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 <fancye...@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 <cutl...@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 <fancye...@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 <cutl...@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 <fancye...@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 <fancye...@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 <cutl...@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 <fancye...@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 <fancye...@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 <fancye...@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 > >> >> >>> >> <jos...@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 <fancye...@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-unsubscr...@spark.apache.org > >> >> >>> >>>> For additional commands, e-mail: dev-h...@spark.apache.org > >> >> >>> >>>> > >> >> >>> >>> > >> >> >>> > >> >> >>> > >> >> >>> > --------------------------------------------------------------------- > >> >> >>> To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org > >> >> >>> For additional commands, e-mail: dev-h...@spark.apache.org > >> >> >>> > >> >> >> > >> > > >> > > > > > >