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https://issues.apache.org/jira/browse/FLINK-6250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15978916#comment-15978916
]
ASF GitHub Bot commented on FLINK-6250:
---------------------------------------
Github user stefanobortoli commented on a diff in the pull request:
https://github.com/apache/flink/pull/3732#discussion_r112712717
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/ProcTimeBoundedDistinctRowsOver.scala
---
@@ -0,0 +1,230 @@
+/*
+ * 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 java.util
+
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.streaming.api.functions.ProcessFunction
+import org.apache.flink.types.Row
+import org.apache.flink.util.{Collector, Preconditions}
+import org.apache.flink.api.common.state.ValueStateDescriptor
+import org.apache.flink.api.java.typeutils.RowTypeInfo
+import org.apache.flink.api.common.state.ValueState
+import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
+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.{List => JList}
+
+import org.apache.flink.api.common.typeinfo.BasicTypeInfo
+import org.apache.flink.api.common.state.ListState
+
+class ProcTimeBoundedDistinctRowsOver(
+ private val aggregates: Array[AggregateFunction[_]],
+ private val aggFields: Array[Array[Int]],
+ private val distinctAggsFlag: Array[Boolean],
+ private val precedingOffset: Long,
+ private val forwardedFieldCount: Int,
+ private val aggregatesTypeInfo: RowTypeInfo,
+ private val inputType: TypeInformation[Row])
+ extends ProcessFunction[Row, Row] {
+
+ Preconditions.checkNotNull(aggregates)
+ Preconditions.checkNotNull(aggFields)
+ Preconditions.checkNotNull(distinctAggsFlag)
+ Preconditions.checkNotNull(distinctAggsFlag.length == aggregates.length)
+ Preconditions.checkArgument(aggregates.length == aggFields.length)
+ Preconditions.checkArgument(precedingOffset > 0)
+
+ private var accumulatorState: ValueState[Row] = _
+ private var rowMapState: MapState[Long, JList[Row]] = _
+ private var output: Row = _
+ private var counterState: ValueState[Long] = _
+ private var smallestTsState: ValueState[Long] = _
+ private var distinctValueStateList: Array[MapState[Any, Long]] = _
+
+ override def open(config: Configuration) {
+
+ output = new Row(forwardedFieldCount + aggregates.length)
+ // We keep the elements received in a Map state keyed
+ // by the ingestion time in the operator.
+ // we also keep counter of processed elements
+ // and timestamp of oldest element
+ val rowListTypeInfo: TypeInformation[JList[Row]] =
+ new
ListTypeInfo[Row](inputType).asInstanceOf[TypeInformation[JList[Row]]]
+
+ val mapStateDescriptor: MapStateDescriptor[Long, JList[Row]] =
+ new MapStateDescriptor[Long, JList[Row]]("windowBufferMapState",
+ BasicTypeInfo.LONG_TYPE_INFO.asInstanceOf[TypeInformation[Long]],
rowListTypeInfo)
+ rowMapState = getRuntimeContext.getMapState(mapStateDescriptor)
+
+ val aggregationStateDescriptor: ValueStateDescriptor[Row] =
+ new ValueStateDescriptor[Row]("aggregationState", aggregatesTypeInfo)
+ accumulatorState =
getRuntimeContext.getState(aggregationStateDescriptor)
+
+ val processedCountDescriptor : ValueStateDescriptor[Long] =
+ new ValueStateDescriptor[Long]("processedCountState", classOf[Long])
+ counterState = getRuntimeContext.getState(processedCountDescriptor)
+
+ val smallestTimestampDescriptor : ValueStateDescriptor[Long] =
+ new ValueStateDescriptor[Long]("smallestTSState", classOf[Long])
+ smallestTsState =
getRuntimeContext.getState(smallestTimestampDescriptor)
+ distinctValueStateList = new Array(aggregates.size)
+ for(i <- 0 until aggregates.size){
+ if(distinctAggsFlag(i)){
+ val distinctValDescriptor = new MapStateDescriptor[Any, Long](
+
"distinctValuesBufferMapState" + i,
+ classOf[Any],
+ classOf[Long])
+
