[Solved] SELECT on MIN column value and CHAR column really slow in MySQL

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SELECT on MIN column value and CHAR column really slow in MySQL

Database type:

I'm reasonable sure the answer to this lies in having a different index. I have a query that's unreasonably slow, but only when it's in the following complete form, if I remove parts of the query it's blazing fast, how can I make it better?

Slow:

SELECT json
  FROM requests
  WHERE spider = 'foo'
    AND load_count = ( SELECT MIN( load_count ) FROM requests )
    AND load_count < 50
  LIMIT 500;

EXPLAIN:

+----+-------------+----------+------+-------------------------+--------------+---------+-------+--------+------------------------------+
| id | select_type | table    | type | possible_keys           | key          | key_len | ref   | rows   | Extra                        |
+----+-------------+----------+------+-------------------------+--------------+---------+-------+--------+------------------------------+
|  1 | PRIMARY     | requests | ref  | load_count,spider_index | spider_index | 90      | const | 200845 | Using where                  |
|  2 | SUBQUERY    | NULL     | NULL | NULL                    | NULL         | NULL    | NULL  |   NULL | Select tables optimized away |
+----+-------------+----------+------+-------------------------+--------------+---------+-------+--------+------------------------------+

Database structure:

CREATE TABLE `requests` (
  `added` int(11) NOT NULL AUTO_INCREMENT,
  `url` char(255) NOT NULL,
  `spider` char(30) NOT NULL,
  `referer` char(255) DEFAULT NULL,
  `json` text NOT NULL,
  `load_count` int(11) NOT NULL DEFAULT '0',
  `processed` tinyint(1) NOT NULL DEFAULT '0',
  `invalid` tinyint(1) NOT NULL DEFAULT '0',
  PRIMARY KEY (`added`),
  UNIQUE KEY `url` (`url`),
  KEY `load_count` (`load_count`),
  KEY `spider_index` (`spider`)
) ENGINE=MyISAM AUTO_INCREMENT=5285840 DEFAULT CHARSET=utf8

After updating my index like Neo suggested I get drastic improvements:

+----+-------------+----------+------+-------------------+-------------------+---------+-------------+------+------------------------------+
| id | select_type | table    | type | possible_keys     | key               | key_len | ref         | rows | Extra                        |
+----+-------------+----------+------+-------------------+-------------------+---------+-------------+------+------------------------------+
|  1 | PRIMARY     | requests | ref  | spider_load_count | spider_load_count | 94      | const,const | 1487 | Using where                  |
|  2 | SUBQUERY    | NULL     | NULL | NULL              | NULL              | NULL    | NULL        | NULL | Select tables optimized away |
+----+-------------+----------+------+-------------------+-------------------+---------+-------------+------+------------------------------+

How to optimize this SQL query?

The following recommendations will help you in your SQL tuning process.
You'll find 3 sections below:

  1. Description of the steps you can take to speed up the query.
  2. The optimal indexes for this query, which you can copy and create in your database.
  3. An automatically re-written query you can copy and execute in your database.
The optimization process and recommendations:
  1. Avoid Subqueries (query line: 8): We advise against using subqueries as they are not optimized well by the optimizer. Therefore, it's recommended to join a newly created temporary table that holds the data, which also includes the relevant search index.
  2. Create Optimal Indexes (modified query below): The recommended indexes are an integral part of this optimization effort and should be created before testing the execution duration of the optimized query.
Optimal indexes for this query:
ALTER TABLE `requests` ADD INDEX `requests_idx_spider_load_count` (`spider`,`load_count`);
ALTER TABLE `requests` ADD INDEX `requests_idx_load_count` (`load_count`);
The optimized query:
SELECT
        requests.json 
    FROM
        requests 
    WHERE
        requests.spider = 'foo' 
        AND requests.load_count = (
            SELECT
                MIN(requests.load_count) 
            FROM
                requests
        ) 
        AND requests.load_count < 50 LIMIT 500

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* original question posted on StackOverflow here.