在 MySql 建表时候一般会指定字符集，大多数情况下为了更好的兼容性无脑选了 utf8mb4。但是有时会因为选错，或历史遗留问题，导致使用了 utf8 字符集。当两个表的字符集不一样，在使用字符型字段进行表连接查询时，就需要特别注意下查询耗时是否符合预期。
有次使用 left join 写一个 SQL，发现用时明显超过预期，经过一顿折腾才发现是两个表字符集不一样，特此记录一下。
问题分析
mysql> SELECT COUNT( *) from app_bind_rel t left join app_config_control_sn p on t.host_sn = p.host_sn ; +-----------+ | COUNT( *) | +-----------+ | 13447 | +-----------+ 1 row in set (0.89 sec)
例如上面的 SQL，左表 1W 条数据，右表 400 多条数据，在 host_sn 字段上都有索引，查询竟然用了近 900ms，怎么会这么慢？
mysql> explain SELECT COUNT( *) from app_bind_rel t left join app_config_control_sn p on t.host_sn = p.host_sn ; +----+-------------+-------+------------+-------+---------------+-------------+---------+------+-------+----------+-----------------------------------------------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+-------------+---------+------+-------+----------+-----------------------------------------------------------------+ | 1 | SIMPLE | t | NULL | index | NULL | idx_host_sn | 122 | NULL | 10791 | 100.00 | Using index | | 1 | SIMPLE | p | NULL | index | NULL | idx_host_sn | 152 | NULL | 457 | 100.00 | Using where; Using index; Using join buffer (Block Nested Loop) | +----+-------------+-------+------------+-------+---------------+-------------+---------+------+-------+----------+-----------------------------------------------------------------+ 2 rows in set, 1 warning (0.00 sec)
查看下执行计划，的确是使用了索引，但是细看 Extra 列发现较正常的连表查询多了“Using join buffer (Block Nested Loop)”这一信息，这个具体是什么意思我们后面再说。
然后我们再看下详细的执行计划，使用 explain formart=json。
{
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "988640.52"
},
"nested_loop": [
{
"table": {
"table_name": "t",
"access_type": "index",
"key": "idx_host_sn",
"used_key_parts": [
"host_sn"
],
"key_length": "122",
"rows_examined_per_scan": 10791,
"rows_produced_per_join": 10791,
"filtered": "100.00",
"using_index": true,
"cost_info": {
"read_cost": "161.00",
"eval_cost": "2158.20",
"prefix_cost": "2319.20",
"data_read_per_join": "2M"
},
"used_columns": [
"host_sn"
]
}
},
{
"table": {
"table_name": "p",
"access_type": "index",
"key": "idx_host_sn",
"used_key_parts": [
"host_sn"
],
"key_length": "152",
"rows_examined_per_scan": 457,
"rows_produced_per_join": 4931487,
"filtered": "100.00",
"using_index": true,
"using_join_buffer": "Block Nested Loop",
"cost_info": {
"read_cost": "23.92",
"eval_cost": "986297.40",
"prefix_cost": "988640.52",
"data_read_per_join": "865M"
},
"used_columns": [
"host_sn"
],
"attached_condition": "(is_not_null_compl(p), (`db0`.`t`.`host_sn` = convert(`db0`.`p`.`host_sn` using utf8mb4)), true)"
}
}
]
}
}
特别需要关注的是这一对 KV
"attached_condition": "(is_not_null_compl(p), (`collection_bullet_0000`.`t`.`host_sn` = convert(`collection_bullet_0000`.`p`.`host_sn` using utf8mb4)), true)"
看字面意思就是当 p 表不为空的时候，执行表连接需要先将 p 表的 host_sn 字段转变为 utf8mb4 字符集。我们应该都知道在表连接中使用了函数的话，是无法使用索引的。
所以再回到上面我看到的“Using join buffer (Block Nested Loop)”问题，来解释下这是一个什么过程。
Nested-Loop Join
MySql 官网对 Nested-Loop Join 有做过解释，其实做开发的同学看到名字就大体知道是啥，不就是循环嵌套嘛。
MySql 中分为 Nested-Loop Join 算法跟 Block Nested-Loop Join 算法。
例如，有如下三个表，t1、t2、t3 使用了这三种 join type。
Table Join Type
t1 range
t2 ref
t3 ALL
当使用 Nested-Loop Join 算法时，其 join 过程如下所示，其实就是简单的三层循环。
for each row in t1 matching range {
for each row in t2 matching reference key {
for each row in t3 {
if row satisfies join conditions, send to client
}
}
}
Block Nested-Loop Join（BNL） 算法是对 Nested-Loop Join 算法的一种优化。BNL 算法缓冲外部循环中读取的行来减少内部循环中读取表的次数。例如，将 10 行数据读取到缓冲器中，并且将缓冲器传递到下一个循环内部，内部循环中读取的每一行与缓冲器中的所有 10 行进行比较。这将使读取内部表的次数减少一个数量级。
for each row in t1 matching range {
for each row in t2 matching reference key {
store used columns from t1, t2 in join buffer
if buffer is full {
