From 5003736ad60c3231bb18264c9561646c08379170 Mon Sep 17 00:00:00 2001
From: Lu WANG <lu.wang@databricks.com>
Date: Mon, 16 Apr 2018 11:27:30 -0500
Subject: [PATCH 01/14] [SPARK-9312][ML] Add RawPrediction, numClasses, and
 numFeatures for OneVsRestModel

add RawPrediction as output column
add numClasses and numFeatures to OneVsRestModel

## What changes were proposed in this pull request?

- Add two val numClasses and numFeatures in OneVsRestModel so that we can inherit from Classifier in the future

- Add rawPrediction output column in transform, the prediction label in calculated by the rawPrediciton like raw2prediction

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: Lu WANG <lu.wang@databricks.com>

Closes #21044 from ludatabricks/SPARK-9312.
---
 .../spark/ml/classification/OneVsRest.scala   | 56 +++++++++++++++----
 .../ml/classification/OneVsRestSuite.scala    |  7 ++-
 2 files changed, 51 insertions(+), 12 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index f04fde2cbbca1..5348d882cfd67 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -32,7 +32,7 @@ import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Since
 import org.apache.spark.ml._
 import org.apache.spark.ml.attribute._
-import org.apache.spark.ml.linalg.Vector
+import org.apache.spark.ml.linalg.{Vector, Vectors}
 import org.apache.spark.ml.param.{Param, ParamMap, ParamPair, Params}
 import org.apache.spark.ml.param.shared.{HasParallelism, HasWeightCol}
 import org.apache.spark.ml.util._
@@ -55,7 +55,7 @@ private[ml] trait ClassifierTypeTrait {
 /**
  * Params for [[OneVsRest]].
  */
-private[ml] trait OneVsRestParams extends PredictorParams
+private[ml] trait OneVsRestParams extends ClassifierParams
   with ClassifierTypeTrait with HasWeightCol {
 
   /**
@@ -138,6 +138,14 @@ final class OneVsRestModel private[ml] (
     @Since("1.4.0") val models: Array[_ <: ClassificationModel[_, _]])
   extends Model[OneVsRestModel] with OneVsRestParams with MLWritable {
 
+  require(models.nonEmpty, "OneVsRestModel requires at least one model for one class")
+
+  @Since("2.4.0")
+  val numClasses: Int = models.length
+
+  @Since("2.4.0")
+  val numFeatures: Int = models.head.numFeatures
+
   /** @group setParam */
   @Since("2.1.0")
   def setFeaturesCol(value: String): this.type = set(featuresCol, value)
@@ -146,6 +154,10 @@ final class OneVsRestModel private[ml] (
   @Since("2.1.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
+  /** @group setParam */
+  @Since("2.4.0")
+  def setRawPredictionCol(value: String): this.type = set(rawPredictionCol, value)
+
   @Since("1.4.0")
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema, fitting = false, getClassifier.featuresDataType)
@@ -181,6 +193,7 @@ final class OneVsRestModel private[ml] (
         val updateUDF = udf { (predictions: Map[Int, Double], prediction: Vector) =>
           predictions + ((index, prediction(1)))
         }
+
         model.setFeaturesCol($(featuresCol))
         val transformedDataset = model.transform(df).select(columns: _*)
         val updatedDataset = transformedDataset
@@ -195,15 +208,34 @@ final class OneVsRestModel private[ml] (
       newDataset.unpersist()
     }
 
-    // output the index of the classifier with highest confidence as prediction
-    val labelUDF = udf { (predictions: Map[Int, Double]) =>
-      predictions.maxBy(_._2)._1.toDouble
-    }
+    if (getRawPredictionCol != "") {
+      val numClass = models.length
 
-    // output label and label metadata as prediction
-    aggregatedDataset
-      .withColumn($(predictionCol), labelUDF(col(accColName)), labelMetadata)
-      .drop(accColName)
+      // output the RawPrediction as vector
+      val rawPredictionUDF = udf { (predictions: Map[Int, Double]) =>
+        val predArray = Array.fill[Double](numClass)(0.0)
+        predictions.foreach { case (idx, value) => predArray(idx) = value }
+        Vectors.dense(predArray)
+      }
+
+      // output the index of the classifier with highest confidence as prediction
+      val labelUDF = udf { (rawPredictions: Vector) => rawPredictions.argmax.toDouble }
+
+      // output confidence as raw prediction, label and label metadata as prediction
+      aggregatedDataset
+        .withColumn(getRawPredictionCol, rawPredictionUDF(col(accColName)))
+        .withColumn(getPredictionCol, labelUDF(col(getRawPredictionCol)), labelMetadata)
+        .drop(accColName)
+    } else {
+      // output the index of the classifier with highest confidence as prediction
+      val labelUDF = udf { (predictions: Map[Int, Double]) =>
+        predictions.maxBy(_._2)._1.toDouble
+      }
+      // output label and label metadata as prediction
+      aggregatedDataset
+        .withColumn(getPredictionCol, labelUDF(col(accColName)), labelMetadata)
+        .drop(accColName)
+    }
   }
 
   @Since("1.4.1")
@@ -297,6 +329,10 @@ final class OneVsRest @Since("1.4.0") (
   @Since("1.5.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
+  /** @group setParam */
+  @Since("2.4.0")
+  def setRawPredictionCol(value: String): this.type = set(rawPredictionCol, value)
+
   /**
    * The implementation of parallel one vs. rest runs the classification for
    * each class in a separate threads.
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index 11e88367108b4..2c3417c7e4028 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -72,11 +72,12 @@ class OneVsRestSuite extends MLTest with DefaultReadWriteTest {
       .setClassifier(new LogisticRegression)
     assert(ova.getLabelCol === "label")
     assert(ova.getPredictionCol === "prediction")
+    assert(ova.getRawPredictionCol === "rawPrediction")
     val ovaModel = ova.fit(dataset)
 
     MLTestingUtils.checkCopyAndUids(ova, ovaModel)
 
-    assert(ovaModel.models.length === numClasses)
+    assert(ovaModel.numClasses === numClasses)
     val transformedDataset = ovaModel.transform(dataset)
 
     // check for label metadata in prediction col
@@ -179,6 +180,7 @@ class OneVsRestSuite extends MLTest with DefaultReadWriteTest {
     val dataset2 = dataset.select(col("label").as("y"), col("features").as("fea"))
     ovaModel.setFeaturesCol("fea")
     ovaModel.setPredictionCol("pred")
+    ovaModel.setRawPredictionCol("")
     val transformedDataset = ovaModel.transform(dataset2)
     val outputFields = transformedDataset.schema.fieldNames.toSet
     assert(outputFields === Set("y", "fea", "pred"))
@@ -190,7 +192,8 @@ class OneVsRestSuite extends MLTest with DefaultReadWriteTest {
     val ovr = new OneVsRest()
       .setClassifier(logReg)
     val output = ovr.fit(dataset).transform(dataset)
-    assert(output.schema.fieldNames.toSet === Set("label", "features", "prediction"))
+    assert(output.schema.fieldNames.toSet
+      === Set("label", "features", "prediction", "rawPrediction"))
   }
 
   test("SPARK-21306: OneVsRest should support setWeightCol") {

From 04614820e103feeae91299dc90dba1dd628fd485 Mon Sep 17 00:00:00 2001
From: WeichenXu <weichen.xu@databricks.com>
Date: Mon, 16 Apr 2018 11:31:24 -0500
Subject: [PATCH 02/14] [SPARK-21088][ML] CrossValidator, TrainValidationSplit
 support collect all models when fitting: Python API

## What changes were proposed in this pull request?

Add python API for collecting sub-models during CrossValidator/TrainValidationSplit fitting.

## How was this patch tested?

UT added.

Author: WeichenXu <weichen.xu@databricks.com>

Closes #19627 from WeichenXu123/expose-model-list-py.
---
 .../spark/ml/tuning/CrossValidator.scala      |  11 ++
 .../ml/tuning/TrainValidationSplit.scala      |  11 ++
 .../ml/param/_shared_params_code_gen.py       |   5 +
 python/pyspark/ml/param/shared.py             |  24 ++++
 python/pyspark/ml/tests.py                    |  78 +++++++++++++
 python/pyspark/ml/tuning.py                   | 107 +++++++++++++-----
 python/pyspark/ml/util.py                     |   4 +
 7 files changed, 211 insertions(+), 29 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index a0b507d2e718c..c2826dcc08634 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -270,6 +270,17 @@ class CrossValidatorModel private[ml] (
     this
   }
 
+  // A Python-friendly auxiliary method
+  private[tuning] def setSubModels(subModels: JList[JList[Model[_]]])
+    : CrossValidatorModel = {
+    _subModels = if (subModels != null) {
+      Some(subModels.asScala.toArray.map(_.asScala.toArray))
+    } else {
+      None
+    }
+    this
+  }
+
   /**
    * @return submodels represented in two dimension array. The index of outer array is the
    *         fold index, and the index of inner array corresponds to the ordering of
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
index 88ff0dfd75e96..8d1b9a8ddab59 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
@@ -262,6 +262,17 @@ class TrainValidationSplitModel private[ml] (
     this
   }
 
+  // A Python-friendly auxiliary method
+  private[tuning] def setSubModels(subModels: JList[Model[_]])
+    : TrainValidationSplitModel = {
+    _subModels = if (subModels != null) {
+      Some(subModels.asScala.toArray)
+    } else {
+      None
+    }
+    this
+  }
+
   /**
    * @return submodels represented in array. The index of array corresponds to the ordering of
    *         estimatorParamMaps
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index db951d81de1e7..6e9e0a34cdfde 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -157,6 +157,11 @@ def get$Name(self):
          "TypeConverters.toInt"),
         ("parallelism", "the number of threads to use when running parallel algorithms (>= 1).",
          "1", "TypeConverters.toInt"),
+        ("collectSubModels", "Param for whether to collect a list of sub-models trained during " +
+         "tuning. If set to false, then only the single best sub-model will be available after " +
+         "fitting. If set to true, then all sub-models will be available. Warning: For large " +
+         "models, collecting all sub-models can cause OOMs on the Spark driver.",
+         "False", "TypeConverters.toBoolean"),
         ("loss", "the loss function to be optimized.", None, "TypeConverters.toString")]
 
     code = []
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 474c38764e5a1..08408ee8fbfcc 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -655,6 +655,30 @@ def getParallelism(self):
         return self.getOrDefault(self.parallelism)
 
 
+class HasCollectSubModels(Params):
+    """
+    Mixin for param collectSubModels: Param for whether to collect a list of sub-models trained during tuning. If set to false, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver.
+    """
+
+    collectSubModels = Param(Params._dummy(), "collectSubModels", "Param for whether to collect a list of sub-models trained during tuning. If set to false, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver.", typeConverter=TypeConverters.toBoolean)
+
+    def __init__(self):
+        super(HasCollectSubModels, self).__init__()
+        self._setDefault(collectSubModels=False)
+
+    def setCollectSubModels(self, value):
+        """
+        Sets the value of :py:attr:`collectSubModels`.
+        """
+        return self._set(collectSubModels=value)
+
+    def getCollectSubModels(self):
+        """
+        Gets the value of collectSubModels or its default value.
+        """
+        return self.getOrDefault(self.collectSubModels)
+
+
 class HasLoss(Params):
     """
     Mixin for param loss: the loss function to be optimized.
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 4ce54547eab09..2ec0be60e9fa9 100755
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -1018,6 +1018,50 @@ def test_parallel_evaluation(self):
         cvParallelModel = cv.fit(dataset)
         self.assertEqual(cvSerialModel.avgMetrics, cvParallelModel.avgMetrics)
 
+    def test_expose_sub_models(self):
+        temp_path = tempfile.mkdtemp()
+        dataset = self.spark.createDataFrame(
+            [(Vectors.dense([0.0]), 0.0),
+             (Vectors.dense([0.4]), 1.0),
+             (Vectors.dense([0.5]), 0.0),
+             (Vectors.dense([0.6]), 1.0),
+             (Vectors.dense([1.0]), 1.0)] * 10,
+            ["features", "label"])
+
+        lr = LogisticRegression()
+        grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
+        evaluator = BinaryClassificationEvaluator()
+
+        numFolds = 3
+        cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator,
+                            numFolds=numFolds, collectSubModels=True)
+
+        def checkSubModels(subModels):
+            self.assertEqual(len(subModels), numFolds)
+            for i in range(numFolds):
+                self.assertEqual(len(subModels[i]), len(grid))
+
+        cvModel = cv.fit(dataset)
+        checkSubModels(cvModel.subModels)
+
+        # Test the default value for option "persistSubModel" to be "true"
+        testSubPath = temp_path + "/testCrossValidatorSubModels"
+        savingPathWithSubModels = testSubPath + "cvModel3"
+        cvModel.save(savingPathWithSubModels)
+        cvModel3 = CrossValidatorModel.load(savingPathWithSubModels)
+        checkSubModels(cvModel3.subModels)
+        cvModel4 = cvModel3.copy()
+        checkSubModels(cvModel4.subModels)
+
+        savingPathWithoutSubModels = testSubPath + "cvModel2"
+        cvModel.write().option("persistSubModels", "false").save(savingPathWithoutSubModels)
+        cvModel2 = CrossValidatorModel.load(savingPathWithoutSubModels)
+        self.assertEqual(cvModel2.subModels, None)
+
+        for i in range(numFolds):
+            for j in range(len(grid)):
+                self.assertEqual(cvModel.subModels[i][j].uid, cvModel3.subModels[i][j].uid)
+
     def test_save_load_nested_estimator(self):
         temp_path = tempfile.mkdtemp()
         dataset = self.spark.createDataFrame(
@@ -1186,6 +1230,40 @@ def test_parallel_evaluation(self):
         tvsParallelModel = tvs.fit(dataset)
         self.assertEqual(tvsSerialModel.validationMetrics, tvsParallelModel.validationMetrics)
 
+    def test_expose_sub_models(self):
+        temp_path = tempfile.mkdtemp()
+        dataset = self.spark.createDataFrame(
+            [(Vectors.dense([0.0]), 0.0),
+             (Vectors.dense([0.4]), 1.0),
+             (Vectors.dense([0.5]), 0.0),
+             (Vectors.dense([0.6]), 1.0),
+             (Vectors.dense([1.0]), 1.0)] * 10,
+            ["features", "label"])
+        lr = LogisticRegression()
+        grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
+        evaluator = BinaryClassificationEvaluator()
+        tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator,
+                                   collectSubModels=True)
+        tvsModel = tvs.fit(dataset)
+        self.assertEqual(len(tvsModel.subModels), len(grid))
+
+        # Test the default value for option "persistSubModel" to be "true"
+        testSubPath = temp_path + "/testTrainValidationSplitSubModels"
+        savingPathWithSubModels = testSubPath + "cvModel3"
+        tvsModel.save(savingPathWithSubModels)
+        tvsModel3 = TrainValidationSplitModel.load(savingPathWithSubModels)
+        self.assertEqual(len(tvsModel3.subModels), len(grid))
+        tvsModel4 = tvsModel3.copy()
+        self.assertEqual(len(tvsModel4.subModels), len(grid))
+
+        savingPathWithoutSubModels = testSubPath + "cvModel2"
+        tvsModel.write().option("persistSubModels", "false").save(savingPathWithoutSubModels)
+        tvsModel2 = TrainValidationSplitModel.load(savingPathWithoutSubModels)
+        self.assertEqual(tvsModel2.subModels, None)
+
+        for i in range(len(grid)):
+            self.assertEqual(tvsModel.subModels[i].uid, tvsModel3.subModels[i].uid)
+
     def test_save_load_nested_estimator(self):
         # This tests saving and loading the trained model only.
         # Save/load for TrainValidationSplit will be added later: SPARK-13786
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 545e24ca05aa5..0c8029f293cfe 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -24,7 +24,7 @@
 from pyspark.ml import Estimator, Model
 from pyspark.ml.common import _py2java
 from pyspark.ml.param import Params, Param, TypeConverters
-from pyspark.ml.param.shared import HasParallelism, HasSeed
+from pyspark.ml.param.shared import HasCollectSubModels, HasParallelism, HasSeed
 from pyspark.ml.util import *
 from pyspark.ml.wrapper import JavaParams
 from pyspark.sql.functions import rand
@@ -33,7 +33,7 @@
            'TrainValidationSplitModel']
 
