[FLINK-6200][SQL] support unbounded event time range window

flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamOverAggregate.scala

@@ -127,14 +127,9 @@ class DataStreamOverAggregate(
         // row-time OVER window
         if (overWindow.lowerBound.isPreceding &&
               overWindow.lowerBound.isUnbounded && overWindow.upperBound.isCurrentRow) {
-          if (overWindow.isRows) {
-            // unbounded preceding OVER ROWS window
-            createUnboundedAndCurrentRowEventTimeOverWindow(inputDS)
-          } else {
-            // unbounded preceding OVER RANGE window
-            throw new TableException(
-              "row-time OVER RANGE UNBOUNDED PRECEDING window is not supported yet.")
-          }
+          // ROWS/RANGE clause unbounded OVER window
+          createUnboundedAndCurrentRowEventTimeOverWindow(inputDS, overWindow.isRows)
+
         } else if (overWindow.lowerBound.isPreceding && overWindow.upperBound.isCurrentRow) {
           // bounded OVER window
           if (overWindow.isRows) {
@@ -240,7 +235,8 @@ class DataStreamOverAggregate(
   }
 
   def createUnboundedAndCurrentRowEventTimeOverWindow(
-    inputDS: DataStream[Row]): DataStream[Row]  = {
+    inputDS: DataStream[Row],
+    isRows: Boolean): DataStream[Row]  = {
 
     val overWindow: Group = logicWindow.groups.get(0)
     val partitionKeys: Array[Int] = overWindow.keys.toArray
@@ -251,7 +247,8 @@ class DataStreamOverAggregate(
 
     val processFunction = AggregateUtil.createUnboundedEventTimeOverProcessFunction(
       namedAggregates,
-      inputType)
+      inputType,
+      isRows)
 
     val result: DataStream[Row] =
       // partitioned aggregation

flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/AggregateUtil.scala

@@ -140,7 +140,8 @@ object AggregateUtil {
     */
   private[flink] def createUnboundedEventTimeOverProcessFunction(
    namedAggregates: Seq[CalcitePair[AggregateCall, String]],
-   inputType: RelDataType): UnboundedEventTimeOverProcessFunction = {
+   inputType: RelDataType,
+   isRows: Boolean): UnboundedEventTimeOverProcessFunction = {
 
     val (aggFields, aggregates) =
       transformToAggregateFunctions(
@@ -150,12 +151,23 @@ object AggregateUtil {
 
     val aggregationStateType: RowTypeInfo = createAccumulatorRowType(aggregates)
 
-    new UnboundedEventTimeOverProcessFunction(
-      aggregates,
-      aggFields,
-      inputType.getFieldCount,
-      aggregationStateType,
-      FlinkTypeFactory.toInternalRowTypeInfo(inputType))
+    if (isRows) {
+      // ROWS unbounded over process function
+      new UnboundedEventTimeRowsOverProcessFunction(
+        aggregates,
+        aggFields,
+        inputType.getFieldCount,
+        aggregationStateType,
+        FlinkTypeFactory.toInternalRowTypeInfo(inputType))
+    } else {
+      // RANGE unbounded over process function
+      new UnboundedEventTimeRangeOverProcessFunction(
+        aggregates,
+        aggFields,
+        inputType.getFieldCount,
+        aggregationStateType,
+        FlinkTypeFactory.toInternalRowTypeInfo(inputType))
+    }
   }
 
   /**

flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/UnboundedEventTimeOverProcessFunction.scala

@@ -41,7 +41,7 @@ import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
   * @param inputType the input row tye which the state saved
   *
   */
-class UnboundedEventTimeOverProcessFunction(
+abstract class UnboundedEventTimeOverProcessFunction(
     private val aggregates: Array[AggregateFunction[_]],
     private val aggFields: Array[Int],
     private val forwardedFieldCount: Int,
@@ -53,7 +53,7 @@ class UnboundedEventTimeOverProcessFunction(
   Preconditions.checkNotNull(aggFields)
   Preconditions.checkArgument(aggregates.length == aggFields.length)
 
-  private var output: Row = _
+  protected var output: Row = _
   // state to hold the accumulators of the aggregations
   private var accumulatorState: ValueState[Row] = _
   // state to hold rows until the next watermark arrives
@@ -162,30 +162,9 @@ class UnboundedEventTimeOverProcessFunction(
         val curRowList = rowMapState.get(curTimestamp)
         collector.setAbsoluteTimestamp(curTimestamp)
 
-        var j = 0
-        while (j < curRowList.size) {
-          val curRow = curRowList.get(j)
-          i = 0
-
-          // copy forwarded fields to output row
-          while (i < forwardedFieldCount) {
-            output.setField(i, curRow.getField(i))
-            i += 1
-          }
-
-          // update accumulators and copy aggregates to output row
-          i = 0
-          while (i < aggregates.length) {
-            val index = forwardedFieldCount + i
-            val accumulator = lastAccumulator.getField(i).asInstanceOf[Accumulator]
-            aggregates(i).accumulate(accumulator, curRow.getField(aggFields(i)))
-            output.setField(index, aggregates(i).getValue(accumulator))
-            i += 1
-          }
-          // emit output row
-          collector.collect(output)
-          j += 1
-        }
+        // process the same timestamp datas, the mechanism is different according ROWS or RANGE
+        processElementsWithSameTimestamp(curRowList, lastAccumulator, collector)
+
         rowMapState.remove(curTimestamp)
       }
 
