Database design for optimisation - php

A couple of years ago I designed a reward system for 11-16yo students in PHP, JavaScript and MySQL.
The premise is straightforward:
Members of staff issue points to students under various categories ("Positive attitude and behaviour", "Model citizen", etc)
Students accrue these points then spend them in our online store (iTunes vouchers, etc)
Existing system
The database structure is also straightforward (probably too much so):
Transactions
239,189 rows
CREATE TABLE `transactions` (
`Transaction_ID` int(9) NOT NULL auto_increment,
`Datetime` date NOT NULL,
`Giver_ID` int(9) NOT NULL,
`Recipient_ID` int(9) NOT NULL,
`Points` int(4) NOT NULL,
`Category_ID` int(3) NOT NULL,
`Reason` text NOT NULL,
PRIMARY KEY (`Transaction_ID`),
KEY `Giver_ID` (`Giver_ID`),
KEY `Datetime` (`Datetime`),
KEY `DatetimeAndGiverID` (`Datetime`,`Giver_ID`),
KEY `Recipient_ID` (`Recipient_ID`)
) ENGINE=InnoDB AUTO_INCREMENT=249069 DEFAULT CHARSET=latin1
Categories
34 rows
CREATE TABLE `categories` (
`Category_ID` int(9) NOT NULL,
`Title` varchar(255) NOT NULL,
`Description` text NOT NULL,
`Default_Points` int(3) NOT NULL,
`Groups` varchar(125) NOT NULL,
`Display_Start` datetime default NULL,
`Display_End` datetime default NULL,
PRIMARY KEY (`Category_ID`)
) ENGINE=InnoDB DEFAULT CHARSET=latin1
Rewards
82 rows
CREATE TABLE `rewards` (
`Reward_ID` int(9) NOT NULL auto_increment,
`Title` varchar(255) NOT NULL,
`Description` text NOT NULL,
`Image_URL` varchar(255) NOT NULL,
`Date_Inactive` datetime NOT NULL,
`Stock_Count` int(3) NOT NULL,
`Cost_to_User` float NOT NULL,
`Cost_to_System` float NOT NULL,
PRIMARY KEY (`Reward_ID`)
) ENGINE=InnoDB AUTO_INCREMENT=91 DEFAULT CHARSET=latin1
Purchases
5,889 rows
CREATE TABLE `purchases` (
`Purchase_ID` int(9) NOT NULL auto_increment,
`Datetime` datetime NOT NULL,
`Reward_ID` int(9) NOT NULL,
`Quantity` int(4) NOT NULL,
`Student_ID` int(9) NOT NULL,
`Student_Name` varchar(255) NOT NULL,
`Date_DealtWith` datetime default NULL,
`Date_Collected` datetime default NULL,
PRIMARY KEY (`Purchase_ID`)
) ENGINE=InnoDB AUTO_INCREMENT=6133 DEFAULT CHARSET=latin1
Problems
The system ran perfectly well for a period of time. It's now starting to slow-down massively on certain queries.
Essentially, every time I need to access a students' reward points total, the required query takes ages. Here is a few example queries and their run-times:
Top 15 students, excluding attendance categories, across whole school
SELECT CONCAT( s.Firstname, " ", s.Surname ) AS `Student` , s.Year_Group AS `Year Group`, SUM( t.Points ) AS `Points`
FROM frog_rewards.transactions t
LEFT JOIN frog_shared.student s ON t.Recipient_ID = s.id
WHERE t.Datetime > '2013-09-01' AND t.Category_ID NOT IN ( 12, 13, 14, 26 )
GROUP BY t.Recipient_ID
ORDER BY `Points` DESC
LIMIT 0 , 15
Run-time: 44.8425 sec
SELECT Recipient_ID, SUM(points) AS Total_Points FROMtransactionsGROUP BY Recipient_ID
Run-time: 9.8698 sec
Now I appreciate that, especially with the second query, I shouldn't ever be running a call which would return such a vast quantity of rows but the limitations of the framework within which the system runs meant that I had no other choice if I wanted to display students' total reward points for teachers/tutors/year managers/leadership to view and analyse.
Time for a solution
Fortunately the framework we've been forced to use is changing. We'll now be using oAuth rather than a horrible, outdated JavaScript widget format.
Unfortunately - or, I guess, fortunately - it means we'll have to rewrite quite a lot of the system.
One of the main areas I intend to look at when rewriting the system is the database structure. As time goes on it will only get bigger, so I need to do a bit of future-proofing.
As such, my main question is this: what is the most efficient and effective way of storing students' point totals?
The only idea I can come up with is to have a separate table called totals with Student_ID and Points fields. Every time a member of staff gives out some points, it adds a row into the transactions table but also updates the totals table.
Is that efficient? Would it be efficient to also have a Points_Since_Monday type field? How would I update/keep on top of that?
On top of the main question, if anyone has suggestions for general improvement with regard to optimisation of the database table, please let me know.
Thanks in advance,
Duncan

There is nothing particularly wrong with your design which should make it as slow as you have reported. I'm thinking there must be other factors at work, such as the server it is running on being overloaded or slow, for example. Only you will be able to find out if that is the case.
In order to test your design I recreated it on the 2008 SQL Server I have running on my desktop computer. I have a standard computer, single hard disc, not SSD, not raid etc. so on a proper database server the results should be even better. I had to make some changes to the design as you are using MySQL but none of the changes should affect performace, it's just so I can run it on my database.