 
-def _parallelFitTasks(est, train, eva, validation, epm):
+def _parallelFitTasks(est, train, eva, validation, epm, collectSubModel):
     """
     Creates a list of callables which can be called from different threads to fit and evaluate
     an estimator in parallel. Each callable returns an `(index, metric)` pair.
@@ -43,14 +43,15 @@ def _parallelFitTasks(est, train, eva, validation, epm):
     :param eva: Evaluator, used to compute `metric`
     :param validation: DataFrame, validation data set, used for evaluation.
     :param epm: Sequence of ParamMap, params maps to be used during fitting & evaluation.
-    :return: (int, float), an index into `epm` and the associated metric value.
+    :param collectSubModel: Whether to collect sub model.
+    :return: (int, float, subModel), an index into `epm` and the associated metric value.
     """
     modelIter = est.fitMultiple(train, epm)
 
     def singleTask():
         index, model = next(modelIter)
         metric = eva.evaluate(model.transform(validation, epm[index]))
-        return index, metric
+        return index, metric, model if collectSubModel else None
 
     return [singleTask] * len(epm)
 
@@ -194,7 +195,8 @@ def _to_java_impl(self):
         return java_estimator, java_epms, java_evaluator
 
 
-class CrossValidator(Estimator, ValidatorParams, HasParallelism, MLReadable, MLWritable):
+class CrossValidator(Estimator, ValidatorParams, HasParallelism, HasCollectSubModels,
+                     MLReadable, MLWritable):
     """
 
     K-fold cross validation performs model selection by splitting the dataset into a set of
@@ -233,10 +235,10 @@ class CrossValidator(Estimator, ValidatorParams, HasParallelism, MLReadable, MLW
 
     @keyword_only
     def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
-                 seed=None, parallelism=1):
+                 seed=None, parallelism=1, collectSubModels=False):
         """
         __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
-                 seed=None, parallelism=1)
+                 seed=None, parallelism=1, collectSubModels=False)
         """
         super(CrossValidator, self).__init__()
         self._setDefault(numFolds=3, parallelism=1)
@@ -246,10 +248,10 @@ def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numF
     @keyword_only
     @since("1.4.0")
     def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
-                  seed=None, parallelism=1):
+                  seed=None, parallelism=1, collectSubModels=False):
         """
         setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
-                  seed=None, parallelism=1):
+                  seed=None, parallelism=1, collectSubModels=False):
         Sets params for cross validator.
         """
         kwargs = self._input_kwargs
@@ -282,6 +284,10 @@ def _fit(self, dataset):
         metrics = [0.0] * numModels
 
         pool = ThreadPool(processes=min(self.getParallelism(), numModels))
+        subModels = None
+        collectSubModelsParam = self.getCollectSubModels()
+        if collectSubModelsParam:
+            subModels = [[None for j in range(numModels)] for i in range(nFolds)]
 
         for i in range(nFolds):
             validateLB = i * h
@@ -290,9 +296,12 @@ def _fit(self, dataset):
             validation = df.filter(condition).cache()
             train = df.filter(~condition).cache()
 
-            tasks = _parallelFitTasks(est, train, eva, validation, epm)
-            for j, metric in pool.imap_unordered(lambda f: f(), tasks):
+            tasks = _parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam)
+            for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks):
                 metrics[j] += (metric / nFolds)
+                if collectSubModelsParam:
+                    subModels[i][j] = subModel
+
             validation.unpersist()
             train.unpersist()
 
@@ -301,7 +310,7 @@ def _fit(self, dataset):
         else:
             bestIndex = np.argmin(metrics)
         bestModel = est.fit(dataset, epm[bestIndex])
-        return self._copyValues(CrossValidatorModel(bestModel, metrics))
+        return self._copyValues(CrossValidatorModel(bestModel, metrics, subModels))
 
     @since("1.4.0")
     def copy(self, extra=None):
@@ -345,9 +354,11 @@ def _from_java(cls, java_stage):
         numFolds = java_stage.getNumFolds()
         seed = java_stage.getSeed()
         parallelism = java_stage.getParallelism()
+        collectSubModels = java_stage.getCollectSubModels()
         # Create a new instance of this stage.
         py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
-                       numFolds=numFolds, seed=seed, parallelism=parallelism)
+                       numFolds=numFolds, seed=seed, parallelism=parallelism,
+                       collectSubModels=collectSubModels)
         py_stage._resetUid(java_stage.uid())
         return py_stage
 
@@ -367,6 +378,7 @@ def _to_java(self):
         _java_obj.setSeed(self.getSeed())
         _java_obj.setNumFolds(self.getNumFolds())
         _java_obj.setParallelism(self.getParallelism())
+        _java_obj.setCollectSubModels(self.getCollectSubModels())
 
         return _java_obj
 
@@ -381,13 +393,15 @@ class CrossValidatorModel(Model, ValidatorParams, MLReadable, MLWritable):
     .. versionadded:: 1.4.0
     """
 
-    def __init__(self, bestModel, avgMetrics=[]):
+    def __init__(self, bestModel, avgMetrics=[], subModels=None):
         super(CrossValidatorModel, self).__init__()
         #: best model from cross validation
         self.bestModel = bestModel
         #: Average cross-validation metrics for each paramMap in
         #: CrossValidator.estimatorParamMaps, in the corresponding order.
         self.avgMetrics = avgMetrics
+        #: sub model list from cross validation
+        self.subModels = subModels
 
     def _transform(self, dataset):
         return self.bestModel.transform(dataset)
@@ -399,6 +413,7 @@ def copy(self, extra=None):
         and some extra params. This copies the underlying bestModel,
         creates a deep copy of the embedded paramMap, and
         copies the embedded and extra parameters over.
+        It does not copy the extra Params into the subModels.
 
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
@@ -407,7 +422,8 @@ def copy(self, extra=None):
             extra = dict()
         bestModel = self.bestModel.copy(extra)
         avgMetrics = self.avgMetrics
-        return CrossValidatorModel(bestModel, avgMetrics)
+        subModels = self.subModels
+        return CrossValidatorModel(bestModel, avgMetrics, subModels)
 
     @since("2.3.0")
     def write(self):
@@ -426,13 +442,17 @@ def _from_java(cls, java_stage):
         Given a Java CrossValidatorModel, create and return a Python wrapper of it.
         Used for ML persistence.
         """
-
         bestModel = JavaParams._from_java(java_stage.bestModel())
         estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)
 
         py_stage = cls(bestModel=bestModel).setEstimator(estimator)
         py_stage = py_stage.setEstimatorParamMaps(epms).setEvaluator(evaluator)
 
+        if java_stage.hasSubModels():
+            py_stage.subModels = [[JavaParams._from_java(sub_model)
+                                   for sub_model in fold_sub_models]
+                                  for fold_sub_models in java_stage.subModels()]
+
         py_stage._resetUid(java_stage.uid())
         return py_stage
 
@@ -454,10 +474,16 @@ def _to_java(self):
         _java_obj.set("evaluator", evaluator)
         _java_obj.set("estimator", estimator)
         _java_obj.set("estimatorParamMaps", epms)
+
+        if self.subModels is not None:
+            java_sub_models = [[sub_model._to_java() for sub_model in fold_sub_models]
+                               for fold_sub_models in self.subModels]
+            _java_obj.setSubModels(java_sub_models)
         return _java_obj
 
 
-class TrainValidationSplit(Estimator, ValidatorParams, HasParallelism, MLReadable, MLWritable):
+class TrainValidationSplit(Estimator, ValidatorParams, HasParallelism, HasCollectSubModels,
+                           MLReadable, MLWritable):
     """
     .. note:: Experimental
 
@@ -492,10 +518,10 @@ class TrainValidationSplit(Estimator, ValidatorParams, HasParallelism, MLReadabl
 
     @keyword_only
     def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
-                 parallelism=1, seed=None):
+                 parallelism=1, collectSubModels=False, seed=None):
         """
         __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
-                 parallelism=1, seed=None)
+                 parallelism=1, collectSubModels=False, seed=None)
         """
         super(TrainValidationSplit, self).__init__()
         self._setDefault(trainRatio=0.75, parallelism=1)
@@ -505,10 +531,10 @@ def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, trai
     @since("2.0.0")
     @keyword_only
     def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,
-                  parallelism=1, seed=None):
+                  parallelism=1, collectSubModels=False, seed=None):
         """
         setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, trainRatio=0.75,\
-                  parallelism=1, seed=None):
+                  parallelism=1, collectSubModels=False, seed=None):
         Sets params for the train validation split.
         """
         kwargs = self._input_kwargs
@@ -541,11 +567,19 @@ def _fit(self, dataset):
         validation = df.filter(condition).cache()
         train = df.filter(~condition).cache()
 
-        tasks = _parallelFitTasks(est, train, eva, validation, epm)
+        subModels = None
+        collectSubModelsParam = self.getCollectSubModels()
+        if collectSubModelsParam:
+            subModels = [None for i in range(numModels)]
+
+        tasks = _parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam)
         pool = ThreadPool(processes=min(self.getParallelism(), numModels))
         metrics = [None] * numModels
-        for j, metric in pool.imap_unordered(lambda f: f(), tasks):
+        for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks):
             metrics[j] = metric
+            if collectSubModelsParam:
+                subModels[j] = subModel
+
         train.unpersist()
         validation.unpersist()
 
@@ -554,7 +588,7 @@ def _fit(self, dataset):
         else:
             bestIndex = np.argmin(metrics)
         bestModel = est.fit(dataset, epm[bestIndex])
-        return self._copyValues(TrainValidationSplitModel(bestModel, metrics))
+        return self._copyValues(TrainValidationSplitModel(bestModel, metrics, subModels))
 
     @since("2.0.0")
     def copy(self, extra=None):
@@ -598,9 +632,11 @@ def _from_java(cls, java_stage):
         trainRatio = java_stage.getTrainRatio()
         seed = java_stage.getSeed()
         parallelism = java_stage.getParallelism()
+        collectSubModels = java_stage.getCollectSubModels()
         # Create a new instance of this stage.
         py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
-                       trainRatio=trainRatio, seed=seed, parallelism=parallelism)
+                       trainRatio=trainRatio, seed=seed, parallelism=parallelism,
+                       collectSubModels=collectSubModels)
         py_stage._resetUid(java_stage.uid())
         return py_stage
 
@@ -620,7 +656,7 @@ def _to_java(self):
         _java_obj.setTrainRatio(self.getTrainRatio())
         _java_obj.setSeed(self.getSeed())
         _java_obj.setParallelism(self.getParallelism())
-
+        _java_obj.setCollectSubModels(self.getCollectSubModels())
         return _java_obj
 
 
@@ -633,12 +669,14 @@ class TrainValidationSplitModel(Model, ValidatorParams, MLReadable, MLWritable):
     .. versionadded:: 2.0.0
     """
 
-    def __init__(self, bestModel, validationMetrics=[]):
+    def __init__(self, bestModel, validationMetrics=[], subModels=None):
         super(TrainValidationSplitModel, self).__init__()
-        #: best model from cross validation
+        #: best model from train validation split
         self.bestModel = bestModel
         #: evaluated validation metrics
         self.validationMetrics = validationMetrics
+        #: sub models from train validation split
+        self.subModels = subModels
 
     def _transform(self, dataset):
         return self.bestModel.transform(dataset)
@@ -651,6 +689,7 @@ def copy(self, extra=None):
         creates a deep copy of the embedded paramMap, and
         copies the embedded and extra parameters over.
         And, this creates a shallow copy of the validationMetrics.
+        It does not copy the extra Params into the subModels.
 
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
@@ -659,7 +698,8 @@ def copy(self, extra=None):
             extra = dict()
         bestModel = self.bestModel.copy(extra)
         validationMetrics = list(self.validationMetrics)
-        return TrainValidationSplitModel(bestModel, validationMetrics)
+        subModels = self.subModels
+        return TrainValidationSplitModel(bestModel, validationMetrics, subModels)
 
     @since("2.3.0")
     def write(self):
@@ -687,6 +727,10 @@ def _from_java(cls, java_stage):
         py_stage = cls(bestModel=bestModel).setEstimator(estimator)
         py_stage = py_stage.setEstimatorParamMaps(epms).setEvaluator(evaluator)
 
+        if java_stage.hasSubModels():
+            py_stage.subModels = [JavaParams._from_java(sub_model)
+                                  for sub_model in java_stage.subModels()]
+
         py_stage._resetUid(java_stage.uid())
         return py_stage
 
@@ -708,6 +752,11 @@ def _to_java(self):
         _java_obj.set("evaluator", evaluator)
         _java_obj.set("estimator", estimator)
         _java_obj.set("estimatorParamMaps", epms)
+
+        if self.subModels is not None:
+            java_sub_models = [sub_model._to_java() for sub_model in self.subModels]
+            _java_obj.setSubModels(java_sub_models)
+
         return _java_obj
 
 
diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py
index c3c47bd79459a..a486c6a3fdeb5 100644
--- a/python/pyspark/ml/util.py
+++ b/python/pyspark/ml/util.py
@@ -169,6 +169,10 @@ def overwrite(self):
         self._jwrite.overwrite()
         return self
 
+    def option(self, key, value):
+        self._jwrite.option(key, value)
+        return self
+
     def context(self, sqlContext):
         """
         Sets the SQL context to use for saving.

From fd990a908b94d1c90c4ca604604f35a13b453d44 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Mon, 16 Apr 2018 22:45:57 +0200
Subject: [PATCH 03/14] [SPARK-23873][SQL] Use accessors in interpreted
 LambdaVariable

## What changes were proposed in this pull request?

Currently, interpreted execution of `LambdaVariable` just uses `InternalRow.get` to access element. We should use specified accessors if possible.

## How was this patch tested?

Added test.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #20981 from viirya/SPARK-23873.
---
 .../spark/sql/catalyst/InternalRow.scala      | 26 ++++++++++++-
 .../catalyst/expressions/BoundAttribute.scala | 22 ++---------
 .../expressions/objects/objects.scala         |  8 +++-
 .../expressions/ExpressionEvalHelper.scala    |  4 +-
 .../expressions/ObjectExpressionsSuite.scala  | 38 ++++++++++++++++++-
 5 files changed, 75 insertions(+), 23 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 29110640d64f2..274d75e680f03 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.{ArrayData, MapData}
-import org.apache.spark.sql.types.{DataType, Decimal, StructType}
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -119,4 +119,28 @@ object InternalRow {
     case v: MapData => v.copy()
     case _ => value
   }
+
+  /**
+   * Returns an accessor for an `InternalRow` with given data type. The returned accessor
+   * actually takes a `SpecializedGetters` input because it can be generalized to other classes
+   * that implements `SpecializedGetters` (e.g., `ArrayData`) too.
+   */
+  def getAccessor(dataType: DataType): (SpecializedGetters, Int) => Any = dataType match {
+    case BooleanType => (input, ordinal) => input.getBoolean(ordinal)
+    case ByteType => (input, ordinal) => input.getByte(ordinal)
+    case ShortType => (input, ordinal) => input.getShort(ordinal)
+    case IntegerType | DateType => (input, ordinal) => input.getInt(ordinal)
+    case LongType | TimestampType => (input, ordinal) => input.getLong(ordinal)
+    case FloatType => (input, ordinal) => input.getFloat(ordinal)
+    case DoubleType => (input, ordinal) => input.getDouble(ordinal)
+    case StringType => (input, ordinal) => input.getUTF8String(ordinal)
+    case BinaryType => (input, ordinal) => input.getBinary(ordinal)
+    case CalendarIntervalType => (input, ordinal) => input.getInterval(ordinal)
+    case t: DecimalType => (input, ordinal) => input.getDecimal(ordinal, t.precision, t.scale)
+    case t: StructType => (input, ordinal) => input.getStruct(ordinal, t.size)
+    case _: ArrayType => (input, ordinal) => input.getArray(ordinal)
+    case _: MapType => (input, ordinal) => input.getMap(ordinal)
+    case u: UserDefinedType[_] => getAccessor(u.sqlType)
+    case _ => (input, ordinal) => input.get(ordinal, dataType)
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
index 5021a567592e0..4cc84b27d9eb0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala
@@ -33,28 +33,14 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean)
 
   override def toString: String = s"input[$ordinal, ${dataType.simpleString}, $nullable]"
 
+  private val accessor: (InternalRow, Int) => Any = InternalRow.getAccessor(dataType)
+
   // Use special getter for primitive types (for UnsafeRow)
   override def eval(input: InternalRow): Any = {
-    if (input.isNullAt(ordinal)) {
+    if (nullable && input.isNullAt(ordinal)) {
       null
     } else {
-      dataType match {
-        case BooleanType => input.getBoolean(ordinal)
-        case ByteType => input.getByte(ordinal)
-        case ShortType => input.getShort(ordinal)
-        case IntegerType | DateType => input.getInt(ordinal)
-        case LongType | TimestampType => input.getLong(ordinal)
-        case FloatType => input.getFloat(ordinal)
-        case DoubleType => input.getDouble(ordinal)
-        case StringType => input.getUTF8String(ordinal)
-        case BinaryType => input.getBinary(ordinal)
-        case CalendarIntervalType => input.getInterval(ordinal)
-        case t: DecimalType => input.getDecimal(ordinal, t.precision, t.scale)
-        case t: StructType => input.getStruct(ordinal, t.size)
-        case _: ArrayType => input.getArray(ordinal)
-        case _: MapType => input.getMap(ordinal)
-        case _ => input.get(ordinal, dataType)
-      }
+      accessor(input, ordinal)
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala
index 50e90ca550807..77802e89e942b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala
@@ -560,11 +560,17 @@ case class LambdaVariable(
     dataType: DataType,
     nullable: Boolean = true) extends LeafExpression with NonSQLExpression {
 
