SQL - query optimization to work with big data - mysql

I have table 33_PROBLEM with columns ROOT and ROOT_CUBED. Ten i have a simple procedure, that insert data, let´s begins with ROOT from -10000 to 10000, which means ROOT_CUBED from -10000^3 to 10000^3.
Question is simple:
How can I get all triplets combinations of ROOT_CUBED values, that add to number given?
Said in different way:
I want to find A, B, C for which is true, that A^3 + B^3 + C^3 = number_given
Here is some example for searched number 33:
SELECT T1.r1,
T2.r2,
T3.r3
FROM (SELECT root_3 AS R1
FROM `33_problem`) AS T1,
(SELECT root_3 AS R2
FROM `33_problem`) AS T2,
(SELECT root_3 AS R3
FROM `33_problem`) AS T3
WHERE T1.r1 + T2.r2 + T3.r3 = 33
It works well ... on a small amount of rows. This query makes (COUNT *)^3 rows, which for 20000 input lines equals to 8e+12 rows !! ... RIP serever ...
what is the right way to solve this one?
( I got the idea from https://www.youtube.com/watch?v=wymmCdLdPvM and I hope, when someone comes with some answers, i will understand better, how SQL works and how queries and databases should be designed to work good even for big data )


1) you could try to only select sequential triplets, such that R1 <= R2 <= R3,
2) if you have duplicates, select distinct
SELECT T1.R1
,T2.R2
,T3.R3
FROM (
SELECT DISTINCT ROOT_3 AS R1
FROM `33_PROBLEM`
) AS T1
,(
SELECT DISTINCT ROOT_3 AS R2
FROM `33_PROBLEM`
WHERE R2>=R1
) AS T2
,(
SELECT DISTINCT ROOT_3 AS R3
FROM `33_PROBLEM`
WHERE R3>=R2
) AS T3
WHERE T1.R1 + T2.R2 + T3.R3 = 33


I tried looking from -10000 to 10000 and there isn't a solution then i watched the youtube video and they say that they already tried up to 10 to the 14th and still no solution.
I did it with python code though when i tried -10000 to 10000...and to optimize of looking for the the C value. First I look at the sum of A cubed and B cubed...the subtract that from 33 and calculate cube root the answer to try to find C in one hit... , this optimizes it a little because then you don't have to loop through all possible values of C.
Since there is no solution for up to 10 to 14th i don't think i can find a solution since just with -10000 to 10000 It took my computer over 2 hours to search. If i looked to 10 to the 14th it would takes like millions of years or something crazy.


You could work with a single table if integers, then do a "self join" using a "cross join".
For R3, you only need to check -ROUND(POW(r1.root_3 + r2.root3, 1/3)). This should significantly speed things up. Also, to make this work, be sure that you have a positive number.
SELECT t1.r1, t2.r2, -ROUND(POW(r1.root_3 + r2.root3, 1/3))
FROM `33_PROBLEM` AS t1
JOIN `33_PROBLEM` AS t2
WHERE t1.root_3 > 0
AND t2.root_3 > -t1.root_3
AND (t1.root_3 + t2.root_3) = ROUND(POW(r1.root_3 + r2.root3, 1/3))

Related

SQL to club records in sequence

I have data in MySQL table, my data looks like
Key, value
A 1
A 2
A 3
A 6
A 7
A 8
A 9
B 1
B 2
and I want to group it based on the continuous sequence. Data is sorted in the table.
Key, min, max
A 1 3
A 6 9
B 1 2
I tried googling it but could find any solution to it. Can someone please help me with this.

This is way easier with a modern DBMS that support window functions, but you can find the upper bounds by checking that there is no successor. In the same way you can find the lower bounds via absence of a predecessor. By combining the lowest upper bound for each lower bound we get the intervals.
select low.keyx, low.valx, min(high.valx)
from (
select t1.keyx, t1.valx from t t1
where not exists (
select 1 from t t2
where t1.keyx = t2.keyx
and t1.valx = t2.valx + 1
)
) as low
join (
select t3.keyx, t3.valx from t t3
where not exists (
select 1 from t t4
where t3.keyx = t4.keyx
and t3.valx = t4.valx - 1
)
) as high
on low.keyx = high.keyx
and low.valx <= high.valx
group by low.keyx, low.valx;
I changed your identifiers since value is a reserved world.
Using a window function is way more compact and efficient. If at all possible, consider upgrading to MySQL 8+, it is superior to 5.7 in so many aspects.
We can create a group by looking at the difference between valx and an enumeration of the vals, if there is a gap the difference increases. Then, we simply pick min and max for each group:
select keyx, min(valx), max(valx)
from (
select keyx, valx
, valx - row_number() over (partition by keyx order by valx) as grp
from t
) as tt
group by keyx, grp;
Fiddle

