看下processlist以及slow query情況，發(fā)現(xiàn)有一個SQL經(jīng)常出現(xiàn)，執(zhí)行計劃中的掃描記錄數(shù)看著還可以，單次執(zhí)行耗時為0.07s，還不算太大。乍一看，可能不是它引發(fā)的，但出現(xiàn)頻率實(shí)在太高，而且執(zhí)行計劃看起來也不夠完美：

mysql> explain SELECT count(1) FROM a , b WHERE a.id = b.video_id and b.state = 1 AND b.column_id = '81'\G

*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: b
type: index_merge
possible_keys: columnid_videoid,column_id,state,video_time_stamp,idx_videoid
key: column_id,state
key_len: 4,4
ref: NULL
rows: 100
Extra: Using intersect(column_id,state); Using where
*************************** 2. row ***************************
id: 1
select_type: SIMPLE
table: a
type: eq_ref
possible_keys: PRIMARY
key: PRIMARY
key_len: 4
ref: b.video_id
rows: 1
Extra: Using where; Using index

再看下該表的索引情況：

mysql> show index from b\G

*************************** 1. row ***************************
Table: b
Non_unique: 0
Key_name: PRIMARY
Seq_in_index: 1
Column_name: id
Collation: A
Cardinality: 167483
Sub_part: NULL
Packed: NULL
Null:
Index_type: BTREE
Comment:
Index_comment:
*************************** 2. row ***************************
Table: b
Non_unique: 1
Key_name: column_id
Seq_in_index: 1
Column_name: column_id
Collation: A
Cardinality: 8374
Sub_part: NULL
Packed: NULL
Null:
Index_type: BTREE
Comment:
Index_comment:
*************************** 3. row ***************************
Table: b
Non_unique: 1
Key_name: state
Seq_in_index: 2
Column_name: state
Collation: A
Cardinality: 5
Sub_part: NULL
Packed: NULL
Null:
Index_type: BTREE
Comment:
Index_comment:

可以看到執(zhí)行計劃中，使用的是index merge，效率自然沒有用聯(lián)合索引(也有的叫做覆蓋索引)來的好了，而且 state 字段的基數(shù)（唯一性）太差，索引效果很差。刪掉兩個獨(dú)立索引，修改成聯(lián)合看看效果如何：

mysql> show index from b;

*************************** 1. row ***************************
Table: b
Non_unique: 0
Key_name: PRIMARY
Seq_in_index: 1
Column_name: id
Collation: A
Cardinality: 128151
Sub_part: NULL
Packed: NULL
Null:
Index_type: BTREE
Comment:
Index_comment:
*************************** 2. row ***************************
Table: b
Non_unique: 1
Key_name: idx_columnid_state
Seq_in_index: 1
Column_name: column_id
Collation: A
Cardinality: 3203
Sub_part: NULL
Packed: NULL
Null:
Index_type: BTREE
Comment:
Index_comment:
*************************** 3. row ***************************
Table: b
Non_unique: 1
Key_name: idx_columnid_state
Seq_in_index: 2
Column_name: state
Collation: A
Cardinality: 3463
Sub_part: NULL
Packed: NULL
Null:
Index_type: BTREE
Comment:
Index_comment:

mysql> explain SELECT count(1) FROM a , b WHERE a.id = b.video_id and b.state = 1 AND b.column_id = '81' \G

*************************** 1. row ***************************
id: 1
select_type: SIMPLE
table: b
type: ref
possible_keys: columnid_videoid,idx_videoid,idx_columnid_state
key: columnid_videoid
key_len: 4
ref: const
rows: 199
Extra: Using where
*************************** 2. row ***************************
id: 1
select_type: SIMPLE
table: a
type: eq_ref
possible_keys: PRIMARY
key: PRIMARY
key_len: 4
ref: b.video_id
rows: 1
Extra: Using where; Using index

可以看到執(zhí)行計劃變成了只用到了 idx_columnid_state 索引，而且 ref 類型也變成了 const，SQL執(zhí)行耗時也從0.07s變成了0.00s，相應(yīng)的CPU負(fù)載也從336%突降到了12%不到。

總結(jié)下，從多次歷史經(jīng)驗(yàn)來看，如果CPU負(fù)載持續(xù)很高，但內(nèi)存和IO都還好的話，這種情況下，首先想到的一定是索引問題，十有八九錯不了。

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