提高 SQL 查询性能
Improve SQL Query perofrmance
我有一个复杂的查询，在我的机器上 运行 需要 700 毫秒。我发现瓶颈是 ORDER BY at_firstname.value 子句，但我如何使用索引来改进它？
SELECT
`e`.*
, `at_default_billing`.`value` AS `default_billing`
, `at_billing_postcode`.`value` AS `billing_postcode`
, `at_billing_city`.`value` AS `billing_city`
, `at_billing_region`.`value` AS `billing_region`
, `at_billing_country_id`.`value` AS `billing_country_id`
, `at_company`.`value` AS `company`
, `at_firstname`.`value` AS `firstname`
, `at_lastname`.`value` AS `lastname`
, CONCAT(at_firstname.value
, " "
, at_lastname.value) AS `full_name`
, `at_phone`.`value` AS `phone`
, IFNULL(at_phone.value,"N/A") AS `telephone`
, `e`.`entity_id` AS `id`
FROM
`customer_entity` AS `e`
LEFT JOIN
`customer_entity_int` AS `at_default_billing`
ON (`at_default_billing`.`entity_id` = `e`.`entity_id`)
AND (`at_default_billing`.`attribute_id` = '13')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_postcode`
ON (`at_billing_postcode`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_postcode`.`attribute_id` = '30')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_city`
ON (`at_billing_city`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_city`.`attribute_id` = '26')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_region`
ON (`at_billing_region`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_region`.`attribute_id` = '28')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_country_id`
ON (`at_billing_country_id`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_country_id`.`attribute_id` = '27')
LEFT JOIN
`customer_address_entity_varchar` AS `at_company`
ON (`at_company`.`entity_id` = `at_default_billing`.`value`)
AND (`at_company`.`attribute_id` = '24')
LEFT JOIN
`customer_entity_varchar` AS `at_firstname`
ON (`at_firstname`.`entity_id` = `e`.`entity_id`)
AND (`at_firstname`.`attribute_id` = '5')
LEFT JOIN
`customer_entity_varchar` AS `at_lastname`
ON (`at_lastname`.`entity_id` = `e`.`entity_id`)
AND (`at_lastname`.`attribute_id` = '7')
LEFT JOIN
`customer_entity_varchar` AS `at_phone`
ON (`at_phone`.`entity_id` = `e`.`entity_id`)
AND (`at_phone`.`attribute_id` = '136')
ORDER BY
`at_firstname`.`value` ASC LIMIT 20
这是执行计划：
查询的解释：
'1','SIMPLE','e',NULL,'ALL',NULL,NULL,NULL,NULL,'19951','100.00','Using temporary; Using filesort'
'1','SIMPLE','at_default_billing',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_INT_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_INT_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_INT_ENTITY_ID,IDX_CUSTOMER_ENTITY_INT_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_INT_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
'1','SIMPLE','at_billing_postcode',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_billing_city',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_billing_region',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_billing_country_id',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_company',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_firstname',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
'1','SIMPLE','at_lastname',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
'1','SIMPLE','at_phone',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
Table结构：
CREATE TABLE `customer_entity_varchar` (
`value_id` int(11) NOT NULL AUTO_INCREMENT COMMENT 'Value Id',
`entity_type_id` smallint(5) unsigned NOT NULL DEFAULT '0' COMMENT 'Entity Type Id',
