Github user d0evi1 commented on a diff in the pull request:
https://github.com/apache/spark/pull/18034#discussion_r117602669
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala ---
@@ -468,7 +469,16 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
val topics = Range(0, k).map { topicInd =>
Data(Vectors.dense((topicsDenseMatrix(::, topicInd).toArray)),
topicInd)
}
-
spark.createDataFrame(topics).repartition(1).write.parquet(Loader.dataPath(path))
+
+ val bufferSize = Utils.byteStringAsBytes(
+ spark.conf.get("spark.kryoserializer.buffer.max", "64m"))
+ // We calculate the approximate size of the model
+ // We only calculate the array size, considering an
+ // average string size of 15 bytes, the formula is:
+ // (floatSize * vectorSize + 15) * numWords
+ val approxSize = (4L * k + 15) * topicsMatrix.numRows
+ val nPartitions = ((approxSize / bufferSize) + 1).toInt
+
spark.createDataFrame(topics).repartition(nPartitions).write.parquet(Loader.dataPath(path))
--- End diff --
why not? i think it does works. the multiple parquet files will may be in
random order, but it will save topic indices. when u call load process, parquet
will restore dataframe, u can check the LocalLDAModel's load method, it will
scan all dataframe's row with the topic indices to rebuild the (topic x vocab)
breeze matrix.
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