Details
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Task
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Status: Open
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Minor
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Resolution: Unresolved
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3.0.2
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None
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None
Description
As described in SPARK-32702 MiMa plugin upgrade caused the detection of new false positives for binary incompatibilities (master against 3.0.0 version).
During upgrade process these false positives were added as exclusions, however they need to be checked if compatibility issues are present or not.
// mima plugin update caused new incompatibilities to be detected // core module ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.shuffle.sort.io.LocalDiskShuffleMapOutputWriter.commitAllPartitions"), ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.shuffle.api.ShuffleMapOutputWriter.commitAllPartitions"), ProblemFilters.exclude[ReversedMissingMethodProblem]("org.apache.spark.shuffle.api.ShuffleMapOutputWriter.commitAllPartitions"), // mllib module ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.totalIterations"), ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.classification.LogisticRegressionTrainingSummary.$init$"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.labels"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.truePositiveRateByLabel"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.falsePositiveRateByLabel"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.precisionByLabel"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.recallByLabel"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.fMeasureByLabel"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.fMeasureByLabel"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.accuracy"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedTruePositiveRate"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedFalsePositiveRate"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedRecall"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedPrecision"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedFMeasure"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.LogisticRegressionSummary.weightedFMeasure"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.roc"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.areaUnderROC"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.pr"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.fMeasureByThreshold"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.precisionByThreshold"), ProblemFilters.exclude[NewMixinForwarderProblem]("org.apache.spark.ml.classification.BinaryLogisticRegressionSummary.recallByThreshold"), ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.classification.FMClassifier.trainImpl"), ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.ml.regression.FMRegressor.trainImpl"),