Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/13509#discussion_r67939355
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala ---
@@ -91,20 +91,39 @@ class Word2VecSuite extends SparkFunSuite with
MLlibTestSparkContext {
}
- ignore("big model load / save") {
- // create a model bigger than 32MB since 9000 * 1000 * 4 > 2^25
- val word2VecMap = Map((0 to 9000).map(i => s"$i" ->
Array.fill(1000)(0.1f)): _*)
+ test("big model load / save") {
+ // backupping old values
+ val oldBufferConfValue =
spark.conf.get("spark.kryoserializer.buffer.max", "64m")
+ val oldBufferMaxConfValue =
spark.conf.get("spark.kryoserializer.buffer", "64k")
+
+ // setting test values to trigger partitioning
+ spark.conf.set("spark.kryoserializer.buffer", "50b")
+ spark.conf.set("spark.kryoserializer.buffer.max", "50b")
+
+ // create a model bigger than 50 Bytes
+ val word2VecMap = Map((0 to 10).map(i => s"$i" ->
Array.fill(10)(0.1f)): _*)
val model = new Word2VecModel(word2VecMap)
+ // est. size of this model, given the formula:
+ // (floatSize * vectorSize + 15) * numWords
+ // (4 * 10 + 15) * 10 = 550
+ // therefore it should generate 12 partitions
--- End diff --
"12 partitions" --> "multiple partitions" (The exact number isn't
important.)
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