+  private val accessor: (InternalRow, Int) => Any = InternalRow.getAccessor(dataType)
+
   // Interpreted execution of `LambdaVariable` always get the 0-index element from input row.
   override def eval(input: InternalRow): Any = {
     assert(input.numFields == 1,
       "The input row of interpreted LambdaVariable should have only 1 field.")
-    input.get(0, dataType)
+    if (nullable && input.isNullAt(0)) {
+      null
+    } else {
+      accessor(input, 0)
+    }
   }
 
   override def genCode(ctx: CodegenContext): ExprCode = {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index a5ecd1b68fac4..b4bf6d7107d7e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -70,7 +70,9 @@ trait ExpressionEvalHelper extends GeneratorDrivenPropertyChecks {
    * Check the equality between result of expression and expected value, it will handle
    * Array[Byte], Spread[Double], MapData and Row.
    */
-  protected def checkResult(result: Any, expected: Any, dataType: DataType): Boolean = {
+  protected def checkResult(result: Any, expected: Any, exprDataType: DataType): Boolean = {
+    val dataType = UserDefinedType.sqlType(exprDataType)
+
     (result, expected) match {
       case (result: Array[Byte], expected: Array[Byte]) =>
         java.util.Arrays.equals(result, expected)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ObjectExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ObjectExpressionsSuite.scala
index b1bc67dfac1b5..b0188b0098def 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ObjectExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ObjectExpressionsSuite.scala
@@ -21,13 +21,14 @@ import java.sql.{Date, Timestamp}
 
 import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
+import scala.util.Random
 
 import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.serializer.{JavaSerializer, KryoSerializer}
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.{RandomDataGenerator, Row}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.ResolveTimeZone
-import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.sql.catalyst.encoders.{ExamplePointUDT, ExpressionEncoder, RowEncoder}
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
 import org.apache.spark.sql.catalyst.expressions.objects._
 import org.apache.spark.sql.catalyst.util._
@@ -381,6 +382,39 @@ class ObjectExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(decodeUsingSerializer, null, InternalRow.fromSeq(Seq(null)))
     }
   }
+
+  test("LambdaVariable should support interpreted execution") {
+    def genSchema(dt: DataType): Seq[StructType] = {
+      Seq(StructType(StructField("col_1", dt, nullable = false) :: Nil),
+        StructType(StructField("col_1", dt, nullable = true) :: Nil))
+    }
+
+    val elementTypes = Seq(BooleanType, ByteType, ShortType, IntegerType, LongType, FloatType,
+      DoubleType, DecimalType.USER_DEFAULT, StringType, BinaryType, DateType, TimestampType,
+      CalendarIntervalType, new ExamplePointUDT())
+    val arrayTypes = elementTypes.flatMap { elementType =>
+      Seq(ArrayType(elementType, containsNull = false), ArrayType(elementType, containsNull = true))
+    }
+    val mapTypes = elementTypes.flatMap { elementType =>
+      Seq(MapType(elementType, elementType, false), MapType(elementType, elementType, true))
+    }
+    val structTypes = elementTypes.flatMap { elementType =>
+      Seq(StructType(StructField("col1", elementType, false) :: Nil),
+        StructType(StructField("col1", elementType, true) :: Nil))
+    }
+
+    val testTypes = elementTypes ++ arrayTypes ++ mapTypes ++ structTypes
+    val random = new Random(100)
+    testTypes.foreach { dt =>
+      genSchema(dt).map { schema =>
+        val row = RandomDataGenerator.randomRow(random, schema)
+        val rowConverter = RowEncoder(schema)
+        val internalRow = rowConverter.toRow(row)
+        val lambda = LambdaVariable("dummy", "dummuIsNull", schema(0).dataType, schema(0).nullable)
+        checkEvaluationWithoutCodegen(lambda, internalRow.get(0, schema(0).dataType), internalRow)
+      }
+    }
+  }
 }
 
 class TestBean extends Serializable {

From 14844a62c025e7299029d7452b8c4003bc221ac8 Mon Sep 17 00:00:00 2001
From: Marco Gaido <marcogaido91@gmail.com>
Date: Tue, 17 Apr 2018 17:55:35 +0900
Subject: [PATCH 04/14] [SPARK-23918][SQL] Add array_min function

## What changes were proposed in this pull request?

The PR adds the SQL function `array_min`. It takes an array as argument and returns the minimum value in it.

## How was this patch tested?

added UTs

Author: Marco Gaido <marcogaido91@gmail.com>

Closes #21025 from mgaido91/SPARK-23918.
---
 python/pyspark/sql/functions.py               | 17 ++++-
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../sql/catalyst/expressions/arithmetic.scala |  6 +-
 .../expressions/codegen/CodeGenerator.scala   | 17 +++++
 .../expressions/collectionOperations.scala    | 64 +++++++++++++++++++
 .../CollectionExpressionsSuite.scala          | 10 +++
 .../org/apache/spark/sql/functions.scala      |  8 +++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 14 ++++
 8 files changed, 131 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index f3492ae42639c..6ca22b610843d 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -2080,6 +2080,21 @@ def size(col):
     return Column(sc._jvm.functions.size(_to_java_column(col)))
 
 
+@since(2.4)
+def array_min(col):
+    """
+    Collection function: returns the minimum value of the array.
+
+    :param col: name of column or expression
+
+    >>> df = spark.createDataFrame([([2, 1, 3],), ([None, 10, -1],)], ['data'])
+    >>> df.select(array_min(df.data).alias('min')).collect()
+    [Row(min=1), Row(min=-1)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.array_min(_to_java_column(col)))
+
+
 @since(2.4)
 def array_max(col):
     """
@@ -2108,7 +2123,7 @@ def sort_array(col, asc=True):
     [Row(r=[1, 2, 3]), Row(r=[1]), Row(r=[])]
     >>> df.select(sort_array(df.data, asc=False).alias('r')).collect()
     [Row(r=[3, 2, 1]), Row(r=[1]), Row(r=[])]
-     """
+    """
     sc = SparkContext._active_spark_context
     return Column(sc._jvm.functions.sort_array(_to_java_column(col), asc))
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 05bfa2dd45340..4dd1ca509bf2c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -409,6 +409,7 @@ object FunctionRegistry {
     expression[MapValues]("map_values"),
     expression[Size]("size"),
     expression[SortArray]("sort_array"),
+    expression[ArrayMin]("array_min"),
     expression[ArrayMax]("array_max"),
     CreateStruct.registryEntry,
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 942dfd4292610..d4e322d23b95b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -595,11 +595,7 @@ case class Least(children: Seq[Expression]) extends Expression {
     val evals = evalChildren.map(eval =>
       s"""
          |${eval.code}
-         |if (!${eval.isNull} && (${ev.isNull} ||
-         |  ${ctx.genGreater(dataType, ev.value, eval.value)})) {
-         |  ${ev.isNull} = false;
-         |  ${ev.value} = ${eval.value};
-         |}
+         |${ctx.reassignIfSmaller(dataType, ev, eval)}
       """.stripMargin
     )
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index c86c5beded9d0..d97611c98ac91 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -699,6 +699,23 @@ class CodegenContext {
     case _ => s"(${genComp(dataType, c1, c2)}) > 0"
   }
 
+  /**
+   * Generates code for updating `partialResult` if `item` is smaller than it.
+   *
+   * @param dataType data type of the expressions
+   * @param partialResult `ExprCode` representing the partial result which has to be updated
+   * @param item `ExprCode` representing the new expression to evaluate for the result
+   */
+  def reassignIfSmaller(dataType: DataType, partialResult: ExprCode, item: ExprCode): String = {
+    s"""
+       |if (!${item.isNull} && (${partialResult.isNull} ||
+       |  ${genGreater(dataType, partialResult.value, item.value)})) {
+       |  ${partialResult.isNull} = false;
+       |  ${partialResult.value} = ${item.value};
+       |}
+      """.stripMargin
+  }
+
   /**
    * Generates code for updating `partialResult` if `item` is greater than it.
    *
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index e2614a179aad8..7c87777eed47a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -288,6 +288,70 @@ case class ArrayContains(left: Expression, right: Expression)
   override def prettyName: String = "array_contains"
 }
 
+/**
+ * Returns the minimum value in the array.
+ */
+@ExpressionDescription(
+  usage = "_FUNC_(array) - Returns the minimum value in the array. NULL elements are skipped.",
+  examples = """
+    Examples:
+      > SELECT _FUNC_(array(1, 20, null, 3));
+       1
+  """, since = "2.4.0")
+case class ArrayMin(child: Expression) extends UnaryExpression with ImplicitCastInputTypes {
+
+  override def nullable: Boolean = true
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(ArrayType)
+
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    val typeCheckResult = super.checkInputDataTypes()
+    if (typeCheckResult.isSuccess) {
+      TypeUtils.checkForOrderingExpr(dataType, s"function $prettyName")
+    } else {
+      typeCheckResult
+    }
+  }
+
+  override protected def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = {
+    val childGen = child.genCode(ctx)
+    val javaType = CodeGenerator.javaType(dataType)
+    val i = ctx.freshName("i")
+    val item = ExprCode("",
+      isNull = JavaCode.isNullExpression(s"${childGen.value}.isNullAt($i)"),
+      value = JavaCode.expression(CodeGenerator.getValue(childGen.value, dataType, i), dataType))
+    ev.copy(code =
+      s"""
+         |${childGen.code}
+         |boolean ${ev.isNull} = true;
+         |$javaType ${ev.value} = ${CodeGenerator.defaultValue(dataType)};
+         |if (!${childGen.isNull}) {
+         |  for (int $i = 0; $i < ${childGen.value}.numElements(); $i ++) {
+         |    ${ctx.reassignIfSmaller(dataType, ev, item)}
+         |  }
+         |}
+      """.stripMargin)
+  }
+
+  override protected def nullSafeEval(input: Any): Any = {
+    var min: Any = null
+    input.asInstanceOf[ArrayData].foreach(dataType, (_, item) =>
+      if (item != null && (min == null || ordering.lt(item, min))) {
+        min = item
+      }
+    )
+    min
+  }
+
+  override def dataType: DataType = child.dataType match {
+    case ArrayType(dt, _) => dt
+    case _ => throw new IllegalStateException(s"$prettyName accepts only arrays.")
+  }
+
+  override def prettyName: String = "array_min"
+}
 
 /**
  * Returns the maximum value in the array.
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala
index a2384019533b7..5a31e3a30edd6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionExpressionsSuite.scala
@@ -106,6 +106,16 @@ class CollectionExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(ArrayContains(a3, Literal.create(null, StringType)), null)
   }
 
+  test("Array Min") {
+    checkEvaluation(ArrayMin(Literal.create(Seq(-11, 10, 2), ArrayType(IntegerType))), -11)
+    checkEvaluation(
+      ArrayMin(Literal.create(Seq[String](null, "abc", ""), ArrayType(StringType))), "")
+    checkEvaluation(ArrayMin(Literal.create(Seq(null), ArrayType(LongType))), null)
+    checkEvaluation(ArrayMin(Literal.create(null, ArrayType(StringType))), null)
+    checkEvaluation(
+      ArrayMin(Literal.create(Seq(1.123, 0.1234, 1.121), ArrayType(DoubleType))), 0.1234)
+  }
+
   test("Array max") {
     checkEvaluation(ArrayMax(Literal.create(Seq(1, 10, 2), ArrayType(IntegerType))), 10)
     checkEvaluation(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index daf407926dca4..642ac056bb809 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -3300,6 +3300,14 @@ object functions {
    */
   def sort_array(e: Column, asc: Boolean): Column = withExpr { SortArray(e.expr, lit(asc).expr) }
 
+  /**
+   * Returns the minimum value in the array.
+   *
+   * @group collection_funcs
+   * @since 2.4.0
+   */
+  def array_min(e: Column): Column = withExpr { ArrayMin(e.expr) }
+
   /**
    * Returns the maximum value in the array.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 5d5d92c84df6d..636e86baedf6f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -413,6 +413,20 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
     )
   }
 
+  test("array_min function") {
+    val df = Seq(
+      Seq[Option[Int]](Some(1), Some(3), Some(2)),
+      Seq.empty[Option[Int]],
+      Seq[Option[Int]](None),
+      Seq[Option[Int]](None, Some(1), Some(-100))
+    ).toDF("a")
+
+    val answer = Seq(Row(1), Row(null), Row(null), Row(-100))
+
+    checkAnswer(df.select(array_min(df("a"))), answer)
+    checkAnswer(df.selectExpr("array_min(a)"), answer)
+  }
+
   test("array_max function") {
     val df = Seq(
       Seq[Option[Int]](Some(1), Some(3), Some(2)),

From 1cc66a072b7fd3bf140fa41596f6b18f8d1bd7b9 Mon Sep 17 00:00:00 2001
From: Jose Torres <torres.joseph.f+github@gmail.com>
Date: Tue, 17 Apr 2018 01:59:38 -0700
Subject: [PATCH 05/14] [SPARK-23687][SS] Add a memory source for continuous
 processing.

## What changes were proposed in this pull request?

Add a memory source for continuous processing.

Note that only one of the ContinuousSuite tests is migrated to minimize the diff here. I'll submit a second PR for SPARK-23688 to change the rest and get rid of waitForRateSourceTriggers.

## How was this patch tested?

unit test

Author: Jose Torres <torres.joseph.f+github@gmail.com>

Closes #20828 from jose-torres/continuousMemory.
---
 .../continuous/ContinuousExecution.scala      |   5 +-
 .../sql/execution/streaming/memory.scala      |  59 +++--
 .../sources/ContinuousMemoryStream.scala      | 211 ++++++++++++++++++
 .../spark/sql/streaming/StreamTest.scala      |   4 +-
 .../continuous/ContinuousSuite.scala          |  31 +--
 5 files changed, 266 insertions(+), 44 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ContinuousMemoryStream.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala
index 1758b3844bd62..951d694355ec5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/continuous/ContinuousExecution.scala
@@ -31,6 +31,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.SQLExecution
 import org.apache.spark.sql.execution.datasources.v2.{StreamingDataSourceV2Relation, WriteToDataSourceV2}
 import org.apache.spark.sql.execution.streaming.{ContinuousExecutionRelation, StreamingRelationV2, _}
+import org.apache.spark.sql.sources.v2
 import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions, StreamWriteSupport}
 import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousReader, PartitionOffset}
 import org.apache.spark.sql.streaming.{OutputMode, ProcessingTime, Trigger}
@@ -317,8 +318,10 @@ class ContinuousExecution(
     synchronized {
       if (queryExecutionThread.isAlive) {
         commitLog.add(epoch)
-        val offset = offsetLog.get(epoch).get.offsets(0).get
+        val offset =
+          continuousSources(0).deserializeOffset(offsetLog.get(epoch).get.offsets(0).get.json)
         committedOffsets ++= Seq(continuousSources(0) -> offset)
+        continuousSources(0).commit(offset.asInstanceOf[v2.reader.streaming.Offset])
       } else {
         return
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala
index 352d4ce9fbcaa..628923d367ce7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/memory.scala
@@ -24,17 +24,19 @@ import javax.annotation.concurrent.GuardedBy
 
 import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, ListBuffer}
+import scala.reflect.ClassTag
 import scala.util.control.NonFatal
 
 import org.apache.spark.internal.Logging
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.encoders.encoderFor
+import org.apache.spark.sql.catalyst.encoders.{encoderFor, ExpressionEncoder}
 import org.apache.spark.sql.catalyst.expressions.{Attribute, UnsafeRow}
-import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, Statistics}
+import org.apache.spark.sql.catalyst.plans.logical.{LeafNode, LogicalPlan, Statistics}
 import org.apache.spark.sql.catalyst.streaming.InternalOutputModes._
+import org.apache.spark.sql.execution.streaming.continuous.ContinuousTrigger
 import org.apache.spark.sql.sources.v2.reader.{DataReader, DataReaderFactory, SupportsScanUnsafeRow}
 import org.apache.spark.sql.sources.v2.reader.streaming.{MicroBatchReader, Offset => OffsetV2}
-import org.apache.spark.sql.streaming.OutputMode
+import org.apache.spark.sql.streaming.{OutputMode, Trigger}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.Utils
 
