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解决网 >>所属分类 >> 程序开发    MySQL   

MySQL 8.0 新特性之统计直方图

标签: MySQL8.0 统计 直方图

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| 译者简介


译者 韩杰·沃趣科技MySQL数据库工程师


熟悉mysql体系架构、主从复制,熟悉问题定位与解决


出品 沃趣科技


原文链接:


https://mysqlserverteam.com/histogram-statistics-in-mysql/


| 概览


MySQL8.0实现了统计直方图。利用直方图,用户可以对一张表的一列做数据分布的统计,特别是针对没有索引的字段。这可以帮助查询优化器找到更优的执行计划。统计直方图的主要使用场景是用来计算字段选择性,即过滤效率。


可以通过以下方式来创建或者删除直方图:


ANALYZETABLEtbl_name UPDATEHISTOGRAM ONcol_name [, col_name] WITHN BUCKETS;


ANALYZETABLEtbl_name DROPHISTOGRAM ONcol_name [, col_name];


buckets默认是100。统计直方图的信息存储在数据字典表"column_statistcs"中,可以通过视图information_schema.COLUMN_STATISTICS访问。直方图以灵活的JSON的格式存储。ANALYZE TABLE会基于表大小自动判断是否要进行取样操作。ANALYZE TABLE也会基于表中列的数据分布情况以及bucket的数量来决定是否要建立等宽直方图(singleton)还是等高直方图(equi-height)。


| 什么是直方图


数据库中,查询优化器负责将SQL转换成最有效的执行计划。有时候,查询优化器会走不到最优的执行计划,导致花费了更多不必要的时间。造成这种情况的主要原因是,查询优化器有时无法准确的知道以下几个问题的答案:


每个表有多少行?

每一列有多少不同的值?

每一列的数据分布情况?

举例说明:一张简单的表,两个字段,一个字段是person_id,另一个字段是time_of_day,表示睡觉时间


CREATETABLEbedtime (


person_id INT,


time_of_day TIME);


对于time_of_day列,大部分人上床时间会在晚上11:00左右。所以下面第一个查询会比第二个查询返回更多的行数:


1) SELECT* FROMbedtime WHEREtime_of_day BETWEEN"22:00:00"AND"23:59:00"


2) SELECT* FROMbedtime WHEREtime_of_day BETWEEN"12:00:00"AND"14:00:00"


如果没有统计数据,优化器会假设time_of_day的值是均匀分配的,即一个人的上床时间在下午3点和晚上11点的概率差不多。如何才能使查询优化器知道数据的分布情况?一个解决方法就是在列上建立统计直方图。


直方图能近似获得一列的数据分布情况,从而让数据库知道它含有哪些数据。直方图有多种形式,MySQL支持了两种:等宽直方图(singleton)、等高直方图(equi-height)。直方图的共同点是,它们都将数据分到了一系列的buckets中去。MySQL会自动将数据划到不同的buckets中,也会自动决定创建哪种类型的直方图。


| 如何创建和删除统计直方图


为了管理统计直方图,ANALYZE TABLE命令新增了两个子句:


ANALYZETABLEtbl_name UPDATEHISTOGRAM ONcol_name [, col_name] WITHN BUCKETS;


ANALYZETABLEtbl_name DROPHISTOGRAM ONcol_name [, col_name];


第一个表示一次可以为一个或多个列创建统计直方图:


mysql> ANALYZE TABLE payment UPDATE HISTOGRAM ON amount WITH 32BUCKETS;


+----------------+-----------+----------+---------------------------------------------------+


| Table |Op | Msg_type |Msg_text |


+----------------+-----------+----------+---------------------------------------------------+


|sakila.payment | histogram |status | Histogram statistics created forcolumn 'amount'. |


+----------------+-----------+----------+---------------------------------------------------+


1row inset ( 0. 27sec)


mysql> ANALYZE TABLE payment UPDATE HISTOGRAM ON amount, payment_date WITH 32BUCKETS;


+----------------+-----------+----------+---------------------------------------------------------+


| Table |Op | Msg_type |Msg_text |


+----------------+-----------+----------+---------------------------------------------------------+


|sakila.payment | histogram |status | Histogram statistics created forcolumn 'amount'. |


| sakila.payment |histogram | status |Histogram statistics created forcolumn 'payment_date'. |


+----------------+-----------+----------+---------------------------------------------------------+


buckets的值必须指定,可以设置为1到1024,默认值是100。


对于不同的数据集合,buckets的值取决于以下几个因素:


这列有多少不同的值

数据的分布情况

需要多高的准确性

但是,某些buckets的值能提升的关于数据分布情况的准确性相当低。所以,建议的做法是,开始的时候将buckets的值设的低一点,比如32,然后如果没有满足期望,再往上增大。


上面这个例子中,我们对于amount列建立了两次直方图。第一个语句,建立了一个新的直方图;第二个语句,amount列的直方图被重写了。


如果需要删除已经创建的直方图,用DROP HISTOGRAM就可以实现:


mysql> ANALYZE TABLE payment DROP HISTOGRAM ON payment_date;


+----------------+-----------+----------+---------------------------------------------------------+


| Table |Op | Msg_type |Msg_text |


+----------------+-----------+----------+---------------------------------------------------------+


|sakila.payment | histogram |status | Histogram statistics removed forcolumn 'payment_date'. |


+----------------+-----------+----------+---------------------------------------------------------+


UPDATE HISTOGRAM可以一次性为多个列创建直方图。如果命令中间写错,ANALYZE TABLE仍然会起作用。比如,你指定了三列,但第二列不存在。MySQL仍然会为第一列和第三列创建直方图。


mysql> ANALYZE TABLE customer UPDATE HISTOGRAM ON c_birth_day, c_foobar, c_birth_month WITH 32BUCKETS;


+----------------+-----------+----------+----------------------------------------------------------+


| Table |Op | Msg_type |Msg_text |


+----------------+-----------+----------+----------------------------------------------------------+


|tpcds.customer | histogram |status | Histogram statistics created forcolumn 'c_birth_day'. |


| tpcds.customer |histogram | status |Histogram statistics created forcolumn 'c_birth_month'. |


|tpcds.customer | histogram |Error | The column 'c_foobar' does notexist. |


+----------------+-----------+----------+----------------------------------------------------------+


3rows inset ( 0. 15sec)


| 数据库内部发生了什么


当你读过MySQL手册,你可能已经注意到新的系统变量histogram_generation_max_mem_size。当用户建立统计直方图,这个值是用来控制大约多少内存能允许被使用。那么,为什么要控制这个呢?


当你在建立直方图的时候,MySQL server会将所有数据读到内存中,然后在内存中进行操作,包括排序。如果对一个很大的表建立直方图,可能会有风险将几百M的数据都读到内存中,但这是不明智的。为了规避这个风险,MySQL会根据给定的histogram_generation_max_mem_size的值计算该将多少行数据读到内存中。如果根据当前histogram_generation_max_mem_size的限制,MySQL认为只能读一部分数据,那么MySQL会进行取样。通过“sampling-rate”属性,可以观察到取样比率。


mysql> SET histogram_generation_max_mem_size = 1000000;


Query OK, 0rows affected ( 0. 00sec)


mysql> ANALYZE TABLE customer UPDATE HISTOGRAM ON c_birth_country WITH 16BUCKETS;


+----------------+-----------+----------+------------------------------------------------------------+


| Table |Op | Msg_type |Msg_text |


+----------------+-----------+----------+------------------------------------------------------------+


|tpcds.customer | histogram |status | Histogram statistics created forcolumn 'c_birth_country'. |


+----------------+-----------+----------+------------------------------------------------------------+


1row inset ( 0. 22sec)


mysql> SELECT histogram- >>'$."sampling-rate"'


-> FROM information_schema.column_statistics


-> WHERE table_name = "customer"


-> AND column_name = "c_birth_country";


+---------------------------------+


| histogram->>'$."sampling-rate"' |


+---------------------------------+


| 0.048743243211626014 |


+---------------------------------+


1row inset ( 0. 00sec)


优化器创建了一个直方图,大约读了c_birth_country列4.8%的数据。取样是不确定的,因此意义不大。同样的数据,同样的两条语句‘‘ANALYZE TABLE tbl UPDATE HISTOGRAM …’’,如果用了取样,得到的直方图可能就不一样。


| 查询案例


统计直方图可以带来些什么?我们可以看个例子,这个例子中用了直方图,在执行时间上会有很大的不同。


环境:


TPC-DS Benchmark with scale factor of 1

Intel Core i7-4770

Debian Stretch

MySQL 8.0 RC1

innodb_buffer_pool_size = 2G

optimizer_switch = "condition_fanout_filter=on"

| Query 90


查询如下:上午售卖的数量与晚上售卖的数量的比率。


mysql> SELECT CAST(amc AS DECIMAL(15, 4)) / CAST(pmc AS DECIMAL(15, 4)) am_pm_ratio