Here's the table structure I used, I had to guess at what you would have in the Student and Staff tables as you do not descibe those. I also took the liberty of changing the field names in the Transaction table for Giver_ID and Receiver_ID as I assume only staff give points and students receive them.
I generated random data to fill the tables with the same number of rows as you said you have in your database
I ran the two queries you said are taking a long time, I've changed them to suit my design but I (hope) the result is the same
SELECT TOP 15
Firstname + ' ' + Surname
,Year_Group
,SUM(Points) AS Points
FROM points.[Transaction]
INNER JOIN points.Student ON points.[Transaction].Student_ID = points.Student.Student_ID
WHERE [Datetime] > '2013-09-01'
AND Category_ID NOT IN ( 12, 13, 14, 26 )
GROUP BY Firstname + ' ' + Surname
,Year_Group
ORDER BY SUM(Points) DESC
SELECT Student_ID
,SUM(Points) AS Total_Points
FROM points.[Transaction]
GROUP BY Student_ID
Both queries returned results in about 1s. I have not created any additional indexes on the tables other than the CLUSTERED indexes generated by default on the primary keys. Looking at the execution plan the query processor estimates that implementing the following index could improve the query cost by 81.0309%
CREATE NONCLUSTERED INDEX [<Name of Missing Index>]
ON [points].[Transaction] ([Datetime],[Category_ID])
INCLUDE ([Student_ID],[Points])
As others have commented I would look elsewhere for bottlenecks before spending a lot of time redesigning your database.
Update:
I realised I never actually addressed your specific question:
what is the most efficient and effective way of storing students'
point totals?
The only idea I can come up with is to have a separate table called
totals with Student_ID and Points fields. Every time a member of staff
gives out some points, it adds a row into the transactions table but
also updates the totals table.
I would not recommend keeping a separate point total unless you have explored every other possible way to speed up the database. A separate tally can become out of sync with the transactions and then you have to reconcile everything and track down what went wrong, and what the correct total should be.
You should always focus on maintaining the correctness and consistency of the data before trying to increase speed. Most of the time a correct (normalised) data model will operate quickly enough.
In one place I worked we found the most cost effective way to speed up our database was simply to upgrade the hardware; much quicker and cheaper than spending many man-hours redesigning the database :)

Related

Doctrine / MySQL Slow query even when using indexes

I cleaned the question a little bit because it was getting very big and unreadable.
Running on my localhost.
As you can see in the image below, the query takes 755.15 ms when selecting from the table Job that contains 15000 rows (with the where conditions returning 6650)
The table Company contains 1000 rows.
The table geo__name contains 84300 rows approx and is not giving me any problem, so I believe the problem is the database structure or something.
The structure of these 2 tables is the following:
Table Job is:
CREATE TABLE `job` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`created_at` datetime NOT NULL,
`updated_at` datetime NOT NULL,
`company_id` int(11) NOT NULL,
`activity_sector_id` int(11) DEFAULT NULL,
`status` int(11) NOT NULL,
`active` datetime NOT NULL,
`contract_type_id` int(11) NOT NULL,
`salary_type_id` int(11) NOT NULL,
`workday_id` int(11) NOT NULL,
`geoname_id` int(11) NOT NULL,
`title` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL,
`minimum_experience` int(11) DEFAULT NULL,
`min_salary` decimal(7,2) DEFAULT NULL,
`max_salary` decimal(7,2) DEFAULT NULL,
`zip_code` int(11) DEFAULT NULL,
`vacancies` int(11) DEFAULT NULL,
`show_salary` tinyint(1) NOT NULL,
PRIMARY KEY (`id`),
KEY `created_at` (`created_at`,`active`,`status`) USING BTREE,
CONSTRAINT `FK_FBD8E0F823F5422B` FOREIGN KEY (`geoname_id`) REFERENCES `geo__name` (`id`),
CONSTRAINT `FK_FBD8E0F8398DEFD0` FOREIGN KEY (`activity_sector_id`) REFERENCES `activity_sector` (`id`),
CONSTRAINT `FK_FBD8E0F85248165F` FOREIGN KEY (`salary_type_id`) REFERENCES `job_salary_type` (`id`),
CONSTRAINT `FK_FBD8E0F8979B1AD6` FOREIGN KEY (`company_id`) REFERENCES `company` (`id`),
CONSTRAINT `FK_FBD8E0F8AB01D695` FOREIGN KEY (`workday_id`) REFERENCES `workday` (`id`),
CONSTRAINT `FK_FBD8E0F8CD1DF15B` FOREIGN KEY (`contract_type_id`) REFERENCES `job_contract_type` (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=15001 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
The table company is:
CREATE TABLE `company` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`name` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL,
`logo` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
`created_at` datetime NOT NULL,
`updated_at` datetime NOT NULL,
`website` varchar(255) COLLATE utf8mb4_unicode_ci DEFAULT NULL,
`user_id` int(11) NOT NULL,
`phone` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL,
`cifnif` varchar(255) COLLATE utf8mb4_unicode_ci NOT NULL,
`type` int(11) NOT NULL,
`subscription_id` int(11) DEFAULT NULL,
PRIMARY KEY (`id`),
UNIQUE KEY `UNIQ_4FBF094FA76ED395` (`user_id`),
KEY `IDX_4FBF094F9A1887DC` (`subscription_id`),
KEY `name` (`name`(191)),
CONSTRAINT `FK_4FBF094F9A1887DC` FOREIGN KEY (`subscription_id`) REFERENCES `subscription` (`id`),
CONSTRAINT `FK_4FBF094FA76ED395` FOREIGN KEY (`user_id`) REFERENCES `user` (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=1001 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
The query is the following:
SELECT
j0_.id AS id_0,
j0_.status AS status_1,
j0_.title AS title_2,
j0_.min_salary AS min_salary_3,
j0_.max_salary AS max_salary_4,
c1_.id AS id_5,
c1_.name AS name_6,
c1_.logo AS logo_7,
a2_.id AS id_8,
a2_.name AS name_9,
g3_.id AS id_10,
g3_.name AS name_11,
j4_.id AS id_12,
j4_.name AS name_13,
j5_.id AS id_14,
j5_.name AS name_15,
w6_.id AS id_16,
w6_.name AS name_17
FROM
job j0_
INNER JOIN company c1_ ON j0_.company_id = c1_.id
INNER JOIN activity_sector a2_ ON j0_.activity_sector_id = a2_.id
INNER JOIN geo__name g3_ ON j0_.geoname_id = g3_.id
INNER JOIN job_salary_type j4_ ON j0_.salary_type_id = j4_.id
INNER JOIN job_contract_type j5_ ON j0_.contract_type_id = j5_.id
INNER JOIN workday w6_ ON j0_.workday_id = w6_.id
WHERE
j0_.active >= CURRENT_TIMESTAMP
AND j0_.status = 1
ORDER BY
j0_.created_at DESC
When executing the above query I have these results:
In MYSQL Workbench: 0.578 sec / 0.016 sec
In Symfony profiler: 755.15 ms
The question is: Is the duration of this query correct? if not, how can I improve the speed of the query? it seems too much.
