radu created FLINK-6075:
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             Summary: Support Limit/Top(Sort) for Stream SQL
                 Key: FLINK-6075
                 URL: https://issues.apache.org/jira/browse/FLINK-6075
             Project: Flink
          Issue Type: Bug
          Components: Table API & SQL
            Reporter: radu


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. 


> see picture in the attached document  



**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());
```




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