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From "radu (JIRA)" <j...@apache.org>
Subject [jira] [Created] (FLINK-6075) Support Limit/Top(Sort) for Stream SQL
Date Thu, 16 Mar 2017 13:52:42 GMT
radu created FLINK-6075:
---------------------------

             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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