The Symfony debug toolbar if it helps:
As you can see in the below image, I'm only getting the data I really need:
The explain query:
The timeline:
The MySQL server can't handle the load being placed on it. This could be due to resource contention, or because it has not been appropriately tuned and it could also be a problem with your hard drive.
First, I would start your performance by adding MySQL keyword "STRAIGHT_JOIN" which tells MySQL to query the data in the order I have provided, dont try to think the relationships for me. However, on your dataset being so small, and already 1/2 second, don't know if that will help as much, but on larger datasets I have known it to SIGNIFICANTLY improve performance.
Next, you appear to be getting lookup descriptions based on the PK/FK relationship results. Not seeing the indexes on those tables, I would suggest doing covering indexes which contain both the key and description so the join can get the data from the index pages it uses for the JOIN instead of use index page, find the actual data pages to get the description and continue.
Last, your job table with the index on (created_at,active,status), might perform better if the index had the index as ( status, active, created_at ).
With your existing index, think of it this way, each day of data is put into a single box. Within each day box that is sorted by an active timestamp (even if simplified by active date), THEN the status.
So, for each day CREATED, you open a box. Look at secondary boxes, one for each "Active" timestamp (ex: by day). Within each Active timestamp (day), only now can you see if the "Status = 1" records. So open each active timestamp day, assess Status = 1, then close each created day box and go to the next created day box and repeat. So look at the labor intensive of open each box per day, each active box within that day.
Now, under the suggested index starting with status. You now have a very finite number of boxes, one for each status. Open only the 1 box for status = 1 These are the only ones you want to consider... All the others you don't care. Inside that, you have the actual records based on ACTIVE Timestamp and that is sub-sorted. From that, you can jump directly to those at the current timestamp. From the first record and the rest within the box, you now have all the records that qualify. Done. Since these records (index) ALSO has the Created_at as part of the index, it can optimize that with the descending sort order.
For ensuring "covering indexes" for the other lookup tables if they do not yet exist, I suggest the following.
table index
company ( id, name, logo )
activity_sector (id, name )
geo__name ( id, name )
job_salary_type ( id, name )
job_contract_type ( id, name )
workday ( id, name )
And the MySQL Keyword...
SELECT STRAIGHT_JOIN (rest of query...)
There are several reasons as to why Symfony is slow.
1. Server fault
First, it could be the server fault. Server performances may hinder your query time.
2. Data size and defered rendering
Then comes the data size. As you can see on the image below, the query on one of my project have a 50Mb data size (currently about 20k rows).
Parsing 50Mb in HTML can take some time, mostly because of loops.
Still, there are solutions about this, like defered rendering.
Defered rendering is quite simple, instead of parsing data in your twig you,
send all data to a javascript varaible, and use javascript to parse/render data once the DOM is loaded.
3. Query optimisation
As I wrote in comment, you can check the following question, on which I explained why custom queries are important.
Are Doctrine relations affecting application performance?
In this question, you will read that order matter... It's in fact the most important thing.
While static data in your databases are often inserted in the right order,
it's rarely the case for dynamic data (data provided by user during the website life)
Which is why, using ORDER BY in your query will often speed up the page rendering,
as doctrine won't be doing extra queries on it's own.
As exemple, One of my site have about 700 entries diplayed on the index.
First, here is the query count while using findAll() :
It show 254 query (253 duplicates) in 144ms, plus 39 render time.
Next, using the second parameter of findBy(), ORDER BY, I get this result :
You can see the full query here (sreenshot is big)
Much better, 1 query only in 8ms, and about the same render time.
But, here, I don't use any fields from associations.
From the moment I will do it, doctrine qui do some extra query, and query count and time will skyrocket.
In the end, it will turn back to something like findAll()
And last, this is the custom query :
In this custom query, the query time went from 8ms to 38ms.
But, unlike the previous query, I got way more data in my result,
which will prevent doctrine from doing extra query.
Again, ORDER BY() matter in this query. Without it, I skyrocket back to 84 queries.