Aggregating row values in MySQl or Snowflake

I would like to calculate the std dev. min and max of the mer_data array into 3 other fields called std_dev,min_mer and max_mer grouped by mac and timestamp.
This needs to be done without flattening the data as each mer_data row consists of 4000 float values and multiplying that with 700k rows gives a very high dimensional table.
The mer_data field is currently saved as varchar(30000) and maybe Json format might help, I'm not sure.
Input:
Output:
This can be done in Snowflake or MySQL.
Also, the query needs to be optimized so that it does not take much computation time.

While you don't want to split the data up, you will need to if you want to do it in pure SQL. Snowflake has no problems with such aggregations.
WITH fake_data(mac, mer_data) AS (
SELECT * FROM VALUES
('abc','43,44.25,44.5,42.75,44,44.25,42.75,43'),
('def','32.75,33.25,34.25,34.5,32.75,34,34.25,32.75,43')
)
SELECT f.mac,
avg(d.value::float) as avg_dev,
stddev(d.value::float) as std_dev,
MIN(d.value::float) as MIN_MER,
Max(d.value::float) as Max_MER
FROM fake_data f, table(split_to_table(f.mer_data,',')) d
GROUP BY 1
ORDER BY 1;
I would however discourage the use of strings in the grouping process, so would break it apart like so:
WITH fake_data(mac, mer_data, timestamp) AS (
SELECT * FROM VALUES
('abc','43,44.25,44.5,42.75,44,44.25,42.75,43', '01-01-22'),
('def','32.75,33.25,34.25,34.5,32.75,34,34.25,32.75,43', '02-01-22')
), boost_data AS (
SELECT seq8() as seq, *
FROM fake_data
), math_step AS (
SELECT f.seq,
avg(d.value::float) as avg_dev,
stddev(d.value::float) as std_dev,
MIN(d.value::float) as MIN_MER,
Max(d.value::float) as Max_MER
FROM boost_data f, table(split_to_table(f.mer_data,',')) d
GROUP BY 1
)
SELECT b.mac,
m.avg_dev,
m.std_dev,
m.MIN_MER,
m.Max_MER,
b.timestamp
FROM boost_data b
JOIN math_step m
ON b.seq = m.seq
ORDER BY 1;
MAC
AVG_DEV
STD_DEV
MIN_MER
MAX_MER
TIMESTAMP
abc
43.5625
0.7529703087
42.75
44.5
01-01-22
def
34.611111111
3.226141056
32.75
43
02-01-22
performance testing:
so using this SQL to make 70K rows of 4000 values each:
create table fake_data_tab AS
WITH cte_a AS (
SELECT SEQ8() as s
FROM TABLE(GENERATOR(ROWCOUNT =>70000))
), cte_b AS (
SELECT a.s, uniform(20::float, 50::float, random()) as v
FROM TABLE(GENERATOR(ROWCOUNT =>4000))
CROSS JOIN cte_a a
)
SELECT s::text as mac
,LISTAGG(v,',') AS mer_data
,dateadd(day,s,'2020-01-01')::date as timestamp
FROM cte_b
GROUP BY 1,3;
takes 79 seconds on a XTRA_SMALL,
now with that we can test the two solutions:
The second set of code (group by numbers, with a join):
WITH boost_data AS (
SELECT seq8() as seq, *
FROM fake_data_tab
), math_step AS (
SELECT f.seq,
avg(d.value::float) as avg_dev,
stddev(d.value::float) as std_dev,
MIN(d.value::float) as MIN_MER,
Max(d.value::float) as Max_MER
FROM boost_data f, table(split_to_table(f.mer_data,',')) d
GROUP BY 1
)
SELECT b.mac,
m.avg_dev,
m.std_dev,
m.MIN_MER,
m.Max_MER,
b.timestamp
FROM boost_data b
JOIN math_step m
ON b.seq = m.seq
ORDER BY 1;
takes 1m47s
the original group by strings/dates
SELECT f.mac,
avg(d.value::float) as avg_dev,
stddev(d.value::float) as std_dev,
MIN(d.value::float) as MIN_MER,
Max(d.value::float) as Max_MER,
f.timestamp
FROM fake_data_tab f, table(split_to_table(f.mer_data,',')) d
GROUP BY 1,6
ORDER BY 1;
takes 1m46s
Hmm, so leaving the "mac" as a number made the code very fast (~3s), and dealing with strings in ether way changed the data processed from 1.5GB for strings and 150MB for numbers.

