srowen commented on a change in pull request #25789: [SPARK-28927][ML] Show
warning when input data to ALS is indeterminate
URL: https://github.com/apache/spark/pull/25789#discussion_r324466135
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File path: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
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@@ -920,6 +924,14 @@ object ALS extends DefaultParamsReadable[ALS] with
Logging {
require(intermediateRDDStorageLevel != StorageLevel.NONE,
"ALS is not designed to run without persisting intermediate RDDs.")
+ // Indeterminate rating RDD causes inconsistent in/out blocks in case of
rerun.
+ // It can cause runtime error when matching in/out user/item blocks.
+ if (ratings.outputDeterministicLevel == DeterministicLevel.INDETERMINATE) {
+ throw new IllegalArgumentException("The output of rating RDD can not be
indeterminate. " +
+ "If your training data has indeterminate RDD computations, like
`randomSplit` or `sample`" +
+ ", please checkpoint the training data before running ALS.")
Review comment:
Yes, I meant that throwing an exception is the thing that would definitely
cause failures, when it currently happens to work fine in practice in almost
all cases.
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