[jira] [Resolved] (FLINK-6650) Fix Non-windowed group-aggregate error when using append-table mode.

["Timo Walther (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-6650?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Timo Walther resolved FLINK-6650.
---------------------------------
       Resolution: Fixed
    Fix Version/s: 1.4.0
                   1.3.0

Fixed in 1.4: 61914abffa83a55d4f0a339dbcf64c540962a9cd
Fixed in 1.3: 0f86deed28ab326c8cdb886e3b5ea32da76beab7

> Fix Non-windowed group-aggregate error when using append-table mode.
> --------------------------------------------------------------------
>
>                 Key: FLINK-6650
>                 URL: https://issues.apache.org/jira/browse/FLINK-6650
>             Project: Flink
>          Issue Type: Sub-task
>          Components: Table API & SQL
>            Reporter: sunjincheng
>            Assignee: sunjincheng
>             Fix For: 1.3.0, 1.4.0
>
>
> When I test Non-windowed group-aggregate with {{stream.toTable(tEnv, 'a, 'b, 'c).select('a.sum, weightAvgFun('a, 'b)).toAppendStream[Row].addSink(new StreamITCase.StringSink)}}, I got the error as follows:
> {code}
> org.apache.flink.table.api.TableException: Table is not an append-only table. Output needs to handle update and delete changes.
> 	at org.apache.flink.table.api.StreamTableEnvironment.translate(StreamTableEnvironment.scala:631)
> 	at org.apache.flink.table.api.StreamTableEnvironment.translate(StreamTableEnvironment.scala:607)
> 	at org.apache.flink.table.api.scala.StreamTableEnvironment.toAppendStream(StreamTableEnvironment.scala:219)
> 	at org.apache.flink.table.api.scala.StreamTableEnvironment.toAppendStream(StreamTableEnvironment.scala:195)
> 	at org.apache.flink.table.api.scala.TableConversions.toAppendStream(TableConversions.scala:121)
> {code}
> The reason is {{DataStreamGroupAggregate#producesUpdates}} as follows:
> {code}
> override def producesUpdates = true
> {code}
> I think in the view of the user, what user want are(for example):
> Data:
> {code}
> val data = List(
>       (1L, 1, "Hello"),
>       (2L, 2, "Hello"),
>       (3L, 3, "Hello"),
>       (4L, 4, "Hello"),
>       (5L, 5, "Hello"),
>       (6L, 6, "Hello"),
>       (7L, 7, "Hello World"),
>       (8L, 8, "Hello World"),
>       (20L, 20, "Hello World"))
> {code}
> * Case1:
> TableAPI
> {code}
>  stream.toTable(tEnv, 'a, 'b, 'c).select('a.sum)
>  .toAppendStream[Row].addSink(new StreamITCase.StringSink)
> {code}
> Result
> {code}
> // StringSink process datas:
> 1
> 3
> 6
> 10
> 15
> 21
> 28
> 36
> 56
> // Last output datas:
> 1
> 3
> 6
> 10
> 15
> 21
> 28
> 36
> 56
> {code}
> * Case 2:
> TableAPI
> {code}
> stream.toTable(tEnv, 'a, 'b, 'c).select('a.sum).toRetractStream[Row]
> .addSink(new StreamITCase.RetractingSink)
> {code}
> Result:
> {code}
> // RetractingSink process datas:
> (true,1)
> (false,1)
> (true,3)
> (false,3)
> (true,6)
> (false,6)
> (true,10)
> (false,10)
> (true,15)
> (false,15)
> (true,21)
> (false,21)
> (true,28)
> (false,28)
> (true,36)
> (false,36)
> (true,56)
> // Last output data:
> 56
> {code}
> In fact about #Case 1,we can using unbounded OVER windows, as follows:
> TableAPI
> {code}
> stream.toTable(tEnv, 'a, 'b, 'c, 'proctime.proctime)
>     .window(Over orderBy 'proctime preceding UNBOUNDED_ROW as 'w)
>     .select('a.sum over 'w)
>     .toAppendStream[Row].addSink(new StreamITCase.StringSink)
> {code}
> Result
> {code}
> Same as #Case1
> {code}
> But after the [FLINK-6649 | https://issues.apache.org/jira/browse/FLINK-6649] OVER can not express the #Case1 with earlyFiring.
> So I still think that Non-windowed group-aggregate not always update-table, user can decide which mode to use.
> Is there any drawback to this improvement? Welcome anyone feedback?



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