`attribute_id` smallint(5) unsigned NOT NULL DEFAULT '0' COMMENT 'Attribute Id',
`entity_id` int(10) unsigned NOT NULL DEFAULT '0' COMMENT 'Entity Id',
`value` varchar(255) DEFAULT NULL COMMENT 'Value',
PRIMARY KEY (`value_id`),
UNIQUE KEY `UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID` (`entity_id`,`attribute_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_TYPE_ID` (`entity_type_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID` (`attribute_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID` (`entity_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE` (`entity_id`,`attribute_id`,`value`),
CONSTRAINT `FK_CSTR_ENTT_VCHR_ATTR_ID_EAV_ATTR_ATTR_ID` FOREIGN KEY (`attribute_id`) REFERENCES `eav_attribute` (`attribute_id`) ON DELETE CASCADE ON UPDATE CASCADE,
CONSTRAINT `FK_CSTR_ENTT_VCHR_ENTT_TYPE_ID_EAV_ENTT_TYPE_ENTT_TYPE_ID` FOREIGN KEY (`entity_type_id`) REFERENCES `eav_entity_type` (`entity_type_id`) ON DELETE CASCADE ON UPDATE CASCADE,
CONSTRAINT `FK_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_CUSTOMER_ENTITY_ENTITY_ID` FOREIGN KEY (`entity_id`) REFERENCES `customer_entity` (`entity_id`) ON DELETE CASCADE ON UPDATE CASCADE
) ENGINE=InnoDB AUTO_INCREMENT=131094 DEFAULT CHARSET=utf8 COMMENT='Customer Entity Varchar';
目前您的查询是：
- 首先执行所有左外连接。
- 然后
ORDER 编辑行。
- 然后
LIMIT 编辑行。
我会先执行严格需要的外连接，然后排序和限制（减少到 20 行），最后我会做所有其他的外连接。总之我会做：
- 首先执行最小左外连接。即只有两张表。
- 然后
ORDER 编辑行。
- 然后
LIMIT 编辑行。这最多产生 20 行。
- 执行所有其余的外部联接。此时这不再是数千行，而只有 20 行。
此更改应该会大大减少 "Unique Key Lookup" 处决。修改后的查询将如下所示：
select
e.*
, `at_default_billing`.`value` AS `default_billing`
, `at_billing_postcode`.`value` AS `billing_postcode`
, `at_billing_city`.`value` AS `billing_city`
, `at_billing_region`.`value` AS `billing_region`
, `at_billing_country_id`.`value` AS `billing_country_id`
, `at_company`.`value` AS `company`
, `at_lastname`.`value` AS `lastname`
, CONCAT(firstname
, " "
, at_lastname.value) AS `full_name`
, `at_phone`.`value` AS `phone`
, IFNULL(at_phone.value,"N/A") AS `telephone`
from ( -- Step #1: joining customer_entity with customer_entity_varchar
SELECT
`e`.*
, `at_firstname`.`value` AS `firstname`
, `e`.`entity_id` AS `id`
FROM
`customer_entity` AS `e`
LEFT JOIN
`customer_entity_varchar` AS `at_firstname`
ON (`at_firstname`.`entity_id` = `e`.`entity_id`)
AND (`at_firstname`.`attribute_id` = '5')
ORDER BY -- Step #2: Sorting (the bare minimum)
`at_firstname`.`value` ASC
LIMIT 20 -- Step #3: Limiting (to 20 rows)
) e
LEFT JOIN -- Step #4: Performing all the rest of outer joins (only few rows now)
`customer_entity_int` AS `at_default_billing`
ON (`at_default_billing`.`entity_id` = `e`.`entity_id`)
AND (`at_default_billing`.`attribute_id` = '13')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_postcode`
ON (`at_billing_postcode`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_postcode`.`attribute_id` = '30')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_city`
ON (`at_billing_city`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_city`.`attribute_id` = '26')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_region`
ON (`at_billing_region`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_region`.`attribute_id` = '28')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_country_id`
ON (`at_billing_country_id`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_country_id`.`attribute_id` = '27')
LEFT JOIN
`customer_address_entity_varchar` AS `at_company`