If the numbers were in rows, not packed together like that, we can discuss how to do it in SQL.
In rows, GROUP_CONCAT(...) can construct a commalist like you show, and MIN(), STDDEV(), etc can do the other stuff.
If you continue to have the commalist, the do the rest of work in you app programming language. (It is very ugly to have SQL pick apart an array.)

Optimizing Parameterized MySQL Queries

I have a query that has a number of parameters which if I run from in MySQLWorkbench takes around a second to run.
If I take this query and get rid of the parameters and instead substitute the values into the query then it takes about 22 seconds to run, same as If I convert this query to a parameterized stored procedure and run it (it then takes about 22 seconds).
I've enabled profiling on MySQL and I can see a few things there. For example, it shows the number of rows examined and there's an order of difference (20,000 to 400,000) which I assume is the reason for the 20x increase in processing time.
The other difference in the profile is that the parameterized query sent from MySQLWorkbench still has the parameters in (e.g. where limit < #lim) while the sproc the values have been set (where limit < 300).
I've tried this a number of different ways, I'm using JetBrains's DataGrip (as well as MySQLWorkbench) and that works like MySQLWorkbench (sends through the # parameters), I've tried executing the queries and the sproc from MySQLWorkbench, DataGrip, Java (JDBC) and .Net. I've also tried prepared statements in Java but I can't get anywhere near the performance of sending the 'raw' SQL to MySQL.
I feel like I'm missing something obvious here but I don't know what it is.
The query is relatively complex, it has a CTE a couple of sub-selects and a couple of joins, but as I said it runs quickly straight from MySQL.
My main question is why the query is 20x faster in one format than another.
Does the way the query is sent to MySQL have anything to do with this (the '#' values sent through and can I replicate this in a stored procedure?
Updated 1st Jan
Thanks for the comments, I didn't post the query originally as I'm more interested in the general concepts around the use of variables/parameters and how I could take advantage of that (or not)
Here is the original query:
with tmp_bat as (select bd.MatchId,
bd.matchtype,
bd.playerid,
bd.teamid,
bd.opponentsid,
bd.inningsnumber,
bd.dismissal,
bd.dismissaltype,
bd.bowlerid,
bd.fielderid,
bd.score,
bd.position,
bd.notout,
bd.balls,
bd.minutes,
bd.fours,
bd.sixes,
bd.hundred,
bd.fifty,
bd.duck,
bd.captain,
bd.wicketkeeper,
m.hometeamid,
m.awayteamid,
m.matchdesignator,
m.matchtitle,
m.location,
m.tossteamid,
m.resultstring,
m.whowonid,
m.howmuch,
m.victorytype,
m.duration,
m.ballsperover,
m.daynight,
m.LocationId
from (select *
from battingdetails
where matchid in
(select id
from matches
where id in (select matchid from battingdetails)
and matchtype = #match_type
)) as bd
join matches m on m.id = bd.matchid
join extramatchdetails emd1
on emd1.MatchId = m.Id
and emd1.TeamId = bd.TeamId
join extramatchdetails emd2
on emd2.MatchId = m.Id
and emd2.TeamId = bd.TeamId
)
select players.fullname name,
