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作者：LuHengXing
来源：http://www.dbapub.cn/2020/09/01/MySQL8.0直方图/
查询优化器负责将SQL查询转换为尽可能高效的执行计划，但随着数据环境不断变化，查询优化器可能无法找到最佳的执行计划，导致SQL效率低下。造成这种情况的原因是优化器对查询的数据了解的不够充足，例如：每个表有多少行数据，每列中有多少不同的值，每列的数据分布情况。
因此MySQL8.0.3推出了直方图(histogram)功能，直方图是列的数据分布的近似值，其向优化器提供更多的统计信息。比如字段NULL的个数，每个不同值的百分比，最大/最小值等。
MySQL的直方图分为：等宽直方图和等高直方图，MySQL会自动分配使用哪种类型的直方图，无法干预
创建和删除直方图
创建语法
ANALYZE TABLE tbl_name UPDATE HISTOGRAM ON col_name [, col_name] WITH N BUCKETS;创建直方图时能够同时为多个列创建直方图，但必须指定bucket数量，范围在1-1024之间，默认100。对于bucket数量应该综合考虑其有多少不同值、数据的倾斜度、精度等，建议从较低的值开始，不符合再依次增加。
删除语法
ANALYZE TABLE tbl_name DROP HISTOGRAM ON col_name [, col_name];直方图信息
MySQL通过字典表column_statistics来保存直方图的定义，每行记录对应一个字段的直方图，已JSON格式保存。另外，MySQL 系列面试题和答案全部整理好了，大家可以在Java面试库小程序在线刷题。
root@employees 13:49: select json_pretty(histogram) from information_schema.column_statistics where table_name='employees' and column_name='first_name';;
{
"buckets": [
[
"base64:type254:QWFtZXI=",
"base64:type254:QWRlbA==",
0.010176045588684237,
13
],
"data-type": "string",
"null-values": 0.0,
"collation-id": 255,
"last-updated": "2020-09-09 05:47:32.548874",
"sampling-rate": 0.163495700259278,
"histogram-type": "equi-height",
"number-of-buckets-specified": 100
}MySQL为employees的first_name字段分配了等高直方图，默认为100个bucket。
当生成直方图时，MySQL会将所有数据都加载到内存中，并在内存中执行所有工作。如果在大表上生成直方图，可能会将几百M的数据读取到内存中的风险，因此我们可以通过参数hitogram_generation_max_mem_size来控制生成直方图最大允许的内存量，当指定内存满足不了所有数据集时就会采用采样的方式。
root@employees 14:12: select histogram->>'$."sampling-rate"' from information_schema.column_statistics where table_name='employees' and column_name='first_name';;
+---------------------------------+
| histogram->>'$."sampling-rate"' |
+---------------------------------+
| 0.163495700259278 |
+---------------------------------+从MySQL8.0.19开始，存储引擎自身提供了存储在表中数据的采样实现，存储引擎不支持时，MySQL使用默认采样需要全表扫描，这样对于大表来说成本太高，采样实现避免了全表扫描提高采样性能。
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通过INNODB_METRICS计数器可以监视数据页的采样情况，这需要提前开启计数器
root@employees 14:26: SELECT NAME, COUNT FROM INFORMATION_SCHEMA.INNODB_METRICS WHERE NAME LIKE 'sampled%'\G
*************************** 1. row ***************************
NAME: sampled_pages_read
COUNT: 430
*************************** 2. row ***************************
NAME: sampled_pages_skipped
COUNT: 456
2 rows in set (0.04 sec)采样率的计算公式为：sampled_page_read/(sampled_pages_read + sampled_pages_skipped)
优化案例
root@employees 14:32: create table employees_like like employees;
Query OK, 0 rows affected (0.03 sec)
root@employees 14:33: insert into employees_like select * from employees;
Query OK, 300024 rows affected (3.59 sec)
Records: 300024 Duplicates: 0 Warnings: 0
root@employees 14:33: ANALYZE TABLE employees_like update HISTOGRAM on birth_date,first_name;
+--------------------------+-----------+----------+-------------------------------------------------------+
| Table | Op | Msg_type | Msg_text |
+--------------------------+-----------+----------+-------------------------------------------------------+
| employees.employees_like | histogram | status | Histogram statistics created for column 'birth_date'. |
| employees.employees_like | histogram | status | Histogram statistics created for column 'first_name'. |
+--------------------------+-----------+----------+-------------------------------------------------------+root@employees 14:43: explain format=json select count(*) from employees where (birth_date between '1953-05-01' and '1954-05-01') and first_name like 'A%';
{
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "30214.45"
},
"table": {
"table_name": "employees",
"access_type": "ALL",
"rows_examined_per_scan": 299822,
"rows_produced_per_join": 3700,
"filtered": "1.23",
"cost_info": {
"read_cost": "29844.37",
"eval_cost": "370.08",
"prefix_cost": "30214.45",
"data_read_per_join": "520K"
},
"used_columns": [
"birth_date",
"first_name"
],
"attached_condition": "((`employees`.`employees`.`birth_date` between '1953-05-01' and '1954-05-01') and (`employees`.`employees`.`first_name` like 'A%'))"
}
}
}
root@employees 14:45: explain format=json select count(*) from employees where (birth_date between '1953-05-01' and '1954-05-01') and first_name like 'A%';
{
"query_block": {
"select_id": 1,
"cost_info": {
"query_cost": "18744.56"
},
"table": {
"table_name": "employees",
"access_type": "range",
"possible_keys": [
"idx_birth",
"idx_first"
],
"key": "idx_first",
"used_key_parts": [
"first_name"
],
"key_length": "58",
"rows_examined_per_scan": 41654,
"rows_produced_per_join": 6221,
"filtered": "14.94",
"index_condition": "(`employees`.`employees`.`first_name` like 'A%')",
"cost_info": {
"read_cost": "18122.38",
"eval_cost": "622.18",
"prefix_cost": "18744.56",
"data_read_per_join": "874K"
},
"used_columns": [
"birth_date",
"first_name"
],
"attached_condition": "(`employees`.`employees`.`birth_date` between '1953-05-01' and '1954-05-01')"
}
}
}可以看出Cost值从30214.45降到了18744.56，扫描行数从299822降到了41654，性能有所提升。另外，关注公众号Java技术栈，在后台回复：面试，可以获取我整理的 MySQL 系列面试题和答案，非常齐全。
参考资料：
https://dev.mysql.com/doc/refman/8.0/en/analyze-table.html#analyze-table-histogram-statistics-analysis
https://mysqlserverteam.com/histogram-statistics-in-mysql/
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