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记一次Innodb慢查询分析过程

先抛出问题

区分度低的索引到底会带来哪方面的影响?

DDL

#数据量约700w
CREATE TABLE `order` (
  `OrderID` varchar(32) NOT NULL COMMENT '订单编号',
  `type` char(1) DEFAULT '0' COMMENT '订单类型:0 流量订单,1 话费订单',
  `Caller` varchar(16) NOT NULL COMMENT '手机号码',
  `Status` smallint(6) NOT NULL DEFAULT '0' COMMENT '-1下单失败、0初始、1待支付、2支付超时、3支付成功、4充值中、5充值成功、6充值失败 7充值超时 8退款中 9退款失败 10退款成功',
  `OrderTime` datetime DEFAULT CURRENT_TIMESTAMP COMMENT '订单下单的时间',
  `appid` varchar(32) DEFAULT NULL COMMENT '应用唯一标识',
  `bizType` varchar(6) DEFAULT NULL COMMENT '应用下业务标识',
  PRIMARY KEY (`OrderID`),
  KEY `Index_OrderID` (`OrderID`),
  KEY `Index_Status` (`Status`),
  KEY `Index_Caller` (`Caller`),
  KEY `Index_OrderTime` (`OrderTime`),
  KEY `index_Type` (`type`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8 ROW_FORMAT=COMPACT COMMENT='订单表';
复制代码

慢查询sql

#耗时约25s
SELECT
	count( * ) AS col_0_0_ 
FROM
	`order` orderentit0_  
WHERE
	1 = 1 
	AND orderentit0_.type = '0' 
	AND STATUS = '5' 
	AND OrderTime >= '2017-08-01 16:17:00' 
	AND OrderTime <= '2017-08-30 16:17:00';
复制代码

分析

从上面的DDL我们可以看到能利用上的索引字段有:

  • type
  • status
  • orderTime

使用Explain命令来进行分析

explain

可以看到利用了status,type两个字段的索引,使用了索引合并(同时走两个索引,取交集)

这边有几个疑问?

问题一: 为何走了索引还这么慢

show index from `order`

show index from order

重点关注Cardinality字段,翻译为基数,可以理解为该索引字段不同结果的总数,用于粗略判断索引区分度(数值越大越好)

可以判断type,status两个字段的索引的区分度十分的低

所以通过这两个索引字段会取出一大堆数据,再一一对orderTime字段进行过滤,效率自然就低了

问题二: 为何走了type,status的索引合并,却不走orderTime的索引

我们使用FORCE INDEX ( Index_OrderTime ) 来强制走orderTime索引

EXPLAIN 
SELECT
	count( * ) AS col_0_0_ 
FROM
	`order` orderentit0_ FORCE INDEX ( Index_OrderTime ) 
WHERE
	1 = 1 
	AND orderentit0_.type = '0' 
	AND STATUS = '5'
	AND OrderTime >= '2017-08-01 16:17:00' 
	AND OrderTime <= '2017-08-30 16:17:00'
	;
复制代码

来看explain分析结果

通过强制走orderTime索引方式,耗时约4s

走orderTime字段索引

结合之前的explain结果做个对比

索引 type key rows
type,status index_merge index_type,index_status 58W
ordertime range index_orderTime 96W

可以看到因为sql优化器判断通过走合并index_status和index_type比走index_orderTime索引需要检索的行数少,所以选择了走索引合并

# 实际结果约47w,执行时间0.4s
SELECT
	count( * ) AS col_0_0_ 
FROM
	`order` orderentit0_ FORCE INDEX ( Index_OrderTime ) 
WHERE
	1 = 1 
	AND OrderTime >= '2017-08-01 16:17:00' 
	AND OrderTime <= '2017-08-30 16:17:00'

# 实际结果约23w,执行时间8s
SELECT 
	count( * ) AS col_0_0_ 
FROM 
	`order` orderentit0_ 
WHERE 
	1 = 1 
	AND orderentit0_.type = '0' 
	AND STATUS = '5' 
复制代码

至此两个问题解释完毕,鼓掌鼓掌👏

但是又带来第三个问题....

问题三: 为何实际执行时间,与explain分析结果相反

1.索引合并,覆盖
#23w,约3.5s
SELECT count( * ) AS col_0_0_ 
FROM `order` orderentit0_ 
WHERE 1 = 1 
AND orderentit0_.type = '0' 
AND STATUS = '5' 

explain:
index_merge		Index_Status,index_Type NULL	587752	

2.orderTime索引,覆盖
#47w,约0.8s
SELECT
	count( * ) AS col_0_0_ 
FROM
	`order` orderentit0_ 
WHERE
	1 = 1 
	AND OrderTime >= '2017-08-01 16:17:00' 
	AND OrderTime <= '2017-08-30 16:17:00'
	
explain:
range	Index_OrderTime	 NULL	963112	
	
3.索引合并,非覆盖
#23w,约22s
SELECT count(appid) AS col_0_0_ 
FROM `order` orderentit0_ 
WHERE 1=1
AND orderentit0_.type = '0' 
AND STATUS = '5' 

explain:
同1

4.orderTime索引,非覆盖
#47w,约4s
SELECT
	count( appid ) AS col_0_0_ 
FROM
	`order` orderentit0_ 
	FORCE INDEX ( Index_OrderTime ) 
WHERE
	1 = 1 
	AND OrderTime >= '2017-08-01 16:17:00' 
	AND OrderTime <= '2017-08-30 16:17:00'
	
explain:
同2

5.status索引,覆盖
#37w,约0.3s
SELECT count( * ) AS col_0_0_ 
FROM `order` orderentit0_ 
WHERE 1 = 1 
AND STATUS = '5' 

6.type索引,覆盖
#400w,约1.7s
SELECT count( * ) AS col_0_0_ 
FROM `order` orderentit0_ 
WHERE 1 = 1 
AND orderentit0_.type = '0' 
复制代码

猜测?:

  1. 由于type字段索引区分度太低,导致多余的磁盘io时间
  2. 对orderTime索引的查询类型为range,优先级较低

本人水平有限,在此抛出问题,请各位大神赐教

结论

  1. 删除type,status,orderTime索引(前二区分度太低),
  2. 添加orderTime,status,type的联合索引(基于业务需求)

=============2018-04-09==================

对于问题三的找到理论依据了

高性能mysql

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