前言： 本文是对这篇博客mysql 8.0 histograms的翻译，翻译如有不当的地方，敬请谅解，请尊重原创和翻译劳动成果，转载的时候请注明出处。谢谢！
英文原文地址：https://lefred.be/content/mysql-8-0-histograms/
翻译原文地址：
在mysql 8.0之前，mysql缺失了其它关系数据库中一个众所周知的功能：优化器的直方图
优化器团队（optimizer team）在越来越多的mysql dba的呼声中实现了这个功能。
直方图定义
但什么是直方图呢？我们来看维基百科的定义吧，直方图是数值数据分布的准确表示。 对于rdbms来说，直方图是特定列内数据分布的近似值。因此在mysql中，直方图能够帮助优化器找到最有效的执行计划。
直方图例子
为了说明直方图是如何影响优化器工作的，我会用dbt3生成的数据来演示。
我们准备了一个简单查询：
select * from orders
join customer on o_custkey = c_custkey
where o_orderdate < '1993-01-01'
and c_mktsegment = "automobile"\g
让我们看一下传统的执行计划的explain输出，以及可视化方式（visual one)：
mysql> explain select * from orders
join customer on o_custkey = c_custkey
where o_orderdate < '1993-01-01' and c_mktsegment = "automobile"\g
*************************** 1. row ***************************
id: 1
select_type: simple
table: customer
partitions: null
type: all
possible_keys: primary
key: null
key_len: null
ref: null
rows: 149050
filtered: 10.00
extra: using where
*************************** 2. row ***************************
id: 1
select_type: simple
table: orders
partitions: null
type: ref
possible_keys: i_o_custkey,i_o_orderdate
key: i_o_custkey
key_len: 5
ref: dbt3.customer.c_custkey
rows: 14
filtered: 30.62
extra: using where
2 rows in set, 1 warning (0.28 sec)
我们看到mysql首先对customer表做了一个全表扫描，并且它的选择估计记录（过滤）是10%；
接下来让我们运行这个查询（我使用了count(*)）,然后我们来看看有多少行记录
mysql> select count(*) from orders
join customer on o_custkey = c_custkey
where o_orderdate < '1993-01-01' and c_mktsegment = "automobile"\g
*************************** 1. row ***************************
count(*): 45127
1 row in set (49.98 sec)
创建直方图
现在，我将在表customer上的字段c_mktsegment上创建一个直方图
mysql> analyze table customer update histogram on c_mktsegment with 1024 buckets;
+---------------+-----------+----------+---------------------------------------------------------+
| table | op | msg_type | msg_text |
+---------------+-----------+----------+---------------------------------------------------------+
| dbt3.customer | histogram | status | histogram statistics created for column 'c_mktsegment'. |
+---------------+-----------+----------+---------------------------------------------------------+
接下来，我们来验证查询的执行计划：
mysql> explain select * from orders
join customer on o_custkey = c_custkey
where o_orderdate < '1993-01-01' and c_mktsegment = "automobile"\g
*************************** 1. row ***************************
id: 1
select_type: simple
table: orders
partitions: null
type: all
possible_keys: i_o_custkey,i_o_orderdate
key: null
key_len: null
ref: null
rows: 1494230
filtered: 30.62
extra: using where
*************************** 2. row ***************************
id: 1
select_type: simple
table: customer
partitions: null
type: eq_ref
possible_keys: primary
key: primary
key_len: 4
ref: dbt3.orders.o_custkey
rows: 1
filtered: 19.84
extra: using where
2 rows in set, 1 warning (1.06 sec)
现在，使用直方图后，我们可以看到customer表的“吸引力”降低了，因为order表按条件过滤的行的百分比（30.62）几乎是customer表按条件过滤行的百分比的两倍（19.84%），这将导致低order表进行查找。
注意：这段感觉没有翻译恰当，英文原文如下，如果感觉翻译比较生硬，参考原文
now with the histogram we can see that it becomes less attractive to start with customer table since almost twice as many rows (19.84%) will cause look-ups into the order table.
