indexes in mysql SELECT AS or using Views - mysql

Question

I'm in over my head with a big mysql query (mysql 5.0), and i'm hoping somebody here can help.
Earlier I asked how to get distinct values from a joined query
mysql count only for distinct values in joined query
The response I got worked (using a subquery with join as)
select *
from media m
inner join
( select uid
from users_tbl
limit 0,30) map
on map.uid = m.uid
inner join users_tbl u
on u.uid = m.uid
unfortunately, my query has grown more unruly, and though I have it running, joining into a derived table is taking too long because there is no indexes available to the derived query.
my query now looks like this
SELECT mdate.bid, mdate.fid, mdate.date, mdate.time, mdate.title, mdate.name,
mdate.address, mdate.rank, mdate.city, mdate.state, mdate.lat, mdate.`long`,
ext.link,
ext.source, ext.pre, meta, mdate.img
FROM ext
RIGHT OUTER JOIN (
SELECT media.bid,
media.date, media.time, media.title, users.name, users.img, users.rank, media.address,
media.city, media.state, media.lat, media.`long`,
GROUP_CONCAT(tags.tagname SEPARATOR ' | ') AS meta
FROM media
JOIN users ON media.bid = users.bid
LEFT JOIN tags ON users.bid=tags.bid
WHERE `long` BETWEEN -122.52224684058 AND -121.79760915942
AND lat BETWEEN 37.07500915942 AND 37.79964684058
AND date = '2009-02-23'
GROUP BY media.bid, media.date
ORDER BY media.date, users.rank DESC
LIMIT 0, 30
) mdate ON (mdate.bid = ext.bid AND mdate.date = ext.date)
phew!
SO, as you can see, if I understand my problem correctly, i have two derivative tables without indexes (and i don't deny that I may have screwed up the Join statements somehow, but I kept messing with different types, is this ended up giving me the result I wanted).
What's the best way to create a query similar to this which will allow me to take advantage of the indexes?
Dare I say, I actually have one more table to add into the mix at a later date.
Currently, my query is taking .8 seconds to complete, but I'm sure if I could take advantage of the indexes, this could be significantly faster.

Answer 1

First, check for indices on ext(bid, date), users(bid) and tags(bid), you should really have them.
It seems, though, that it's LONG and LAT that cause you most problems. You should try keeping your LONG and LAT as a (coordinate POINT), create a SPATIAL INDEX on this column and query like that:
WHERE MBRContains(#MySquare, coordinate)
If you can't change your schema for some reason, you can try creating additional indices that include date as a first field:
CREATE INDEX ix_date_long ON media (date, `long`)
CREATE INDEX ix_date_lat ON media (date, lat)
These indices will be more efficient for you query, as you use exact search on date combined with a ranged search on axes.

Answer 2

Starting fresh:
Question - why are you grouping by both media.bid and media.date? Can a bid have records for more than one date?
Here's a simpler version to try:
SELECT
mdate.bid,
mdate.fid,
mdate.date,
mdate.time,
mdate.title,
mdate.name,
mdate.address,
mdate.rank,
mdate.city,
mdate.state,
mdate.lat,
mdate.`long`,
ext.link,
ext.source,
ext.pre,
meta,
mdate.img,
( SELECT GROUP_CONCAT(tags.tagname SEPARATOR ' | ')
FROM tags
WHERE ext.bid = tags.bid
ORDER BY tags.bid GROUP BY tags.bid
) AS meta
FROM
ext
LEFT JOIN
media ON ext.bid = media.bid AND ext.date = media.date
JOIN
users ON ext.bid = users.bid
WHERE
`long` BETWEEN -122.52224684058 AND -121.79760915942
AND lat BETWEEN 37.07500915942 AND 37.79964684058
AND ext.date = '2009-02-23'
AND users.userid IN
(
SELECT userid FROM users ORDER BY rank DESC LIMIT 30
)
ORDER BY
media.date,
users.rank DESC
LIMIT 0, 30

Answer 3

You might want to compare your perforamnces against using a temp table for each selection, and joining those tables together.
create table #whatever
create table #whatever2
insert into #whatever select...
insert into #whatever2 select...
select from #whatever join #whatever 2
....
drop table #whatever
drop table #whatever2
If your system has enough memory to hold full tables this might work out much faster. It depends on how big your database is.