for each row in t3 {
for each t1, t2 combination in join buffer {
if row satisfies join conditions, send to client
}
}
empty join buffer
}
}
}
if buffer is not empty {
for each row in t3 {
for each t1, t2 combination in join buffer {
if row satisfies join conditions, send to client
}
}
}
算法实现如上，只有当 “join buffer” 满的时候才会触发 t3 表的读取，如果 “join buffer” 的 size = 10 那么就可以减少 10 倍的 t3 表被读取次数，从内存中读取数据的效率显然要比从磁盘读取的效率高的多。从而提升 join 的效率。
但其实再好的优化毕竟也是嵌套循环，做开发的同学应该都知道 O(N²) 的时间复杂度是无法接受的。这也是我们这个查询这么慢的根因。
解决办法
解决办法其实很简单，修改右表的字符集就可以解决。
在变更数据集之前我们先用 show table status 查看下当前表的状态。
mysql> show table status like 'app_config_control_sn'; +-----------------------+--------+---------+------------+------+----------------+-------------+-----------------+--------------+-----------+----------------+---------------------+---------------------+------------+-----------------+----------+----------------+---------+ | Name | Engine | Version | Row_format | Rows | Avg_row_length | Data_length | Max_data_length | Index_length | Data_free | Auto_increment | Create_time | Update_time | Check_time | Collation | Checksum | Create_options | Comment | +-----------------------+--------+---------+------------+------+----------------+-------------+-----------------+--------------+-----------+----------------+---------------------+---------------------+------------+-----------------+----------+----------------+---------+ | app_config_control_sn | InnoDB | 10 | Dynamic | 457 | 143 | 65536 | 0 | 32768 | 0 | 1041 | 2023-04-17 03:25:45 | 2023-04-17 03:27:24 | NULL | utf8_general_ci | NULL | | SN | +-----------------------+--------+---------+------------+------+----------------+-------------+-----------------+--------------+-----------+----------------+---------------------+---------------------+------------+-----------------+----------+----------------+---------+ 1 row in set (0.00 sec)
接着使用如下命令变更表的字符集。
mysql> ALTER TABLE app_config_control_sn CONVERT TO CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci; Query OK, 457 rows affected (0.09 sec) Records: 457 Duplicates: 0 Warnings: 0
再次使用 show table status 命令查看下表的状态。
mysql> show table status like 'app_config_control_sn'; +-----------------------+--------+---------+------------+------+----------------+-------------+-----------------+--------------+-----------+----------------+---------------------+---------------------+------------+--------------------+----------+----------------+---------+ | Name | Engine | Version | Row_format | Rows | Avg_row_length | Data_length | Max_data_length | Index_length | Data_free | Auto_increment | Create_time | Update_time | Check_time | Collation | Checksum | Create_options | Comment | +-----------------------+--------+---------+------------+------+----------------+-------------+-----------------+--------------+-----------+----------------+---------------------+---------------------+------------+--------------------+----------+----------------+---------+ | app_config_control_sn | InnoDB | 10 | Dynamic | 457 | 143 | 65536 | 0 | 32768 | 0 | 1041 | 2023-04-17 03:50:11 | 2023-04-17 03:50:11 | NULL | utf8mb4_general_ci | NULL | | SN | +-----------------------+--------+---------+------------+------+----------------+-------------+-----------------+--------------+-----------+----------------+---------------------+---------------------+------------+--------------------+----------+----------------+---------+ 1 row in set (0.01 sec)
可以看到表的字符集已经发生了变化，那我们再次执行开始的 SQL 及 explain 语句，确认下问题是否已经解决。