teams.teams team,
'' opponents,
players.sortnamepart,
innings.matches,
innings.innings,
innings.notouts,
innings.runs,
HS.score highestscore,
HS.NotOut,
CAST(TRUNCATE(innings.runs / (CAST((Innings.Innings - innings.notOuts) AS DECIMAL)),
2) AS DECIMAL(7, 2)) 'Avg',
innings.hundreds,
innings.fifties,
innings.ducks,
innings.fours,
innings.sixes,
innings.balls,
CONCAT(grounds.CountryName, ' - ', grounds.KnownAs) Ground,
'' Year,
'' CountryName
from (select count(case when inningsnumber = 1 then 1 end) matches,
count(case when dismissaltype != 11 and dismissaltype != 14 then 1 end) innings,
LocationId,
playerid,
MatchType,
SUM(score) runs,
SUM(notout) notouts,
SUM(hundred) Hundreds,
SUM(fifty) Fifties,
SUM(duck) Ducks,
SUM(fours) Fours,
SUM(sixes) Sixes,
SUM(balls) Balls
from tmp_bat
group by MatchType, playerid, LocationId) as innings
JOIN players ON players.id = innings.playerid
join grounds on Grounds.GroundId = LocationId and grounds.MatchType = innings.MatchType
join
(select pt.playerid, t.matchtype, GROUP_CONCAT(t.name SEPARATOR ', ') as teams
from playersteams pt
join teams t on pt.teamid = t.id
group by pt.playerid, t.matchtype)
as teams on teams.playerid = innings.playerid and teams.matchtype = innings.MatchType
JOIN
(SELECT playerid,
LocationId,
MAX(Score) Score,
MAX(NotOut) NotOut
FROM (SELECT battingdetails.playerid,
battingdetails.score,
battingdetails.notout,
battingdetails.LocationId
FROM tmp_bat as battingdetails
JOIN (SELECT battingdetails.playerid,
battingdetails.LocationId,
MAX(battingdetails.Score) AS score
FROM tmp_bat as battingdetails
GROUP BY battingdetails.playerid,
battingdetails.LocationId,
battingdetails.playerid) AS maxscore
ON battingdetails.score = maxscore.score
AND battingdetails.playerid = maxscore.playerid
AND battingdetails.LocationId = maxscore.LocationId ) AS internal
GROUP BY internal.playerid, internal.LocationId) AS HS
ON HS.playerid = innings.playerid and hs.LocationId = innings.LocationId
where innings.runs >= #runs_limit
order by runs desc, KnownAs, SortNamePart
limit 0, 300;
Wherever you see '#match_type' then I substitute that for a value ('t'). This query takes ~1.1 secs to run. The query with the hard coded values rather than the variables down to ~3.5 secs (see the other note below). The EXPLAIN for this query gives this:
1,PRIMARY,<derived7>,,ALL,,,,,219291,100,Using temporary; Using filesort
1,PRIMARY,players,,eq_ref,PRIMARY,PRIMARY,4,teams.playerid,1,100,
1,PRIMARY,<derived2>,,ref,<auto_key3>,<auto_key3>,26,"teams.playerid,teams.matchtype",11,100,Using where
1,PRIMARY,grounds,,ref,GroundId,GroundId,4,innings.LocationId,1,10,Using where
1,PRIMARY,<derived8>,,ref,<auto_key0>,<auto_key0>,8,"teams.playerid,innings.LocationId",169,100,
8,DERIVED,<derived3>,,ALL,,,,,349893,100,Using temporary
8,DERIVED,<derived14>,,ref,<auto_key0>,<auto_key0>,13,"battingdetails.PlayerId,battingdetails.LocationId,battingdetails.Score",10,100,Using index
14,DERIVED,<derived3>,,ALL,,,,,349893,100,Using temporary
7,DERIVED,t,,ALL,PRIMARY,,,,3323,100,Using temporary; Using filesort
7,DERIVED,pt,,ref,TeamId,TeamId,4,t.Id,65,100,
2,DERIVED,<derived3>,,ALL,,,,,349893,100,Using temporary