4. Partials
When you do custom query, you can load partials objects instead of full data.
As you said in your question, description field seems to slow down your loading speed,
with partials, you can avoid to load some fields from the table, which will speed up query speed.
First, instead of your regular syntax, this is how you will create the query builder :
$em=$this->getEntityManager();
$qb=$em->createQueryBuilder();
Just in case, I prefer to keep $em as a separate variable (if I want to fetch some class repository for example).
Then you can start your partial select. Careful, first select can't include any association fields :
$qb->select("partial job.{id, status, title, minimum_experience, min_salary, max_salary, zip_code, vacancies")
->from(Job::class, "job");
Then you can add your associations :
$qb->addSelect("company")
->join("job.company", "company");
Or even add partial association in case you don't need all the data of the association :
$qb->addSelect("partial activitySector.{id}")
->join("job.activitySector", "activitySector");
$qb->addSelect("partial job.{id, company_id, activity_sector_id, status, active, contract_type_id, salary_type_id, workday_id, geoname_id, title, minimum_experience, min_salary, max_salary, zip_code, vacancies, show_salary");
5. Caches
You could also use various caches, like Zend OPCache for PHP, which you will find some advices in this question: Why Symfony3 so slow?
There is also the SQL cache Varnish.
This round up about everything I can share to lower your loading time.
Hope it will prove useful and you will be able to solve your problem.
So many keys , try to minimize the number of keys.

Slow GroupBy query for filter results Laravel [duplicate]

I have 2 tables. 1 is music and 2 is listenTrack. listenTrack tracks the unique plays of each song. I am trying to get results for popular songs of the month. I'm getting my results but they are just taking too long. Below is my tables and query
430,000 rows
CREATE TABLE `listentrack` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`sessionId` varchar(50) NOT NULL,
`url` varchar(50) NOT NULL,
`date_created` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
`ip` varchar(150) NOT NULL,
`user_id` int(11) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=MyISAM AUTO_INCREMENT=731306 DEFAULT CHARSET=utf8
12500 rows
CREATE TABLE `music` (
`music_id` int(11) NOT NULL AUTO_INCREMENT,
`user_id` int(11) NOT NULL,
`title` varchar(50) DEFAULT NULL,
`artist` varchar(50) DEFAULT NULL,
`description` varchar(255) DEFAULT NULL,
`genre` int(4) DEFAULT NULL,
`file` varchar(255) NOT NULL,
`url` varchar(50) NOT NULL,
`allow_download` int(2) NOT NULL DEFAULT '1',
`plays` bigint(20) NOT NULL,
`downloads` bigint(20) NOT NULL,
`faved` bigint(20) NOT NULL,
`dateadded` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (`music_id`)
) ENGINE=MyISAM AUTO_INCREMENT=15146 DEFAULT CHARSET=utf8
SELECT COUNT(listenTrack.url) AS total, listenTrack.url
FROM listenTrack
LEFT JOIN music ON music.url = listenTrack.url
WHERE DATEDIFF(DATE(date_created),'2009-08-15') = 0
GROUP BY listenTrack.url
ORDER BY total DESC
LIMIT 0,10
this query isn't very complex and the rows aren't too large, i don't think.
Is there any way to speed this up? Or can you suggest a better solution? This is going to be a cron job at the beggining of every month but I would also like to do by the day results as well.
Oh btw i am running this locally, over 4 min to run, but on prod it takes about 45 secs
I'm more of a SQL Server guy but these concepts should apply.
I'd add indexes:
On ListenTrack, add an index with url, and date_created
On Music, add an index with url
These indexes should speed the query up tremendously (I originally had the table names mixed up - fixed in the latest edit).
For the most part you should also index any column that is used in a JOIN. In your case, you should index both listentrack.url and music.url
#jeff s - An index music.date_created wouldnt help because you are running that through a function first so MySQL cannot use an index on that column. Often, you can rewrite a query so that the indexed referenced column is used statically like:
DATEDIFF(DATE(date_created),'2009-08-15') = 0
becomes
date_created >= '2009-08-15' and date_created < '2009-08-15'
This will filter down records that are from 2009-08-15 and allow any indexes on that column to be candidates. Note that MySQL might NOT use that index, it depends on other factors.
Your best bet is to make a dual index on listentrack(url, date_created)
and then another index on music.url
These 2 indexes will cover this particular query.
Note that if you run EXPLAIN on this query you are still going to get a using filesort because it has to write the records to a temporary table on disk to do the ORDER BY.
In general you should always run your query under EXPLAIN to get an idea on how MySQL will execute the query and then go from there. See the EXPLAIN documentation:
http://dev.mysql.com/doc/refman/5.0/en/using-explain.html
Try creating an index that will help with the join:
CREATE INDEX idx_url ON music (url);
I think I might have missed the obvious before. Why are you joining the music table at all? You do not appear to be using the data in that table at all and you are performing a left join which is not required, right? I think this table being in the query will make it much slower and will not add any value. Take all references to music out, unless the url inclusion is required, in which case you need a right join to force it to not include a row without a matching value.
I would add new indexes, as the others mention. Specifically I would add:
music url
listentrack date_created,url
This will improve your join a ton.
Then I would look at the query, you are forcing the system to perform work on each row of the table. It would be better to rephrase the date restriction as a range.
Not sure of the syntax off the top of my head:
where '2009-08-15 00:00:00' <= date_created < 2009-08-16 00:00:00
That should allow it to rapidly use the index to locate the appropriate records. The combined two key index on music should allow it to find the records based on the date and URL. You should experiment, they might be better off going in the other direction url,date_created on the index.
