[SPARK-19851] Add support for EVERY and ANY (SOME) aggregates

python/pyspark/sql/functions.py

@@ -169,6 +169,12 @@ def _():
                'measured in radians.',
 }
 
+_functions_2_2 = {
+    'to_date': 'Converts a string date into a DateType using the (optionally) specified format.',
+    'to_timestamp': 'Converts a string timestamp into a timestamp type using the ' +
+                    '(optionally) specified format.',
+}
+
 # math functions that take two arguments as input
 _binary_mathfunctions = {
     'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' +
@@ -350,6 +356,28 @@ def countDistinct(col, *cols):
     return Column(jc)
 
 
+def every(col):
+    """Aggregate function: returns true if all values in a group are true.
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.every(_to_java_column(col))
+    return Column(jc)
+
+
+def any(col):
+    """Aggregate function: returns true if at least one value in the group is true.
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.any(_to_java_column(col))
+    return Column(jc)
+
+
+def some(col):
+    """Aggregate function: returns true if at least one value in the group is true.
+    """
+    return any(col)
+
+
 @since(1.3)
 def first(col, ignorenulls=False):
     """Aggregate function: returns the first value in a group.

python/pyspark/sql/tests.py

@@ -1158,6 +1158,24 @@ def test_cov(self):
         cov = df.stat.cov("a", "b")
         self.assertTrue(abs(cov - 55.0 / 3) < 1e-6)
 
+    def test_every_any(self):
+        from pyspark.sql import functions
+        data = [
+            Row(key="a", value=False),
+            Row(key="a", value=True),
+            Row(key="a", value=False),
+            Row(key="b", value=True),
+            Row(key="b", value=True),
+            Row(key="c", value=False),
+            Row(key="d", value=True),
+            Row(key="d", value=None)
+        ]
+        df = self.sc.parallelize(data).toDF()
+        df2 = df.select(functions.every(df.value).alias('a'),
+                        functions.any(df.value).alias('b'),
+                        functions.some(df.value).alias('c'))
+        self.assertEqual([Row(a=False, b=True, c=True)], df2.collect())
+
     def test_crosstab(self):
         df = self.sc.parallelize([Row(a=i % 3, b=i % 2) for i in range(1, 7)]).toDF()
         ct = df.stat.crosstab("a", "b").collect()
@@ -2631,6 +2649,75 @@ def test_window_functions_cumulative_sum(self):
         for r, ex in zip(rs, expected):
             self.assertEqual(tuple(r), ex[:len(r)])
 
+    def test_window_functions_every_any(self):
+        df = self.spark.createDataFrame([
+            ("a", False),
+            ("a", True),
+            ("a", False),
+            ("b", True),
+            ("b", True),
+            ("c", False),
+            ("d", True),
+            ("d", None)
+        ], ["key", "value"])
+        w = Window \
+            .partitionBy("key").orderBy("value") \
+            .rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)
+        from pyspark.sql import functions as F
+        sel = df.select(df.key,
+                        df.value,
+                        F.every("value").over(w),
+                        F.any("value").over(w),
+                        F.some("value").over(w))
+        rs = sel.collect()
+        expected = [
+            ("a", False, False, True, True),
+            ("a", False, False, True, True),
+            ("a", True, False, True, True),
+            ("b", True, True, True, True),
+            ("b", True, True, True, True),
+            ("c", False, False, False, False),
+            ("d", None, False, True, True),
+            ("d", True, False, True, True)
+        ]
+        for r, ex in zip(rs, expected):
+            self.assertEqual(tuple(r), ex[:len(r)])
+
+    def test_window_functions_every_any_without_partitionBy(self):
+        df = self.spark.createDataFrame([
+            (False,),
+            (True,),
+            (False,),
+            (True,),
+            (True,),
+            (False,),
+            (True,),
+            (None,)
+        ], ["value"])
+        w1 = Window.orderBy("value").rowsBetween(Window.unboundedPreceding, 0)
+        w2 = Window.orderBy("value").rowsBetween(-1, 0)
+        from pyspark.sql import functions as F
+        sel = df.select(df.value,
+                        F.every("value").over(w1),
+                        F.any("value").over(w1),
+                        F.some("value").over(w1),
+                        F.every("value").over(w2),
+                        F.any("value").over(w2),
+                        F.some("value").over(w2))
+        rs = sel.collect()
+        expected = [
+            (None, False, False, False, False, False, False),
+            (False, False, False, False, False, False, False),
+            (False, False, False, False, False, False, False),
+            (False, False, False, False, False, False, False),
+            (True, False, True, True, False, True, True),
+            (True, False, True, True, True, True, True),
+            (True, False, True, True, True, True, True),
+            (True, False, True, True, True, True, True)
+        ]
+        for r, ex in zip(rs, expected):
+            self.assertEqual(tuple(r), ex[:len(r)])
+
     def test_collect_functions(self):
         df = self.spark.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
         from pyspark.sql import functions

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala

@@ -299,6 +299,9 @@ object FunctionRegistry {
     expression[CollectList]("collect_list"),
     expression[CollectSet]("collect_set"),
     expression[CountMinSketchAgg]("count_min_sketch"),
+    expression[Every]("every"),
+    expression[AnyAgg]("any"),
+    expression[AnyAgg]("some"),
 
