[翻译]——MySQL 8.0 Histograms

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：https://www.cnblogs.com/kerrycode/p/11817026.html

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。

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