[jira] [Commented] (SPARK-20797) mllib lda load and save out of memory.

["Sean Owen (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-20797?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16015657#comment-16015657 ] 

Sean Owen commented on SPARK-20797:
-----------------------------------

It's not clear what you're describing here. Can you reduce this to focus on the specific problem and change?
How many topics?

> mllib lda load and save out of memory. 
> ---------------------------------------
>
>                 Key: SPARK-20797
>                 URL: https://issues.apache.org/jira/browse/SPARK-20797
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.6.1, 1.6.3, 2.0.0, 2.0.2, 2.1.1
>            Reporter: d0evi1
>
> when i try online lda model with large text data, the training step went well, but the save step failed. but  something like below happened (etc. 1.6.1):
> 1.bigger than spark.kryoserializer.buffer.max.  (turning bigger the param can fixed),
> 2. exceed spark.akka.frameSize. (turning this param too bigger will fail, version > 2.0.0, exceeds max allowed: spark.rpc.message.maxSize).
> when topics  num is large, and vocab size is large too. this problem will appear.
> so i found this:
> https://github.com/apache/spark/pull/9989, word2vec's problem has been fixed, 
> this is word2vec's  save method from latest code:
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala:
>       val approxSize = (4L * vectorSize + 15) * numWords
>       val nPartitions = ((approxSize / bufferSize) + 1).toInt
>       val dataArray = model.toSeq.map { case (w, v) => Data(w, v) }
>       spark.createDataFrame(dataArray).repartition(nPartitions).write.parquet(Loader.dataPath(path))
> but the code in mllib.clustering.LDAModel's save:
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
> you'll see:
>       val topicsDenseMatrix = topicsMatrix.asBreeze.toDenseMatrix
>       val topics = Range(0, k).map { topicInd =>
>         Data(Vectors.dense((topicsDenseMatrix(::, topicInd).toArray)), topicInd)
>       }
>       spark.createDataFrame(topics).repartition(1).write.parquet(Loader.dataPath(path))
> i try word2vec's save, replace numWords to topic K, repartition(nPartitions), recompile the code, deploy the new lda's project with large data on our machine cluster, it works.
> hopes it will fixed in the next version.



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