MySQL Join Optimisation: Improving join type with derived tables and GROUP BY

I am trying to improve a query which does the following:

For every job, add up all the costs, add up the invoiced amount, and calculate a profit/loss. The costs come from several different tables, e.g. purchaseorders, users_events (engineer allocated time/time he spent on site), stock used etc.

The query also needs to output some other columns like the name of the site for the work, so that that column can be sorted by (an ORDER BY is appended after all of this).

SELECT
    jobs.job_id,
    jobs.start_date,
    jobs.end_date,
    events.time,
    sites.name site,
    IFNULL(stock_cost,0) stock_cost,
    labour,
    materials,
    labour+materials+plant+expenses revenue,
    (labour+materials+plant)-(time*3557/360000+IFNULL(orders_cost,0)+IFNULL(stock_cost,0)) profit,
    ((labour+materials+plant)-(time*3557/360000+IFNULL(orders_cost,0)+IFNULL(stock_cost,0)))/(time*3557/360000+IFNULL(orders_cost,0)+IFNULL(stock_cost,0)) ratio

FROM
    jobs

    LEFT JOIN (
        SELECT
            job_id,
            SUM(labour_charge) labour,
            SUM(materials_charge) materials,
            SUM(plant_hire_charge) plant,
            SUM(expenses) expenses
        FROM invoices
        GROUP BY job_id
        ORDER BY NULL
    ) invoices USING(job_id)

    LEFT JOIN (
        SELECT
            job_id,
            SUM(IF(start_onsite && end_onsite,end_onsite-start_onsite,end-start)) time,
            SUM(travel+parking+materials) user_expenses
        FROM users_events
        WHERE type='job'
        GROUP BY job_id
        ORDER BY NULL
    ) events USING(job_id)

    LEFT JOIN (
        SELECT
            job_id,
            SUM(IFNULL(total,0))*0.01 orders_cost
        FROM purchaseorders
        GROUP BY job_id
        ORDER BY NULL
    ) purchaseorders USING(job_id)

    LEFT JOIN (
        SELECT
            location job_id,
            SUM(amount*cost))*0.01 stock_cost
        FROM stock_location
        LEFT JOIN stock_items ON stock_items.id=stock_location.stock_id
        WHERE location>=3000 AND amount>0 AND cost>0
        GROUP BY location
        ORDER BY NULL
    ) stock USING(job_id)

    LEFT JOIN contacts_sites sites ON sites.id=jobs.site_id;

I read this: http://dev.mysql.com/doc/refman/5.0/en/group-by-optimization.html but don't see how/if I can apply anything therein. For testing purposes, I have tried adding all sorts of indices on fields left, right and centre with no improvement to the EXPLAIN output:

+----+-------------+----------------+--------+------------------------+---------+---------+------------------------------------+-------+-------------------------------+
| id | select_type | table          | type   | possible_keys          | key     | key_len | ref                                | rows  | Extra                         |
+----+-------------+----------------+--------+------------------------+---------+---------+------------------------------------+-------+-------------------------------+
|  1 | PRIMARY     | jobs           | ALL    | NULL                   | NULL    | NULL    | NULL                               |  7088 |                               |
|  1 | PRIMARY     | <derived2>     | ALL    | NULL                   | NULL    | NULL    | NULL                               |  5038 |                               |
|  1 | PRIMARY     | <derived3>     | ALL    | NULL                   | NULL    | NULL    | NULL                               |  6476 |                               |
|  1 | PRIMARY     | <derived4>     | ALL    | NULL                   | NULL    | NULL    | NULL                               |   904 |                               |
|  1 | PRIMARY     | <derived5>     | ALL    | NULL                   | NULL    | NULL    | NULL                               |   531 |                               |
|  1 | PRIMARY     | sites          | eq_ref | PRIMARY                | PRIMARY | 4       | bestbee_db.jobs.site_id            |     1 |                               |
|  5 | DERIVED     | stock_location | ALL    | stock,location,amount,…| NULL    | NULL    | NULL                               |  5426 | Using where; Using temporary; |
|  5 | DERIVED     | stock_items    | eq_ref | PRIMARY                | PRIMARY | 4       | bestbee_db.stock_location.stock_id |     1 | Using where                   |
|  4 | DERIVED     | purchaseorders | ALL    | NULL                   | NULL    | NULL    | NULL                               |  1445 | Using temporary;              |
|  3 | DERIVED     | users_events   | ALL    | type,type_job          | NULL    | NULL    | NULL                               | 11295 | Using where; Using temporary; |
|  2 | DERIVED     | invoices       | ALL    | NULL                   | NULL    | NULL    | NULL                               |  5320 | Using temporary;              |
+----+-------------+----------------+--------+------------------------+---------+---------+------------------------------------+-------+-------------------------------+

The rows produced is 5 x 10^21 (down from 3 x 10^42 before I started optimising this query!)

It currently takes seven seconds to execute (down from 26) but I wo开发者_开发问答uld like that to be under one second.

By the way: GROUP BY x ORDER BY NULL is a great way to eliminate unnecessary filesorts from subqueries! (from http://www.mysqlperformanceblog.com/2006/09/04/group_concat-useful-group-by-extension/)

Based on your comment to my question, I would do the following...

At the very top...

SELECT STRAIGHT_JOIN (just add the "STRAIGH_JOIN" keyword)

Then, for each of your subqueries for invoices, events, p/o's, etc, change the ORDER BY to the JOB_ID explicitly so it might help the optimization against the primary JOBS table join.

Finally, ensure each of your subquery tables HAS an index on the Job_ID (Invoices, User_events, PurchaseOrders, Stock_Location)

Additionally, for the Stock_Location table, you might want to help the WHERE clause for your subquery by having a compound index on
(job_id, location, amount) Three fields deep should be enough even though you have the key plus 3 where condition elements.