@@ -47,16 +49,43 @@ object MemoryStream {
     new MemoryStream[A](memoryStreamId.getAndIncrement(), sqlContext)
 }
 
+/**
+ * A base class for memory stream implementations. Supports adding data and resetting.
+ */
+abstract class MemoryStreamBase[A : Encoder](sqlContext: SQLContext) extends BaseStreamingSource {
+  protected val encoder = encoderFor[A]
+  protected val attributes = encoder.schema.toAttributes
+
+  def toDS(): Dataset[A] = {
+    Dataset[A](sqlContext.sparkSession, logicalPlan)
+  }
+
+  def toDF(): DataFrame = {
+    Dataset.ofRows(sqlContext.sparkSession, logicalPlan)
+  }
+
+  def addData(data: A*): Offset = {
+    addData(data.toTraversable)
+  }
+
+  def readSchema(): StructType = encoder.schema
+
+  protected def logicalPlan: LogicalPlan
+
+  def addData(data: TraversableOnce[A]): Offset
+}
+
 /**
  * A [[Source]] that produces value stored in memory as they are added by the user.  This [[Source]]
  * is intended for use in unit tests as it can only replay data when the object is still
  * available.
  */
 case class MemoryStream[A : Encoder](id: Int, sqlContext: SQLContext)
-    extends MicroBatchReader with SupportsScanUnsafeRow with Logging {
-  protected val encoder = encoderFor[A]
-  private val attributes = encoder.schema.toAttributes
-  protected val logicalPlan = StreamingExecutionRelation(this, attributes)(sqlContext.sparkSession)
+    extends MemoryStreamBase[A](sqlContext)
+      with MicroBatchReader with SupportsScanUnsafeRow with Logging {
+
+  protected val logicalPlan: LogicalPlan =
+    StreamingExecutionRelation(this, attributes)(sqlContext.sparkSession)
   protected val output = logicalPlan.output
 
   /**
@@ -70,7 +99,7 @@ case class MemoryStream[A : Encoder](id: Int, sqlContext: SQLContext)
   protected var currentOffset: LongOffset = new LongOffset(-1)
 
   @GuardedBy("this")
-  private var startOffset = new LongOffset(-1)
+  protected var startOffset = new LongOffset(-1)
 
   @GuardedBy("this")
   private var endOffset = new LongOffset(-1)
@@ -82,18 +111,6 @@ case class MemoryStream[A : Encoder](id: Int, sqlContext: SQLContext)
   @GuardedBy("this")
   protected var lastOffsetCommitted : LongOffset = new LongOffset(-1)
 
-  def toDS(): Dataset[A] = {
-    Dataset(sqlContext.sparkSession, logicalPlan)
-  }
-
-  def toDF(): DataFrame = {
-    Dataset.ofRows(sqlContext.sparkSession, logicalPlan)
-  }
-
-  def addData(data: A*): Offset = {
-    addData(data.toTraversable)
-  }
-
   def addData(data: TraversableOnce[A]): Offset = {
     val objects = data.toSeq
     val rows = objects.iterator.map(d => encoder.toRow(d).copy().asInstanceOf[UnsafeRow]).toArray
@@ -114,8 +131,6 @@ case class MemoryStream[A : Encoder](id: Int, sqlContext: SQLContext)
     }
   }
 
-  override def readSchema(): StructType = encoder.schema
-
   override def deserializeOffset(json: String): OffsetV2 = LongOffset(json.toLong)
 
   override def getStartOffset: OffsetV2 = synchronized {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ContinuousMemoryStream.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ContinuousMemoryStream.scala
new file mode 100644
index 0000000000000..c28919b8b729b
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/sources/ContinuousMemoryStream.scala
@@ -0,0 +1,211 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.streaming.sources
+
+import java.{util => ju}
+import java.util.Optional
+import java.util.concurrent.atomic.AtomicInteger
+import javax.annotation.concurrent.GuardedBy
+
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ListBuffer
+
+import org.json4s.NoTypeHints
+import org.json4s.jackson.Serialization
+
+import org.apache.spark.SparkEnv
+import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, ThreadSafeRpcEndpoint}
+import org.apache.spark.sql.{Encoder, Row, SQLContext}
+import org.apache.spark.sql.execution.streaming._
+import org.apache.spark.sql.execution.streaming.sources.ContinuousMemoryStream.GetRecord
+import org.apache.spark.sql.sources.v2.{ContinuousReadSupport, DataSourceOptions}
+import org.apache.spark.sql.sources.v2.reader.DataReaderFactory
+import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousDataReader, ContinuousReader, Offset, PartitionOffset}
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.RpcUtils
+
+/**
+ * The overall strategy here is:
+ *  * ContinuousMemoryStream maintains a list of records for each partition. addData() will
+ *    distribute records evenly-ish across partitions.
+ *  * RecordEndpoint is set up as an endpoint for executor-side
+ *    ContinuousMemoryStreamDataReader instances to poll. It returns the record at the specified
+ *    offset within the list, or null if that offset doesn't yet have a record.
+ */
+class ContinuousMemoryStream[A : Encoder](id: Int, sqlContext: SQLContext)
+  extends MemoryStreamBase[A](sqlContext) with ContinuousReader with ContinuousReadSupport {
+  private implicit val formats = Serialization.formats(NoTypeHints)
+  private val NUM_PARTITIONS = 2
+
+  protected val logicalPlan =
+    StreamingRelationV2(this, "memory", Map(), attributes, None)(sqlContext.sparkSession)
+
+  // ContinuousReader implementation
+
+  @GuardedBy("this")
+  private val records = Seq.fill(NUM_PARTITIONS)(new ListBuffer[A])
+
+  @GuardedBy("this")
+  private var startOffset: ContinuousMemoryStreamOffset = _
+
+  private val recordEndpoint = new RecordEndpoint()
+  @volatile private var endpointRef: RpcEndpointRef = _
+
+  def addData(data: TraversableOnce[A]): Offset = synchronized {
+    // Distribute data evenly among partition lists.
+    data.toSeq.zipWithIndex.map {
+      case (item, index) => records(index % NUM_PARTITIONS) += item
+    }
+
+    // The new target offset is the offset where all records in all partitions have been processed.
+    ContinuousMemoryStreamOffset((0 until NUM_PARTITIONS).map(i => (i, records(i).size)).toMap)
+  }
+
+  override def setStartOffset(start: Optional[Offset]): Unit = synchronized {
+    // Inferred initial offset is position 0 in each partition.
+    startOffset = start.orElse {
+      ContinuousMemoryStreamOffset((0 until NUM_PARTITIONS).map(i => (i, 0)).toMap)
+    }.asInstanceOf[ContinuousMemoryStreamOffset]
+  }
+
+  override def getStartOffset: Offset = synchronized {
+    startOffset
+  }
+
+  override def deserializeOffset(json: String): ContinuousMemoryStreamOffset = {
+    ContinuousMemoryStreamOffset(Serialization.read[Map[Int, Int]](json))
+  }
+
+  override def mergeOffsets(offsets: Array[PartitionOffset]): ContinuousMemoryStreamOffset = {
+    ContinuousMemoryStreamOffset(
+      offsets.map {
+        case ContinuousMemoryStreamPartitionOffset(part, num) => (part, num)
+      }.toMap
+    )
+  }
+
+  override def createDataReaderFactories(): ju.List[DataReaderFactory[Row]] = {
+    synchronized {
+      val endpointName = s"ContinuousMemoryStreamRecordEndpoint-${java.util.UUID.randomUUID()}-$id"
+      endpointRef =
+        recordEndpoint.rpcEnv.setupEndpoint(endpointName, recordEndpoint)
+
+      startOffset.partitionNums.map {
+        case (part, index) =>
+          new ContinuousMemoryStreamDataReaderFactory(
+            endpointName, part, index): DataReaderFactory[Row]
+      }.toList.asJava
+    }
+  }
+
+  override def stop(): Unit = {
+    if (endpointRef != null) recordEndpoint.rpcEnv.stop(endpointRef)
+  }
+
+  override def commit(end: Offset): Unit = {}
+
+  // ContinuousReadSupport implementation
+  // This is necessary because of how StreamTest finds the source for AddDataMemory steps.
+  def createContinuousReader(
+      schema: Optional[StructType],
+      checkpointLocation: String,
+      options: DataSourceOptions): ContinuousReader = {
+    this
+  }
+
+  /**
+   * Endpoint for executors to poll for records.
+   */
+  private class RecordEndpoint extends ThreadSafeRpcEndpoint {
+    override val rpcEnv: RpcEnv = SparkEnv.get.rpcEnv
+
+    override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+      case GetRecord(ContinuousMemoryStreamPartitionOffset(part, index)) =>
+        ContinuousMemoryStream.this.synchronized {
+          val buf = records(part)
+          val record = if (buf.size <= index) None else Some(buf(index))
+
+          context.reply(record.map(Row(_)))
+        }
+    }
+  }
+}
+
+object ContinuousMemoryStream {
+  case class GetRecord(offset: ContinuousMemoryStreamPartitionOffset)
+  protected val memoryStreamId = new AtomicInteger(0)
+
+  def apply[A : Encoder](implicit sqlContext: SQLContext): ContinuousMemoryStream[A] =
+    new ContinuousMemoryStream[A](memoryStreamId.getAndIncrement(), sqlContext)
+}
+
+/**
+ * Data reader factory for continuous memory stream.
+ */
+class ContinuousMemoryStreamDataReaderFactory(
+    driverEndpointName: String,
+    partition: Int,
+    startOffset: Int) extends DataReaderFactory[Row] {
+  override def createDataReader: ContinuousMemoryStreamDataReader =
+    new ContinuousMemoryStreamDataReader(driverEndpointName, partition, startOffset)
+}
+
+/**
+ * Data reader for continuous memory stream.
+ *
+ * Polls the driver endpoint for new records.
+ */
+class ContinuousMemoryStreamDataReader(
+    driverEndpointName: String,
+    partition: Int,
+    startOffset: Int) extends ContinuousDataReader[Row] {
+  private val endpoint = RpcUtils.makeDriverRef(
+    driverEndpointName,
+    SparkEnv.get.conf,
+    SparkEnv.get.rpcEnv)
+
+  private var currentOffset = startOffset
+  private var current: Option[Row] = None
+
+  override def next(): Boolean = {
+    current = None
+    while (current.isEmpty) {
+      Thread.sleep(10)
+      current = endpoint.askSync[Option[Row]](
+          GetRecord(ContinuousMemoryStreamPartitionOffset(partition, currentOffset)))
+    }
+    currentOffset += 1
+    true
+  }
+
+  override def get(): Row = current.get
+
+  override def close(): Unit = {}
+
+  override def getOffset: ContinuousMemoryStreamPartitionOffset =
+    ContinuousMemoryStreamPartitionOffset(partition, currentOffset)
+}
+
+case class ContinuousMemoryStreamOffset(partitionNums: Map[Int, Int])
+  extends Offset {
+  private implicit val formats = Serialization.formats(NoTypeHints)
+  override def json(): String = Serialization.write(partitionNums)
+}
+
+case class ContinuousMemoryStreamPartitionOffset(partition: Int, numProcessed: Int)
+  extends PartitionOffset
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala
index 00741d660dd2d..af0268fa47871 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/StreamTest.scala
@@ -99,7 +99,7 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be
    * been processed.
    */
   object AddData {
-    def apply[A](source: MemoryStream[A], data: A*): AddDataMemory[A] =
+    def apply[A](source: MemoryStreamBase[A], data: A*): AddDataMemory[A] =
       AddDataMemory(source, data)
   }
 
@@ -131,7 +131,7 @@ trait StreamTest extends QueryTest with SharedSQLContext with TimeLimits with Be
     def runAction(): Unit
   }
 
-  case class AddDataMemory[A](source: MemoryStream[A], data: Seq[A]) extends AddData {
+  case class AddDataMemory[A](source: MemoryStreamBase[A], data: Seq[A]) extends AddData {
     override def toString: String = s"AddData to $source: ${data.mkString(",")}"
 
     override def addData(query: Option[StreamExecution]): (BaseStreamingSource, Offset) = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala
index ef74efef156d5..c318b951ff992 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/ContinuousSuite.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.v2.DataSourceV2ScanExec
 import org.apache.spark.sql.execution.streaming._
 import org.apache.spark.sql.execution.streaming.continuous._
+import org.apache.spark.sql.execution.streaming.sources.ContinuousMemoryStream
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.streaming.{StreamTest, Trigger}
 import org.apache.spark.sql.test.TestSparkSession
@@ -53,32 +54,24 @@ class ContinuousSuiteBase extends StreamTest {
   // A continuous trigger that will only fire the initial time for the duration of a test.
   // This allows clean testing with manual epoch advancement.
   protected val longContinuousTrigger = Trigger.Continuous("1 hour")
+
+  override protected val defaultTrigger = Trigger.Continuous(100)
+  override protected val defaultUseV2Sink = true
 }
 
 class ContinuousSuite extends ContinuousSuiteBase {
   import testImplicits._
 
-  test("basic rate source") {
-    val df = spark.readStream
-      .format("rate")
-      .option("numPartitions", "5")
-      .option("rowsPerSecond", "5")
-      .load()
-      .select('value)
+  test("basic") {
+    val input = ContinuousMemoryStream[Int]
 
-    testStream(df, useV2Sink = true)(
-      StartStream(longContinuousTrigger),
-      AwaitEpoch(0),
-      Execute(waitForRateSourceTriggers(_, 2)),
-      IncrementEpoch(),
-      CheckAnswerRowsContains(scala.Range(0, 10).map(Row(_))),
+    testStream(input.toDF())(
+      AddData(input, 0, 1, 2),
+      CheckAnswer(0, 1, 2),
       StopStream,
-      StartStream(longContinuousTrigger),
-      AwaitEpoch(2),
-      Execute(waitForRateSourceTriggers(_, 2)),
-      IncrementEpoch(),
-      CheckAnswerRowsContains(scala.Range(0, 20).map(Row(_))),
-      StopStream)
+      AddData(input, 3, 4, 5),
+      StartStream(),
+      CheckAnswer(0, 1, 2, 3, 4, 5))
   }
 
   test("map") {

From 05ae74778a10fbdd7f2cbf7742de7855966b7d35 Mon Sep 17 00:00:00 2001
From: Efim Poberezkin <efim@poberezkin.ru>
Date: Tue, 17 Apr 2018 04:13:17 -0700
Subject: [PATCH 06/14] [SPARK-23747][STRUCTURED STREAMING] Add
 EpochCoordinator unit tests

## What changes were proposed in this pull request?

Unit tests for EpochCoordinator that test correct sequencing of committed epochs. Several tests are ignored since they test functionality implemented in SPARK-23503 which is not yet merged, otherwise they fail.

