>> >>> wrote:
> >> >> >>> >>>>
> >> >> >>> >>>> I ran my lda example in a yarn 2.6.2 cluster with spark
> 1.5.2.
> >> >> >>> >>>> it throws exception in line: Matrix topics =
> >> >> >>> >>>> ldaModel.topicsMatrix();
> >> >> >>> >>>> But in yarn job history ui, it's successful. What's wrong
> with
> >> >> >>> >>>> it?
> >> >> >>> >>>> I submit job with
> >> >> >>> >>>> .bin/spark-submit --class Myclass \
> >> >> >>> >>>> --master yarn-client \
> >> >> >>> >>>> --num-executors 2 \
> >> >> >>> >>>> --driver-memory 4g \
> >> >> >>> >>>> --executor-memory 4g \
> >> >> >>> >>>> --executor-cores 1 \
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>> My codes:
> >> >> >>> >>>>
> >> >> >>> >>>> corpus.cache();
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>> // Cluster the documents into three topics using LDA
> >> >> >>> >>>>
> >> >> >>> >>>> DistributedLDAModel ldaModel = (DistributedLDAModel) new
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> LDA().setOptimizer("em").setMaxIterations(iterNumber).setK(topicNumber).run(corpus);
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>> // Output topics. Each is a distribution over words
> >> >> >>> >>>> (matching
> >> >> >>> >>>> word
> >> >> >>> >>>> count vectors)
> >> >> >>> >>>>
> >> >> >>> >>>> System.out.println("Learned topics (as distributions
> over
> >> >> >>> >>>> vocab
> >> >> >>> >>>> of
> >> >> >>> >>>> " + ldaModel.vocabSize()
> >> >> >>> >>>>
> >> >> >>> >>>> + " words):");
> >> >> >>> >>>>
> >> >> >>> >>>> //Line81, exception here: Matrix topics =
> >> >> >>> >>>> ldaModel.topicsMatrix();
> >> >> >>> >>>>
> >> >> >>> >>>> for (int topic = 0; topic < topicNumber; topic++) {
> >> >> >>> >>>>
> >> >> >>> >>>> System.out.print("Topic " + topic + ":");
> >> >> >>> >>>>
> >> >> >>> >>>> for (int word = 0; word < ldaModel.vocabSize();
> word++)
> >> >> >>> >>>> {
> >> >> >>> >>>>
> >> >> >>> >>>> System.out.print(" " + topics.apply(word, topic));
> >> >> >>> >>>>
> >> >> >>> >>>> }
> >> >> >>> >>>>
> >> >> >>> >>>> System.out.println();
> >> >> >>> >>>>
> >> >> >>> >>>> }
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>> ldaModel.save(sc.sc(), modelPath);
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>> Exception in thread "main"
> >> >> >>> >>>> java.lang.IndexOutOfBoundsException:
> >> >> >>> >>>> (1025,0) not in [-58,58) x [-100,100)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> breeze.linalg.DenseMatrix$mcD$sp.update$mcD$sp(DenseMatrix.scala:112)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:534)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.mllib.clustering.DistributedLDAModel$$anonfun$topicsMatrix$1.apply(LDAModel.scala:531)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix$lzycompute(LDAModel.scala:531)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.mllib.clustering.DistributedLDAModel.topicsMatrix(LDAModel.scala:523)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> com.mobvoi.knowledgegraph.textmining.lda.ReviewLDA.main(ReviewLDA.java:81)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> sun.reflect.NativeMethodAccessorImpl.invoke0(Native
> >> >> >>> >>>> Method)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> >> >> >>> >>>>
> >> >> >>> >>>> at java.lang.reflect.Method.invoke(Method.java:606)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:674)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:180)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>>
> >> >> >>> >>>>
> org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:120)
> >> >> >>> >>>>
> >> >> >>> >>>> at
> >> >> >>> >>>> org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
> >> >> >>> >>>>
> >> >> >>> >>>> 15/12/23 00:01:16 INFO spark.SparkContext: Invoking stop()
> >> >> >>> >>>> from
> >> >> >>> >>>> shutdown
> >> >> >>> >>>> hook
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> >> >> >>> >>>>
> ---------------------------------------------------------------------
> >> >> >>> >>>> To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
> >> >> >>> >>>> For additional commands, e-mail: dev-help@spark.apache.org
> >> >> >>> >>>>
> >> >> >>> >>>
> >> >> >>>
> >> >> >>>
> >> >> >>>
> ---------------------------------------------------------------------
> >> >> >>> To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
> >> >> >>> For additional commands, e-mail: dev-help@spark.apache.org
> >> >> >>>
> >> >> >>
> >> >
> >> >
> >
> >
>