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https://issues.apache.org/jira/browse/FLINK-5658?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15882521#comment-15882521
]
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_r102927451
--- Diff:
flink-libraries/flink-table/src/main/scala/org/apache/flink/table/plan/nodes/datastream/DataStreamSlideEventTimeRowAgg.scala
---
@@ -0,0 +1,179 @@
+/*
+ * 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.plan.nodes.datastream
+
+import org.apache.calcite.plan.{RelOptCluster, RelTraitSet}
+import org.apache.calcite.rel.`type`.RelDataType
+import org.apache.calcite.rel.core.AggregateCall
+import org.apache.calcite.rel.{RelNode, RelWriter, SingleRel}
+import org.apache.flink.api.java.tuple.Tuple
+import org.apache.flink.types.Row
+import org.apache.flink.table.calcite.{FlinkRelBuilder, FlinkTypeFactory}
+import FlinkRelBuilder.NamedWindowProperty
+import org.apache.flink.table.runtime.aggregate.AggregateUtil._
+import org.apache.flink.table.runtime.aggregate._
+import org.apache.flink.streaming.api.datastream.{AllWindowedStream,
DataStream, WindowedStream}
+import org.apache.flink.streaming.api.windowing.assigners._
+import org.apache.flink.streaming.api.windowing.windows.{Window =>
DataStreamWindow}
+import org.apache.flink.table.api.StreamTableEnvironment
+import org.apache.flink.table.plan.nodes.CommonAggregate
+
+class DataStreamSlideEventTimeRowAgg(
+ namedProperties: Seq[NamedWindowProperty],
+ cluster: RelOptCluster,
+ traitSet: RelTraitSet,
+ inputNode: RelNode,
+ namedAggregates: Seq[CalcitePair[AggregateCall, String]],
+ rowRelDataType: RelDataType,
+ inputType: RelDataType,
+ grouping: Array[Int])
+ extends SingleRel(cluster, traitSet, inputNode)
+ with CommonAggregate
+ with DataStreamRel {
+
+ override def deriveRowType(): RelDataType = rowRelDataType
+
+ override def copy(traitSet: RelTraitSet, inputs:
java.util.List[RelNode]): RelNode = {
+ new DataStreamSlideEventTimeRowAgg(
+ namedProperties,
+ cluster,
+ traitSet,
+ inputs.get(0),
+ namedAggregates,
+ getRowType,
+ inputType,
+ grouping)
--- End diff --
I think `I check whether the current data is out of order in WindowOperator
isLate function, and now just discard if islate.` will not work well. Because
the logic in this method is:
```
if (windowAssigner instanceof GlobalEventTimeRowWindowAssigner) {
return
windowAssignerContext.getCurrentElementTime() <
windowAssignerContext.getCurrentMaxTime();
}
```
e.g. Test Data:
```
1, 1L, "Hi", 1400000L
2, 2L, "Hello", 1400005L
3, 2L, "Hello w", 1300000
4, 3L, "Hello world", 1400010L
```
You do not know which element first comes, so you will get different
results every time you run it，Just like：
`SELECT` d, SUM(a) over (order by rowtime() range between unbounded
preceding and current row) from T1`
You can get the following results：
The first time:
```
1400005,2
1400010,6
```
The second time
```
1400000,1
1400010,5
```
The third time
```
1300000,3
1400005,5
1400010,9
```
So，IMHO. Event-time over must handle the situation above. How do you think?
> 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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