Author: Efim Poberezkin <efim@poberezkin.ru>

Closes #20983 from efimpoberezkin/pr/EpochCoordinator-tests.
---
 .../continuous/EpochCoordinatorSuite.scala    | 224 ++++++++++++++++++
 1 file changed, 224 insertions(+)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/EpochCoordinatorSuite.scala

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/EpochCoordinatorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/EpochCoordinatorSuite.scala
new file mode 100644
index 0000000000000..99e30561f81d5
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/EpochCoordinatorSuite.scala
@@ -0,0 +1,224 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.streaming.continuous
+
+import org.mockito.InOrder
+import org.mockito.Matchers.{any, eq => eqTo}
+import org.mockito.Mockito._
+import org.scalatest.BeforeAndAfterEach
+import org.scalatest.mockito.MockitoSugar
+
+import org.apache.spark._
+import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.sql.LocalSparkSession
+import org.apache.spark.sql.execution.streaming.continuous._
+import org.apache.spark.sql.sources.v2.reader.streaming.{ContinuousReader, PartitionOffset}
+import org.apache.spark.sql.sources.v2.writer.WriterCommitMessage
+import org.apache.spark.sql.sources.v2.writer.streaming.StreamWriter
+import org.apache.spark.sql.test.TestSparkSession
+
+class EpochCoordinatorSuite
+  extends SparkFunSuite
+    with LocalSparkSession
+    with MockitoSugar
+    with BeforeAndAfterEach {
+
+  private var epochCoordinator: RpcEndpointRef = _
+
+  private var writer: StreamWriter = _
+  private var query: ContinuousExecution = _
+  private var orderVerifier: InOrder = _
+
+  override def beforeEach(): Unit = {
+    val reader = mock[ContinuousReader]
+    writer = mock[StreamWriter]
+    query = mock[ContinuousExecution]
+    orderVerifier = inOrder(writer, query)
+
+    spark = new TestSparkSession()
+
+    epochCoordinator
+      = EpochCoordinatorRef.create(writer, reader, query, "test", 1, spark, SparkEnv.get)
+  }
+
+  test("single epoch") {
+    setWriterPartitions(3)
+    setReaderPartitions(2)
+
+    commitPartitionEpoch(0, 1)
+    commitPartitionEpoch(1, 1)
+    commitPartitionEpoch(2, 1)
+    reportPartitionOffset(0, 1)
+    reportPartitionOffset(1, 1)
+
+    // Here and in subsequent tests this is called to make a synchronous call to EpochCoordinator
+    // so that mocks would have been acted upon by the time verification happens
+    makeSynchronousCall()
+
+    verifyCommit(1)
+  }
+
+  test("single epoch, all but one writer partition has committed") {
+    setWriterPartitions(3)
+    setReaderPartitions(2)
+
+    commitPartitionEpoch(0, 1)
+    commitPartitionEpoch(1, 1)
+    reportPartitionOffset(0, 1)
+    reportPartitionOffset(1, 1)
+
+    makeSynchronousCall()
+
+    verifyNoCommitFor(1)
+  }
+
+  test("single epoch, all but one reader partition has reported an offset") {
+    setWriterPartitions(3)
+    setReaderPartitions(2)
+
+    commitPartitionEpoch(0, 1)
+    commitPartitionEpoch(1, 1)
+    commitPartitionEpoch(2, 1)
+    reportPartitionOffset(0, 1)
+
+    makeSynchronousCall()
+
+    verifyNoCommitFor(1)
+  }
+
+  test("consequent epochs, messages for epoch (k + 1) arrive after messages for epoch k") {
+    setWriterPartitions(2)
+    setReaderPartitions(2)
+
+    commitPartitionEpoch(0, 1)
+    commitPartitionEpoch(1, 1)
+    reportPartitionOffset(0, 1)
+    reportPartitionOffset(1, 1)
+
+    commitPartitionEpoch(0, 2)
+    commitPartitionEpoch(1, 2)
+    reportPartitionOffset(0, 2)
+    reportPartitionOffset(1, 2)
+
+    makeSynchronousCall()
+
+    verifyCommitsInOrderOf(List(1, 2))
+  }
+
+  ignore("consequent epochs, a message for epoch k arrives after messages for epoch (k + 1)") {
+    setWriterPartitions(2)
+    setReaderPartitions(2)
+
+    commitPartitionEpoch(0, 1)
+    commitPartitionEpoch(1, 1)
+    reportPartitionOffset(0, 1)
+
+    commitPartitionEpoch(0, 2)
+    commitPartitionEpoch(1, 2)
+    reportPartitionOffset(0, 2)
+    reportPartitionOffset(1, 2)
+
+    // Message that arrives late
+    reportPartitionOffset(1, 1)
+
+    makeSynchronousCall()
+
+    verifyCommitsInOrderOf(List(1, 2))
+  }
+
+  ignore("several epochs, messages arrive in order 1 -> 3 -> 4 -> 2") {
+    setWriterPartitions(1)
+    setReaderPartitions(1)
+
+    commitPartitionEpoch(0, 1)
+    reportPartitionOffset(0, 1)
+
+    commitPartitionEpoch(0, 3)
+    reportPartitionOffset(0, 3)
+
+    commitPartitionEpoch(0, 4)
+    reportPartitionOffset(0, 4)
+
+    commitPartitionEpoch(0, 2)
+    reportPartitionOffset(0, 2)
+
+    makeSynchronousCall()
+
+    verifyCommitsInOrderOf(List(1, 2, 3, 4))
+  }
+
+  ignore("several epochs, messages arrive in order 1 -> 3 -> 5 -> 4 -> 2") {
+    setWriterPartitions(1)
+    setReaderPartitions(1)
+
+    commitPartitionEpoch(0, 1)
+    reportPartitionOffset(0, 1)
+
+    commitPartitionEpoch(0, 3)
+    reportPartitionOffset(0, 3)
+
+    commitPartitionEpoch(0, 5)
+    reportPartitionOffset(0, 5)
+
+    commitPartitionEpoch(0, 4)
+    reportPartitionOffset(0, 4)
+
+    commitPartitionEpoch(0, 2)
+    reportPartitionOffset(0, 2)
+
+    makeSynchronousCall()
+
+    verifyCommitsInOrderOf(List(1, 2, 3, 4, 5))
+  }
+
+  private def setWriterPartitions(numPartitions: Int): Unit = {
+    epochCoordinator.askSync[Unit](SetWriterPartitions(numPartitions))
+  }
+
+  private def setReaderPartitions(numPartitions: Int): Unit = {
+    epochCoordinator.askSync[Unit](SetReaderPartitions(numPartitions))
+  }
+
+  private def commitPartitionEpoch(partitionId: Int, epoch: Long): Unit = {
+    val dummyMessage: WriterCommitMessage = mock[WriterCommitMessage]
+    epochCoordinator.send(CommitPartitionEpoch(partitionId, epoch, dummyMessage))
+  }
+
+  private def reportPartitionOffset(partitionId: Int, epoch: Long): Unit = {
+    val dummyOffset: PartitionOffset = mock[PartitionOffset]
+    epochCoordinator.send(ReportPartitionOffset(partitionId, epoch, dummyOffset))
+  }
+
+  private def makeSynchronousCall(): Unit = {
+    epochCoordinator.askSync[Long](GetCurrentEpoch)
+  }
+
+  private def verifyCommit(epoch: Long): Unit = {
+    orderVerifier.verify(writer).commit(eqTo(epoch), any())
+    orderVerifier.verify(query).commit(epoch)
+  }
+
+  private def verifyNoCommitFor(epoch: Long): Unit = {
+    verify(writer, never()).commit(eqTo(epoch), any())
+    verify(query, never()).commit(epoch)
+  }
+
+  private def verifyCommitsInOrderOf(epochs: Seq[Long]): Unit = {
+    epochs.foreach(verifyCommit)
+  }
+}

From 30ffb53cad84283b4f7694bfd60bdd7e1101b04e Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@gmail.com>
Date: Tue, 17 Apr 2018 15:09:36 +0200
Subject: [PATCH 07/14] [SPARK-23875][SQL] Add IndexedSeq wrapper for ArrayData

## What changes were proposed in this pull request?

We don't have a good way to sequentially access `UnsafeArrayData` with a common interface such as `Seq`. An example is `MapObject` where we need to access several sequence collection types together. But `UnsafeArrayData` doesn't implement `ArrayData.array`. Calling `toArray` will copy the entire array. We can provide an `IndexedSeq` wrapper for `ArrayData`, so we can avoid copying the entire array.

## How was this patch tested?

Added test.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #20984 from viirya/SPARK-23875.
---
 .../expressions/objects/objects.scala         |   2 +-
 .../spark/sql/catalyst/util/ArrayData.scala   |  30 +++++-
 .../util/ArrayDataIndexedSeqSuite.scala       | 100 ++++++++++++++++++
 3 files changed, 130 insertions(+), 2 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ArrayDataIndexedSeqSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala
index 77802e89e942b..72b202b3a5020 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/objects/objects.scala
@@ -708,7 +708,7 @@ case class MapObjects private(
         }
       }
     case ArrayType(et, _) =>
-      _.asInstanceOf[ArrayData].array
+      _.asInstanceOf[ArrayData].toSeq[Any](et)
   }
 
   private lazy val mapElements: Seq[_] => Any = customCollectionCls match {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ArrayData.scala
index 9beef41d639f3..2cf59d567c08c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/ArrayData.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.catalyst.util
 
 import scala.reflect.ClassTag
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{SpecializedGetters, UnsafeArrayData}
-import org.apache.spark.sql.types.DataType
+import org.apache.spark.sql.types._
 
 object ArrayData {
   def toArrayData(input: Any): ArrayData = input match {
@@ -42,6 +43,9 @@ abstract class ArrayData extends SpecializedGetters with Serializable {
 
   def array: Array[Any]
 
+  def toSeq[T](dataType: DataType): IndexedSeq[T] =
+    new ArrayDataIndexedSeq[T](this, dataType)
+
   def setNullAt(i: Int): Unit
 
   def update(i: Int, value: Any): Unit
@@ -164,3 +168,27 @@ abstract class ArrayData extends SpecializedGetters with Serializable {
     }
   }
 }
+
+/**
+ * Implements an `IndexedSeq` interface for `ArrayData`. Notice that if the original `ArrayData`
+ * is a primitive array and contains null elements, it is better to ask for `IndexedSeq[Any]`,
+ * instead of `IndexedSeq[Int]`, in order to keep the null elements.
+ */
+class ArrayDataIndexedSeq[T](arrayData: ArrayData, dataType: DataType) extends IndexedSeq[T] {
+
+  private val accessor: (SpecializedGetters, Int) => Any = InternalRow.getAccessor(dataType)
+
+  override def apply(idx: Int): T =
+    if (0 <= idx && idx < arrayData.numElements()) {
+      if (arrayData.isNullAt(idx)) {
+        null.asInstanceOf[T]
+      } else {
+        accessor(arrayData, idx).asInstanceOf[T]
+      }
+    } else {
+      throw new IndexOutOfBoundsException(
+        s"Index $idx must be between 0 and the length of the ArrayData.")
+    }
+
+  override def length: Int = arrayData.numElements()
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ArrayDataIndexedSeqSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ArrayDataIndexedSeqSuite.scala
new file mode 100644
index 0000000000000..6400898343ae7
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/ArrayDataIndexedSeqSuite.scala
@@ -0,0 +1,100 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.util
+
+import scala.util.Random
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.catalyst.encoders.{ExamplePointUDT, RowEncoder}
+import org.apache.spark.sql.catalyst.expressions.{FromUnsafeProjection, UnsafeArrayData, UnsafeProjection}
+import org.apache.spark.sql.types._
+
+class ArrayDataIndexedSeqSuite extends SparkFunSuite {
+  private def compArray(arrayData: ArrayData, elementDt: DataType, array: Array[Any]): Unit = {
+    assert(arrayData.numElements == array.length)
+    array.zipWithIndex.map { case (e, i) =>
+      if (e != null) {
+        elementDt match {
+          // For NaN, etc.
+          case FloatType | DoubleType => assert(arrayData.get(i, elementDt).equals(e))
+          case _ => assert(arrayData.get(i, elementDt) === e)
+        }
+      } else {
+        assert(arrayData.isNullAt(i))
+      }
+    }
+
+    val seq = arrayData.toSeq[Any](elementDt)
+    array.zipWithIndex.map { case (e, i) =>
+      if (e != null) {
+        elementDt match {
+          // For Nan, etc.
+          case FloatType | DoubleType => assert(seq(i).equals(e))
+          case _ => assert(seq(i) === e)
+        }
+      } else {
+        assert(seq(i) == null)
+      }
+    }
+
+    intercept[IndexOutOfBoundsException] {
+      seq(-1)
+    }.getMessage().contains("must be between 0 and the length of the ArrayData.")
+
+    intercept[IndexOutOfBoundsException] {
+      seq(seq.length)
+    }.getMessage().contains("must be between 0 and the length of the ArrayData.")
+  }
+
+  private def testArrayData(): Unit = {
+    val elementTypes = Seq(BooleanType, ByteType, ShortType, IntegerType, LongType, FloatType,
+      DoubleType, DecimalType.USER_DEFAULT, StringType, BinaryType, DateType, TimestampType,
+      CalendarIntervalType, new ExamplePointUDT())
+    val arrayTypes = elementTypes.flatMap { elementType =>
+      Seq(ArrayType(elementType, containsNull = false), ArrayType(elementType, containsNull = true))
+    }
+    val random = new Random(100)
+    arrayTypes.foreach { dt =>
+      val schema = StructType(StructField("col_1", dt, nullable = false) :: Nil)
+      val row = RandomDataGenerator.randomRow(random, schema)
+      val rowConverter = RowEncoder(schema)
+      val internalRow = rowConverter.toRow(row)
+
+      val unsafeRowConverter = UnsafeProjection.create(schema)
+      val safeRowConverter = FromUnsafeProjection(schema)
+
+      val unsafeRow = unsafeRowConverter(internalRow)
+      val safeRow = safeRowConverter(unsafeRow)
+
+      val genericArrayData = safeRow.getArray(0).asInstanceOf[GenericArrayData]
+      val unsafeArrayData = unsafeRow.getArray(0).asInstanceOf[UnsafeArrayData]
+
+      val elementType = dt.elementType
+      test("ArrayDataIndexedSeq - UnsafeArrayData - " + dt.toString) {
+        compArray(unsafeArrayData, elementType, unsafeArrayData.toArray[Any](elementType))
+      }
+
+      test("ArrayDataIndexedSeq - GenericArrayData - " + dt.toString) {
+        compArray(genericArrayData, elementType, genericArrayData.toArray[Any](elementType))
+      }
+    }
+  }
+
+  testArrayData()
+}

From 0a9172a05e604a4a94adbb9208c8c02362afca00 Mon Sep 17 00:00:00 2001
From: Marco Gaido <marcogaido91@gmail.com>
Date: Tue, 17 Apr 2018 21:45:20 +0800
Subject: [PATCH 08/14] [SPARK-23835][SQL] Add not-null check to Tuples'
 arguments deserialization

## What changes were proposed in this pull request?

There was no check on nullability for arguments of `Tuple`s. This could lead to have weird behavior when a null value had to be deserialized into a non-nullable Scala object: in those cases, the `null` got silently transformed in a valid value (like `-1` for `Int`), corresponding to the default value we are using in the SQL codebase. This situation was very likely to happen when deserializing to a Tuple of primitive Scala types (like Double, Int, ...).

The PR adds the `AssertNotNull` to arguments of tuples which have been asked to be converted to non-nullable types.

## How was this patch tested?

added UT

Author: Marco Gaido <marcogaido91@gmail.com>

Closes #20976 from mgaido91/SPARK-23835.
---
 .../sql/kafka010/KafkaContinuousSinkSuite.scala    |  6 +++---
 .../apache/spark/sql/kafka010/KafkaSinkSuite.scala |  2 +-
 .../spark/sql/catalyst/ScalaReflection.scala       | 14 +++++++-------
 .../spark/sql/catalyst/ScalaReflectionSuite.scala  | 12 +++++++++++-
 .../scala/org/apache/spark/sql/DatasetSuite.scala  |  5 +++++
 5 files changed, 27 insertions(+), 12 deletions(-)

diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSinkSuite.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSinkSuite.scala
index fc890a0cfdac3..ddfc0c1a4be2d 100644
--- a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSinkSuite.scala
+++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaContinuousSinkSuite.scala
@@ -79,7 +79,7 @@ class KafkaContinuousSinkSuite extends KafkaContinuousTest {
     val reader = createKafkaReader(topic)
       .selectExpr("CAST(key as STRING) key", "CAST(value as STRING) value")
       .selectExpr("CAST(key as INT) key", "CAST(value as INT) value")
-      .as[(Int, Int)]
+      .as[(Option[Int], Int)]
       .map(_._2)
 
     try {
@@ -119,7 +119,7 @@ class KafkaContinuousSinkSuite extends KafkaContinuousTest {
     val reader = createKafkaReader(topic)
       .selectExpr("CAST(key as STRING) key", "CAST(value as STRING) value")
       .selectExpr("CAST(key as INT) key", "CAST(value as INT) value")
-      .as[(Int, Int)]
+      .as[(Option[Int], Int)]
       .map(_._2)
 
     try {
@@ -167,7 +167,7 @@ class KafkaContinuousSinkSuite extends KafkaContinuousTest {
     val reader = createKafkaReader(topic)
       .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)")
       .selectExpr("CAST(key AS INT)", "CAST(value AS INT)")
-      .as[(Int, Int)]
+      .as[(Option[Int], Int)]
       .map(_._2)
 
     try {
diff --git a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala
index 42f8b4c7657e2..7079ac6453ffc 100644
--- a/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala
+++ b/external/kafka-0-10-sql/src/test/scala/org/apache/spark/sql/kafka010/KafkaSinkSuite.scala
@@ -138,7 +138,7 @@ class KafkaSinkSuite extends StreamTest with SharedSQLContext {
     val reader = createKafkaReader(topic)
       .selectExpr("CAST(key as STRING) key", "CAST(value as STRING) value")
       .selectExpr("CAST(key as INT) key", "CAST(value as INT) value")
-      .as[(Int, Int)]
+      .as[(Option[Int], Int)]
       .map(_._2)
 
     try {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
index 1aae3aea3a31a..e4274aaa9727e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala
@@ -382,22 +382,22 @@ object ScalaReflection extends ScalaReflection {
           val clsName = getClassNameFromType(fieldType)
           val newTypePath = s"""- field (class: "$clsName", name: "$fieldName")""" +: walkedTypePath
           // For tuples, we based grab the inner fields by ordinal instead of name.
-          if (cls.getName startsWith "scala.Tuple") {
+          val constructor = if (cls.getName startsWith "scala.Tuple") {
             deserializerFor(
               fieldType,
               Some(addToPathOrdinal(i, dataType, newTypePath)),
               newTypePath)
           } else {
-            val constructor = deserializerFor(
+            deserializerFor(
               fieldType,
               Some(addToPath(fieldName, dataType, newTypePath)),
               newTypePath)
+          }
 
