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https://issues.apache.org/jira/browse/FLINK-6250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15978893#comment-15978893
]
ASF GitHub Bot commented on FLINK-6250:
---------------------------------------
Github user fhueske commented on a diff in the pull request:
https://github.com/apache/flink/pull/3732#discussion_r112700998
--- 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](
--- End diff --
We should use the concrete type of the function argument here. Otherwise,
the values won't be efficiently serialized.
> 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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