The explain plan for this query should say "using index" on the right hand column for both. That means that it will not have to hit the data in the table to calculate your sums.
I would also check the memory settings that you have configured for MySQL. It sounds like you do not have enough memory allocated. Be very careful on the differences between server based settings and thread based settings. The server with a 10MB cache is pretty small, a thread with a 10MB cache can use a lot of memory quickly.
Jacob
Pre-grouping and then joining makes things a lot faster with MySQL/MyISAM. (I'm suspicious less of this is needed with other DB's)
This should perform about as fast as the non-joined version:
SELECT
total, a.url, title
FROM
(
SELECT COUNT(*) as total, url
from listenTrack
WHERE DATEDIFF(DATE(date_created),'2009-08-15') = 0
GROUP BY url
ORDER BY total DESC
LIMIT 0,10
) as a
LEFT JOIN music ON music.url = a.url
;
P.S. - Mapping between the two tables with an id instead of a url is sound advice.
Why are you repeating the url in both tables?
Have listentrack hold a music_id instead, and join on that. Gets rid of the text search as well as the extra index.
Besides, it's arguably more correct. You're tracking the times that a particular track was listened to, not the url. What if the url changes?
After you add indexes then you may want to explore adding a new column for the date_created to be a unix_timestamp, which will make math operations quicker.
I am not certain why you have the diff function though, as it appears you are looking for all rows that were updated on a particular date.
You may want to look at your query as it seems to have an error.
If you use unit tests then you can compare the results of your query and a query using a unix timestamp instead.
you might want to add an index to the url field of both tables.
having said that, when i converted from mysql to sql server 2008, with the same queries and same database structures, the queries ran 1-3 orders of magnitude faster.
i think some of it had to do with the rdbms (mysql optimizers are not so good...) and some of it might have had to do with how the rdbms reserve system resources. although, the comparisons were made on production systems where only the db would run.
This below would probably work to speed up the query.
CREATE INDEX music_url_index ON music (url) USING BTREE;
CREATE INDEX listenTrack_url_index ON listenTrack (url) USING BTREE;
You really need to know the total number of comparisons and row scans that are happening. To get that answer look at the code here of how to do that using explain http://www.siteconsortium.com/h/p1.php?id=mysql002.

MySQL - Ranking with millions of entries

I'm working on a project and right now I'm implementing a leaderboard. Before I start working on it, I need some advices for better practice of my leaderboard's structure.
First of all the leaderboard will be displayed on two pages, the one is on the home page of each player's which will contain the first 10 teams (same 10 teams for all players) and the other leaderboard will be in the leaderboard's page, which there, will have all the teams with sorting functionalities.
The structure of the leaderboard of each row is the following:
• ranking position
• team name
• team value
• total of the games the team won
• total of the games the team defeated
• total of the games the team had draw
• sum of the goals the team has made
• sum of the goals the team has conceded
• the last 4 game results of the team
Below is my database's tables
challenges table
CREATE TABLE `challenges` (
id` int(10) unsigned NOT NULL AUTO_INCREMENT,
'challenge_date` datetime NOT NULL,
`status` varchar(20) COLLATE utf8_unicode_ci NOT NULL,
`created_at` timestamp NULL DEFAULT NULL,
`updated_at` timestamp NULL DEFAULT NULL,
PRIMARY KEY (`id`),
KEY `challanges_id_index` (`id`),
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci;
challenges results
CREATE TABLE `challenges_results` (
`id` int(11) unsigned NOT NULL AUTO_INCREMENT,
`challenge_id` int(11) NOT NULL,
`team_id` int(11) NOT NULL,
`goals` int(11) NOT NULL,
`result` char(1) DEFAULT NULL,
`challenge_date` datetime NOT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
On challenges results result column can be W for wins, D for draws and L for defeats
team values
CREATE TABLE `team_values` (
`id` int(11) unsigned NOT NULL AUTO_INCREMENT,
`team_id` int(11) DEFAULT NULL,
`value` double(15,8) DEFAULT '1500.00000000',
`created_at` datetime DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
team
CREATE TABLE `teams` (
`id` int(10) unsigned NOT NULL AUTO_INCREMENT,
`name` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`avatar` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`founded` date NOT NULL,
`residense_city_id` int(10) unsigned NOT NULL,
`slug` varchar(255) COLLATE utf8_unicode_ci NOT NULL,
`primary_color` char(10) COLLATE utf8_unicode_ci NOT NULL,
`secondary_color` char(10) COLLATE utf8_unicode_ci NOT NULL,
`status` varchar(20) COLLATE utf8_unicode_ci NOT NULL,
`created_at` timestamp NULL DEFAULT NULL,
`updated_at` timestamp NULL DEFAULT NULL,
PRIMARY KEY (`id`),
UNIQUE KEY `teams_slug_unique` (`slug`),
KEY `teams_id_index` (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci;
One team can have many values (teams_values) but only the recent will be displayed.
One team can be in many challenges.
One team can have many results from different challenges.
The leaderboard will work as follow. The teams will be sorted with the highest values from teams_values table. That value is calculated and stored every time the team is having a challenge.
In case where two or more teams have the same value we need to apply the following three rules. The rules also needs to be executed one by one, for example if I run the first rule and still there are teams which are equal also on value and goals scored then I will apply the second rule and so on.
• Best offense (higher Number of goals scored)
• Best Defense (Less Number of goals conceded)
• The team with the most wins in the games between them
So I came with three solutions which still I don't know which one is the better and if there is a better from the three.