-            if (!nullable) {
-              AssertNotNull(constructor, newTypePath)
-            } else {
-              constructor
-            }
+          if (!nullable) {
+            AssertNotNull(constructor, newTypePath)
+          } else {
+            constructor
           }
         }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
index 8c3db48a01f12..353b8344658f2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala
@@ -21,7 +21,7 @@ import java.sql.{Date, Timestamp}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.expressions.{BoundReference, Literal, SpecificInternalRow, UpCast}
+import org.apache.spark.sql.catalyst.expressions.{BoundReference, Expression, Literal, SpecificInternalRow, UpCast}
 import org.apache.spark.sql.catalyst.expressions.objects.{AssertNotNull, NewInstance}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -365,4 +365,14 @@ class ScalaReflectionSuite extends SparkFunSuite {
         StructField("_2", NullType, nullable = true))),
       nullable = true))
   }
+
+  test("SPARK-23835: add null check to non-nullable types in Tuples") {
+    def numberOfCheckedArguments(deserializer: Expression): Int = {
+      assert(deserializer.isInstanceOf[NewInstance])
+      deserializer.asInstanceOf[NewInstance].arguments.count(_.isInstanceOf[AssertNotNull])
+    }
+    assert(numberOfCheckedArguments(deserializerFor[(Double, Double)]) == 2)
+    assert(numberOfCheckedArguments(deserializerFor[(java.lang.Double, Int)]) == 1)
+    assert(numberOfCheckedArguments(deserializerFor[(java.lang.Integer, java.lang.Integer)]) == 0)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
index 9b745befcb611..e0f4d2ba685e1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
@@ -1453,6 +1453,11 @@ class DatasetSuite extends QueryTest with SharedSQLContext {
     val group2 = cached.groupBy("x").agg(min(col("z")) as "value")
     checkAnswer(group1.union(group2), Row(4, 5) :: Row(1, 2) :: Row(4, 6) :: Row(1, 3) :: Nil)
   }
+
+  test("SPARK-23835: null primitive data type should throw NullPointerException") {
+    val ds = Seq[(Option[Int], Option[Int])]((Some(1), None)).toDS()
+    intercept[NullPointerException](ds.as[(Int, Int)].collect())
+  }
 }
 
 case class TestDataUnion(x: Int, y: Int, z: Int)

From ed4101d29f50d54fd7846421e4c00e9ecd3599d0 Mon Sep 17 00:00:00 2001
From: jinxing <jinxing6042@126.com>
Date: Tue, 17 Apr 2018 21:52:33 +0800
Subject: [PATCH 09/14] [SPARK-22676] Avoid iterating all partition paths when
 spark.sql.hive.verifyPartitionPath=true
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

## What changes were proposed in this pull request?

In current code, it will scanning all partition paths when spark.sql.hive.verifyPartitionPath=true.
e.g. table like below:
```
CREATE TABLE `test`(
`id` int,
`age` int,
`name` string)
PARTITIONED BY (
`A` string,
`B` string)
load data local inpath '/tmp/data0' into table test partition(A='00', B='00')
load data local inpath '/tmp/data1' into table test partition(A='01', B='01')
load data local inpath '/tmp/data2' into table test partition(A='10', B='10')
load data local inpath '/tmp/data3' into table test partition(A='11', B='11')
```
If I query with SQL – "select * from test where A='00' and B='01'  ", current code will scan all partition paths including '/data/A=00/B=00', '/data/A=00/B=00', '/data/A=01/B=01', '/data/A=10/B=10', '/data/A=11/B=11'. It costs much time and memory cost.

This pr proposes to avoid iterating all partition paths. Add a config `spark.files.ignoreMissingFiles` and ignore the `file not found` when `getPartitions/compute`(for hive table scan). This is much like the logic brought by
`spark.sql.files.ignoreMissingFiles`(which is for datasource scan).

## How was this patch tested?
UT

Author: jinxing <jinxing6042@126.com>

Closes #19868 from jinxing64/SPARK-22676.
---
 .../spark/internal/config/package.scala       |  6 ++
 .../org/apache/spark/rdd/HadoopRDD.scala      | 43 +++++++++---
 .../org/apache/spark/rdd/NewHadoopRDD.scala   | 45 ++++++++----
 .../scala/org/apache/spark/FileSuite.scala    | 69 ++++++++++++++++++-
 .../apache/spark/sql/internal/SQLConf.scala   |  3 +-
 .../spark/sql/hive/QueryPartitionSuite.scala  | 40 +++++++++++
 6 files changed, 181 insertions(+), 25 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/internal/config/package.scala b/core/src/main/scala/org/apache/spark/internal/config/package.scala
index 407545aa4a47a..99d779fb600e8 100644
--- a/core/src/main/scala/org/apache/spark/internal/config/package.scala
+++ b/core/src/main/scala/org/apache/spark/internal/config/package.scala
@@ -301,6 +301,12 @@ package object config {
     .booleanConf
     .createWithDefault(false)
 
+  private[spark] val IGNORE_MISSING_FILES = ConfigBuilder("spark.files.ignoreMissingFiles")
+    .doc("Whether to ignore missing files. If true, the Spark jobs will continue to run when " +
+        "encountering missing files and the contents that have been read will still be returned.")
+    .booleanConf
+    .createWithDefault(false)
+
   private[spark] val APP_CALLER_CONTEXT = ConfigBuilder("spark.log.callerContext")
     .stringConf
     .createOptional
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index 2480559a41b7a..44895abc7bd4d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.rdd
 
-import java.io.IOException
+import java.io.{FileNotFoundException, IOException}
 import java.text.SimpleDateFormat
 import java.util.{Date, Locale}
 
@@ -28,6 +28,7 @@ import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.mapred._
 import org.apache.hadoop.mapred.lib.CombineFileSplit
 import org.apache.hadoop.mapreduce.TaskType
+import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.util.ReflectionUtils
 
 import org.apache.spark._
@@ -134,6 +135,8 @@ class HadoopRDD[K, V](
 
   private val ignoreCorruptFiles = sparkContext.conf.get(IGNORE_CORRUPT_FILES)
 
+  private val ignoreMissingFiles = sparkContext.conf.get(IGNORE_MISSING_FILES)
+
   private val ignoreEmptySplits = sparkContext.conf.get(HADOOP_RDD_IGNORE_EMPTY_SPLITS)
 
   // Returns a JobConf that will be used on slaves to obtain input splits for Hadoop reads.
@@ -197,17 +200,24 @@ class HadoopRDD[K, V](
     val jobConf = getJobConf()
     // add the credentials here as this can be called before SparkContext initialized
     SparkHadoopUtil.get.addCredentials(jobConf)
-    val allInputSplits = getInputFormat(jobConf).getSplits(jobConf, minPartitions)
-    val inputSplits = if (ignoreEmptySplits) {
-      allInputSplits.filter(_.getLength > 0)
-    } else {
-      allInputSplits
-    }
-    val array = new Array[Partition](inputSplits.size)
-    for (i <- 0 until inputSplits.size) {
-      array(i) = new HadoopPartition(id, i, inputSplits(i))
+    try {
+      val allInputSplits = getInputFormat(jobConf).getSplits(jobConf, minPartitions)
+      val inputSplits = if (ignoreEmptySplits) {
+        allInputSplits.filter(_.getLength > 0)
+      } else {
+        allInputSplits
+      }
+      val array = new Array[Partition](inputSplits.size)
+      for (i <- 0 until inputSplits.size) {
+        array(i) = new HadoopPartition(id, i, inputSplits(i))
+      }
+      array
+    } catch {
+      case e: InvalidInputException if ignoreMissingFiles =>
+        logWarning(s"${jobConf.get(FileInputFormat.INPUT_DIR)} doesn't exist and no" +
+            s" partitions returned from this path.", e)
+        Array.empty[Partition]
     }
-    array
   }
 
   override def compute(theSplit: Partition, context: TaskContext): InterruptibleIterator[(K, V)] = {
@@ -256,6 +266,12 @@ class HadoopRDD[K, V](
         try {
           inputFormat.getRecordReader(split.inputSplit.value, jobConf, Reporter.NULL)
         } catch {
+          case e: FileNotFoundException if ignoreMissingFiles =>
+            logWarning(s"Skipped missing file: ${split.inputSplit}", e)
+            finished = true
+            null
+          // Throw FileNotFoundException even if `ignoreCorruptFiles` is true
+          case e: FileNotFoundException if !ignoreMissingFiles => throw e
           case e: IOException if ignoreCorruptFiles =>
             logWarning(s"Skipped the rest content in the corrupted file: ${split.inputSplit}", e)
             finished = true
@@ -276,6 +292,11 @@ class HadoopRDD[K, V](
         try {
           finished = !reader.next(key, value)
         } catch {
+          case e: FileNotFoundException if ignoreMissingFiles =>
+            logWarning(s"Skipped missing file: ${split.inputSplit}", e)
+            finished = true
+          // Throw FileNotFoundException even if `ignoreCorruptFiles` is true
+          case e: FileNotFoundException if !ignoreMissingFiles => throw e
           case e: IOException if ignoreCorruptFiles =>
             logWarning(s"Skipped the rest content in the corrupted file: ${split.inputSplit}", e)
             finished = true
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index e4dd1b6a82498..ff66a04859d10 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.rdd
 
-import java.io.IOException
+import java.io.{FileNotFoundException, IOException}
 import java.text.SimpleDateFormat
 import java.util.{Date, Locale}
 
@@ -28,7 +28,7 @@ import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.input.{CombineFileSplit, FileSplit}
+import org.apache.hadoop.mapreduce.lib.input.{CombineFileSplit, FileInputFormat, FileSplit, InvalidInputException}
 import org.apache.hadoop.mapreduce.task.{JobContextImpl, TaskAttemptContextImpl}
 
 import org.apache.spark._
@@ -90,6 +90,8 @@ class NewHadoopRDD[K, V](
 
   private val ignoreCorruptFiles = sparkContext.conf.get(IGNORE_CORRUPT_FILES)
 
+  private val ignoreMissingFiles = sparkContext.conf.get(IGNORE_MISSING_FILES)
+
   private val ignoreEmptySplits = sparkContext.conf.get(HADOOP_RDD_IGNORE_EMPTY_SPLITS)
 
   def getConf: Configuration = {
@@ -124,17 +126,25 @@ class NewHadoopRDD[K, V](
         configurable.setConf(_conf)
       case _ =>
     }
-    val allRowSplits = inputFormat.getSplits(new JobContextImpl(_conf, jobId)).asScala
-    val rawSplits = if (ignoreEmptySplits) {
-      allRowSplits.filter(_.getLength > 0)
-    } else {
-      allRowSplits
-    }
-    val result = new Array[Partition](rawSplits.size)
-    for (i <- 0 until rawSplits.size) {
-      result(i) = new NewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
+    try {
+      val allRowSplits = inputFormat.getSplits(new JobContextImpl(_conf, jobId)).asScala
+      val rawSplits = if (ignoreEmptySplits) {
+        allRowSplits.filter(_.getLength > 0)
+      } else {
+        allRowSplits
+      }
+      val result = new Array[Partition](rawSplits.size)
+      for (i <- 0 until rawSplits.size) {
+        result(i) =
+            new NewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
+      }
+      result
+    } catch {
+      case e: InvalidInputException if ignoreMissingFiles =>
+        logWarning(s"${_conf.get(FileInputFormat.INPUT_DIR)} doesn't exist and no" +
+            s" partitions returned from this path.", e)
+        Array.empty[Partition]
     }
-    result
   }
 
   override def compute(theSplit: Partition, context: TaskContext): InterruptibleIterator[(K, V)] = {
@@ -189,6 +199,12 @@ class NewHadoopRDD[K, V](
           _reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
           _reader
         } catch {
+          case e: FileNotFoundException if ignoreMissingFiles =>
+            logWarning(s"Skipped missing file: ${split.serializableHadoopSplit}", e)
+            finished = true
+            null
+          // Throw FileNotFoundException even if `ignoreCorruptFiles` is true
+          case e: FileNotFoundException if !ignoreMissingFiles => throw e
           case e: IOException if ignoreCorruptFiles =>
             logWarning(
               s"Skipped the rest content in the corrupted file: ${split.serializableHadoopSplit}",
@@ -213,6 +229,11 @@ class NewHadoopRDD[K, V](
           try {
             finished = !reader.nextKeyValue
           } catch {
+            case e: FileNotFoundException if ignoreMissingFiles =>
+              logWarning(s"Skipped missing file: ${split.serializableHadoopSplit}", e)
+              finished = true
+            // Throw FileNotFoundException even if `ignoreCorruptFiles` is true
+            case e: FileNotFoundException if !ignoreMissingFiles => throw e
             case e: IOException if ignoreCorruptFiles =>
               logWarning(
                 s"Skipped the rest content in the corrupted file: ${split.serializableHadoopSplit}",
diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala
index 55a9122cf9026..a441b9c8ab97a 100644
--- a/core/src/test/scala/org/apache/spark/FileSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileSuite.scala
@@ -23,6 +23,7 @@ import java.util.zip.GZIPOutputStream
 
 import scala.io.Source
 
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.io._
 import org.apache.hadoop.io.compress.DefaultCodec
@@ -32,7 +33,7 @@ import org.apache.hadoop.mapreduce.lib.input.{FileSplit => NewFileSplit, TextInp
 import org.apache.hadoop.mapreduce.lib.output.{TextOutputFormat => NewTextOutputFormat}
 
 import org.apache.spark.internal.config._
-import org.apache.spark.rdd.{HadoopRDD, NewHadoopRDD}
+import org.apache.spark.rdd.{HadoopRDD, NewHadoopRDD, RDD}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.Utils
 
@@ -596,4 +597,70 @@ class FileSuite extends SparkFunSuite with LocalSparkContext {
       actualPartitionNum = 5,
       expectedPartitionNum = 2)
   }
+
+  test("spark.files.ignoreMissingFiles should work both HadoopRDD and NewHadoopRDD") {
+    // "file not found" can happen both when getPartitions or compute in HadoopRDD/NewHadoopRDD,
+    // We test both cases here.
+
+    val deletedPath = new Path(tempDir.getAbsolutePath, "test-data-1")
+    val fs = deletedPath.getFileSystem(new Configuration())
+    fs.delete(deletedPath, true)
+    intercept[FileNotFoundException](fs.open(deletedPath))
+
+    def collectRDDAndDeleteFileBeforeCompute(newApi: Boolean): Array[_] = {
+      val dataPath = new Path(tempDir.getAbsolutePath, "test-data-2")
+      val writer = new OutputStreamWriter(new FileOutputStream(new File(dataPath.toString)))
+      writer.write("hello\n")
+      writer.write("world\n")
+      writer.close()
+      val rdd = if (newApi) {
+        sc.newAPIHadoopFile(dataPath.toString, classOf[NewTextInputFormat],
+          classOf[LongWritable], classOf[Text])
+      } else {
+        sc.textFile(dataPath.toString)
+      }
+      rdd.partitions
+      fs.delete(dataPath, true)
+      // Exception happens when initialize record reader in HadoopRDD/NewHadoopRDD.compute
+      // because partitions' info already cached.
+      rdd.collect()
+    }
+
+    // collect HadoopRDD and NewHadoopRDD when spark.files.ignoreMissingFiles=false by default.
+    sc = new SparkContext("local", "test")
+    intercept[org.apache.hadoop.mapred.InvalidInputException] {
+      // Exception happens when HadoopRDD.getPartitions
+      sc.textFile(deletedPath.toString).collect()
+    }
+
+    var e = intercept[SparkException] {
+      collectRDDAndDeleteFileBeforeCompute(false)
+    }
+    assert(e.getCause.isInstanceOf[java.io.FileNotFoundException])
+
+    intercept[org.apache.hadoop.mapreduce.lib.input.InvalidInputException] {
+      // Exception happens when NewHadoopRDD.getPartitions
+      sc.newAPIHadoopFile(deletedPath.toString, classOf[NewTextInputFormat],
+        classOf[LongWritable], classOf[Text]).collect
+    }
+
+    e = intercept[SparkException] {
+      collectRDDAndDeleteFileBeforeCompute(true)
+    }
+    assert(e.getCause.isInstanceOf[java.io.FileNotFoundException])
+
+    sc.stop()
+
+    // collect HadoopRDD and NewHadoopRDD when spark.files.ignoreMissingFiles=true.
+    val conf = new SparkConf().set(IGNORE_MISSING_FILES, true)
+    sc = new SparkContext("local", "test", conf)
+    assert(sc.textFile(deletedPath.toString).collect().isEmpty)
+
+    assert(collectRDDAndDeleteFileBeforeCompute(false).isEmpty)
+
+    assert(sc.newAPIHadoopFile(deletedPath.toString, classOf[NewTextInputFormat],
+      classOf[LongWritable], classOf[Text]).collect().isEmpty)
+
+    assert(collectRDDAndDeleteFileBeforeCompute(true).isEmpty)
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
index 0dc47bfe075d0..3729bd5293eca 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
@@ -437,7 +437,8 @@ object SQLConf {
 
   val HIVE_VERIFY_PARTITION_PATH = buildConf("spark.sql.hive.verifyPartitionPath")
     .doc("When true, check all the partition paths under the table\'s root directory " +
-         "when reading data stored in HDFS.")
+         "when reading data stored in HDFS. This configuration will be deprecated in the future " +
+         "releases and replaced by spark.files.ignoreMissingFiles.")
     .booleanConf
     .createWithDefault(false)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
index b2dc401ce1efc..78156b17fb43b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
@@ -23,6 +23,7 @@ import java.sql.Timestamp
 import com.google.common.io.Files
 import org.apache.hadoop.fs.FileSystem
 
