Help optimizing a query for MySQL

I have a MySQL query that goes as follows

SELECT
    count(`clicks`.`user_id`) as total,
    `users`.`fullname`
FROM
    `users`,
    `clicks`,
WHERE
    `users`.`id` = `clicks`.`user_id`
GROUP BY
    `clicks`.`user_id`
ORDER BY
    `total` desc
LIMIT
    0,20;

I am running statistics on several button pressing type games. It has a user table and a clicks table. It logs clicks from a specific user. The user can click the button whenever they want. Twenty clicks one day, thirty another, etc. They aren't all in a row.

Currently there are about ~180k clicks for ~2k users. This query takes 1.38 seconds to run on average. I would like to speed it up if that's at all possible.

1. Is USERS.id defined as the primary key for the table? It should be...
2. Assuming INNODB, does CLICKS.user_id have a foreign key constraint on it to associate its values with USERS.id?
3. USERS.id and CLICKS.user_id are a numeric data type (IE: INT), not text based?
4. Indexes should be added (if they don't already exist) for:
   • CLICKS.user_id
   • USERS.fullname

5. If indexes exist, have you tried refreshes the table statistics:

   ANALYZE TABLE USERS;
   ANALYZE TABLE CLICKS;
   

That query is probably as fast as it gets, provided that you have the columns clicks.user_id and users.id indexed.

One thing about it that I can imagine being responsible for a lot of sluggishness, which is the ORDER BY clause. Seeing that it's an aggregate field it probably has to first get all the data and then sort it afterward without much optimization. Sorting is a good candidate when anything is slow.

Another idea though, is to maintain a separate table that contains the total clicks. If you need those records, then you may end up having to run 2 queries per click... one for the existing table, and another one for updating the user/click table, which would only have user_id and click_count plus whatever else you think is appropriate. That way, SELECTs should become lightning fast, even with lots and lots of users because you're only retrieving the absolute necessary minimum amount of rows as opposed to a whole bunch of them that then only get aggregated anyway.

Be sure that you've created indexes on users.id and clicks.user_id. You can also try counting the clicks before performing the join, but I suspect that if this would actually improve performance that the engine would do it for you anyway.

Create an index on clicks.userid for starters. This will make a difference

I just thought of something else. You might get better results by using INNER JOINs.

Untested:

SELECT
    count(`clicks`.`user_id`) as total,
    `users`.`fullname`
FROM
    `users`
    INNER JOIN `clicks` ON `clicks`.`user_id` = `users`.`id`
GROUP BY
    `clicks`.`user_id`
ORDER BY
    `total` desc
LIMIT
    0,20;

Try this (untested):

SELECT
    C.total,
    `users`.`fullname`
FROM
    `users`
INNER JOIN
    (SELECT COUNT(*) AS total, user_id 
     FROM 
         `clicks` 
     GROUP BY 
        `user_id` 
     ORDER BY 
        COUNT(*) 
     LIMIT 0,20) C
ON C.user_id = users.user_id

ORDER BY
    C.total desc

Counting the rows first might save you a little time.