SQL魔术 - 查询不应该花费15个小时,但确实如此

Question

好的,所以我有一个非常可怕的MySQL表(900k记录,总共180 MB),我想从子组中提取更高的记录date_updated并计算每组中的加权平均值.计算运行约15个小时,我有一种强烈的感觉,我做错了.

一,怪异的表格布局:

• category
• element_id
• date_updated
• value
• weight
• source_prefix
• source_name

这里只有关键点element_id(BTREE,~8k独特元素).

和计算过程:

为每个组和子组创建哈希.

CREATE TEMPORARY TABLE `temp1` (INDEX ( `ds_hash` ))
                SELECT `category`, 
                `element_id`, 
                `source_prefix`, 
                `source_name`, 
                `date_updated`, 
                `value`, 
                `weight`, 
                MD5(CONCAT(`category`, `element_id`, `source_prefix`, `source_name`)) AS `subcat_hash`, 
                MD5(CONCAT(`category`, `element_id`, `date_updated`)) AS `cat_hash` 
                FROM `bigbigtable` WHERE `date_updated` <= '2009-04-28'

我真的不明白哈希这个大惊小怪,但这种方式更快.我猜想,黑暗魔法.

查找每个子组的最大日期

CREATE TEMPORARY TABLE `temp2` (INDEX ( `subcat_hash` ))

                SELECT MAX(`date_updated`) AS `maxdate` , `subcat_hash`
                FROM `temp1`
                GROUP BY `subcat_hash`;

使用temp2加入temp1以查找类别的加权平均值

CREATE TEMPORARY TABLE `valuebycats` (INDEX ( `category` ))
            SELECT `temp1`.`element_id`, 
                   `temp1`.`category`, 
                   `temp1`.`source_prefix`, 
                   `temp1`.`source_name`, 
                   `temp1`.`date_updated`, 
                   AVG(`temp1`.`value`) AS `avg_value`,
            SUM(`temp1`.`value` * `temp1`.`weight`) / SUM(`weight`) AS `rating`

            FROM `temp1` LEFT JOIN `temp2` ON `temp1`.`subcat_hash` = `temp2`.`subcat_hash`
            WHERE `temp2`.`subcat_hash` = `temp1`.`subcat_hash`
            AND `temp1`.`date_updated` = `temp2`.`maxdate`

            GROUP BY `temp1`.`cat_hash`;

(现在我浏览了它并将其写下来,在我看来,我应该在最后一个查询中使用INNER JOIN(以避免900k*900k临时表)).

不过,有没有正常的方法呢？

UPD:一些图片供参考:

删除了死的ImageShack链接

UPD:解释建议的解决方案:

+----+-------------+-------+------+---------------+------------+---------+--------------------------------------------------------------------------------------+--------+----------+----------------------------------------------+
| id | select_type | table | type | possible_keys | key        | key_len | ref                                                                                  | rows   | filtered | Extra                                        |
+----+-------------+-------+------+---------------+------------+---------+--------------------------------------------------------------------------------------+--------+----------+----------------------------------------------+
|  1 | SIMPLE      | cur   | ALL  | NULL          | NULL       | NULL    | NULL                                                                                 | 893085 |   100.00 | Using where; Using temporary; Using filesort |
|  1 | SIMPLE      | next  | ref  | prefix        | prefix     | 1074    | bigbigtable.cur.source_prefix,bigbigtable.cur.source_name,bigbigtable.cur.element_id |      1 |   100.00 | Using where                                  |
+----+-------------+-------+------+---------------+------------+---------+--------------------------------------------------------------------------------------+--------+----------+----------------------------------------------+    

Answer 1

使用散列是数据库引擎执行连接的方式之一.你必须编写自己的基于哈希的连接是非常罕见的; 这当然看起来不像其中之一,有900k行表和一些聚合.

根据您的评论,此查询可能会执行您要查找的内容:

SELECT cur.source_prefix, 
       cur.source_name, 
       cur.category, 
       cur.element_id,
       MAX(cur.date_updated) AS DateUpdated, 
       AVG(cur.value) AS AvgValue,
       SUM(cur.value * cur.weight) / SUM(cur.weight) AS Rating
FROM eev0 cur
LEFT JOIN eev0 next
    ON next.date_updated < '2009-05-01'
    AND next.source_prefix = cur.source_prefix 
    AND next.source_name = cur.source_name
    AND next.element_id = cur.element_id
    AND next.date_updated > cur.date_updated
WHERE cur.date_updated < '2009-05-01'
AND next.category IS NULL
GROUP BY cur.source_prefix, cur.source_name, 
    cur.category, cur.element_id

GROUP BY执行每个源+类别+元素的计算.

JOIN用于过滤掉旧条目.它查找以后的条目,然后WHERE语句过滤掉后面的条目所在的行.像这样的连接受益于(source_prefix,source_name,element_id,date_updated)上的索引.

有许多方法可以过滤掉旧条目,但是这个方法往往表现得非常好.