+import org.apache.spark.internal.config._
 import org.apache.spark.sql._
 import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.internal.SQLConf
@@ -70,6 +71,45 @@ class QueryPartitionSuite extends QueryTest with SQLTestUtils with TestHiveSingl
     }
   }
 
+  test("Replace spark.sql.hive.verifyPartitionPath by spark.files.ignoreMissingFiles") {
+    withSQLConf((SQLConf.HIVE_VERIFY_PARTITION_PATH.key, "false")) {
+      sparkContext.conf.set(IGNORE_MISSING_FILES.key, "true")
+      val testData = sparkContext.parallelize(
+        (1 to 10).map(i => TestData(i, i.toString))).toDF()
+      testData.createOrReplaceTempView("testData")
+
+      val tmpDir = Files.createTempDir()
+      // create the table for test
+      sql(s"CREATE TABLE table_with_partition(key int,value string) " +
+          s"PARTITIONED by (ds string) location '${tmpDir.toURI}' ")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='1') " +
+          "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='2') " +
+          "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='3') " +
+          "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='4') " +
+          "SELECT key,value FROM testData")
+
+      // test for the exist path
+      checkAnswer(sql("select key,value from table_with_partition"),
+        testData.toDF.collect ++ testData.toDF.collect
+            ++ testData.toDF.collect ++ testData.toDF.collect)
+
+      // delete the path of one partition
+      tmpDir.listFiles
+          .find { f => f.isDirectory && f.getName().startsWith("ds=") }
+          .foreach { f => Utils.deleteRecursively(f) }
+
+      // test for after delete the path
+      checkAnswer(sql("select key,value from table_with_partition"),
+        testData.toDF.collect ++ testData.toDF.collect ++ testData.toDF.collect)
+
+      sql("DROP TABLE IF EXISTS table_with_partition")
+      sql("DROP TABLE IF EXISTS createAndInsertTest")
+    }
+  }
+
   test("SPARK-21739: Cast expression should initialize timezoneId") {
     withTable("table_with_timestamp_partition") {
       sql("CREATE TABLE table_with_timestamp_partition(value int) PARTITIONED BY (ts TIMESTAMP)")

From 3990daaf3b6ca2c5a9f7790030096262efb12cb2 Mon Sep 17 00:00:00 2001
From: jinxing <jinxing6042@126.com>
Date: Tue, 17 Apr 2018 08:55:01 -0500
Subject: [PATCH 10/14] [SPARK-23948] Trigger mapstage's job listener in
 submitMissingTasks

## What changes were proposed in this pull request?

SparkContext submitted a map stage from `submitMapStage` to `DAGScheduler`,
`markMapStageJobAsFinished` is called only in (https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L933 and https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L1314);

But think about below scenario:
1. stage0 and stage1 are all `ShuffleMapStage` and stage1 depends on stage0;
2. We submit stage1 by `submitMapStage`;
3. When stage 1 running, `FetchFailed` happened, stage0 and stage1 got resubmitted as stage0_1 and stage1_1;
4. When stage0_1 running, speculated tasks in old stage1 come as succeeded, but stage1 is not inside `runningStages`. So even though all splits(including the speculated tasks) in stage1 succeeded, job listener in stage1 will not be called;
5. stage0_1 finished, stage1_1 starts running. When `submitMissingTasks`, there is no missing tasks. But in current code, job listener is not triggered.

We should call the job listener for map stage in `5`.

## How was this patch tested?

Not added yet.

Author: jinxing <jinxing6042@126.com>

Closes #21019 from jinxing64/SPARK-23948.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 33 ++++++------
 .../spark/scheduler/DAGSchedulerSuite.scala   | 52 +++++++++++++++++++
 2 files changed, 70 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 8c46a84323392..78b6b34b5d2bb 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1092,17 +1092,16 @@ class DAGScheduler(
       // the stage as completed here in case there are no tasks to run
       markStageAsFinished(stage, None)
 
-      val debugString = stage match {
+      stage match {
         case stage: ShuffleMapStage =>
-          s"Stage ${stage} is actually done; " +
-            s"(available: ${stage.isAvailable}," +
-            s"available outputs: ${stage.numAvailableOutputs}," +
-            s"partitions: ${stage.numPartitions})"
+          logDebug(s"Stage ${stage} is actually done; " +
+              s"(available: ${stage.isAvailable}," +
+              s"available outputs: ${stage.numAvailableOutputs}," +
+              s"partitions: ${stage.numPartitions})")
+          markMapStageJobsAsFinished(stage)
         case stage : ResultStage =>
-          s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})"
+          logDebug(s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})")
       }
-      logDebug(debugString)
-
       submitWaitingChildStages(stage)
     }
   }
@@ -1307,13 +1306,7 @@ class DAGScheduler(
                   shuffleStage.findMissingPartitions().mkString(", "))
                 submitStage(shuffleStage)
               } else {
-                // Mark any map-stage jobs waiting on this stage as finished
-                if (shuffleStage.mapStageJobs.nonEmpty) {
-                  val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep)
-                  for (job <- shuffleStage.mapStageJobs) {
-                    markMapStageJobAsFinished(job, stats)
-                  }
-                }
+                markMapStageJobsAsFinished(shuffleStage)
                 submitWaitingChildStages(shuffleStage)
               }
             }
@@ -1433,6 +1426,16 @@ class DAGScheduler(
     }
   }
 
+  private[scheduler] def markMapStageJobsAsFinished(shuffleStage: ShuffleMapStage): Unit = {
+    // Mark any map-stage jobs waiting on this stage as finished
+    if (shuffleStage.isAvailable && shuffleStage.mapStageJobs.nonEmpty) {
+      val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep)
+      for (job <- shuffleStage.mapStageJobs) {
+        markMapStageJobAsFinished(job, stats)
+      }
+    }
+  }
+
   /**
    * Responds to an executor being lost. This is called inside the event loop, so it assumes it can
    * modify the scheduler's internal state. Use executorLost() to post a loss event from outside.
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index d812b5bd92c1b..8b6ec37625eec 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -2146,6 +2146,58 @@ class DAGSchedulerSuite extends SparkFunSuite with LocalSparkContext with TimeLi
     assertDataStructuresEmpty()
   }
 
+  test("Trigger mapstage's job listener in submitMissingTasks") {
+    val rdd1 = new MyRDD(sc, 2, Nil)
+    val dep1 = new ShuffleDependency(rdd1, new HashPartitioner(2))
+    val rdd2 = new MyRDD(sc, 2, List(dep1), tracker = mapOutputTracker)
+    val dep2 = new ShuffleDependency(rdd2, new HashPartitioner(2))
+
+    val listener1 = new SimpleListener
+    val listener2 = new SimpleListener
+
+    submitMapStage(dep1, listener1)
+    submitMapStage(dep2, listener2)
+
+    // Complete the stage0.
+    assert(taskSets(0).stageId === 0)
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", rdd1.partitions.length)),
+      (Success, makeMapStatus("hostB", rdd1.partitions.length))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet ===
+        HashSet(makeBlockManagerId("hostA"), makeBlockManagerId("hostB")))
+    assert(listener1.results.size === 1)
+
+    // When attempting stage1, trigger a fetch failure.
+    assert(taskSets(1).stageId === 1)
+    complete(taskSets(1), Seq(
+      (Success, makeMapStatus("hostC", rdd2.partitions.length)),
+      (FetchFailed(makeBlockManagerId("hostA"), dep1.shuffleId, 0, 0, "ignored"), null)))
+    scheduler.resubmitFailedStages()
+    // Stage1 listener should not have a result yet
+    assert(listener2.results.size === 0)
+
+    // Speculative task succeeded in stage1.
+    runEvent(makeCompletionEvent(
+      taskSets(1).tasks(1),
+      Success,
+      makeMapStatus("hostD", rdd2.partitions.length)))
+    // stage1 listener still should not have a result, though there's no missing partitions
+    // in it. Because stage1 has been failed and is not inside `runningStages` at this moment.
+    assert(listener2.results.size === 0)
+
+    // Stage0 should now be running as task set 2; make its task succeed
+    assert(taskSets(2).stageId === 0)
+    complete(taskSets(2), Seq(
+      (Success, makeMapStatus("hostC", rdd2.partitions.length))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet ===
+        Set(makeBlockManagerId("hostC"), makeBlockManagerId("hostB")))
+
+    // After stage0 is finished, stage1 will be submitted and found there is no missing
+    // partitions in it. Then listener got triggered.
+    assert(listener2.results.size === 1)
+    assertDataStructuresEmpty()
+  }
+
   /**
    * In this test, we run a map stage where one of the executors fails but we still receive a
    * "zombie" complete message from that executor. We want to make sure the stage is not reported

From f39e82ce150b6a7ea038e6858ba7adbaba3cad88 Mon Sep 17 00:00:00 2001
From: Marco Gaido <marcogaido91@gmail.com>
Date: Wed, 18 Apr 2018 00:35:44 +0800
Subject: [PATCH 11/14] [SPARK-23986][SQL] freshName can generate non-unique
 names

## What changes were proposed in this pull request?

We are using `CodegenContext.freshName` to get a unique name for any new variable we are adding. Unfortunately, this method currently fails to create a unique name when we request more than one instance of variables with starting name `name1` and an instance with starting name `name11`.

The PR changes the way a new name is generated by `CodegenContext.freshName` so that we generate unique names in this scenario too.

## How was this patch tested?

added UT

Author: Marco Gaido <marcogaido91@gmail.com>

Closes #21080 from mgaido91/SPARK-23986.
---
 .../catalyst/expressions/codegen/CodeGenerator.scala  | 11 +++--------
 .../catalyst/expressions/CodeGenerationSuite.scala    | 10 ++++++++++
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index d97611c98ac91..f6b6775923ac6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -572,14 +572,9 @@ class CodegenContext {
     } else {
       s"${freshNamePrefix}_$name"
     }
-    if (freshNameIds.contains(fullName)) {
-      val id = freshNameIds(fullName)
-      freshNameIds(fullName) = id + 1
-      s"$fullName$id"
-    } else {
-      freshNameIds += fullName -> 1
-      fullName
-    }
+    val id = freshNameIds.getOrElse(fullName, 0)
+    freshNameIds(fullName) = id + 1
+    s"${fullName}_$id"
   }
 
   /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index f7c023111ff59..5b71becee2de0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -489,4 +489,14 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
       assert(!ctx.subExprEliminationExprs.contains(ref))
     }
   }
+
+  test("SPARK-23986: freshName can generate duplicated names") {
+    val ctx = new CodegenContext
+    val names1 = ctx.freshName("myName1") :: ctx.freshName("myName1") ::
+      ctx.freshName("myName11") :: Nil
+    assert(names1.distinct.length == 3)
+    val names2 = ctx.freshName("a") :: ctx.freshName("a") ::
+      ctx.freshName("a_1") :: ctx.freshName("a_0") :: Nil
+    assert(names2.distinct.length == 4)
+  }
 }

From 1ca3c50fefb34532c78427fa74872db3ecbf7ba2 Mon Sep 17 00:00:00 2001
From: WeichenXu <weichen.xu@databricks.com>
Date: Tue, 17 Apr 2018 10:11:08 -0700
Subject: [PATCH 12/14] [SPARK-21741][ML][PYSPARK] Python API for
 DataFrame-based multivariate summarizer

## What changes were proposed in this pull request?

Python API for DataFrame-based multivariate summarizer.

## How was this patch tested?

doctest added.

Author: WeichenXu <weichen.xu@databricks.com>

Closes #20695 from WeichenXu123/py_summarizer.
---
 python/pyspark/ml/stat.py | 193 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 192 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/stat.py b/python/pyspark/ml/stat.py
index 93d0f4fd9148f..a06ab31a7a56a 100644
--- a/python/pyspark/ml/stat.py
+++ b/python/pyspark/ml/stat.py
@@ -19,7 +19,9 @@
 
 from pyspark import since, SparkContext
 from pyspark.ml.common import _java2py, _py2java
-from pyspark.ml.wrapper import _jvm
+from pyspark.ml.wrapper import JavaWrapper, _jvm
+from pyspark.sql.column import Column, _to_seq
+from pyspark.sql.functions import lit
 
 
 class ChiSquareTest(object):
@@ -195,6 +197,195 @@ def test(dataset, sampleCol, distName, *params):
                                              _jvm().PythonUtils.toSeq(params)))
 
