[SPARK-10566] [Core] SnappyCompressionCodec init exception handling masks important error information

[dimfeld]

When throwing an IllegalArgumentException in SnappyCompressionCodec.init, chain the existing exception. This allows potentially important debugging info to be passed to the user.

Manual testing shows the exception chained properly, and the test suite still looks fine as well.

This contribution is my original work and I license the work to the project under the project's open source license.

[srowen]

Looks fine to me. I can see why just not catching the Error and letting it fly works as well. For now, fine to just preserve the same behavior but chain the cause.

[SparkQA]

Test build #1744 has finished for PR 8725 at commit 16bf56d.

• This patch passes all tests.
• This patch merges cleanly.
• This patch adds the following public classes (experimental):
  • class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
  • class MultilayerPerceptronClassificationModel(JavaModel):
  • class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol):
  • class MinMaxScalerModel(JavaModel):
  • ("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
  • class HasElasticNetParam(Params):
  • class HasFitIntercept(Params):
  • class HasStandardization(Params):
  • class HasThresholds(Params):
  • thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")
  • self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")