[SPARK-18497][SS]Make ForeachSink support watermark

sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/ForeachSink.scala

@@ -47,22 +47,22 @@ class ForeachSink[T : Encoder](writer: ForeachWriter[T]) extends Sink with Seria
     // method supporting incremental planning. But in the long run, we should generally make newly
     // created Datasets use `IncrementalExecution` where necessary (which is SPARK-16264 tries to
     // resolve).
-
+    val incrementalExecution = data.queryExecution.asInstanceOf[IncrementalExecution]
     val datasetWithIncrementalExecution =
-      new Dataset(data.sparkSession, data.logicalPlan, implicitly[Encoder[T]]) {
+      new Dataset(data.sparkSession, incrementalExecution, implicitly[Encoder[T]]) {
         override lazy val rdd: RDD[T] = {
           val objectType = exprEnc.deserializer.dataType
           val deserialized = CatalystSerde.deserialize[T](logicalPlan)
 
           // was originally: sparkSession.sessionState.executePlan(deserialized) ...
-          val incrementalExecution = new IncrementalExecution(
+          val newIncrementalExecution = new IncrementalExecution(
             this.sparkSession,
             deserialized,
-            data.queryExecution.asInstanceOf[IncrementalExecution].outputMode,
-            data.queryExecution.asInstanceOf[IncrementalExecution].checkpointLocation,
-            data.queryExecution.asInstanceOf[IncrementalExecution].currentBatchId,
-            data.queryExecution.asInstanceOf[IncrementalExecution].currentEventTimeWatermark)
-          incrementalExecution.toRdd.mapPartitions { rows =>
+            incrementalExecution.outputMode,
+            incrementalExecution.checkpointLocation,
+            incrementalExecution.currentBatchId,
+            incrementalExecution.currentEventTimeWatermark)
+          newIncrementalExecution.toRdd.mapPartitions { rows =>
             rows.map(_.get(0, objectType))
           }.asInstanceOf[RDD[T]]
         }

sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/ForeachSinkSuite.scala

@@ -25,6 +25,7 @@ import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkException
 import org.apache.spark.sql.ForeachWriter
+import org.apache.spark.sql.functions.{count, window}
 import org.apache.spark.sql.streaming.{OutputMode, StreamingQueryException, StreamTest}
 import org.apache.spark.sql.test.SharedSQLContext
 
@@ -169,6 +170,40 @@ class ForeachSinkSuite extends StreamTest with SharedSQLContext with BeforeAndAf
       assert(errorEvent.error.get.getMessage === "error")
     }
   }
+
+  test("foreach with watermark") {
+    val inputData = MemoryStream[Int]
+
+    val windowedAggregation = inputData.toDF()
+      .withColumn("eventTime", $"value".cast("timestamp"))
+      .withWatermark("eventTime", "10 seconds")
+      .groupBy(window($"eventTime", "5 seconds") as 'window)
+      .agg(count("*") as 'count)
+      .select($"count".as[Long])
+      .map(_.toInt)
+      .repartition(1)
+
+    val query = windowedAggregation
+      .writeStream
+      .outputMode(OutputMode.Complete)
+      .foreach(new TestForeachWriter())
+      .start()
+    try {
+      inputData.addData(10, 11, 12)
+      query.processAllAvailable()
+
+      val allEvents = ForeachSinkSuite.allEvents()
+      assert(allEvents.size === 1)
+      val expectedEvents = Seq(
+        ForeachSinkSuite.Open(partition = 0, version = 0),
+        ForeachSinkSuite.Process(value = 3),
+        ForeachSinkSuite.Close(None)
+      )
+      assert(allEvents === Seq(expectedEvents))
+    } finally {
+      query.stop()
+    }
+  }
 }
 
 /** A global object to collect events in the executor */