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https://issues.apache.org/jira/browse/FLINK-5658?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15934017#comment-15934017
]
ASF GitHub Bot commented on FLINK-5658:
---------------------------------------
Github user sunjincheng121 commented on a diff in the pull request:
https://github.com/apache/flink/pull/3386#discussion_r107062397
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/runtime/aggregate/UnboundedEventTimeOverProcessFunction.scala
---
@@ -0,0 +1,181 @@
+/*
+ * 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.api.common.typeinfo.{BasicTypeInfo,
TypeInformation}
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.types.Row
+import org.apache.flink.streaming.api.functions.ProcessFunction
+import org.apache.flink.util.{Collector, Preconditions}
+import org.apache.flink.api.common.state._
+import org.apache.flink.api.common.typeutils.TypeSerializer
+import org.apache.flink.api.java.tuple.Tuple2
+import org.apache.flink.api.java.typeutils.TupleTypeInfo
+import org.apache.flink.streaming.api.operators.TimestampedCollector
+import org.apache.flink.table.functions.{Accumulator, AggregateFunction}
+
+
+/**
+ * A ProcessFunction to support unbounded event-time over-window
+ *
+ * @param aggregates the aggregate functions
+ * @param aggFields the filed index which the aggregate functions use
+ * @param forwardedFieldCount the input fields count
+ * @param intermediateType the intermediate row tye which the state saved
+ * @param inputType the input row tye which the state saved
+ *
+ */
+class UnboundedEventTimeOverProcessFunction(
+ private val aggregates: Array[AggregateFunction[_]],
+ private val aggFields: Array[Int],
+ private val forwardedFieldCount: Int,
+ private val intermediateType: TypeInformation[Row],
+ 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 rowState: ListState[Tuple2[Long, Row]] = _
--- End diff --
I think `ListState` can not work well for event-time case. because we must
deal with out of order datas,for example:
If we allowedLateness = 2 ( the length of time that the user configures the
allowable data delay)
InputData:
```
(1L, 1, "Hello"),
(2L, 2, "Hello"),
**(4L, 4, "Hello"),** // We should handle `4L` and `3L` elements
correctly,because
**(3L, 3, "Hello"),** //`allowedLateness=2`
(7L, 7, "Hello"),
(7L, 8, "Hello"),
(5L, 5, "Hello"),
(8L, 8, "Hello World"),
**(20L, 20, "Hello World"),**
**(9L, 9, "Hello World"))** // we can ignore `9L`, Because 20L-9L =
11L > 2
```
So, I suggest that we can use `MapState[Long, List[Row]] ` and
`PriorityQueue[(Long, Long)]` to deal with this case. then we should consider
two things:
1. Out of order but not late event.
2. add `allowedLateness` config which use can definition.
What do you think? @hongyuhong @fhueske
> Add event time OVER RANGE BETWEEN UNBOUNDED PRECEDING aggregation to SQL
> ------------------------------------------------------------------------
>
> Key: FLINK-5658
> URL: https://issues.apache.org/jira/browse/FLINK-5658
> Project: Flink
> Issue Type: Sub-task
> Components: Table API & SQL
> Reporter: Fabian Hueske
> Assignee: Yuhong Hong
>
> The goal of this issue is to add support for OVER RANGE aggregations on event
> 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 rowTime() RANGE BETWEEN UNBOUNDED
> PRECEDING AND CURRENT ROW) AS sumB,
> MIN(b) OVER (PARTITION BY c ORDER BY rowTime() RANGE BETWEEN UNBOUNDED
> 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 rowTime() as parameter. rowTime() is a
> parameterless scalar function that just indicates processing time mode.
> - bounded PRECEDING is not supported (see FLINK-5655)
> - 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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