mysql> SELECT COUNT( *) from app_bind_rel t left join app_config_control_sn p on t.host_sn = p.host_sn ; +-----------+ | COUNT( *) | +-----------+ | 13447 | +-----------+ 1 row in set (0.03 sec) mysql> explain SELECT COUNT( *) from app_bind_rel t left join app_config_control_sn p on t.host_sn = p.host_sn ; +----+-------------+-------+------------+-------+---------------+-------------+---------+---------------+-------+----------+--------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+-------------+---------+---------------+-------+----------+--------------------------+ | 1 | SIMPLE | t | NULL | index | NULL | idx_host_sn | 122 | NULL | 10791 | 100.00 | Using index | | 1 | SIMPLE | p | NULL | ref | idx_host_sn | idx_host_sn | 202 | db0.t.host_sn | 2 | 100.00 | Using where; Using index | +----+-------------+-------+------------+-------+---------------+-------------+---------+---------------+-------+----------+--------------------------+ 2 rows in set, 1 warning (0.00 sec)
可以看到耗时已经只需要 30ms 左右，这个就比较符合预期，而在执行计划中也不再会有“Using join buffer (Block Nested Loop)”信息。
其他
mysql> SELECT COUNT( *) from app_bind_rel t join app_config_control_sn p on t.host_sn = p.host_sn ; +-----------+ | COUNT( *) | +-----------+ | 730 | +-----------+ 1 row in set (0.01 sec)
在没有变更字符集之前，当我们将 left join 修改为 join 的时候会发现耗时减少了 100 倍，只用了 10 ms，这是为什么呢？
mysql> explain SELECT COUNT( *) from app_bind_rel t join app_config_control_sn p on t.host_sn = p.host_sn ; +----+-------------+-------+------------+-------+---------------+-------------+---------+------+------+----------+--------------------------+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +----+-------------+-------+------------+-------+---------------+-------------+---------+------+------+----------+--------------------------+ | 1 | SIMPLE | p | NULL | index | NULL | idx_host_sn | 152 | NULL | 457 | 100.00 | Using index | | 1 | SIMPLE | t | NULL | ref | idx_host_sn | idx_host_sn | 122 | func | 1 | 100.00 | Using where; Using index | +----+-------------+-------+------------+-------+---------------+-------------+---------+------+------+----------+--------------------------+ 2 rows in set, 1 warning (0.00 sec)
查看执行计划，发现使用 join 的时候不会有 “Using join buffer (Block Nested Loop)”。再细看执行计划，发现驱动表已经由 t 表变为了 p 表。
{
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "643.80"
},
"nested_loop": [
{
"table": {
"table_name": "p",
"access_type": "index",
"key": "idx_host_sn",
"used_key_parts": [
"host_sn"
],
"key_length": "152",
"rows_examined_per_scan": 457,
"rows_produced_per_join": 457,
"filtered": "100.00",
"using_index": true,
"cost_info": {
"read_cost": "4.00",
"eval_cost": "91.40",
"prefix_cost": "95.40",
"data_read_per_join": "82K"
},
"used_columns": [
"host_sn"
]
}
},
{
"table": {
"table_name": "t",
"access_type": "ref",
"possible_keys": [
"idx_host_sn"
],
"key": "idx_host_sn",
"used_key_parts": [
"host_sn"
],
"key_length": "122",
"ref": [
"func"
],
"rows_examined_per_scan": 1,
"rows_produced_per_join": 457,
"filtered": "100.00",
"using_index": true,
"cost_info": {
"read_cost": "457.00",
"eval_cost": "91.40",
"prefix_cost": "643.80",
"data_read_per_join": "117K"
},
"used_columns": [
"host_sn"
],
"attached_condition": "(`db0`.`t`.`host_sn` = convert(`db0`.`p`.`host_sn` using utf8mb4))"
}
}
]
}
}
查看详细的执行计划，可以看到
"attached_condition": "(`collection_bullet_0000`.`t`.`host_sn` = convert(`collection_bullet_0000`.`p`.`host_sn` using utf8mb4))"
这对 KV 依然是存在的，但是 “using_join_buffer”: “Block Nested Loop” 已经不存在了。这个其实主要是因为当 p 表变为驱动表的时候，会先将自己的 host_sn 字段转为 utf8mb4 字符集，再与 t 表进行关联。t 表由于本来就是 utf8mb4 字符集且存在索引，就可以正常走数据库索引了，所以查询耗时也就大大降低。而使用 left join 时候，t 表作为驱动表是无法优化改变的。
可见在表连接中即使使用了函数也不一定就没法走索引，关键还是要看用法及明确处理过程。
记得刚学习数据库的时候，老师还特别强调驱动表一定要写在左边，而随着数据库技术的不断迭代发展，数据库已经能更智能的自动帮我们优化处理过程，之前很多的数据库规则也不需要了。
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