-> FROM (SELECT COUNT(*) amc


-> FROM web_sales,


-> household_demographics,


-> time_dim,


-> web_page


-> WHERE ws_sold_time_sk = time_dim.t_time_sk


-> AND ws_ship_hdemo_sk = household_demographics.hd_demo_sk


-> AND ws_web_page_sk = web_page.wp_web_page_sk


-> AND time_dim.t_hour BETWEEN 9 AND 9 + 1


-> AND household_demographics.hd_dep_count = 2


-> AND web_page.wp_char_count BETWEEN 5000 AND 5200) at,


-> (SELECT COUNT(*) pmc


-> FROM web_sales,


-> household_demographics,


-> time_dim,


-> web_page


-> WHERE ws_sold_time_sk = time_dim.t_time_sk


-> AND ws_ship_hdemo_sk = household_demographics.hd_demo_sk


-> AND ws_web_page_sk = web_page.wp_web_page_sk


-> AND time_dim.t_hour BETWEEN 15 AND 15 + 1


-> AND household_demographics.hd_dep_count = 2


-> AND web_page.wp_char_count BETWEEN 5000 AND 5200) pt


-> ORDER BY am_pm_ratio


-> LIMIT 100;


+-------------+


| am_pm_ratio |


+-------------+


| 1.27619048 |


+-------------+


1 row in set (1.48 sec)


可以看到,查询花费了1.5秒左右。看起来不算多,但是通过在一列上建立直方图,可以让执行速度快三倍。


mysql> ANALYZE TABLE web_page UPDATE HISTOGRAM ON wp_char_count WITH 8BUCKETS;


+ ----------------+-----------+----------+----------------------------------------------------------+


| Table | Op | Msg_type | Msg_text |


+ ----------------+-----------+----------+----------------------------------------------------------+


| tpcds.web_page | histogram | status| Histogram statistics created forcolumn 'wp_char_count'. |


+ ----------------+-----------+----------+----------------------------------------------------------+


1row inset ( 0.06sec)


mysql> SELECT ...


+ -------------+


| am_pm_ratio |


+ -------------+


| 1.27619048|


+ -------------+


1row inset ( 0.50sec)


通过这个直方图,查询花费了0.5秒左右。原因呢?主要的原因是,查询语句中的谓词“web_page.wp_char_count BETWEEN 5000 AND 5200”。没有直方图的时候,优化器会假设web_page表中符合谓词“web_page.wp_char_count BETWEEN 5000 AND 5200”的数据占到总数据11.11%左右。但,这是错误的。用下面的查询语句,可以看到实际上满足条件的数据只有1.6%。


mysql> SELECT


-> (SELECT COUNT(*) FROM web_page WHERE web_page.wp_char_count BETWEEN 5000 AND 5200)


-> /


-> (SELECT COUNT(*) FROM web_page) AS ratio;


+--------+


| ratio |


+--------+


| 0.0167 |


+--------+


1 row in set (0.00 sec)


通过直方图,优化器会知道这个信息,并且更早进行表join,因此执行时间快了三倍。


| Query 61


查询如下:在给定的年份和月份,有和没有广告宣传的情况下货物的售卖比率。


mysql>SELECT promotions, -> total,


->CAST(promotions AS DECIMAL(15, 4)) / CAST(total AS DECIMAL(15, 4)) * 100


->FROM (SELECT SUM(ss_ext_sales_price) promotions


->FROM store_sales,


->store,


->promotion,


->date_dim,


->customer,


->customer_address,


->item


->WHERE ss_sold_date_sk = d_date_sk


->AND ss_store_sk = s_store_sk


->AND ss_promo_sk = p_promo_sk


->AND ss_customer_sk = c_customer_sk


->AND ca_address_sk = c_current_addr_sk


->AND ss_item_sk = i_item_sk


->AND ca_gmt_offset = -5


->AND i_category = 'Home'


->AND ( p_channel_dmail = 'Y'


->OR p_channel_email = 'Y'


->OR p_channel_tv = 'Y')


->AND s_gmt_offset = -5


->AND d_year = 2000


->AND d_moy = 12) promotional_sales,


->(SELECT SUM(ss_ext_sales_price) total


->FROM store_sales,


->store,


->date_dim,


->customer,


->customer_address,


->item


->WHERE ss_sold_date_sk = d_date_sk


->AND ss_store_sk = s_store_sk


->AND ss_customer_sk = c_customer_sk


->AND ca_address_sk = c_current_addr_sk


->AND ss_item_sk = i_item_sk


->AND ca_gmt_offset = -5


->AND i_category = 'Home'


