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Posted to issues@spark.apache.org by "Denis Pyshev (Jira)" <ji...@apache.org> on 2020/09/27 23:03:00 UTC
[jira] [Created] (SPARK-33009) Verify list of binary
incompatibilities after MiMa upgrade
Denis Pyshev created SPARK-33009:
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Summary: Verify list of binary incompatibilities after MiMa upgrade
Key: SPARK-33009
URL: https://issues.apache.org/jira/browse/SPARK-33009
Project: Spark
Issue Type: Task
Components: Build
Affects Versions: 3.0.2
Reporter: Denis Pyshev
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.
{noformat}
// 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"),
{noformat}
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