zhengruifeng commented on issue #25178: [SPARK-28421][ML] SparseVector.apply performance optimization URL: https://github.com/apache/spark/pull/25178#issuecomment-514041986 The expected cost without range check is `E(cost(apply2)) = log(NNZ)`; while the one with range check is `E(cost(apply3)) = 2 + P(key in range)*log(NNZ)`; The diff is `E(cost(apply3) - cost(apply2)) = 2 - P(key out of range) * log(NNZ)`, so the optimization is high related to the key distribution and the `NNZ`. The above suite suppose the input key is from an uniform distribution. And show that, if the `NNZ` is small, range check will cost extra 10% cost; otherwise, the range check will save about 50% cost.
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