     // string functions
     expression[Ascii]("ascii"),

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/AnyAgg.scala

@@ -0,0 +1,63 @@
+/*
+ * 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.expressions.aggregate
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.util.TypeUtils
+import org.apache.spark.sql.types._
+
+@ExpressionDescription(
+  usage = "_FUNC_(expr) - Returns true if at least one value of `expr` is true.")
+case class AnyAgg(child: Expression) extends DeclarativeAggregate with ImplicitCastInputTypes {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  override def dataType: DataType = BooleanType
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(BooleanType)
+
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForBooleanExpr(child.dataType, "function any")
+
+  private lazy val some = AttributeReference("some", BooleanType)()
+
+  private lazy val emptySet = AttributeReference("emptySet", BooleanType)()
+
+  override lazy val aggBufferAttributes = some :: emptySet :: Nil
+
+  override lazy val initialValues: Seq[Expression] = Seq(
+    Literal(false),
+    Literal(true)
+  )
+
+  override lazy val updateExpressions: Seq[Expression] = Seq(
+    Or(some, Coalesce(Seq(child, Literal(false)))),
+    Literal(false)
+  )
+
+  override lazy val mergeExpressions: Seq[Expression] = Seq(
+    Or(some.left, some.right),
+    And(emptySet.left, emptySet.right)
+  )
+
+  override lazy val evaluateExpression: Expression = And(!emptySet, some)
+}

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/Every.scala

@@ -0,0 +1,63 @@
+/*
+ * 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.expressions.aggregate
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.util.TypeUtils
+import org.apache.spark.sql.types._
+
+@ExpressionDescription(
+  usage = "_FUNC_(expr) - Returns true if all values of `expr` are true.")
+case class Every(child: Expression) extends DeclarativeAggregate with ImplicitCastInputTypes {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  override def dataType: DataType = BooleanType
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(BooleanType)
+
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForBooleanExpr(child.dataType, "function every")
+
+  private lazy val every = AttributeReference("every", BooleanType)()
+
+  private lazy val emptySet = AttributeReference("emptySet", BooleanType)()
+
+  override lazy val aggBufferAttributes = every :: emptySet :: Nil
+
+  override lazy val initialValues: Seq[Expression] = Seq(
+    Literal(true),
+    Literal(true)
+  )
+
+  override lazy val updateExpressions: Seq[Expression] = Seq(
+    And(every, Coalesce(Seq(child, Literal(false)))),
+    Literal(false)
+  )
+
+  override lazy val mergeExpressions: Seq[Expression] = Seq(
+    And(every.left, every.right),
+    And(emptySet.left, emptySet.right)
+  )
+
+  override lazy val evaluateExpression: Expression = And(!emptySet, every)
+}

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala

@@ -33,6 +33,14 @@ object TypeUtils {
     }
   }
 
+  def checkForBooleanExpr(dt: DataType, caller: String): TypeCheckResult = {
+    if (dt.isInstanceOf[BooleanType] || dt == NullType) {
+      TypeCheckResult.TypeCheckSuccess
+    } else {
+      TypeCheckResult.TypeCheckFailure(s"$caller requires boolean types, not $dt")
+    }
+  }
+
   def checkForOrderingExpr(dt: DataType, caller: String): TypeCheckResult = {
     if (RowOrdering.isOrderable(dt)) {
       TypeCheckResult.TypeCheckSuccess

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/ExpressionTypeCheckingSuite.scala

@@ -143,6 +143,8 @@ class ExpressionTypeCheckingSuite extends SparkFunSuite {
     assertSuccess(Sum('stringField))
     assertSuccess(Average('stringField))
     assertSuccess(Min('arrayField))
+    assertSuccess(Every('booleanField))
+    assertSuccess(AnyAgg('booleanField))
 
     assertError(Min('mapField), "min does not support ordering on type")
     assertError(Max('mapField), "max does not support ordering on type")

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/aggregate/AnyTestSuite.scala

@@ -0,0 +1,80 @@
+/*
+ * 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.expressions.aggregate
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Literal}
+import org.apache.spark.sql.types.BooleanType
+
+class AnyTestSuite extends SparkFunSuite {
+  val input = AttributeReference("input", BooleanType, nullable = true)()
+  val evaluator = DeclarativeAggregateEvaluator(AnyAgg(input), Seq(input))
+
+  test("empty buffer") {
+    assert(evaluator.initialize() === InternalRow(false, true))
+  }
+
+  test("update") {
+    val result = evaluator.update(
+      InternalRow(true),
+      InternalRow(false),
+      InternalRow(true))
+    assert(result === InternalRow(true, false))
+  }
+
+  test("merge") {
+    // Empty merge
+    val p0 = evaluator.initialize()
+    assert(evaluator.merge(p0) === InternalRow(false, true))
+
+    // Single merge
+    val p1 = evaluator.update(InternalRow(true), InternalRow(true))
+    assert(evaluator.merge(p1) === InternalRow(true, false))
+
+    // Multiple merges.
+    val p2 = evaluator.update(InternalRow(false), InternalRow(null))
+    assert(evaluator.merge(p1, p2) === InternalRow(true, false))
+
+    // Empty partitions (p0 is empty)
+    assert(evaluator.merge(p0, p2) === InternalRow(false, false))
+    assert(evaluator.merge(p2, p1, p0) === InternalRow(true, false))
+  }
+
+  test("eval") {
+    // Null Eval
+    assert(evaluator.eval(InternalRow(null, true)) === InternalRow(false))
+
+    // Empty Eval
+    val p0 = evaluator.initialize()
+    assert(evaluator.eval(p0) === InternalRow(false))
+
+    // Update - Eval
+    val p1 = evaluator.update(InternalRow(true), InternalRow(null))
+    assert(evaluator.eval(p1) === InternalRow(true))
+
+    // Update - Merge - Eval
+    val p2 = evaluator.update(InternalRow(false), InternalRow(false))
+    val m1 = evaluator.merge(p0, p2)
+    assert(evaluator.eval(m1) === InternalRow(false))
+
+    // Update - Merge - Eval (empty partition at the end)
+    val m2 = evaluator.merge(p2, p1, p0)
+    assert(evaluator.eval(m2) === InternalRow(true))
+  }
+
+}