zhengruifeng edited a comment 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.***
---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org