The first option that I though is to use options like inner join, union etc to collect the information from the tables and apply the rules on the same SQL query. So every time that I want to view the leaderboard, I will execute this SQL. The problem with this solution is that I don't know how effective will be in case that we want the leaderboard to be always up to date with the latest results. Because imaging having 10k visitors per day and everyone executing this query.
Second option is to collect the information and in case of duplicate values, I will use PHP to get the duplicate teams, apply the rules and then based on the results of the rules swipe the teams in the array. From performance site I don't know how effective is this option.
Third solution is to create another table called leaderboard which I will store all this information in case the team doesn't exist or I will update the record if exist based on the results of the latest challenge e.g increasing the goals if the team scored. Then I will use only the leaderboard table for filtering the data and printing the ranking of the teams. I believe this option is better because I need to deal only with one table and I will update the record only when a team finished a challenge.
We will use cache, but for now we are thinking that the leaderboard should be always up to date and not updating it once a day.
Which one is better solution and why and in case of a better solution I'm open for suggestions. Thanks
Since you're running on a shared account on a virtual private server, the chances are very going you're going to theoretically run into cases where you contend for the use of server resources, disk usage, memory usage, cpu processing power.
First and foremost, try do all your database calculations in MySQL, and only return the data to PHP once you've completed all operations on them. MySQL is optimised for the job, whereas PHP is better at general computing problems.
Your one option to take some load off the server would be to use PHP to create a webpage that is viewable in the browser, every single time the leaderboard is updated. That way, you run through calculations only once every time the leaderboard needs to be updated.
If I was building the system and the system was never going to reach enterprise-grade level, but instead remain small and functional, I would write a PHP script early on, because you can save a lot of processing power that way alone.
For what it's worth, if the server is well configured, you shouldn't be worried about getting 10k user requests a day, unless your code is really terribly written.
EDIT: As an afterthought, you can install a program like https://memcached.org/, which caches your SQL data in RAM. Sites like LiveJournal and Wordpress use it, but you'd need to configure it in a way that works for the rest of the vps users unless the box is really high spec.

Optimizing an SQL query with generated GROUP BY statement

I have this query:
SELECT ROUND(AVG(temp)*multT + conT,2) as temp,
FLOOR(timestamp/$secondInterval) as meh
FROM sensor_locass
LEFT JOIN sensor_data USING(sensor_id)
WHERE sensor_id = '$id'
AND project_id = '$project'
GROUP BY meh
ORDER BY timestamp ASC
The purpose is to select data for drawing a graph, I use the average over a pixels worth of data to make the graph faithful to the data.
So far optimization has included adding indexes, switching between MyISAM and InnoDB but no luck.
Since the time interval changes with graph zoom and period of data collection I cannot make a seperate column for the GROUP BY statement, the query however is slow. Does anyone have ideas for optimizing this query or the table to make this grouping faster, I currently have an index on the timestamp, sensor_id and project_id columns, the timestamp index is not used however.
When running explain extended with the query I get the following:
1 SIMPLE sensor_locass ref sensor_id_lookup,project_id_lookup sensor_id_lookup 4 const 2 100.00 Using where; Using temporary; Using filesort
1 SIMPLE sensor_data ref idsensor_lookup idsensor_lookup 4 webstech.sensor_locass.sensor_id 66857 100.00
The sensor_data table contains at the moment 2.7 million datapoints which is only a small fraction of the amount of data i will end up having to work with. Any helpful ideas, comments or solution would be most welcome
EDIT table definitions:
CREATE TABLE `sensor_data` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`gateway_id` int(11) NOT NULL,
`timestamp` int(10) NOT NULL,
`v1` int(11) NOT NULL,
`v2` int(11) NOT NULL,
`v3` int(11) NOT NULL,
`sensor_id` int(11) NOT NULL,
`temp` decimal(5,3) NOT NULL,
`oxygen` decimal(5,3) NOT NULL,
`batVol` decimal(4,3) NOT NULL,
PRIMARY KEY (`id`),
KEY `gateway_id` (`gateway_id`),
KEY `time_lookup` (`timestamp`),
KEY `idsensor_lookup` (`sensor_id`)
) ENGINE=MyISAM AUTO_INCREMENT=2741126 DEFAULT CHARSET=latin1
CREATE TABLE `sensor_locass` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`project_id` int(11) NOT NULL,
`sensor_id` int(11) NOT NULL,
`start` date NOT NULL,
`end` date NOT NULL,
`multT` decimal(6,3) NOT NULL,
`conT` decimal(6,3) NOT NULL,
`multO` decimal(6,3) NOT NULL,
`conO` decimal(6,3) NOT NULL,
`xpos` decimal(4,2) NOT NULL,
`ypos` decimal(4,2) NOT NULL,
`lat` decimal(9,6) NOT NULL,
`lon` decimal(9,6) NOT NULL,
`isRef` tinyint(1) NOT NULL,
PRIMARY KEY (`id`),
KEY `sensor_id_lookup` (`sensor_id`),
KEY `project_id_lookup` (`project_id`)
) ENGINE=MyISAM AUTO_INCREMENT=238 DEFAULT CHARSET=latin1
Despite everyone's answers, changing the primary key to optimize the search on the table with 238 rows isn't gonna change anything, especially when the EXPLAIN shows a single key narrowing the search to two rows. And adding timestamp to the primary key on sensor_data won't work either since nothing is querying the timestamp, just calculating on it (unless you can restrict on the timestamp values as galymzhan suggests).
