Github user Krimit commented on a diff in the pull request:
https://github.com/apache/spark/pull/18034#discussion_r124916809
--- 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 --
@srowen what's your concern with saving multiple files instead of one?
We've encountered crippling errors saving large LDA models in the ml api as
well. The problem there is even worse, since the entire matrix is treated as
one single datum
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