3,DERIVED,matches,,ALL,PRIMARY,,,,114162,10,Using where
3,DERIVED,m,,eq_ref,PRIMARY,PRIMARY,4,matches.Id,1,100,
3,DERIVED,emd1,,ref,"PRIMARY,TeamId",PRIMARY,4,matches.Id,1,100,Using index
3,DERIVED,emd2,,eq_ref,"PRIMARY,TeamId",PRIMARY,8,"matches.Id,emd1.TeamId",1,100,Using index
3,DERIVED,battingdetails,,ref,"TeamId,MatchId,match_team",match_team,8,"emd1.TeamId,matches.Id",15,100,
3,DERIVED,battingdetails,,ref,MatchId,MatchId,4,matches.Id,31,100,Using index; FirstMatch(battingdetails)
and the EXPLAIN for the query with the hardcoded values looks like this:
1,PRIMARY,<derived8>,,ALL,,,,,20097,100,Using temporary; Using filesort
1,PRIMARY,players,,eq_ref,PRIMARY,PRIMARY,4,HS.PlayerId,1,100,
1,PRIMARY,grounds,,ref,GroundId,GroundId,4,HS.LocationId,1,100,Using where
1,PRIMARY,<derived2>,,ref,<auto_key0>,<auto_key0>,30,"HS.LocationId,HS.PlayerId,grounds.MatchType",17,100,Using where
1,PRIMARY,<derived7>,,ref,<auto_key0>,<auto_key0>,46,"HS.PlayerId,innings.MatchType",10,100,Using where
8,DERIVED,matches,,ALL,PRIMARY,,,,114162,10,Using where; Using temporary
8,DERIVED,m,,eq_ref,"PRIMARY,LocationId",PRIMARY,4,matches.Id,1,100,
8,DERIVED,emd1,,ref,"PRIMARY,TeamId",PRIMARY,4,matches.Id,1,100,Using index
8,DERIVED,emd2,,eq_ref,"PRIMARY,TeamId",PRIMARY,8,"matches.Id,emd1.TeamId",1,100,Using index
8,DERIVED,<derived14>,,ref,<auto_key2>,<auto_key2>,4,m.LocationId,17,100,
8,DERIVED,battingdetails,,ref,"PlayerId,TeamId,Score,MatchId,match_team",MatchId,8,"matches.Id,maxscore.PlayerId",1,3.56,Using where
8,DERIVED,battingdetails,,ref,MatchId,MatchId,4,matches.Id,31,100,Using index; FirstMatch(battingdetails)
14,DERIVED,matches,,ALL,PRIMARY,,,,114162,10,Using where; Using temporary
14,DERIVED,m,,eq_ref,PRIMARY,PRIMARY,4,matches.Id,1,100,
14,DERIVED,emd1,,ref,"PRIMARY,TeamId",PRIMARY,4,matches.Id,1,100,Using index
14,DERIVED,emd2,,eq_ref,"PRIMARY,TeamId",PRIMARY,8,"matches.Id,emd1.TeamId",1,100,Using index
14,DERIVED,battingdetails,,ref,"TeamId,MatchId,match_team",match_team,8,"emd1.TeamId,matches.Id",15,100,
14,DERIVED,battingdetails,,ref,MatchId,MatchId,4,matches.Id,31,100,Using index; FirstMatch(battingdetails)
7,DERIVED,t,,ALL,PRIMARY,,,,3323,100,Using temporary; Using filesort
7,DERIVED,pt,,ref,TeamId,TeamId,4,t.Id,65,100,
2,DERIVED,matches,,ALL,PRIMARY,,,,114162,10,Using where; Using temporary
2,DERIVED,m,,eq_ref,PRIMARY,PRIMARY,4,matches.Id,1,100,
2,DERIVED,emd1,,ref,"PRIMARY,TeamId",PRIMARY,4,matches.Id,1,100,Using index
2,DERIVED,emd2,,eq_ref,"PRIMARY,TeamId",PRIMARY,8,"matches.Id,emd1.TeamId",1,100,Using index
2,DERIVED,battingdetails,,ref,"TeamId,MatchId,match_team",match_team,8,"emd1.TeamId,matches.Id",15,100,
2,DERIVED,battingdetails,,ref,MatchId,MatchId,4,matches.Id,31,100,Using index; FirstMatch(battingdetails)
Pointers as to ways to improve my SQL are always welcome (I'm definitely not a database person), but I''d still like to understand whether I can use the SQL with the variables from code and why that improves the performance by so much
Update 2 1st Jan
AAArrrggghhh. My machine rebooted overnight and now the queries are generally running much quicker. It's still 1 sec vs 3 secs but the 20 times slowdown does seem to have disappeared

