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From "radu (JIRA)" <j...@apache.org>
Subject [jira] [Created] (FLINK-6073) Support for SQL inner queries for proctime
Date Thu, 16 Mar 2017 13:20:41 GMT
radu created FLINK-6073:

             Summary: Support for SQL inner queries for proctime
                 Key: FLINK-6073
                 URL: https://issues.apache.org/jira/browse/FLINK-6073
             Project: Flink
          Issue Type: Sub-task
          Components: Table API & SQL
            Reporter: radu
            Priority: Critical

Time target: Proc Time

**SQL targeted query examples:**
Q1) `Select  item, (select item2 from stream2 ) as itemExtern from stream1;`

Comments: This is the main functionality targeted by this JIRA to enable to combine in the
main query results from an inner query.

Q2) `Select  s1.item, (Select a2 from table as t2 where table.id = s1.id  limit 1) from s1;`

Another equivalent way to write the first example of inner query is with limit 1. This ensures
the equivalency with the SingleElementAggregation used when translated the main target syntax
for inner query. We must ensure that the 2 syntaxes are supported and implemented with the
same functionality. 
There is the option also to select elements in the inner query from a table not just from
a different stream. This should be a sub-JIRA issue implement this support.

Parsing the SQL inner query via calcite is translated to a join function (left join with always
true condition) between the output of the query on the main stream and the output of a single
output aggregation operation on the inner query. The translation logic is shown below
LogicalJoin [condition=true;type=LEFT]
		…logic of inner query (LogicalProject, LogicalScan…)
	…logical of main,external query (LogicalProject, LogicalScan…))

`LogicalJoin[condition=true;type=LEFT] `– it can be considered as a special case operation
rather than a proper join to be implemented between stream-to-stream. The implementation behavior
should attach to the main stream output a value from a different query. 

`LogicalSingleValue[type=aggregation]` – it can be interpreted as the holder of the single
value that results from the inner query. As this operator is the guarantee that the inner
query will bring to the join no more than one value, there are several options on how to consider
it’s functionality in the streaming context:
1. 	Throw an error if the inner query returns more than one result. This would be a typical
behavior in the case of standard SQL over DB. However, it is very unlikely that a stream would
only emit a single value. Therefore, such a behavior would be very limited for streams in
the inner query. However, such a behavior might be more useful and common if the inner query
is over a table. 
1. 	We can interpret the usage of this parameter as the guarantee that at one moment only
one value is selected. Therefore the behavior would rather be as a filter to select one value.
This brings the option that the output of this operator evolves in time with the second stream
that drives the inner query. The decision on when to evolve the stream should depend on what
marks the evolution of the stream (processing time, watermarks/event time, ingestion time,
window time partitions…).

 In this JIRA issue the evolution would be marked by the processing time. For this implementation
the operator would work based on option 2. Hence at every moment the state of the operator
that holds one value can evolve with the last elements. In this way the logic of the inner
query is to select always the last element (fields, or other query related transformations
based on the last value). This behavior is needed in many scenarios: (e.g., the typical problem
of computing the total income, when incomes are in multiple currencies and the total needs
to be computed in one currency by using always the last exchange rate).
This behavior is motivated also by the functionality of the 3rd SQL query example – Q3 
(using inner query as the input source for FROM ). In such scenarios, the selection in the
main query would need to be done based on latest elements. Therefore with such a behavior
the 2 types of queries (Q1 and Q3) would provide the same, intuitive result.

**Functionality example**
Based on the logical translation plan, we exemplify next the behavior of the inner query applied
on 2 streams that operate on processing time.
SELECT amount, (SELECT exchange FROM inputstream1) AS field1 FROM inputstream2

<style type="text/css">
<table class="tg">
    <th class="tg-9hbo">Time</th>
    <th class="tg-9hbo">Stream1</th>
    <th class="tg-9hbo">Stream2</th>
    <th class="tg-9hbo">Output</th>
    <td class="tg-yw4l">T1</td>
    <td class="tg-yw4l"></td>
    <td class="tg-yw4l">1.2</td>
    <td class="tg-yw4l"></td>
    <td class="tg-yw4l">T2</td>
    <td class="tg-yw4l">User1,10</td>
    <td class="tg-yw4l"></td>
    <td class="tg-yw4l">(10,1.2)</td>
    <td class="tg-yw4l">T3</td>
    <td class="tg-yw4l">User2,11</td>
    <td class="tg-yw4l"></td>
    <td class="tg-yw4l">(11,1.2)</td>
    <td class="tg-yw4l">T4</td>
    <td class="tg-yw4l"></td>
    <td class="tg-yw4l">1.3</td>
    <td class="tg-yw4l"></td>
    <td class="tg-yw4l">T5</td>
    <td class="tg-yw4l">User3,9</td>
    <td class="tg-yw4l"></td>
    <td class="tg-yw4l">(9,1.3)</td>
    <td class="tg-9hbo" colspan="4">...</td>

Note 1. For streams that would operate on event time, at moment T3 we would need to retract
the previous outputs ((10, 1.2), (11,1.2) ) and reemit them as ((10,1.3), (11,1.3) ). 

Note 2. Rather than failing when a new value comes in the inner query we just update the state
that holds the single value. If option 1 for the behavior of LogicalSingleValue is chosen,
than an error should be triggered at moment T3.

**Implementation option**
Considering the notes and the option for the behavior the operator would be implemented by
using the join function of flink  with a custom always true join condition and an inner selection
for the output based on the incoming direction (to mimic the left join). The single value
selection can be implemented over a statefull flat map. In case the join is executed in parallel
by multiple operators, than we either use a parallelism of 1 for the statefull flatmap (option
1) or we broadcast the outputs of the flatmap to all join instances to ensure consistency
of the results (option 2). Considering that the flatMap functionality of selecting one value
is light, option 1 is better.  The design schema is shown below.

> [See Picture in the document] 

**General logic of Join**
                 .where(new ConstantConditionSelector())
                 .equalTo(new ConstantConditionSelector())
                .trigger(new LeftFireTrigger())
                .evictor(new Evictor())

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