Oh, and you can drop the LEFT in your query, since matching on project_id makes it irrelevant anyway (but doesn't slow anything down). And please don't interpolate variables directly into a query if those variables come from customer input to avoid $project_id = "'; DROP TABLES; --" type sql injection exploits.
Adjusting your heap sizes could work for a while but you'll have to continue adjusting it if you need to scale.
The answer vdrmrt suggests might work but then you'd need to populate your aggregate table with every single possible value for $secondInterval which I'm assuming isn't very plausible given the flexibility that you said you needed. In the same vein, you could consider rrdtool, either using it directly or modifying your data in the same way that it does. What I'm referring to specifically is that it keeps the raw data for a given period of time (usually a few days), then averages the data points together over larger and larger periods of time. The end result is that you can zoom in to high detail for recent periods of time but if you look back further, the data has been effectively lossy-compressed to averages over large periods of time (e.g. one data point per second for a day, one data point per minute for a week, one data point per hour for a month, etc). You could customize those averages initially but unless you kept both the raw data and the summarized data, you wouldn't be able to go back and adjust. In particular, you could not dynamically zoom in to high detail on some older arbitrary point (such as looking at the per second data for a 1 hour of time occuring six months ago).
So you'll have to decide whether such restrictions are reasonable given your requirements.
If not, I would then argue that you are trying to do something in MySQL that it was not designed for. I would suggest pulling the raw data you need and taking the averages in php, rather than in your query. As has already been pointed out, the main reason your query takes a long time is because the GROUP BY clause is forcing mysql to crunch all the data in memory but since its too much data its actually writing that data temporarily to disk. (Hence the using filesort). However, you have much more flexibility in terms of how much memory you can use in php. Furthermore, since you are combining nearby rows, you could pull the data out row by row, combining it on the fly and thereby never needing to keep all the rows in memory in your php process. You could then drop the GROUP BY and avoid the filesort. Use an ORDER BY timestamp instead and if mysql doesn't optimize it correctly, then make sure you use FORCE INDEX FOR ORDER BY (timestamp)
I'd suggest that you find a natural primary key to your tables and switch to InnoDB. This a guess at what your data looks like:
sensor_data:
PRIMARY KEY (sensor_id, timestamp)
sensor_locass:
PRIMARY KEY (sensor_id, project_id)
InnoDB will order all the data in this way so rows you're likely to SELECT together will be together on disk. I think you're group by will always cause some trouble. If you can keep it below the size where it switches over to a file sort (tmp_table_size and max_heap_table_size), it'll be much faster.
How many rows are you generally returning? How long is it taking now?
As Joshua suggested, you should define (sensor_id, project_id) as a primary key for sensor_locass table, because at the moment table has 2 separate indexes on each of the columns. According to mysql docs, SELECT will choose only one index from them (most restrictive, which finds fewer rows), while primary key allows to use both columns for indexing data.
However, EXPLAIN shows that MySQL examined 66857 rows on a joined table, so you should somehow optimize that too. Maybe you could query sensor data for a given interval of time, like timestamp BETWEEN (begin, end) ?
I agree that the first step should be to define sensor_id, project_id as primary key for sensor_locass.
If that is not enough and your data is relative static you can create an aggregated table that you can refresh for example everyday and than query from there.
What you still have to do is to define a range for secondInterval, store that in new table and add that field to the primary key of your aggregated table.
The query to populate the aggregated table will be something like this:
INSERT INTO aggregated_sensor_data (sensor_id,project_id,secondInterval,timestamp,temp,meh)
SELECT
sensor_locass.sensor_id,
sensor_locass.project_id,
secondInterval,
timestamp,
ROUND(AVG(temp)*multT + conT,2) as temp,
FLOOR(timestamp/secondInterval) as meh
FROM
sensor_locass
LEFT JOIN sensor_data
USING(sensor_id)
LEFT JOIN secondIntervalRange
ON 1 = 1
WHERE
sensor_id = '$id'
AND
project_id = '$project'
GROUP BY
sensor_locass.sensor_id,
sensor_locass.project_id,
meh
ORDER BY
timestamp ASC
And you can use this query to extract the aggregated data:
SELECT
temp,
meh
FROM
aggregated_sensor_data
WHERE
sensor_id = '$id'
AND project_id = '$project'
AND secondInterval = $secondInterval
ORDER BY
timestamp ASC
If you want to use timestamp index, you will have to tell explicitly to use that index. MySQL 5.1 supports USE INDEX FOR ORDER BY/FORCE INDEX FOR ORDER BY. Have a look at it here http://dev.mysql.com/doc/refman/5.1/en/index-hints.html

MySQL Slow on join. Any way to speed up

I have 2 tables. 1 is music and 2 is listenTrack. listenTrack tracks the unique plays of each song. I am trying to get results for popular songs of the month. I'm getting my results but they are just taking too long. Below is my tables and query
430,000 rows
CREATE TABLE `listentrack` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`sessionId` varchar(50) NOT NULL,
`url` varchar(50) NOT NULL,
`date_created` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
`ip` varchar(150) NOT NULL,
`user_id` int(11) DEFAULT NULL,
PRIMARY KEY (`id`)
) ENGINE=MyISAM AUTO_INCREMENT=731306 DEFAULT CHARSET=utf8
12500 rows
CREATE TABLE `music` (
`music_id` int(11) NOT NULL AUTO_INCREMENT,
`user_id` int(11) NOT NULL,
`title` varchar(50) DEFAULT NULL,
`artist` varchar(50) DEFAULT NULL,
`description` varchar(255) DEFAULT NULL,
`genre` int(4) DEFAULT NULL,
`file` varchar(255) NOT NULL,
`url` varchar(50) NOT NULL,
`allow_download` int(2) NOT NULL DEFAULT '1',
`plays` bigint(20) NOT NULL,
`downloads` bigint(20) NOT NULL,
`faved` bigint(20) NOT NULL,
`dateadded` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (`music_id`)
) ENGINE=MyISAM AUTO_INCREMENT=15146 DEFAULT CHARSET=utf8
SELECT COUNT(listenTrack.url) AS total, listenTrack.url
FROM listenTrack
LEFT JOIN music ON music.url = listenTrack.url
WHERE DATEDIFF(DATE(date_created),'2009-08-15') = 0
GROUP BY listenTrack.url
ORDER BY total DESC
LIMIT 0,10
this query isn't very complex and the rows aren't too large, i don't think.
