Reputation: 828
I have a big table with more than 10,000 rows and it will grow to 1,000,000 in the near future, and I need to run a query which gives back a Time value for each keyword for each user. I have one right now which is quite slow because I use left joins and it needs one subquery / keyword:
SELECT rawdata.user, t1.Facebook_Time, t2.Outlook_Time, t3.Excel_time
FROM
rawdata left join
(SELECT user, sec_to_time(SuM(time_to_sec(EndTime-StartTime))) as 'Facebook_Time'
FROM rawdata
WHERE MainWindowTitle LIKE '%Facebook%'
GROUP by user)t1 on rawdata.user = t1.user left join
(SELECT user, sec_to_time(SuM(time_to_sec(EndTime-StartTime))) as 'Outlook_Time'
FROM rawdata
WHERE MainWindowTitle LIKE '%Outlook%'
GROUP by user)t2 on rawdata.user = t2.user left join
(SELECT user, sec_to_time(SuM(time_to_sec(EndTime-StartTime))) as 'Excel_Time'
FROM rawdata
WHERE MainWindowTitle LIKE '%Excel%'
GROUP by user)t3 on rawdata.user = t3.user
The table looks like this:
WindowTitle | StartTime | EndTime | User
------------|-----------|---------|---------
Form1 | DateTime | DateTime| user1
Form2 | DateTime | DateTime| user2
... | ... | ... | ...
Form_n | DateTime | DateTime| user_n
The output should looks like this:
User | Keyword | SUM(EndTime-StartTime)
-------|-----------|-----------------------
User1 | 'Facebook'| 00:34:12
User1 | 'Outlook' | 00:12:34
User1 | 'Excel' | 00:43:13
User2 | 'Facebook'| 00:34:12
User2 | 'Outlook' | 00:12:34
User2 | 'Excel' | 00:43:13
... | ... | ...
User_n | ... | ...
And the question is, which is the fastest way in MySQL to do this?
Upvotes: 4
Views: 161
Reputation: 14233
I think your wildcard searches are probably what's slowing it down the most, since you can't really utilize indexes on those fields. Also if you can avoid doing sub-queries and just do a straight join, it might help, but the wildcard searches are far worse. Is there anyway you could change the table to have a categoryName or categoryID that can have an index and not require a wildcard search? Like "where categoryName = 'Outlook'"
To optimize the data in your tables, add a categoryID (ideally this would reference a separate table, but let's just use arbitrary numbers for this example):
alter table rawData add column categoryID int not null
alter table rawData add index (categoryID)
Then populate the categoryID field for the existing data:
update rawData set categoryID=1 where name like '%Outlook%'
update rawData set categoryID=2 where name like '%Facebook%'
-- etc...
Then change your insert to follow the same rules.
Then make your SELECT query like this (changed wild cards to categoryID):
SELECT rawdata.user, t1.Facebook_Time, t2.Outlook_Time, t3.Excel_time
FROM
rawdata left join
(SELECT user, sec_to_time(SuM(time_to_sec(EndTime-StartTime))) as 'Facebook_Time'
FROM rawdata
WHERE categoryID = 2
GROUP by user)t1 on rawdata.user = t1.user left join
(SELECT user, sec_to_time(SuM(time_to_sec(EndTime-StartTime))) as 'Outlook_Time'
FROM rawdata
WHERE categoryID = 1
GROUP by user)t2 on rawdata.user = t2.user left join
(SELECT user, sec_to_time(SuM(time_to_sec(EndTime-StartTime))) as 'Excel_Time'
FROM rawdata
WHERE categoryID = 3
GROUP by user)t3 on rawdata.user = t3.user
Upvotes: 3