In your WITH construct, are you overthinking your select in ( select in ( select in ))) ... overstating what could just be simplified to the with Innings I have in my solution.
Also, you were joining to the extraMatchDetails TWICE, but joined on the same conditions on match and team, but never utliized either of those tables in the "WITH CTE" rendering that component useless, doesn't it? However, the MATCH table has homeTeamID and AwayTeamID which is what I THINK your actual intent was
Also, your WITH CTE is pulling many columns not needed or used in subsequent return such as Captain, WicketKeeper.
So, I have restructured... pre-query the batting details once up front and summarized, then you should be able to join off that.
Hopefully this MIGHT be a better fit, function and performance for your needs.
with innings as
(
select
bd.matchId,
bd.matchtype,
bd.playerid,
m.locationId,
count(case when bd.inningsnumber = 1 then 1 end) matches,
count(case when bd.dismissaltype in ( 11, 14 ) then 0 else 1 end) innings,
SUM(bd.score) runs,
SUM(bd.notout) notouts,
SUM(bd.hundred) Hundreds,
SUM(bd.fifty) Fifties,
SUM(bd.duck) Ducks,
SUM(bd.fours) Fours,
SUM(bd.sixes) Sixes,
SUM(bd.balls) Balls
from
battingDetails bd
join Match m
on bd.MatchID = m.MatchID
where
matchtype = #match_type
group by
bd.matchId,
bd.matchType,
bd.playerid,
m.locationId
)
select
p.fullname playerFullName,
p.sortnamepart,
CONCAT(g.CountryName, ' - ', g.KnownAs) Ground,
t.team,
i.matches,
i.innings,
i.runs,
i.notouts,
i.hundreds,
i.fifties,
i.ducks,
i.fours,
i.sixes,
i.balls,
CAST( TRUNCATE( i.runs / (CAST((i.Innings - i.notOuts) AS DECIMAL)), 2) AS DECIMAL(7, 2)) 'Avg',
hs.maxScore,
hs.maxNotOut,
'' opponents,
'' Year,
'' CountryName
from
innings i
JOIN players p
ON i.playerid = p.id
join grounds g
on i.locationId = g.GroundId
and i.matchType = g.matchType
join
(select
pt.playerid,
t.matchtype,
GROUP_CONCAT(t.name SEPARATOR ', ') team
from
playersteams pt
join teams t
on pt.teamid = t.id
group by
pt.playerid,
t.matchtype) as t
on i.playerid = t.playerid
and i.MatchType = t.matchtype
join
( select
i2.playerid,
i2.locationid,
max( i2.score ) maxScore,
max( i2.notOut ) maxNotOut
from
innings i2
group by
i2.playerid,
i2.LocationId ) HS
on i.playerid = HS.playerid
AND i.locationid = HS.locationid
FROM
where
i.runs >= #runs_limit
order by
i.runs desc,
g.KnownAs,
p.SortNamePart
limit
0, 300;
Now, I know that you stated that after the server reboot, performance is better, but really, what you DO have appears to really have overbloated queries.