优化器选择对order表进行全表扫描（full sacn），此时执行计划的代价看起来似乎还高一些，，让我们看一下sql的执行时间：
mysql> select count(*) from orders
join customer on o_custkey = c_custkey
where o_orderdate < '1993-01-01' and c_mktsegment = "automobile"\g
*************************** 1. row ***************************
count(*): 45127
1 row in set (6.35 sec)
sql语句的执行时间更短，明显比之前要快了
查看数据的分布
直方图数据存贮在information_schema.column_statistics表中，这个表的定义如下
+-------------+-------------+------+-----+---------+-------+
| field | type | null | key | default | extra |
+-------------+-------------+------+-----+---------+-------+
| schema_name | varchar(64) | no | | null | |
| table_name | varchar(64) | no | | null | |
| column_name | varchar(64) | no | | null | |
| histogram | json | no | | null | |
+-------------+-------------+------+-----+---------+-------+
它的一条记录类似下面这样：
select schema_name, table_name, column_name, json_pretty(histogram)
from information_schema.column_statistics
where column_name = 'c_mktsegment'\g
*************************** 1. row ***************************
schema_name: dbt3
table_name: customer
column_name: c_mktsegment
json_pretty(histogram): {
"buckets": [
[
"base64:type254:qvvut01pqklmrq==",
0.19837010534684954
],
[
"base64:type254:qlvjterjtkc=",
0.3983104750546611
],
[
"base64:type254:rlvstkluvvjf",
0.5978433710991851
],
[
"base64:type254:se9vu0vit0xe",
0.799801232359372
],
[
"base64:type254:tufdselorvjz",
1.0
]
],
"data-type": "string",
"null-values": 0.0,
"collation-id": 255,
"last-updated": "2018-03-02 20:21:48.271523",
"sampling-rate": 0.6709158000670916,
"histogram-type": "singleton",
"number-of-buckets-specified": 1024
}
而且可以查看分布
select from_base64(substring_index(v, ':', -1)) value, concat(round(c*100,1),'%') cumulfreq,
concat(round((c - lag(c, 1, 0) over()) * 100,1), '%') freq
from information_schema.column_statistics, json_table(histogram->'$.buckets',
'$[*]' columns(v varchar(60) path '$[0]', c double path '$[1]')) hist
where schema_name = 'dbt3' and table_name = 'customer' and column_name = 'c_mktsegment';
+------------+-----------+-------+
| value | cumulfreq | freq |
+------------+-----------+-------+
| automobile | 19.8% | 19.8% |
| building | 39.9% | 20.1% |
| furniture | 59.9% | 19.9% |
| household | 79.9% | 20.1% |
| machinery | 100.0% | 20.1% |
+------------+-----------+-------+
你也可以用下面语法删除直方图信息。
mysql> analyze table customer drop histogram on c_mktsegment;
+---------------+-----------+----------+---------------------------------------------------------+
| table | op | msg_type | msg_text |
+---------------+-----------+----------+---------------------------------------------------------+
| dbt3.customer | histogram | status | histogram statistics removed for column 'c_mktsegment'. |
+---------------+-----------+----------+---------------------------------------------------------+
1 row in set (0.00 sec)
buckets
你会注意到，当我们创建一个直方图时，我们需要指定buckets的数量，事实上，数据被分成包含特定值以及他们基数（cardinality）的一组buckets，如果在上一个例子中检查直方图的类型，你会发现它是等宽直方图（singleton）
"histogram-type": "singleton",
这种类型的直方图最好的，因为基数是针对单个特定值。 如果这次我仅使用2个存储桶（buckets）来重新创建直方图（请记住，在c_mktsegment列中有4个不同的值）：
mysql> analyze table customer update histogram on c_mktsegment with 2 buckets;
+---------------+-----------+----------+---------------------------------------------------------+
| table | op | msg_type | msg_text |
+---------------+-----------+----------+---------------------------------------------------------+
| dbt3.customer | histogram | status | histogram statistics created for column 'c_mktsegment'. |
+---------------+-----------+----------+---------------------------------------------------------+
如果我检查直方图的类型：
mysql> select schema_name, table_name, column_name,
json_pretty(histogram)
from information_schema.column_statistics
where column_name = 'c_mktsegment'\g
*************************** 1. row ***************************
schema_name: dbt3
table_name: customer
column_name: c_mktsegment
json_pretty(histogram): {
"buckets": [
[
"base64:type254:qvvut01pqklmrq==",
"base64:type254:rlvstkluvvjf",
0.5996992690844636,
3
],
[
"base64:type254:se9vu0vit0xe",
"base64:type254:tufdselorvjz",
1.0,
2
]
],
"data-type": "string",
"null-values": 0.0,
"collation-id": 255,
"last-updated": "2018-03-02 20:42:26.165898",
"sampling-rate": 0.6709158000670916,
"histogram-type": "equi-height",
"number-of-buckets-specified": 2
}
现在的直方图类型是等高直方图，这意味着将连续范围的值分组到存储桶中，以使落入每个存储桶的数据项的数量相同。
结论：
直方图对那些不是索引中第一列的列非常有用，这些列用于join、in子查询（in-subqueries）或order by…limit的查询的where条件下使用。
另外， 可以考虑尝试使用足够的存储通来获取等宽直方图。