->AND s_gmt_offset = -5


->AND d_year = 2000


->AND d_moy = 12) all_sales


->ORDER BY promotions,


->total


->LIMIT 100;


+------------+------------+--------------------------------------------------------------------------+


| promotions | total | CAST(promotions AS DECIMAL(15, 4)) / CAST(total AS DECIMAL(15, 4)) * 100 |


+------------+------------+--------------------------------------------------------------------------+


| 3213210.07 | 5966836.78 | 53.85114741 |


+------------+------------+--------------------------------------------------------------------------+


1 row in set (2.78 sec)


可以看到,查询花费了2.8秒左右。但是,查询优化器不知道s_gmt_offset列只有一个不同的值。没有统计数据的情况下,优化器会用所谓的“hard-coded guesstimates”,会假设10%的数据符合条件“ca_gmt_offset = -5“。如果在这个列上增加一个直方图,优化器会知道所有的数据都符合条件,因此会走一个更好的执行计划。


mysql> ANALYZE TABLE store UPDATE HISTOGRAM ON s_gmt_offset WITH 8BUCKETS;


+-------------+-----------+----------+---------------------------------------------------------+


| Table |Op | Msg_type |Msg_text |


+-------------+-----------+----------+---------------------------------------------------------+


|tpcds.store | histogram |status | Histogram statistics created forcolumn 's_gmt_offset'. |


+-------------+-----------+----------+---------------------------------------------------------+


1row inset ( 0. 06sec)


mysql> SELECT ...


+------------+------------+--------------------------------------------------------------------------+


| promotions |total | CAST(promotions AS DECIMAL(15, 4)) / CAST(total AS DECIMAL(15, 4)) * 100 |


+------------+------------+--------------------------------------------------------------------------+


| 3213210.07 |5966836.78| 53.85114741 |


+------------+------------+--------------------------------------------------------------------------+


1row inset ( 1.37sec)


有了直方图,查询花了不到1.4秒,差不多提升了2倍。原因是:


第一个执行计划,优化器选择了第一个派生表在store表上做了全表扫描,然后对表item, store_sales, date_dim, customer,customer_address分别做了主键查找。

但是,当MySQL意识到store表会比它猜测的返回更多的数据时,优化器会在item表上做全表扫描,然后对store_sales, store, date_dim, customer,customer_address 分别做主键查找。

| 为什么不用索引?


索引往往也能做上述工作,比如:


mysql> CREATE INDEX s_gmt_offset_idx ON store (s_gmt_offset);


Query OK, 0rows affected ( 0. 53sec)


Records:0Duplicates:0Warnings:0


mysql> SELECT ...


+------------+------------+--------------------------------------------------------------------------+


| promotions |total | CAST(promotions AS DECIMAL(15, 4)) / CAST(total AS DECIMAL(15, 4)) * 100 |


+------------+------------+--------------------------------------------------------------------------+


| 3213210.07 |5966836.78| 53.85114741 |


+------------+------------+--------------------------------------------------------------------------+


1row inset ( 1.41sec)


但是,用直方图而不是索引有以下两个原因:


维护一个索引有代价。每一次的insert、update、delete都会需要更新索引,会对性能有一定的影响。而直方图一次创建永不更新,除非明确去更新它。所以不会影响insert、update、delete的性能。

如果有索引,优化器用使用index dives技术来估算符合条件范围的记录数量。这种方式也是有代价的,特别是查询语句条件中有很长的IN列表。直方图相对而言代价小,因此可能更合适。

| 检索统计直方图


统计直方图以JSON的形式存在数据字典中。可以用内建的JSON函数built-in JSON functions从直方图获取一些信息。举例来说,如果需要知道amount列的直方图的创建或者更新时间,可以用JSON unquoting extraction operator来获取信息:


mysql>SELECT


->HISTOGRAM->>'$."last-updated"'AS last_updated


->FROM INFORMATION_SCHEMA.COLUMN_STATISTICS


->WHERE


->SCHEMA_NAME = "sakila"


->AND TABLE_NAME = "payment"


->AND COLUMN_NAME = "amount";


+----------------------------+


| last_updated |


+----------------------------+


| 2017-09-15 11:54:25.000000 |


+----------------------------+


如果要查找实际有多少个buckets,以及用analyze table时指定了多少个buckets,可以如下:


mysql> SELECT


-> TABLE_NAME,


-> COLUMN_NAME,


-> HISTOGRAM- >>'$."number-of-buckets-specified"'AS num_buckets_specified,


-> JSON_LENGTH(HISTOGRAM, '$.buckets') AS num_buckets_created


-> FROM INFORMATION_SCHEMA.COLUMN_STATISTICS


-> WHERE


-> SCHEMA_NAME = "sakila";