Not sure this is the correct answer but I thought I'd post this in case other people have the same issue.
The issue seems to be the use of CTEs in a stored procedure. I have a query that creates a CTE and then uses that CTE 8 times. If I run this query using interpolated variables it takes about 0.8 sec, if I turn it into a stored procedure and use the stored procedure parameters then it takes about to a minute (between 45 and 63 seconds) to run!
I've found a couple of ways of fixing this, one is to use multiple temporary tables (8 in this case) as MySQL cannot re-use a temp table in a query. This gets the query time right down but just doesn't fell like a maintainable or scalable solution. The other fix is to leave the variables in place and assign them from the stored procedure parameters, this also has no real performance issues. So my sproc looks like this:
create procedure bowling_individual_career_records_by_year_for_team_vs_opponent(IN team_id INT,
IN opponents_id INT)
begin
set #team_id = team_id;
set #opponents_id = opponents_id;
# use these variables in the SQL below
...
end
Not sure this is the best solution but it works for me and keeps the structure of the SQL the same as it was previously.

MySQL ranking in presence of indexes using variables

Using the classic trick of using #N=#N + 1 to get the rank of items on some ordered column. Now before ordering I need to filter out some values from the base table by inner joining it with some other table. So the query looks like this -:
SET #N=0;
SELECT
#N := #N + 1 AS rank,
fa.id,
fa.val
FROM
table1 AS fa
INNER JOIN table2 AS em
ON em.id = fa.id
AND em.type = "A"
ORDER BY fa.val ;
The issue is if I don't have an index on the em.type, then everything works fine but if I put an index on em.type then hell unleashes and the rank values instead of coming ordered by the val column comes in the order the rows are stored in the em table.
here are sample outputs -:
without index-:
rank id val
1 05F8C7 55050.000000
2 05HJDG 51404.733458
3 05TK1Z 46972.008208
4 05F2TR 46900.000000
5 05F349 44433.412847
6 06C2BT 43750.000000
7 0012X3 42000.000000
8 05MMPK 39430.399658
9 05MLW5 39054.046383
10 062D20 35550.000000
with index-:
rank id val
480 05F8C7 55050.000000
629 05HJDG 51404.733458
1603 05TK1Z 46972.008208
466 05F2TR 46900.000000
467 05F349 44433.412847
3534 06C2BT 43750.000000
15 0012X3 42000.000000
1109 05MMPK 39430.399658
1087 05MLW5 39054.046383
2544 062D20 35550.000000
I believe the use of indexes should be completely transparent and outputs should not be effected by it. Is this a bug in MySQL?

This "trick" was a bomb waiting to explode. A clever optimizer will evaluate a query as it sees fits, optimizing for speed - that's why it's called optimizer. I don't think this use of MySQL variables was documented to work as you expect it to work, but it was working.
Was working, up until recent improvements on the MariaDB optimizer. It will probably break as well in the mainstream MySQL as there are several improvements on the optimizer in the (yet to be released, still beta) 5.6 version.
What you can do (until MySQL implemented window functions) is to use a self-join and a grouping. Results will be consistent, no matter what future improvements are done in the optimizer. Downside is that that it may not be very efficient:
SELECT
COUNT(*) AS rank,
fa.id,
fa.val
FROM
table1 AS fa
INNER JOIN table2 AS em
ON em.id = fa.id
AND em.type = 'A'
INNER JOIN
table1 AS fa2
INNER JOIN table2 AS em2
ON em2.id = fa2.id
AND em2.type = 'A'
ON fa2.id <= fa.id
--- assuming that `id` is the Primary Key of the table
GROUP BY fa.id
ORDER BY fa.val ;

Need Help streamlining a SQL query to avoid redundant math operations in the WHERE and SELECT

