[jira] [Commented] (FLINK-6075) ORDER BY *time ASC

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/FLINK-6075?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16092740#comment-16092740 ] 

ASF GitHub Bot commented on FLINK-6075:
---------------------------------------

Github user rtudoran commented on the issue:

    https://github.com/apache/flink/pull/4263
  
    @fhueske ,@wuchong  thanks for the feedback. I will modify all these today - tomorrow and ping you when they are pushed.
    
    @wuchong - TopN for each group should not be hard to implement. In fact if you look at the logic of how things are implemented we are in a similar setup as for OVER windows. We have the input stream and we apply a keyBy and the processfunction (for order by / fetch/offset). Thus we would only need the Calcite syntax to make sure the partition fields are pushed in the LogicalSort object. Did you try to see if you have directly a group by if this is pushed in the sort object?
    Otherwise we can make a rule to combine a Group object with the sort object


> ORDER BY *time ASC
> ------------------
>
>                 Key: FLINK-6075
>                 URL: https://issues.apache.org/jira/browse/FLINK-6075
>             Project: Flink
>          Issue Type: New Feature
>          Components: Table API & SQL
>            Reporter: radu
>              Labels: features
>         Attachments: sort.png
>
>
> These will be split in 3 separated JIRA issues. However, the design is the same only the processing function differs in terms of the output. Hence, the design is the same for all of them.
> Time target: Proc Time
> **SQL targeted query examples:**
> *Sort example*
> Q1)` SELECT a FROM stream1 GROUP BY HOP(proctime, INTERVAL '1' HOUR, INTERVAL '3' HOUR) ORDER BY b` 
> Comment: window is defined using GROUP BY
> Comment: ASC or DESC keywords can be placed to mark the ordering type
> *Limit example*
> Q2) `SELECT a FROM stream1 WHERE rowtime BETWEEN current_timestamp - INTERVAL '1' HOUR AND current_timestamp ORDER BY b LIMIT 10`
> Comment: window is defined using time ranges in the WHERE clause
> Comment: window is row triggered
> *Top example*
> Q3) `SELECT sum(a) OVER (ORDER BY proctime RANGE INTERVAL '1' HOUR PRECEDING LIMIT 10) FROM stream1`  
> Comment: limit over the contents of the sliding window
> General Comments:
> -All these SQL clauses are supported only over windows (bounded collections of data). 
> -Each of the 3 operators will be supported with each of the types of expressing the windows. 
> **Description**
> The 3 operations (limit, top and sort) are similar in behavior as they all require a sorted collection of the data on which the logic will be applied (i.e., select a subset of the items or the entire sorted set). These functions would make sense in the streaming context only in the context of a window. Without defining a window the functions could never emit as the sort operation would never trigger. If an SQL query will be provided without limits an error will be thrown (`SELECT a FROM stream1 TOP 10` -> ERROR). Although not targeted by this JIRA, in the case of working based on event time order, the retraction mechanisms of windows and the lateness mechanisms can be used to deal with out of order events and retraction/updates of results.
> **Functionality example**
> We exemplify with the query below for all the 3 types of operators (sorting, limit and top). Rowtime indicates when the HOP window will trigger – which can be observed in the fact that outputs are generated only at those moments. The HOP windows will trigger at every hour (fixed hour) and each event will contribute/ be duplicated for 2 consecutive hour intervals. Proctime indicates the processing time when a new event arrives in the system. Events are of the type (a,b) with the ordering being applied on the b field.
> `SELECT a FROM stream1 HOP(proctime, INTERVAL '1' HOUR, INTERVAL '2' HOUR) ORDER BY b (LIMIT 2/ TOP 2 / [ASC/DESC] `)
> ||Rowtime||	Proctime||	Stream1||	Limit 2||	Top 2||	Sort [ASC]||
> |         |10:00:00  |(aaa, 11)	|	        |	      |            |
> |         |10:05:00	 |(aab, 7)  |           |	      |            |
> |10-11	  |11:00:00  |          |	aab,aaa |aab,aaa  |	aab,aaa    |
> |         |11:03:00  |(aac,21)  |           |         |            |			
> |11-12    |12:00:00  |          |	aab,aaa |aab,aaa  |	aab,aaa,aac|
> |         |12:10:00  |(abb,12)  |           |         |            |			
> |         |12:15:00  |(abb,12)  |           |         |            |			
> |12-13	  |13:00:00  |          |	abb,abb	| abb,abb |	abb,abb,aac|
> |...|
> **Implementation option**
> Considering that the SQL operators will be associated with window boundaries, the functionality will be implemented within the logic of the window as follows.
> * Window assigner – selected based on the type of window used in SQL (TUMBLING, SLIDING…)
> * Evictor/ Trigger – time or count evictor based on the definition of the window boundaries
> * Apply – window function that sorts data and selects the output to trigger (based on LIMIT/TOP parameters). All data will be sorted at once and result outputted when the window is triggered
> An alternative implementation can be to use a fold window function to sort the elements as they arrive, one at a time followed by a flatMap to filter the number of outputs. 
> !sort.png!
> **General logic of Join**
> ```
> inputDataStream.window(new [Slide/Tumble][Time/Count]Window())
> //.trigger(new [Time/Count]Trigger()) – use default
> //.evictor(new [Time/Count]Evictor()) – use default
> 		.apply(SortAndFilter());
> ```
> ------------
> JIRA will contain 
> ORDER BY *time ASC OFFSET FETCH
> ORDER BY *time DESC OFFSET FETCH
> ORDER BY * OFFSET FETCH



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