Mysql – Correlated subqueries. Count Visits after Last Purchase Date

This specific case (the exists predicate) is a bit of an outlier as far as correlated subqueries go. ish...

Some correlated subqueries are certainly evaluated once for every row in the outer query, but I don't think you can apply that across the board, and I specifically think that's a statement that cannot be applied with authority to the exists predicate.

The SQL example below this paragraph is highly contrived, but nevertheless illustrates an "evaluated once for each row in the outer query" correlated subquery. It's "highly contrived" because a straight relational join would be a much better solution, and in this simple case the join is super easy to write.

select
    team_name
    , (
       select league_name
       from leagues
       where leagues.league_id = teams.league_id
    ) as league_name
from teams

In this instance, each row in the result set is going to contain the name of the league that the team belongs to, because of the correlated subquery. And the correlated subquery is probably going to be run once for each row in the outer query. Maybe.

The thing is, the database's query optimizer might rewrite this as a more efficient join, depending on the database. So even this simple example might not be executed "once per row in the outer query."

Either way, the subquery is dependent on the values returned from the table in the outer query, and that makes it a correlated subquery--because of the correlation with the outer table.

Part of the point of SQL in general is that the specific implementation is not important to you.

You should think in terms of sets of data (think set arithmetic), not in terms of iterations and comparisons per iteration.

The exists predicate doesn't make you any promises about the particular implementation. In general, exists is supposed to take advantage of table statistics and indexes in such a way that it produces a more efficient query plan than many other approaches will (and more efficient than you're likely to come up with on your own).

The actual query that ends up being run is going to be some kind of join, or multiple joins. But you don't need to get worked up about that. That's why your exists clause contains select * instead of identifying specific fields.

The query plan produced by the database engine will figure out which fields it wants to use, which key or keys and which index or indexes matter, etc.

So your exists example is still a correlated subquery, despite the underlying implementation, because the inner query refers to and is logically dependent upon the table in the outer query.

SELECT DISTINCT store_type FROM stores
WHERE EXISTS
    (
    SELECT * FROM cities_stores
    WHERE cities_stores.store_type = stores.store_type
    );

You can use conditional sum for this calculation something as

select 
date_format(created,'%d-%m') as created_date , 
sum( case when status='open' then 1 else 0 end )+
sum( case when status='new' then 1 else 0 end )+
sum( case when status='resolved' and date(resolved) > date(created) then 1 else 0 end ) as open
from table_name
where
YEAR(Created) = '2015'
group by created_date ;

Here is a test case

mysql> select * from test ;
+------+---------------------+---------------------+----------+
| id   | created             | resolved            | status   |
+------+---------------------+---------------------+----------+
|    1 | 2015-05-10 00:00:00 | 1970-01-01 00:00:00 | open     |
|    2 | 2015-05-10 00:00:00 | 1970-01-01 00:00:00 | new      |
|    3 | 2015-05-10 00:00:00 | 2015-05-12 00:00:00 | resolved |
|    4 | 2015-05-11 00:00:00 | 1970-01-01 00:00:00 | open     |
|    5 | 2015-05-11 00:00:00 | 1970-01-01 00:00:00 | new      |
|    6 | 2015-05-11 00:00:00 | 2015-05-11 00:00:00 | resolved |
+------+---------------------+---------------------+----------+
6 rows in set (0.00 sec)

mysql> select 
    -> date_format(created,'%d-%m') as created_date , 
    -> sum( case when status='open' then 1 else 0 end )+
    -> sum( case when status='new' then 1 else 0 end )+
    -> sum( case when status='resolved' and date(resolved) > date(created) then 1 else 0 end ) as open
    -> from test
    -> where
    -> YEAR(Created) = '2015'
    -> group by created_date ;
+--------------+------+
| created_date | open |
+--------------+------+
| 10-05        |    3 |
| 11-05        |    2 |
+--------------+------+
2 rows in set (0.00 sec)

UPDATE The count of open is a incremental as per the comment. So the following should do the trick

select
created_date,
@open:= @open+open_ticket-resolved as open
from(
 select 
 date_format(created,'%d-%m') as created_date , 
 sum( case when status='open' then 1 else 0 end )+
 sum( case when status='new' then 1 else 0 end )+
 sum( case when status='resolved' and ( date(resolved) > date(created) or date(resolved) = date(created)) then 1 else 0 end ) as open_ticket,
 sum(case when status = 'resolved' and date(resolved) = date(created) then 1 else 0 end) as resolved
 from test
 where
 YEAR(Created) = '2015'
 group by created_date
)x,(select @open:=0)r order by created_date ; 

From the above sample data it will have

 +--------------+------+
| created_date | open |
+--------------+------+
| 10-05        |    3 |
| 11-05        |    5 |
+--------------+------+

UPDATE:

Count the previous tickets which got resolved on a given date and deduct that from the current day open ticket, for that you need a left join something as

select
created_date,
@open:= @open+open_ticket-resolved_ticket as open
from(
 select 
 date_format(t.created,'%d-%m') as created_date , 
 sum( case when t.status='open' then 1 else 0 end )+
 sum( case when t.status='new' then 1 else 0 end )+
 sum( case when t.status='resolved' and ( date(t.resolved) > date(created) or date(t.resolved) = date(created)) then 1 else 0 end ) as open_ticket,
 coalesce(tot,0) as resolved_ticket
 from test t left join(
    select count(*) as tot, date(resolved) as resolved from test where status='resolved' group by date(resolved)
 )x on date(x.resolved) = date(t.created) 
 where
 YEAR(t.created) = '2015'
 group by created_date
)x,(select @open:=0)r order by created_date ; 

OUTPUT :

+--------------+------+
| created_date | open |
+--------------+------+
| 10-05        |    3 |
| 11-05        |    5 |
| 12-05        |    7 |
+--------------+------+