*Hey everyone, I am working on a query and am unsure how to make it process as quickly as possible and with as little redundancy as possible. I am really hoping someone there can help me come up with a good way of doing this.
Thanks in advance for the help!*
Okay, so here is what I have as best I can explain it. I have simplified the tables and math to just get across what I am trying to understand.
Basically I have a smallish table that never changes and will always only have 50k records like this:
Values_Table
ID Value1 Value2
1 2 7
2 2 7.2
3 3 7.5
4 33 10
….50000 44 17.2
And a couple tables that constantly change and are rather large, eg a potential of up to 5 million records:
Flags_Table
Index Flag1 Type
1 0 0
2 0 1
3 1 0
4 1 1
….5,000,000 1 1
Users_Table
Index Name ASSOCIATED_ID
1 John 1
2 John 1
3 Paul 3
4 Paul 3
….5,000,000 Richard 2
I need to tie all 3 tables together. The most results that are likely to ever be returned from the small table is somewhere in the neighborhood of 100 results. The large tables are joined on the index and these are then joined to the Values_Table ON Values_Table.ID = Users_Table.ASSOCIATED_ID …. That part is easy enough.
Where it gets tricky for me is that I need to return, as quickly as possible, a list limited to 10 results where value1 and value2 are mathematically operated on to return a new_ value where that new_value is less than 10 and the result is sorted by that new_value and any other where statements I need can be applied to the flags. I do need to be able to move along the limit. EG LIMIT 0,10 / 11,10 / 21,10 etc...
In a subsequent (or the same if possible) query I need to get the top 10 count of all types that matched that criteria before the limit was applied.
So for example I want to join all of these and return anything where Value1 + Value2 < 10 AND I also need the count.
So what I want is:
Index Name Flag1 New_Value
1 John 0 9
2 John 0 9
5000000 Richard 1 9.2
The second response would be:
ID (not index) Count
1 2
2 1
I tried this a few ways and ultimately came up with the following somewhat ugly query:
SELECT INDEX, NAME, Flag1, (Value1 * some_variable + Value2) as New_Value
FROM Values_Table
JOIN Users_Table ON ASSOCIATED_ID = ID
JOIN Flags_Table ON Flags_Table.Index = Users_Table.Index
WHERE (Value1 * some_variable + Value1) < 10
ORDER BY New_Value
LIMIT 0,10
And then for the count:
SELECT ID, COUNT(TYPE) as Count, (Value1 * some_variable + Value2) as New_Value
FROM Values_Table
JOIN Users_Table ON ASSOCIATED_ID = ID
JOIN Flags_Table ON Flags_Table.Index = Users_Table.Index
WHERE (Value1 * some_variable + Value1) < 10
GROUP BY TYPE
ORDER BY New_Value
LIMIT 0,10
Being able to filter on the different flags and such in my WHERE clause is important; that may sound stupid to comment on but I mention that because from what I could see a quicker method would have been to use the HAVING statement but I don't believe that will work in certain instance depending on what I want to use my WHERE clause to filter against.
And when filtering using the flags table :
SELECT INDEX, NAME, Flag1, (Value1 * some_variable + Value2) as New_Value
FROM Values_Table
JOIN Users_Table ON ASSOCIATED_ID = ID
JOIN Flags_Table ON Flags_Table.Index = Users_Table.Index
WHERE (Value1 * some_variable + Value1) < 10 AND Flag1 = 0
ORDER BY New_Value
LIMIT 0,10
...filtered count:
SELECT ID, COUNT(TYPE) as Count, (Value1 * some_variable + Value2) as New_Value
FROM Values_Table
JOIN Users_Table ON ASSOCIATED_ID = ID
JOIN Flags_Table ON Flags_Table.Index = Users_Table.Index
WHERE (Value1 * some_variable + Value1) < 10 AND Flag1 = 0
GROUP BY TYPE
ORDER BY New_Value
LIMIT 0,10
That works fine but has to run the math multiple times for each row, and I get the nagging feeling that it is also running the math multiple times on the same row in the Values_table table. My thought was that I should just get only the valid responses from the Values_table first and then join those to the other tables to cut down on the processing; with how SQL optimizes things though I wasn't sure if it might not already be doing that. I know I could use a HAVING clause to only run the math once if I did it that way but I am uncertain how I would then best join things.
My questions are:
Can I avoid running that math twice and still make the query work
(or I suppose if there is a good way
to make the first one work as well
that would be great)
What is the fastest way to do this
as this is something that will
be running very often.
It seems like this should be painfully simple but I am just missing something stupid.
I contemplated pulling into a temp table then joining that table to itself but that seems like I would trade math for iterations against the table and still end up slow.
Thank you all for your help in this and please let me know if I need to clarify anything here!
** To clarify on a question, I can't use a 3rd column with the values pre-calculated because in reality the math is much more complex then addition, I just simplified it for illustration's sake.

Do you have a benchmark query to compare against? Usually it doesn't work to try to outsmart the optimizer. If you have acceptable performance from a starting query, then you can see where extra work is being expended (indicated by disk reads, cache consumption, etc.) and focus on that.
Avoid the temptation to break it into pieces and solve those. That's an antipattern. That includes temp tables especially.
Redundant math is usually ok - what hurts is disk activity. I've never seen a query that needed CPU work reduction on pure calculations.

Gather your results and put them in a temp table
SELECT * into TempTable FROM (SELECT INDEX, NAME, Type, ID, Flag1, (Value1 + Value2) as New_Value
FROM Values_Table
JOIN Users_Table ON ASSOCIATED_ID = ID
JOIN Flags_Table ON Flags_Table.Index = Users_Table.Index
WHERE New_Value < 10)
ORDER BY New_Value
LIMIT 0,10
Return Result for First Query
SELECT INDEX, NAME, Flag1, New_Value
FROM TempTable
Return Results for count of Types
Select ID, Count(Type)
FROM TempTable
GROUP BY TYPE

Is there any chance that you can add a third column to the values_table with the pre-calculated value? Even if the result of your calculation is dependent on other variables, you could run the calculation for the whole table but only when those variables change.