ON (`at_company`.`entity_id` = `at_default_billing`.`value`)
AND (`at_company`.`attribute_id` = '24')
LEFT JOIN
`customer_entity_varchar` AS `at_lastname`
ON (`at_lastname`.`entity_id` = `e`.`entity_id`)
AND (`at_lastname`.`attribute_id` = '7')
LEFT JOIN
`customer_entity_varchar` AS `at_phone`
ON (`at_phone`.`entity_id` = `e`.`entity_id`)
AND (`at_phone`.`attribute_id` = '136')
不幸的是，SELECT whole_mess_of_rows FROM many_tables ORDER BY one_col LIMIT small_number 是一个臭名昭著的性能反模式。为什么？因为它对一个大的结果集进行排序，只是为了丢弃其中的大部分。
诀窍是便宜地找出哪些行在 LIMIT small_number 中，然后只从更大的查询中检索那些行。
你想要哪几行？在我看来，此查询将检索它们的 customer_entity.id 值。但是很难确定，所以你应该测试这个子查询。
SELECT customer_entity.entity_id
FROM customer_entity
LEFT JOIN customer_entity_varchar AS at_firstname
ON (at_firstname.entity_id = e.entity_id)
AND (at_firstname.attribute_id = '5')
ORDER BY at_firstname.value ASC
LIMIT 20
这应该给出二十个相关的 entity_id 值。测试它。看它的执行计划。如果需要，将适当的索引添加到 customer_entity。该索引可能是 (firstname_attribute_id, firstname_entity_id, firstname_value) 但我猜。
然后您可以将它放在主查询的末尾，就在 ORDER BY 之前。
WHERE e.entity_id IN (
SELECT customer_entity.entity_id
FROM customer_entity
LEFT JOIN customer_entity_varchar AS at_firstname
ON (at_firstname.entity_id = e.entity_id)
AND (at_firstname.attribute_id = '5')
ORDER BY at_firstname.value ASC
LIMIT 20
)
而且事情应该会快一点。
我同意前面的答案，但想强调更多的反模式：过度规范化。
您的架构是已经很糟糕的 EAV 架构模式的奇怪（且低效）变体。
将 customer_address_entity_varchar 拆分为 5 table 几乎没有优势，也有一些劣势。 customer_entity_varchar.
同样
地址应该（通常）存储为单个 table 中的几列；没有 JOINs 给其他 tables.
名字+姓氏也是如此。
Phone 可能是另一个问题，因为 person/company/entity 可能有多个 phone 号码（手机、家庭、工作、传真等）。但那是另外一回事了。
我有一个复杂的查询，在我的机器上 运行 需要 700 毫秒。我发现瓶颈是 ORDER BY at_firstname.value 子句，但我如何使用索引来改进它？
SELECT
`e`.*
, `at_default_billing`.`value` AS `default_billing`
, `at_billing_postcode`.`value` AS `billing_postcode`
, `at_billing_city`.`value` AS `billing_city`
, `at_billing_region`.`value` AS `billing_region`
, `at_billing_country_id`.`value` AS `billing_country_id`
, `at_company`.`value` AS `company`
, `at_firstname`.`value` AS `firstname`
, `at_lastname`.`value` AS `lastname`
, CONCAT(at_firstname.value
, " "
, at_lastname.value) AS `full_name`
, `at_phone`.`value` AS `phone`
, IFNULL(at_phone.value,"N/A") AS `telephone`
, `e`.`entity_id` AS `id`
FROM
`customer_entity` AS `e`
LEFT JOIN
`customer_entity_int` AS `at_default_billing`
ON (`at_default_billing`.`entity_id` = `e`.`entity_id`)
AND (`at_default_billing`.`attribute_id` = '13')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_postcode`
ON (`at_billing_postcode`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_postcode`.`attribute_id` = '30')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_city`
ON (`at_billing_city`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_city`.`attribute_id` = '26')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_region`
ON (`at_billing_region`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_region`.`attribute_id` = '28')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_country_id`
ON (`at_billing_country_id`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_country_id`.`attribute_id` = '27')
LEFT JOIN
`customer_address_entity_varchar` AS `at_company`
ON (`at_company`.`entity_id` = `at_default_billing`.`value`)
AND (`at_company`.`attribute_id` = '24')
LEFT JOIN
`customer_entity_varchar` AS `at_firstname`
ON (`at_firstname`.`entity_id` = `e`.`entity_id`)
AND (`at_firstname`.`attribute_id` = '5')
LEFT JOIN
`customer_entity_varchar` AS `at_lastname`
