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https://issues.apache.org/jira/browse/FLINK-5654?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15948651#comment-15948651
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ASF GitHub Bot commented on FLINK-5654:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3641#discussion_r108867389
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/ProcTimeBoundedProcessingOverProcessFunction.scala
---
@@ -0,0 +1,166 @@
+/*
+ * 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.flink.table.runtime.aggregate
+
+import org.apache.flink.api.common.state.{ ListState, ListStateDescriptor }
+import org.apache.flink.api.java.typeutils.RowTypeInfo
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.runtime.state.{ FunctionInitializationContext,
FunctionSnapshotContext }
+import org.apache.flink.streaming.api.functions.ProcessFunction
+import org.apache.flink.table.functions.{ Accumulator, AggregateFunction }
+import org.apache.flink.types.Row
+import org.apache.flink.util.{ Collector, Preconditions }
+import org.apache.flink.api.common.state.ValueState
+import org.apache.flink.api.common.state.ValueStateDescriptor
+import scala.util.control.Breaks._
+import org.apache.flink.api.java.tuple.{ Tuple2 => JTuple2 }
+import org.apache.flink.api.common.state.MapState
+import org.apache.flink.api.common.state.MapStateDescriptor
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.api.java.typeutils.ListTypeInfo
+import java.util.{ ArrayList, LinkedList, List => JList }
+import org.apache.flink.api.common.typeinfo.BasicTypeInfo
+
+/**
+ * Process Function used for the aggregate in bounded proc-time OVER window
+ * [[org.apache.flink.streaming.api.datastream.DataStream]]
+ *
+ * @param aggregates the list of all
[[org.apache.flink.table.functions.AggregateFunction]]
+ * used for this aggregation
+ * @param aggFields the position (in the input Row) of the input value
for each aggregate
+ * @param forwardedFieldCount Is used to indicate fields in the current
element to forward
+ * @param rowTypeInfo Is used to indicate the field schema
+ * @param timeBoundary Is used to indicate the processing time boundaries
+ * @param inputType It is used to mark the Row type of the input
+ */
+class ProcTimeBoundedProcessingOverProcessFunction(
+ private val aggregates: Array[AggregateFunction[_]],
+ private val aggFields: Array[Int],
+ private val forwardedFieldCount: Int,
+ private val rowTypeInfo: RowTypeInfo,
+ private val timeBoundary: Long,
+ private val inputType: TypeInformation[Row])
+ extends ProcessFunction[Row, Row] {
+
+ Preconditions.checkNotNull(aggregates)
+ Preconditions.checkNotNull(aggFields)
+ Preconditions.checkArgument(aggregates.length == aggFields.length)
+
+ private var output: Row = _
+ private var accumulatorState: ValueState[Row] = _
+ private var rowMapState: MapState[Long, JList[Row]] = _
+
+ override def open(config: Configuration) {
+ output = new Row(forwardedFieldCount + aggregates.length)
+
+ // We keep the elements received in a list state
+ // together with the ingestion time in the operator
+ val rowListTypeInfo: TypeInformation[JList[Row]] =
+ new
ListTypeInfo[Row](inputType).asInstanceOf[TypeInformation[JList[Row]]]
+ val mapStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
+ new MapStateDescriptor[Long, JList[Row]]("rowmapstate",
+ BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]],
rowListTypeInfo)
+ rowMapState = getRuntimeContext.getMapState(mapStateDescriptor)
+
+ val stateDescriptor: ValueStateDescriptor[Row] =
+ new ValueStateDescriptor[Row]("overState", rowTypeInfo)
+ accumulatorState = getRuntimeContext.getState(stateDescriptor)
+ }
+
+ override def processElement(
+ input: Row,
+ ctx: ProcessFunction[Row, Row]#Context,
+ out: Collector[Row]): Unit = {
+
+ val currentTime = ctx.timerService().currentProcessingTime()
+ //buffer the event incoming event
+
+ var i = 0
+
+ //initialize the accumulators
+ var accumulators = accumulatorState.value()
+
+ if (null == accumulators) {
+ accumulators = new Row(aggregates.length)
+ i = 0
+ while (i < aggregates.length) {
+ accumulators.setField(i, aggregates(i).createAccumulator())
+ i += 1
+ }
+ }
+
+ //set the fields of the last event to carry on with the aggregates
+ i = 0
+ while (i < forwardedFieldCount) {
+ output.setField(i, input.getField(i))
+ i += 1
+ }
+
+ //update the elements to be removed and retract them from aggregators
+ val limit = currentTime - timeBoundary
+
+ // we iterate through all elements in the window buffer based on
timestampt keys
+ // when we find timestamps that are out of interest, we need to get
the corresponding elements
+ // and eliminate them. Multiple elements can be received at the same
timestamp
+ val iter = rowMapState.keys.iterator
+ var markToRemove = new ArrayList[Long]()
+ while (iter.hasNext()) {
+ val elementKey = iter.next
+ if (elementKey < limit) {
+ val elementsRemove = rowMapState.get(elementKey)
+ val iterRemove = elementsRemove.iterator()
--- End diff --
We would save the `Iterator` object instance. Probably micro optimization,
but since we iterate most lists with `while(i < x)` we could do the same here.
> Add processing time OVER RANGE BETWEEN x PRECEDING aggregation to SQL
> ---------------------------------------------------------------------
>
> Key: FLINK-5654
> URL: https://issues.apache.org/jira/browse/FLINK-5654
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Fabian Hueske
> Assignee: radu
>
> The goal of this issue is to add support for OVER RANGE aggregations on
> processing time streams to the SQL interface.
> Queries similar to the following should be supported:
> {code}
> SELECT
> a,
> SUM(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1'
> HOUR PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY procTime() RANGE BETWEEN INTERVAL '1'
> HOUR PRECEDING AND CURRENT ROW) AS minB
> FROM myStream
> {code}
> The following restrictions should initially apply:
> - All OVER clauses in the same SELECT clause must be exactly the same.
> - The PARTITION BY clause is optional (no partitioning results in single
> threaded execution).
> - The ORDER BY clause may only have procTime() as parameter. procTime() is a
> parameterless scalar function that just indicates processing time mode.
> - UNBOUNDED PRECEDING is not supported (see FLINK-5657)
> - FOLLOWING is not supported.
> The restrictions will be resolved in follow up issues. If we find that some
> of the restrictions are trivial to address, we can add the functionality in
> this issue as well.
> This issue includes:
> - Design of the DataStream operator to compute OVER ROW aggregates
> - Translation from Calcite's RelNode representation (LogicalProject with
> RexOver expression).
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