+------------+--------------+-----------------------+---------------------+


| TABLE_NAME |COLUMN_NAME | num_buckets_specified |num_buckets_created |


+------------+--------------+-----------------------+---------------------+


|payment | amount |32| 19 |


| payment |payment_date | 32 |32|


+------------+--------------+-----------------------+---------------------+


经测试,num_buckets_created与字段的distinct值很接近,近似相等;但是num_buckets_created不会大于num_buckets_specified。如果num_buckets_created与num_buckets_specified相等,那么存在可能,在创建直方图的时候指定的buckets不够多,那么此时可以通过增加buckets的数量,来提高直方图的准确性。


buckets可以设置为1到1024


| 优化器trace


如果你想要知道直方图做了什么,最简单的方式就是看一下执行计划:


mysql> EXPLAIN SELECT * FROM customer WHERE c_birth_day BETWEEN 1AND 10;


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


| id |select_type | table |partitions | type |possible_keys | key |key_len | ref |rows | filtered |Extra |


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


|1| SIMPLE |customer | NULL |ALL | NULL |NULL | NULL |NULL | 98633 |11.11| Using where |


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


1row inset, 1warning ( 0. 00sec)


mysql> ANALYZE TABLE customer UPDATE HISTOGRAM ON c_birth_day WITH 32BUCKETS;


+----------------+-----------+----------+--------------------------------------------------------+


| Table |Op | Msg_type |Msg_text |


+----------------+-----------+----------+--------------------------------------------------------+


|tpcds.customer | histogram |status | Histogram statistics created forcolumn 'c_birth_day'. |


+----------------+-----------+----------+--------------------------------------------------------+


1row inset ( 0. 10sec)


mysql> EXPLAIN SELECT * FROM customer WHERE c_birth_day BETWEEN 1AND 10;


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


| id |select_type | table |partitions | type |possible_keys | key |key_len | ref |rows | filtered |Extra |


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


|1| SIMPLE |customer | NULL |ALL | NULL |NULL | NULL |NULL | 98633 |32.12| Using where |


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


1row inset, 1warning ( 0. 00sec)


可以看到filtered列,从默认的11.11%变成了更精确的32.12%。但是,如果有多个条件,有些有直方图,有些没有,就比较难判断优化器做了什么改进:


mysql> EXPLAIN SELECT * FROM customer WHERE c_birth_day <= 20AND c_birth_year = 1967;


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


| id |select_type | table |partitions | type |possible_keys | key |key_len | ref |rows | filtered |Extra |


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


|1| SIMPLE |customer | NULL |ALL | NULL |NULL | NULL |NULL | 98633 |6.38| Using where |


+----+-------------+----------+------------+------+---------------+------+---------+------+-------+----------+-------------+


1row inset, 1warning ( 0. 00sec)


如果想要知道更多关于直方图统计的细节,可以使用trace:


mysql> SET OPTIMIZER_TRACE = "enabled=on";


Query OK, 0rows affected ( 0.00sec)


mysql> SET OPTIMIZER_TRACE_MAX_MEM_SIZE = 1000000;


Query OK, 0rows affected ( 0.00sec)


mysql> EXPLAIN SELECT * FROM customer WHERE c_birth_day <= 20AND c_birth_year = 1967;


mysql> SELECT JSON_EXTRACT(TRACE, "$**.filtering_effect") FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE;


+ ----------------------------------------------------------------------------------------+


| JSON_EXTRACT(TRACE, "$**.filtering_effect") |


+ ----------------------------------------------------------------------------------------+


| [[{"condition": "(`customer`.`c_birth_day` <= 20)", "histogram_selectivity": 0.6376}]]|


+ ----------------------------------------------------------------------------------------+


1row inset ( 0.00sec)


这里用了JSON_EXTRACT从trace里取出相关的部分。对于每个条件,直方图被使用的话,就会看到估算过的字段的选择性。在这个例子里,通过直方图,对“c_birth_day <= 20”条件,估算出63.76%的数据满足条件。事实上,与实际的数据分布情况基本一致:


mysql> SELECT


-> (SELECT count(*) FROM customer WHERE c_birth_day <= 20)


-> /


-> (SELECT COUNT(*) FROM customer) AS ratio;


+--------+


| ratio |


+--------+


| 0.6376 |


+--------+


1 row in set (0.03 sec)

 

 

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