Pablowe

The Dangers of Having status fields

Having a status column is very common in databases today. It can be used to denote a user status:

CREATE TABLE IF NOT EXISTS `user` (
`user_id` int(10) unsigned NOT NULL auto_increment,
`email` varchar(32) NOT NULL,
`pw_hash` char(40) NOT NULL COLLATE latin1_general_cs,
`status` enum('PENDING', 'ACTIVE', 'DISABLED') default 'PENDING',
`date_created` timestamp NOT NULL default CURRENT_TIMESTAMP,
PRIMARY KEY (`user_id`),
UNIQUE KEY `idx_email` (`email`)
);

or user-uploaded media status:

CREATE TABLE IF NOT EXISTS `media` (
`media_id` int unsigned NOT NULL auto_increment,
`owner_user_id` int(10) unsigned NOT NULL,
`title` varchar(32) NOT NULL,
`path` varchar(255) NOT NULL,
`description` varchar(128) NOT NULL,
`status` enum('PROCESSING', 'ACTIVE', 'FAILED', 'DISABLED') default 'PROCESSING',
`date_created` timestamp NOT NULL default CURRENT_TIMESTAMP,
PRIMARY KEY (`media_id`),
KEY `idx_owner` (`owner_user_id`)
);

Let’s say that we needed to retrieve a list of media associated with users. This can be easily accomplished with the following query:

SELECT SQL_NO_CACHE
`u`.`user_id`,
`m`.`media_id`
FROM `user` AS `u`
JOIN `media` AS `m` ON (`u`.`user_id` = `m`.`owner_user_id`);

If there are status columns involved, the query then becomes:

SELECT SQL_NO_CACHE
`u`.`user_id`,
`m`.`media_id`
FROM `user` AS `u`
JOIN `media` AS `m` ON (`u`.`user_id` = `m`.`owner_user_id`)
WHERE `u`.`status` = 'ACTIVE'
AND `m`.`status` = 'ACTIVE';

If non-active records are removed, the status columns dropped, and the first query run, there is a 15% increase in qps over the second query on the original tables in my test environment. The difference can becomes even more pronounced as the number of tables referenced in a JOIN increases!

If you must have status fields, choose the right data type. But if they can be avoided by archiving disabled records to “cold” databases and keeping “pending” records separate (say, until a user completes email verification), that is generally a better solution.

Must we always escape values?

One of the cardinal rules of writing web applications is to escape user-generated input with functions like PHP’s real_escape_string. This is a great rule, but one that can have a negative impact on your application’s performance if used unnecessarily. For instance, when querying data with an integer parameter that is passed internally (not user-generated):

$query = "SELECT SQL_NO_CACHE * FROM `user` WHERE `user_id` = '" .
$mysqli->real_escape_string ( self::$user_id ) . "'";
$res = $mysqli->query ( $query );


The above code takes an average of 0.000922918319702 seconds to execute.

Whereas:

$query = "SELECT SQL_NO_CACHE * FROM `user` WHERE `user_id` = " . self::$user_id;
$res = $mysqli->query ( $query );


takes an average of only 0.000418901443481 seconds to execute.

Although the improvement is small (~0.0005 seconds), when your site runs millions (or tens-of-millions) of queries per day, the benefits begin to add up.

Improving WordPress Performance with Basic MySQL Maintenance

If your blog is anything like mine, the vast majority of comments are spam. Most blogs have at least a 50% ratio of spam-to-valid comments, and Pablowe has a 99.4% ratio (which is probably why there are so many Anti-Spam plugins for WordPress).

One of the most oft-executed queries (based on the MySQL general log statistics) is:

SELECT DISTINCT ID, post_title, post_password, comment_ID,
comment_post_ID, comment_author, comment_date_gmt, comment_approved,
comment_type,comment_author_url,
SUBSTRING(comment_content,1,30) AS com_excerpt
FROM comments
LEFT OUTER JOIN posts ON (comments.comment_post_ID = posts.ID)
WHERE comment_approved = '1' AND comment_type = '' AND
post_password = ''
ORDER BY comment_date_gmt DESC
LIMIT 10;

In order to keep this (and other) queries performing well, I put the following script in cron and schedule it to execute weekly:

<?php
// This should match your values in wp_config.php
$table_prefix = '';
$db_host = '';
$db_name = '';
$db_user = '';
$db_password = '';
// How long (in days) before comments are purged
$purge_age = 7; // DEFAULT: One Week
if ( $mysqli = new mysqli( $db_host,$db_user,$db_password,$db_name) ) {
$purge_query = 'DELETE FROM ' . $table_prefix .
'comments WHERE comment_date < DATE_SUB(NOW(), INTERVAL ' .
$purge_age . " DAY) AND comment_approved = 'spam'";
if ( ! $mysqli->query ( $purge_query ) ) {
die ( “Could Not Issue Query: Please check configuration\n” );
}
$mysqli->close ( );
} else {
die ( “Could Not Connect: Please check configuration\n” );
}
?>

Will this make your blog Digg Proof? Not by itself, but it can play an important part in the overall performance and scalability of your blog.

OmniSQL 0.0.6 Released

OmniSQL (a command line tool for DBAs needing to issue ad-hoc queries against sharded data) version 0.0.6 is officially released.

Instead of logging in separately to multiple databases to issue the same query, groups of databases can be specified in a configuration file and queries will be automatically issued against all targeted MySQL instances.

Let me know of any bugs found or features you would like to see in upcoming releases!

Download at http://code.google.com/p/omnisql/.

CHANGE LOG
- Fixed Bug #1: Multiple databases on the same host
- Fixed Bug #2: Multiple queries from STDIN
- Changed XML format of omnisql.cnf
- Added a README to show how to install and use

The Query Performance Improvement Process

The purpose of this post is to outline a general flow-chart for improving the performance of queryies in MySQL. Much has been written on using EXPLAIN to optimize queries, but there is a whole process that should be followed in order to maximize the effectiveness of query performance tuning. Following is a visual flow-chart of the process:

Assuming that the problem query has been identified, the first question to be asked is:

1) Can the query be gotten rid of?

Surprisingly, the answer is often “yes”. As a result of the Rapid Application Development paradigm followed by many “Web 2.0″ companies, class interfaces are constantly changing and a data-set that used to be required, could no longer be. Equally as often, the query could be rolled up into another query with minimal effect on the other query’s performance. If the query can’t be gotten rid of or the data retrieved elsewhere,

2) Can the query be changed to select a smaller dataset with a more selective WHERE clause or eliminating columns that are returned but not used from the SELECT clause?

Some developers are trained to use SELECT * (link goes to an explanation of why “SELECT *” is evil) in all of their queries under the guise of “flexibility”, using perhaps only one or two columns. This can bog down the network if the unneeded columns are large, and eliminate the possibility of using covering indexes that could eliminate the need to read from disk when retrieving the result set. Additionally, a result can be made smaller by using a LIMIT clause if only a known few of the records will be used by the application. If neither of these approaches work,

3) Can the query be re-written?

If the query has a SUBQUERY, it might have more desireable performance characteristics if it were to be rewritten as a JOIN (tutorial here as well). Although the query re-writing process is outside the scope of this post, there are many good tutorials available elsewhere online.

4) Can indexes be added or changed?

Most people jump the gun and add/change indexes first, before trying to re-write the query. I do not suggest this because there are downsides to having too many indexes (slower INSERTs, poor use of memory, etc…). Some good rules-of-thumb for indexes are:

• Index columns that are used in JOINs (typically Foreign Keys)
• Use UNIQUE indexes when possible
• Find places where you can use covering indexes
• Ensure adequate selectivity on indexed columns
• Use small datatypes (int vs. varchar)
• Keep track of your data (selectivity can change over time)

5) Can the schema or code be changed?

Sometimes fixing a query is as simple as ensuring that joined columns have the same datatype or creating summary tables so that calculations are not performed each time the query is executed. More often, however, it can require that entire sections of code are re-written and data structures changed. This step is last in the list because it is usually the most time-consuming and error-prone.

Cache The Result

There are those who feel that caching a result (using memcached, for example) is a solution for badly-performing queries. I am not one of those people. If your cache servers die, for instance, wouldn’t you rather have the result be that the application performs more slowly, rather than becoming completely unresponsive? Only after your queries have achieved an acceptable level of performance should you explore the option of caching the results.

If you have gone through the entire process and been unable to fix the query, I would encourage you to repeat the process with a fresh set of eyes.

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