@@ -204,21 +183,145 @@ class UnboundedEventTimeOverProcessFunction(
    * If timestamps arrive in order (as in case of using the RocksDB state backend) this is just
    * an append with O(1).
    */
-  private def insertToSortedList(recordTimeStamp: Long) = {
+  private def insertToSortedList(recordTimestamp: Long) = {
     val listIterator = sortedTimestamps.listIterator(sortedTimestamps.size)
     var continue = true
     while (listIterator.hasPrevious && continue) {
       val timestamp = listIterator.previous
-      if (recordTimeStamp >= timestamp) {
+      if (recordTimestamp >= timestamp) {
         listIterator.next
-        listIterator.add(recordTimeStamp)
+        listIterator.add(recordTimestamp)
         continue = false
       }
     }
 
     if (continue) {
-      sortedTimestamps.addFirst(recordTimeStamp)
+      sortedTimestamps.addFirst(recordTimestamp)
     }
   }
 
+  /**
+   * Process the same timestamp datas, the mechanism is different between
+   * rows and range window.
+   */
+  def processElementsWithSameTimestamp(
+    curRowList: JList[Row],
+    lastAccumulator: Row,
+    out: Collector[Row]): Unit
+
+}
+
+/**
+  * A ProcessFunction to support unbounded ROWS window.
+  * With the ROWS option you define on a physical level how many rows are included in your window frame
+  */
+class UnboundedEventTimeRowsOverProcessFunction(
+   aggregates: Array[AggregateFunction[_]],
+   aggFields: Array[Int],
+   forwardedFieldCount: Int,
+   intermediateType: TypeInformation[Row],
+   inputType: TypeInformation[Row])
+  extends UnboundedEventTimeOverProcessFunction(
+    aggregates,
+    aggFields,
+    forwardedFieldCount,
+    intermediateType,
+    inputType) {
+
+  override def processElementsWithSameTimestamp(
+    curRowList: JList[Row],
+    lastAccumulator: Row,
+    out: Collector[Row]): Unit = {
+
+    var j = 0
+    var i = 0
+    while (j < curRowList.size) {
+      val curRow = curRowList.get(j)
+      i = 0
+
+      // copy forwarded fields to output row
+      while (i < forwardedFieldCount) {
+        output.setField(i, curRow.getField(i))
+        i += 1
+      }
+
+      // update accumulators and copy aggregates to output row
+      i = 0
+      while (i < aggregates.length) {
+        val index = forwardedFieldCount + i
+        val accumulator = lastAccumulator.getField(i).asInstanceOf[Accumulator]
+        aggregates(i).accumulate(accumulator, curRow.getField(aggFields(i)))
+        output.setField(index, aggregates(i).getValue(accumulator))
+        i += 1
+      }
+      // emit output row
+      out.collect(output)
+      j += 1
+    }
+  }
+}
+
+
+/**
+  * A ProcessFunction to support unbounded RANGE window.
+  * The RANGE option includes all the rows within the window frame
+  * that have the same ORDER BY values as the current row
+  */
+class UnboundedEventTimeRangeOverProcessFunction(
+    aggregates: Array[AggregateFunction[_]],
+    aggFields: Array[Int],
+    forwardedFieldCount: Int,
+    intermediateType: TypeInformation[Row],
+    inputType: TypeInformation[Row])
+  extends UnboundedEventTimeOverProcessFunction(
+    aggregates,
+    aggFields,
+    forwardedFieldCount,
+    intermediateType,
+    inputType) {
+
+  override def processElementsWithSameTimestamp(
+    curRowList: JList[Row],
+    lastAccumulator: Row,
+    out: Collector[Row]): Unit = {
+
+    var j = 0
+    var i = 0
+    // all same timestamp data should have same aggregation value.
+    while (j < curRowList.size) {
+      val curRow = curRowList.get(j)
+      i = 0
+      while (i < aggregates.length) {
+        val index = forwardedFieldCount + i
+        val accumulator = lastAccumulator.getField(i).asInstanceOf[Accumulator]
+        aggregates(i).accumulate(accumulator, curRow.getField(aggFields(i)))
+        i += 1
+      }
+      j += 1
+    }
+
+    // emit output row
+    j = 0
+    while (j < curRowList.size) {
+      val curRow = curRowList.get(j)
+
+      // copy forwarded fields to output row
+      i = 0
+      while (i < forwardedFieldCount) {
+        output.setField(i, curRow.getField(i))
+        i += 1
+      }
+
+      //copy aggregates to output row
+      i = 0
+      while (i < aggregates.length) {
+        val index = forwardedFieldCount + i
+        val accumulator = lastAccumulator.getField(i).asInstanceOf[Accumulator]
+        output.setField(index, aggregates(i).getValue(accumulator))
+        i += 1
+      }
+      out.collect(output)
+      j += 1
+    }
+  }
 }