ON (`at_lastname`.`entity_id` = `e`.`entity_id`)
AND (`at_lastname`.`attribute_id` = '7')
LEFT JOIN
`customer_entity_varchar` AS `at_phone`
ON (`at_phone`.`entity_id` = `e`.`entity_id`)
AND (`at_phone`.`attribute_id` = '136')
ORDER BY
`at_firstname`.`value` ASC LIMIT 20
这是执行计划：
查询的解释：
'1','SIMPLE','e',NULL,'ALL',NULL,NULL,NULL,NULL,'19951','100.00','Using temporary; Using filesort'
'1','SIMPLE','at_default_billing',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_INT_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_INT_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_INT_ENTITY_ID,IDX_CUSTOMER_ENTITY_INT_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_INT_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
'1','SIMPLE','at_billing_postcode',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_billing_city',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_billing_region',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_billing_country_id',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_company',NULL,'eq_ref','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ADDRESS_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.at_default_billing.value,const','1','100.00','Using where'
'1','SIMPLE','at_firstname',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
'1','SIMPLE','at_lastname',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
'1','SIMPLE','at_phone',NULL,'eq_ref','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID,IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE','UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID','6','lazurd.e.entity_id,const','1','100.00',NULL
Table结构：
CREATE TABLE `customer_entity_varchar` (
`value_id` int(11) NOT NULL AUTO_INCREMENT COMMENT 'Value Id',
`entity_type_id` smallint(5) unsigned NOT NULL DEFAULT '0' COMMENT 'Entity Type Id',
`attribute_id` smallint(5) unsigned NOT NULL DEFAULT '0' COMMENT 'Attribute Id',
`entity_id` int(10) unsigned NOT NULL DEFAULT '0' COMMENT 'Entity Id',
`value` varchar(255) DEFAULT NULL COMMENT 'Value',
PRIMARY KEY (`value_id`),
UNIQUE KEY `UNQ_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID` (`entity_id`,`attribute_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_TYPE_ID` (`entity_type_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ATTRIBUTE_ID` (`attribute_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID` (`entity_id`),
KEY `IDX_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_ATTRIBUTE_ID_VALUE` (`entity_id`,`attribute_id`,`value`),
CONSTRAINT `FK_CSTR_ENTT_VCHR_ATTR_ID_EAV_ATTR_ATTR_ID` FOREIGN KEY (`attribute_id`) REFERENCES `eav_attribute` (`attribute_id`) ON DELETE CASCADE ON UPDATE CASCADE,
CONSTRAINT `FK_CSTR_ENTT_VCHR_ENTT_TYPE_ID_EAV_ENTT_TYPE_ENTT_TYPE_ID` FOREIGN KEY (`entity_type_id`) REFERENCES `eav_entity_type` (`entity_type_id`) ON DELETE CASCADE ON UPDATE CASCADE,
CONSTRAINT `FK_CUSTOMER_ENTITY_VARCHAR_ENTITY_ID_CUSTOMER_ENTITY_ENTITY_ID` FOREIGN KEY (`entity_id`) REFERENCES `customer_entity` (`entity_id`) ON DELETE CASCADE ON UPDATE CASCADE
) ENGINE=InnoDB AUTO_INCREMENT=131094 DEFAULT CHARSET=utf8 COMMENT='Customer Entity Varchar';
目前您的查询是：
- 首先执行所有左外连接。
- 然后
ORDER编辑行。 - 然后
LIMIT编辑行。
我会先执行严格需要的外连接，然后排序和限制（减少到 20 行），最后我会做所有其他的外连接。总之我会做：
- 首先执行最小左外连接。即只有两张表。
- 然后
ORDER编辑行。 - 然后
LIMIT编辑行。这最多产生 20 行。 - 执行所有其余的外部联接。此时这不再是数千行，而只有 20 行。
此更改应该会大大减少 "Unique Key Lookup" 处决。修改后的查询将如下所示：
select
e.*
, `at_default_billing`.`value` AS `default_billing`
