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I'm building a large scale application and was wondering the difference between static counts vs dynamic counts.

Right now, I'm using dynamic counts. For an example:

SELECT *,  
(SELECT COUNT(1) FROM CarImages WHERE CarImages.carid = Cars.id) as ImageCount, 
(SELECT COUNT(1) FROM CarBuyers WHERE CarBuyers.carid = Cars.id) as BuyerCount, 
(SELECT COUNT(1) FROM CarSellers WHERE CarSellers.carid = Cars.id) as SellerCount, 
 FROM Cars WHERE Cars.CarCondition > 2 AND IsCarHasGoodRecord(Cars.carid); 

Inside IsCarHasGoodRecord function:  
function IsCarHasGoodRecrd(carid INT) RETURNS BOOLEAN 
BEGIN 
   DECLARE GoodCondition INT; DECLARE CarMaxConditionId INT; 
   SELECT 1 INTO CarMaxConditionId  FROM CarMaxConditions WHERE carid = carid; 
   SELECT 1 INTO GoodCondition FROM KellyBlueBookConfirm WHERE carid = carid AND carmaxid = CarMaxConditionId;

--If the current "car row" is good condition, return true so that the car will be included in the result set 
    if GoodCondition  = 1 THEN  
       RETURN TRUE; 
    ELSE 
     RETURN FALSE; 
    END IF; 
END$;

What happens is that for each car row, carimage count, car buyer count, car seller count is retrieved. Then the car id is passed to the IsCarHasGoodRecrd to find out if the car has a good record and whether to include it in the result set. As you can tell from the query above, about 3 correlated subqueries are executed in the main query, and 2 other correlate subqueries are returned in the function...totaling 5 correlated subqueries for each row in car table.


Now, if everything is properly INDEXED, on a high-end server (2x dual opteron, 4gigs ram, 2x 250 gig)...with a large concurrent userbase and 20 million rows in all tables......what is performance going to be like? I never dealt with such databases or userbase so I don't know what to expect.

I was thinking of using static counts (where the there is a column for each count in the parent table) but it's too much maintenance and i would have to use transactions everytime i update it to make sure the counts are not off.

Table Schema:

[Car] 
id - identity 
name - carname 
status - active, deleted etc 
created  
modified 

[CarImage] 
id - identity 
carid - foreign key, points to [car].id 
name - image name 
status 
created 

[CarBuyers] 
id - identity 
carid - foreign key, points to [car].id 
buyerid - foreign key, points to [users].id 
created 
status 

[CarSellers] 
id - identity 
carid - foreign key, points to [car].id 
sellerid - foreign key, points to [users].id 
created 
status 

[users] 
id - identity 
name 
created 
status

The reason I use a stored function is to keep the SQL syntax simple. There's alot of tables OTHER than [car] table which follows the SAME pattern (like a [parts] table OR [upgrades] table). If I put the logic in a stored procedure...I could just write:

SELECT * FROM ([car] OR [parts] or [upgrades] or whatever that follows the same pattern) WHERE iscarvalid(blahblahblah);

I could do that with left joins but it's very long and tedious to write. I heard left joins maybe faster.

However, if everything is indexed..what performance am I expecting with the server configuration in my above post?

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Last Post by cfAllie
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I'll let someone else tackle the issue of expectations, but I did have a few thoughts....

Left joins may be tedious to write, but performance is more important. I could reduce a 10 line query to 1 line. But if the 1 line query takes 45 minutes longer to run, its hardly worth it.

I'm not an expert but I would be leery of using that many correlated subqueries with that kind of volume. Afaik, correlated subqueries execute once for each value in the outer query. So _generally_ subqueries _tend_ to be less efficient than joins.

However, databases and optimizers differ. Some may be capable of generating more efficient plans for subqueries, resulting in better performance. While other optimizers might not, yielding poorer performance for subqueries. Especially when dealing with a large volume of data.

I would read up on your db specifically, and learn how it handles joins versus correlated subqueries. That way you could make more of an informed decision.

I don't know if that helps any, but there it is ;-)

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