distinctValueStateList(i)=getRuntimeContext.getMapState(distinctValDescriptor)
+ }
+ }
+ }
+
+ override def processElement(
+ input: Row,
+ ctx: ProcessFunction[Row, Row]#Context,
+ out: Collector[Row]): Unit = {
+
+ val currentTime = ctx.timerService.currentProcessingTime
+ var i = 0
+
+ // initialize state for the processed element
+ var accumulators = accumulatorState.value
+ if (accumulators == null) {
+ accumulators = new Row(aggregates.length)
+ while (i < aggregates.length) {
+ accumulators.setField(i, aggregates(i).createAccumulator())
+ i += 1
+ }
+ }
+
+ // get smallest timestamp
+ var smallestTs = smallestTsState.value
+ if (smallestTs == 0L) {
+ smallestTs = currentTime
+ smallestTsState.update(smallestTs)
+ }
+ // get previous counter value
+ var counter = counterState.value
+
+ if (counter == precedingOffset) {
+ val retractList = rowMapState.get(smallestTs)
+
+ // get oldest element beyond buffer size
+ // and if oldest element exist, retract value
+ var removeCounter :Integer = 0
+ var distinctCounter : Integer = 0
+ var retractVal : Object = null
+ i = 0
+ while (i < aggregates.length) {
+ val accumulator =
accumulators.getField(i).asInstanceOf[Accumulator]
+ retractVal = retractList.get(0).getField(aggFields(i)(0))
+ if(distinctAggsFlag(i)){
+ var distinctValCounter: Long =
distinctValueStateList(i).get(retractVal)
+ // if the value to be retract is the last one added
+ // the remove it and retract the value
+ if(distinctValCounter == 1L){
+ aggregates(i).retract(accumulator, retractVal)
+ distinctValueStateList(i).remove(retractVal)
+ } // if the are other values in the buffer
+ // decrease the counter and continue
+ else {
+ distinctValCounter -= 1
+ distinctValueStateList(i).put(retractVal,distinctValCounter)
+ }
+ }else {
+ aggregates(i).retract(accumulator, retractVal)
+ }
+ i += 1
+ }
+ retractList.remove(0)
+ // if reference timestamp list not empty, keep the list
+ if (!retractList.isEmpty) {
+ rowMapState.put(smallestTs, retractList)
+ } // if smallest timestamp list is empty, remove and find new
smallest
+ else {
+ rowMapState.remove(smallestTs)
+ val iter = rowMapState.keys.iterator
+ var currentTs: Long = 0L
+ var newSmallestTs: Long = Long.MaxValue
+ while (iter.hasNext) {
+ currentTs = iter.next
+ if (currentTs < newSmallestTs) {
+ newSmallestTs = currentTs
+ }
+ }
+ smallestTsState.update(newSmallestTs)
+ }
+ } // we update the counter only while buffer is getting filled
+ else {
+ counter += 1
+ counterState.update(counter)
+ }
+
+ // copy forwarded fields in output row
+ i = 0
+ while (i < forwardedFieldCount) {
+ output.setField(i, input.getField(i))
+ i += 1
+ }
+
+ // accumulate current row and set aggregate in output row
+ i = 0
+ while (i < aggregates.length) {
+ val index = forwardedFieldCount + i
+ val accumulator = accumulators.getField(i).asInstanceOf[Accumulator]
+ val inputValue = input.getField(aggFields(i)(0))
+ // check if distinct aggregation
+ if(distinctAggsFlag(i)){
+ // if first time we see value, set counter and aggregate
+ var distinctValCounter: Long =
distinctValueStateList(i).get(inputValue)
+ // if counter is 0L first time we aggregate
+ // for a seen value but never accumulated
+ if(distinctValCounter == 0L){
--- End diff --
if it is a long, it returns 0L, don't ask me why. :-D
> Distinct procTime with Rows boundaries
> --------------------------------------
>
> Key: FLINK-6250
> URL: https://issues.apache.org/jira/browse/FLINK-6250
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: radu
> Assignee: Stefano Bortoli
>
> Support proctime with rows boundaries
> Q1.1. `SELECT SUM( DISTINCT b) OVER (ORDER BY procTime() ROWS BETWEEN 2
> PRECEDING AND CURRENT ROW) FROM stream1`
> Q1.1. `SELECT COUNT(b), SUM( DISTINCT b) OVER (ORDER BY procTime() ROWS
> BETWEEN 2 PRECEDING AND CURRENT ROW) FROM stream1`
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