, `at_billing_postcode`.`value` AS `billing_postcode`
, `at_billing_city`.`value` AS `billing_city`
, `at_billing_region`.`value` AS `billing_region`
, `at_billing_country_id`.`value` AS `billing_country_id`
, `at_company`.`value` AS `company`
, `at_lastname`.`value` AS `lastname`
, CONCAT(firstname
, " "
, at_lastname.value) AS `full_name`
, `at_phone`.`value` AS `phone`
, IFNULL(at_phone.value,"N/A") AS `telephone`
from ( -- Step #1: joining customer_entity with customer_entity_varchar
SELECT
`e`.*
, `at_firstname`.`value` AS `firstname`
, `e`.`entity_id` AS `id`
FROM
`customer_entity` AS `e`
LEFT JOIN
`customer_entity_varchar` AS `at_firstname`
ON (`at_firstname`.`entity_id` = `e`.`entity_id`)
AND (`at_firstname`.`attribute_id` = '5')
ORDER BY -- Step #2: Sorting (the bare minimum)
`at_firstname`.`value` ASC
LIMIT 20 -- Step #3: Limiting (to 20 rows)
) e
LEFT JOIN -- Step #4: Performing all the rest of outer joins (only few rows now)
`customer_entity_int` AS `at_default_billing`
ON (`at_default_billing`.`entity_id` = `e`.`entity_id`)
AND (`at_default_billing`.`attribute_id` = '13')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_postcode`
ON (`at_billing_postcode`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_postcode`.`attribute_id` = '30')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_city`
ON (`at_billing_city`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_city`.`attribute_id` = '26')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_region`
ON (`at_billing_region`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_region`.`attribute_id` = '28')
LEFT JOIN
`customer_address_entity_varchar` AS `at_billing_country_id`
ON (`at_billing_country_id`.`entity_id` = `at_default_billing`.`value`)
AND (`at_billing_country_id`.`attribute_id` = '27')
LEFT JOIN
`customer_address_entity_varchar` AS `at_company`
ON (`at_company`.`entity_id` = `at_default_billing`.`value`)
AND (`at_company`.`attribute_id` = '24')
LEFT JOIN
`customer_entity_varchar` AS `at_lastname`
ON (`at_lastname`.`entity_id` = `e`.`entity_id`)
AND (`at_lastname`.`attribute_id` = '7')
LEFT JOIN
`customer_entity_varchar` AS `at_phone`
ON (`at_phone`.`entity_id` = `e`.`entity_id`)
AND (`at_phone`.`attribute_id` = '136')
不幸的是，SELECT whole_mess_of_rows FROM many_tables ORDER BY one_col LIMIT small_number 是一个臭名昭著的性能反模式。为什么？因为它对一个大的结果集进行排序，只是为了丢弃其中的大部分。
诀窍是便宜地找出哪些行在 LIMIT small_number 中，然后只从更大的查询中检索那些行。
你想要哪几行？在我看来，此查询将检索它们的 customer_entity.id 值。但是很难确定，所以你应该测试这个子查询。
SELECT customer_entity.entity_id
FROM customer_entity
LEFT JOIN customer_entity_varchar AS at_firstname
ON (at_firstname.entity_id = e.entity_id)
AND (at_firstname.attribute_id = '5')
ORDER BY at_firstname.value ASC
LIMIT 20
这应该给出二十个相关的 entity_id 值。测试它。看它的执行计划。如果需要，将适当的索引添加到 customer_entity。该索引可能是 (firstname_attribute_id, firstname_entity_id, firstname_value) 但我猜。
然后您可以将它放在主查询的末尾，就在 ORDER BY 之前。
WHERE e.entity_id IN (
SELECT customer_entity.entity_id
FROM customer_entity
LEFT JOIN customer_entity_varchar AS at_firstname
ON (at_firstname.entity_id = e.entity_id)
AND (at_firstname.attribute_id = '5')
ORDER BY at_firstname.value ASC
LIMIT 20
)
而且事情应该会快一点。
我同意前面的答案，但想强调更多的反模式：过度规范化。
您的架构是已经很糟糕的 EAV 架构模式的奇怪（且低效）变体。
将 customer_address_entity_varchar 拆分为 5 table 几乎没有优势，也有一些劣势。 customer_entity_varchar.
地址应该（通常）存储为单个 table 中的几列；没有 JOINs 给其他 tables.
名字+姓氏也是如此。
Phone 可能是另一个问题，因为 person/company/entity 可能有多个 phone 号码（手机、家庭、工作、传真等）。但那是另外一回事了。