Is there any way to speed this up? Or can you suggest a better solution? This is going to be a cron job at the beggining of every month but I would also like to do by the day results as well.
Oh btw i am running this locally, over 4 min to run, but on prod it takes about 45 secs
I'm more of a SQL Server guy but these concepts should apply.
I'd add indexes:
On ListenTrack, add an index with url, and date_created
On Music, add an index with url
These indexes should speed the query up tremendously (I originally had the table names mixed up - fixed in the latest edit).
For the most part you should also index any column that is used in a JOIN. In your case, you should index both listentrack.url and music.url
#jeff s - An index music.date_created wouldnt help because you are running that through a function first so MySQL cannot use an index on that column. Often, you can rewrite a query so that the indexed referenced column is used statically like:
DATEDIFF(DATE(date_created),'2009-08-15') = 0
becomes
date_created >= '2009-08-15' and date_created < '2009-08-15'
This will filter down records that are from 2009-08-15 and allow any indexes on that column to be candidates. Note that MySQL might NOT use that index, it depends on other factors.
Your best bet is to make a dual index on listentrack(url, date_created)
and then another index on music.url
These 2 indexes will cover this particular query.
Note that if you run EXPLAIN on this query you are still going to get a using filesort because it has to write the records to a temporary table on disk to do the ORDER BY.
In general you should always run your query under EXPLAIN to get an idea on how MySQL will execute the query and then go from there. See the EXPLAIN documentation:
http://dev.mysql.com/doc/refman/5.0/en/using-explain.html
Try creating an index that will help with the join:
CREATE INDEX idx_url ON music (url);
I think I might have missed the obvious before. Why are you joining the music table at all? You do not appear to be using the data in that table at all and you are performing a left join which is not required, right? I think this table being in the query will make it much slower and will not add any value. Take all references to music out, unless the url inclusion is required, in which case you need a right join to force it to not include a row without a matching value.
I would add new indexes, as the others mention. Specifically I would add:
music url
listentrack date_created,url
This will improve your join a ton.
Then I would look at the query, you are forcing the system to perform work on each row of the table. It would be better to rephrase the date restriction as a range.
Not sure of the syntax off the top of my head:
where '2009-08-15 00:00:00' <= date_created < 2009-08-16 00:00:00
That should allow it to rapidly use the index to locate the appropriate records. The combined two key index on music should allow it to find the records based on the date and URL. You should experiment, they might be better off going in the other direction url,date_created on the index.
The explain plan for this query should say "using index" on the right hand column for both. That means that it will not have to hit the data in the table to calculate your sums.
I would also check the memory settings that you have configured for MySQL. It sounds like you do not have enough memory allocated. Be very careful on the differences between server based settings and thread based settings. The server with a 10MB cache is pretty small, a thread with a 10MB cache can use a lot of memory quickly.
Jacob
Pre-grouping and then joining makes things a lot faster with MySQL/MyISAM. (I'm suspicious less of this is needed with other DB's)
This should perform about as fast as the non-joined version:
SELECT
total, a.url, title
FROM
(
SELECT COUNT(*) as total, url
from listenTrack
WHERE DATEDIFF(DATE(date_created),'2009-08-15') = 0
GROUP BY url
ORDER BY total DESC
LIMIT 0,10
) as a
LEFT JOIN music ON music.url = a.url
;
P.S. - Mapping between the two tables with an id instead of a url is sound advice.
Why are you repeating the url in both tables?
Have listentrack hold a music_id instead, and join on that. Gets rid of the text search as well as the extra index.
Besides, it's arguably more correct. You're tracking the times that a particular track was listened to, not the url. What if the url changes?
After you add indexes then you may want to explore adding a new column for the date_created to be a unix_timestamp, which will make math operations quicker.
I am not certain why you have the diff function though, as it appears you are looking for all rows that were updated on a particular date.
You may want to look at your query as it seems to have an error.
If you use unit tests then you can compare the results of your query and a query using a unix timestamp instead.
you might want to add an index to the url field of both tables.
having said that, when i converted from mysql to sql server 2008, with the same queries and same database structures, the queries ran 1-3 orders of magnitude faster.
i think some of it had to do with the rdbms (mysql optimizers are not so good...) and some of it might have had to do with how the rdbms reserve system resources. although, the comparisons were made on production systems where only the db would run.
This below would probably work to speed up the query.
CREATE INDEX music_url_index ON music (url) USING BTREE;
CREATE INDEX listenTrack_url_index ON listenTrack (url) USING BTREE;
You really need to know the total number of comparisons and row scans that are happening. To get that answer look at the code here of how to do that using explain http://www.siteconsortium.com/h/p1.php?id=mysql002.

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