 
+class Summarizer(object):
+    """
+    .. note:: Experimental
+
+    Tools for vectorized statistics on MLlib Vectors.
+    The methods in this package provide various statistics for Vectors contained inside DataFrames.
+    This class lets users pick the statistics they would like to extract for a given column.
+
+    >>> from pyspark.ml.stat import Summarizer
+    >>> from pyspark.sql import Row
+    >>> from pyspark.ml.linalg import Vectors
+    >>> summarizer = Summarizer.metrics("mean", "count")
+    >>> df = sc.parallelize([Row(weight=1.0, features=Vectors.dense(1.0, 1.0, 1.0)),
+    ...                      Row(weight=0.0, features=Vectors.dense(1.0, 2.0, 3.0))]).toDF()
+    >>> df.select(summarizer.summary(df.features, df.weight)).show(truncate=False)
+    +-----------------------------------+
+    |aggregate_metrics(features, weight)|
+    +-----------------------------------+
+    |[[1.0,1.0,1.0], 1]                 |
+    +-----------------------------------+
+    <BLANKLINE>
+    >>> df.select(summarizer.summary(df.features)).show(truncate=False)
+    +--------------------------------+
+    |aggregate_metrics(features, 1.0)|
+    +--------------------------------+
+    |[[1.0,1.5,2.0], 2]              |
+    +--------------------------------+
+    <BLANKLINE>
+    >>> df.select(Summarizer.mean(df.features, df.weight)).show(truncate=False)
+    +--------------+
+    |mean(features)|
+    +--------------+
+    |[1.0,1.0,1.0] |
+    +--------------+
+    <BLANKLINE>
+    >>> df.select(Summarizer.mean(df.features)).show(truncate=False)
+    +--------------+
+    |mean(features)|
+    +--------------+
+    |[1.0,1.5,2.0] |
+    +--------------+
+    <BLANKLINE>
+
+    .. versionadded:: 2.4.0
+
+    """
+    @staticmethod
+    @since("2.4.0")
+    def mean(col, weightCol=None):
+        """
+        return a column of mean summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "mean")
+
+    @staticmethod
+    @since("2.4.0")
+    def variance(col, weightCol=None):
+        """
+        return a column of variance summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "variance")
+
+    @staticmethod
+    @since("2.4.0")
+    def count(col, weightCol=None):
+        """
+        return a column of count summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "count")
+
+    @staticmethod
+    @since("2.4.0")
+    def numNonZeros(col, weightCol=None):
+        """
+        return a column of numNonZero summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "numNonZeros")
+
+    @staticmethod
+    @since("2.4.0")
+    def max(col, weightCol=None):
+        """
+        return a column of max summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "max")
+
+    @staticmethod
+    @since("2.4.0")
+    def min(col, weightCol=None):
+        """
+        return a column of min summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "min")
+
+    @staticmethod
+    @since("2.4.0")
+    def normL1(col, weightCol=None):
+        """
+        return a column of normL1 summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "normL1")
+
+    @staticmethod
+    @since("2.4.0")
+    def normL2(col, weightCol=None):
+        """
+        return a column of normL2 summary
+        """
+        return Summarizer._get_single_metric(col, weightCol, "normL2")
+
+    @staticmethod
+    def _check_param(featuresCol, weightCol):
+        if weightCol is None:
+            weightCol = lit(1.0)
+        if not isinstance(featuresCol, Column) or not isinstance(weightCol, Column):
+            raise TypeError("featureCol and weightCol should be a Column")
+        return featuresCol, weightCol
+
+    @staticmethod
+    def _get_single_metric(col, weightCol, metric):
+        col, weightCol = Summarizer._check_param(col, weightCol)
+        return Column(JavaWrapper._new_java_obj("org.apache.spark.ml.stat.Summarizer." + metric,
+                                                col._jc, weightCol._jc))
+
+    @staticmethod
+    @since("2.4.0")
+    def metrics(*metrics):
+        """
+        Given a list of metrics, provides a builder that it turns computes metrics from a column.
+
+        See the documentation of [[Summarizer]] for an example.
+
+        The following metrics are accepted (case sensitive):
+         - mean: a vector that contains the coefficient-wise mean.
+         - variance: a vector tha contains the coefficient-wise variance.
+         - count: the count of all vectors seen.
+         - numNonzeros: a vector with the number of non-zeros for each coefficients
+         - max: the maximum for each coefficient.
+         - min: the minimum for each coefficient.
+         - normL2: the Euclidian norm for each coefficient.
+         - normL1: the L1 norm of each coefficient (sum of the absolute values).
+
+        :param metrics:
+         metrics that can be provided.
+        :return:
+         an object of :py:class:`pyspark.ml.stat.SummaryBuilder`
+
+        Note: Currently, the performance of this interface is about 2x~3x slower then using the RDD
+        interface.
+        """
+        sc = SparkContext._active_spark_context
+        js = JavaWrapper._new_java_obj("org.apache.spark.ml.stat.Summarizer.metrics",
+                                       _to_seq(sc, metrics))
+        return SummaryBuilder(js)
+
+
+class SummaryBuilder(JavaWrapper):
+    """
+    .. note:: Experimental
+
+    A builder object that provides summary statistics about a given column.
+
+    Users should not directly create such builders, but instead use one of the methods in
+    :py:class:`pyspark.ml.stat.Summarizer`
+
+    .. versionadded:: 2.4.0
+
+    """
+    def __init__(self, jSummaryBuilder):
+        super(SummaryBuilder, self).__init__(jSummaryBuilder)
+
+    @since("2.4.0")
+    def summary(self, featuresCol, weightCol=None):
+        """
+        Returns an aggregate object that contains the summary of the column with the requested
+        metrics.
+
+        :param featuresCol:
+         a column that contains features Vector object.
+        :param weightCol:
+         a column that contains weight value. Default weight is 1.0.
+        :return:
+         an aggregate column that contains the statistics. The exact content of this
+         structure is determined during the creation of the builder.
+        """
+        featuresCol, weightCol = Summarizer._check_param(featuresCol, weightCol)
+        return Column(self._java_obj.summary(featuresCol._jc, weightCol._jc))
+
+
 if __name__ == "__main__":
     import doctest
     import pyspark.ml.stat

From 5fccdae18911793967b315c02c058eb737e46174 Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Tue, 17 Apr 2018 21:08:42 -0500
Subject: [PATCH 13/14] [SPARK-22968][DSTREAM] Throw an exception on partition
 revoking issue

## What changes were proposed in this pull request?

Kafka partitions can be revoked when new consumers joined in the consumer group to rebalance the partitions. But current Spark Kafka connector code makes sure there's no partition revoking scenarios, so trying to get latest offset from revoked partitions will throw exceptions as JIRA mentioned.

Partition revoking happens when new consumer joined the consumer group, which means different streaming apps are trying to use same group id. This is fundamentally not correct, different apps should use different consumer group. So instead of throwing an confused exception from Kafka, improve the exception message by identifying revoked partition and directly throw an meaningful exception when partition is revoked.

Besides, this PR also fixes bugs in `DirectKafkaWordCount`, this example simply cannot be worked without the fix.

```
8/01/05 09:48:27 INFO internals.ConsumerCoordinator: Revoking previously assigned partitions [kssh-7, kssh-4, kssh-3, kssh-6, kssh-5, kssh-0, kssh-2, kssh-1] for group use_a_separate_group_id_for_each_stream
18/01/05 09:48:27 INFO internals.AbstractCoordinator: (Re-)joining group use_a_separate_group_id_for_each_stream
18/01/05 09:48:27 INFO internals.AbstractCoordinator: Successfully joined group use_a_separate_group_id_for_each_stream with generation 4
18/01/05 09:48:27 INFO internals.ConsumerCoordinator: Setting newly assigned partitions [kssh-7, kssh-4, kssh-6, kssh-5] for group use_a_separate_group_id_for_each_stream
```

## How was this patch tested?

This is manually verified in local cluster, unfortunately I'm not sure how to simulate it in UT, so propose the PR without UT added.

Author: jerryshao <sshao@hortonworks.com>

Closes #21038 from jerryshao/SPARK-22968.
---
 .../streaming/DirectKafkaWordCount.scala        | 17 +++++++++++++----
 .../kafka010/DirectKafkaInputDStream.scala      | 12 ++++++++++++
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala
index def06026bde96..2082fb71afdf1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala
@@ -18,6 +18,9 @@
 // scalastyle:off println
 package org.apache.spark.examples.streaming
 
+import org.apache.kafka.clients.consumer.ConsumerConfig
+import org.apache.kafka.common.serialization.StringDeserializer
+
 import org.apache.spark.SparkConf
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.kafka010._
@@ -26,18 +29,20 @@ import org.apache.spark.streaming.kafka010._
  * Consumes messages from one or more topics in Kafka and does wordcount.
  * Usage: DirectKafkaWordCount <brokers> <topics>
  *   <brokers> is a list of one or more Kafka brokers
+ *   <groupId> is a consumer group name to consume from topics
  *   <topics> is a list of one or more kafka topics to consume from
  *
  * Example:
  *    $ bin/run-example streaming.DirectKafkaWordCount broker1-host:port,broker2-host:port \
- *    topic1,topic2
+ *    consumer-group topic1,topic2
  */
 object DirectKafkaWordCount {
   def main(args: Array[String]) {
-    if (args.length < 2) {
+    if (args.length < 3) {
       System.err.println(s"""
         |Usage: DirectKafkaWordCount <brokers> <topics>
         |  <brokers> is a list of one or more Kafka brokers
+        |  <groupId> is a consumer group name to consume from topics
         |  <topics> is a list of one or more kafka topics to consume from
         |
         """.stripMargin)
@@ -46,7 +51,7 @@ object DirectKafkaWordCount {
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Array(brokers, topics) = args
+    val Array(brokers, groupId, topics) = args
 
     // Create context with 2 second batch interval
     val sparkConf = new SparkConf().setAppName("DirectKafkaWordCount")
@@ -54,7 +59,11 @@ object DirectKafkaWordCount {
 
     // Create direct kafka stream with brokers and topics
     val topicsSet = topics.split(",").toSet
-    val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
+    val kafkaParams = Map[String, Object](
+      ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG -> brokers,
+      ConsumerConfig.GROUP_ID_CONFIG -> groupId,
+      ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer],
+      ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG -> classOf[StringDeserializer])
     val messages = KafkaUtils.createDirectStream[String, String](
       ssc,
       LocationStrategies.PreferConsistent,
diff --git a/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/DirectKafkaInputDStream.scala b/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/DirectKafkaInputDStream.scala
index 215b7cab703fb..c3221481556f5 100644
--- a/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/DirectKafkaInputDStream.scala
+++ b/external/kafka-0-10/src/main/scala/org/apache/spark/streaming/kafka010/DirectKafkaInputDStream.scala
@@ -190,8 +190,20 @@ private[spark] class DirectKafkaInputDStream[K, V](
 
     // make sure new partitions are reflected in currentOffsets
     val newPartitions = parts.diff(currentOffsets.keySet)
+
+    // Check if there's any partition been revoked because of consumer rebalance.
+    val revokedPartitions = currentOffsets.keySet.diff(parts)
+    if (revokedPartitions.nonEmpty) {
+      throw new IllegalStateException(s"Previously tracked partitions " +
+        s"${revokedPartitions.mkString("[", ",", "]")} been revoked by Kafka because of consumer " +
+        s"rebalance. This is mostly due to another stream with same group id joined, " +
+        s"please check if there're different streaming application misconfigure to use same " +
+        s"group id. Fundamentally different stream should use different group id")
+    }
+
     // position for new partitions determined by auto.offset.reset if no commit
     currentOffsets = currentOffsets ++ newPartitions.map(tp => tp -> c.position(tp)).toMap
+
     // don't want to consume messages, so pause
     c.pause(newPartitions.asJava)
     // find latest available offsets

From 1e3b8762a854a07c317f69fba7fa1a7bcdc58ff3 Mon Sep 17 00:00:00 2001
From: maryannxue <maryann.xue@gmail.com>
Date: Wed, 18 Apr 2018 10:36:41 +0800
Subject: [PATCH 14/14] [SPARK-21479][SQL] Outer join filter pushdown in null
 supplying table when condition is on one of the joined columns

## What changes were proposed in this pull request?

Added `TransitPredicateInOuterJoin` optimization rule that transits constraints from the preserved side of an outer join to the null-supplying side. The constraints of the join operator will remain unchanged.

## How was this patch tested?

Added 3 tests in `InferFiltersFromConstraintsSuite`.

Author: maryannxue <maryann.xue@gmail.com>

Closes #20816 from maryannxue/spark-21479.
---
 .../sql/catalyst/optimizer/Optimizer.scala    | 42 +++++++++++++++++--
 .../plans/logical/QueryPlanConstraints.scala  | 25 +++++++++--
 .../InferFiltersFromConstraintsSuite.scala    | 36 ++++++++++++++++
 3 files changed, 96 insertions(+), 7 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 5fb59ef350b8b..913354e4df0e6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -637,8 +637,11 @@ object CollapseWindow extends Rule[LogicalPlan] {
  * constraints. These filters are currently inserted to the existing conditions in the Filter
  * operators and on either side of Join operators.
  *
- * Note: While this optimization is applicable to all types of join, it primarily benefits Inner and
- * LeftSemi joins.
+ * In addition, for left/right outer joins, infer predicate from the preserved side of the Join
+ * operator and push the inferred filter over to the null-supplying side. For example, if the
+ * preserved side has constraints of the form 'a > 5' and the join condition is 'a = b', in
+ * which 'b' is an attribute from the null-supplying side, a [[Filter]] operator of 'b > 5' will
+ * be applied to the null-supplying side.
  */
 object InferFiltersFromConstraints extends Rule[LogicalPlan] with PredicateHelper {
 
@@ -671,11 +674,42 @@ object InferFiltersFromConstraints extends Rule[LogicalPlan] with PredicateHelpe
       val newConditionOpt = conditionOpt match {
         case Some(condition) =>
           val newFilters = additionalConstraints -- splitConjunctivePredicates(condition)
-          if (newFilters.nonEmpty) Option(And(newFilters.reduce(And), condition)) else None
+          if (newFilters.nonEmpty) Option(And(newFilters.reduce(And), condition)) else conditionOpt
         case None =>
           additionalConstraints.reduceOption(And)
       }
-      if (newConditionOpt.isDefined) Join(left, right, joinType, newConditionOpt) else join
+      // Infer filter for left/right outer joins
+      val newLeftOpt = joinType match {
+        case RightOuter if newConditionOpt.isDefined =>
+          val inferredConstraints = left.getRelevantConstraints(
+            left.constraints
+              .union(right.constraints)
+              .union(splitConjunctivePredicates(newConditionOpt.get).toSet))
+          val newFilters = inferredConstraints
+            .filterNot(left.constraints.contains)
+            .reduceLeftOption(And)
+          newFilters.map(Filter(_, left))
+        case _ => None
+      }
+      val newRightOpt = joinType match {
+        case LeftOuter if newConditionOpt.isDefined =>
+          val inferredConstraints = right.getRelevantConstraints(
+            right.constraints
+              .union(left.constraints)
+              .union(splitConjunctivePredicates(newConditionOpt.get).toSet))
+          val newFilters = inferredConstraints
+            .filterNot(right.constraints.contains)
+            .reduceLeftOption(And)
+          newFilters.map(Filter(_, right))
+        case _ => None
+      }
+
+      if ((newConditionOpt.isDefined && (newConditionOpt ne conditionOpt))
+        || newLeftOpt.isDefined || newRightOpt.isDefined) {
+        Join(newLeftOpt.getOrElse(left), newRightOpt.getOrElse(right), joinType, newConditionOpt)
+      } else {
+        join
+      }
   }
 }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala
index 046848875548b..a29f3d29236c7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/QueryPlanConstraints.scala
@@ -41,9 +41,7 @@ trait QueryPlanConstraints { self: LogicalPlan =>
    * example, if this set contains the expression `a = 2` then that expression is guaranteed to
    * evaluate to `true` for all rows produced.
    */
-  lazy val constraints: ExpressionSet = ExpressionSet(allConstraints.filter { c =>
-        c.references.nonEmpty && c.references.subsetOf(outputSet) && c.deterministic
-      })
+  lazy val constraints: ExpressionSet = ExpressionSet(allConstraints.filter(selfReferenceOnly))
 
   /**
    * This method can be overridden by any child class of QueryPlan to specify a set of constraints
@@ -55,6 +53,23 @@ trait QueryPlanConstraints { self: LogicalPlan =>
    */
   protected def validConstraints: Set[Expression] = Set.empty
 
+  /**
+   * Returns an [[ExpressionSet]] that contains an additional set of constraints, such as
+   * equality constraints and `isNotNull` constraints, etc., and that only contains references
+   * to this [[LogicalPlan]] node.
+   */
+  def getRelevantConstraints(constraints: Set[Expression]): ExpressionSet = {
+    val allRelevantConstraints =
+      if (conf.constraintPropagationEnabled) {
+        constraints
+          .union(inferAdditionalConstraints(constraints))
+          .union(constructIsNotNullConstraints(constraints))
+      } else {
+        constraints
+      }
+    ExpressionSet(allRelevantConstraints.filter(selfReferenceOnly))
+  }
+
   /**
    * Infers a set of `isNotNull` constraints from null intolerant expressions as well as
    * non-nullable attributes. For e.g., if an expression is of the form (`a > 5`), this
@@ -120,4 +135,8 @@ trait QueryPlanConstraints { self: LogicalPlan =>
       destination: Attribute): Set[Expression] = constraints.map(_ transform {
     case e: Expression if e.semanticEquals(source) => destination
   })
+
+  private def selfReferenceOnly(e: Expression): Boolean = {
+    e.references.nonEmpty && e.references.subsetOf(outputSet) && e.deterministic
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala
index f78c2356e35a5..e068f51044589 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/InferFiltersFromConstraintsSuite.scala
@@ -204,4 +204,40 @@ class InferFiltersFromConstraintsSuite extends PlanTest {
     val optimized = Optimize.execute(originalQuery)
     comparePlans(optimized, correctAnswer)
   }
+
+  test("SPARK-21479: Outer join after-join filters push down to null-supplying side") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+    val condition = Some("x.a".attr === "y.a".attr)
+    val originalQuery = x.join(y, LeftOuter, condition).where("x.a".attr === 2).analyze
+    val left = x.where(IsNotNull('a) && 'a === 2)
+    val right = y.where(IsNotNull('a) && 'a === 2)
+    val correctAnswer = left.join(right, LeftOuter, condition).analyze
+    val optimized = Optimize.execute(originalQuery)
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("SPARK-21479: Outer join pre-existing filters push down to null-supplying side") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+    val condition = Some("x.a".attr === "y.a".attr)
+    val originalQuery = x.join(y.where("y.a".attr > 5), RightOuter, condition).analyze
+    val left = x.where(IsNotNull('a) && 'a > 5)
+    val right = y.where(IsNotNull('a) && 'a > 5)
+    val correctAnswer = left.join(right, RightOuter, condition).analyze
+    val optimized = Optimize.execute(originalQuery)
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("SPARK-21479: Outer join no filter push down to preserved side") {
+    val x = testRelation.subquery('x)
+    val y = testRelation.subquery('y)
+    val condition = Some("x.a".attr === "y.a".attr)
+    val originalQuery = x.join(y.where("y.a".attr === 1), LeftOuter, condition).analyze
+    val left = x
+    val right = y.where(IsNotNull('a) && 'a === 1)
+    val correctAnswer = left.join(right, LeftOuter, condition).analyze
+    val optimized = Optimize.execute(originalQuery)
+    comparePlans(optimized, correctAnswer)
+  }
 }