-- 建立源表
create table t_source  
(  
  item_id int,  
  created_time datetime,  
  modified_time datetime,  
  item_name varchar(20),  
  other varchar(20)  
);  

-- 建立目标表
create table t_target like t_source; 

-- 生成100万测试数据，其中有50万created_time和item_name重复
delimiter //      
create procedure sp_generate_data()    
begin     
    set @i := 1;   
    
    while @i<=500000 do  
        set @created_time := date_add('2017-01-01',interval @i second);  
        set @modified_time := @created_time;  
        set @item_name := concat('a',@i);  
        insert into t_source  
        values (@i,@created_time,@modified_time,@item_name,'other');  
        set @i:=@i+1;    
    end while;  
    commit;    
    
    set @last_insert_id := 500000;  
    insert into t_source  
    select item_id + @last_insert_id,  
           created_time,  
           date_add(modified_time,interval @last_insert_id second),  
           item_name,  
           'other'   
      from t_source;  
    commit;
end     
//      
delimiter ;     
    
call sp_generate_data();  

-- 源表没有主键或唯一性约束，有可能存在两条完全一样的数据，所以再插入一条记录模拟这种情况。
insert into t_source select * from t_source where item_id=1;

源表中有1000001条记录，去重后的目标表应该有500000条记录。

mysql> select count(*),count(distinct created_time,item_name) from t_source;
+----------+----------------------------------------+
| count(*) | count(distinct created_time,item_name) |
+----------+----------------------------------------+
|  1000001 |                                 500000 |
+----------+----------------------------------------+
1 row in set (1.92 sec)

一、巧用索引与变量

1. 无索引对比测试

（1）使用相关子查询

truncate t_target;  
insert into t_target  
select distinct t1.* from t_source t1 where item_id in   
(select min(item_id) from t_source t2 where t1.created_time=t2.created_time and t1.item_name=t2.item_name);

这个语句很长时间都出不来结果，只看一下执行计划吧。

mysql> explain select distinct t1.* from t_source t1 where item_id in   
    -> (select min(item_id) from t_source t2 where t1.created_time=t2.created_time and t1.item_name=t2.item_name);  
+----+--------------------+-------+------------+------+---------------+------+---------+------+--------+----------+------------------------------+
| id | select_type        | table | partitions | type | possible_keys | key  | key_len | ref  | rows   | filtered | Extra                        |
+----+--------------------+-------+------------+------+---------------+------+---------+------+--------+----------+------------------------------+
|  1 | PRIMARY            | t1    | NULL       | ALL  | NULL          | NULL | NULL    | NULL | 997282 |   100.00 | Using where; Using temporary |
|  2 | DEPENDENT SUBQUERY | t2    | NULL       | ALL  | NULL          | NULL | NULL    | NULL | 997282 |     1.00 | Using where                  |
+----+--------------------+-------+------------+------+---------------+------+---------+------+--------+----------+------------------------------+
2 rows in set, 3 warnings (0.00 sec)

主查询和相关子查询都是全表扫描，一共要扫描100万*100万数据行，难怪出不来结果。

（2）使用表连接

truncate t_target;  
insert into t_target  
select distinct t1.* from t_source t1,  
(select min(item_id) item_id,created_time,item_name from t_source group by created_time,item_name) t2  
where t1.item_id = t2.item_id;

这种方法用时14秒，查询计划如下：

mysql> explain select distinct t1.* from t_source t1,   (select min(item_id) item_id,created_time,item_name from t_source group by created_time,item_name) t2   where t1.item_id = t2.item_id;
+----+-------------+------------+------------+------+---------------+-------------+---------+-----------------+--------+----------+------------------------------+
| id | select_type | table      | partitions | type | possible_keys | key         | key_len | ref             | rows   | filtered | Extra                        |
+----+-------------+------------+------------+------+---------------+-------------+---------+-----------------+--------+----------+------------------------------+
|  1 | PRIMARY     | t1         | NULL       | ALL  | NULL          | NULL        | NULL    | NULL            | 997282 |   100.00 | Using where; Using temporary |
|  1 | PRIMARY     | <derived2> | NULL       | ref  | <auto_key0>   | <auto_key0> | 5       | test.t1.item_id |     10 |   100.00 | Distinct                     |
|  2 | DERIVED     | t_source   | NULL       | ALL  | NULL          | NULL        | NULL    | NULL            | 997282 |   100.00 | Using temporary              |
+----+-------------+------------+------------+------+---------------+-------------+---------+-----------------+--------+----------+------------------------------+
3 rows in set, 1 warning (0.00 sec)

内层查询扫描t_source表的100万行，建立临时表，找出去重后的最小item_id，生成导出表derived2，此导出表有50万行。
MySQL会在导出表derived2上自动创建一个item_id字段的索引auto_key0。
外层查询也要扫描t_source表的100万行数据，在与导出表做链接时，对t_source表每行的item_id，使用auto_key0索引查找导出表中匹配的行，并在此时优化distinct操作，在找到第一个匹配的行后即停止查找同样值的动作。

（3）使用变量

set @a:='1000-01-01 00:00:00';  
set @b:=' ';  
set @f:=0;  
truncate t_target;  
insert into t_target  
select item_id,created_time,modified_time,item_name,other  
  from   
(select t0.*,if(@a=created_time and @b=item_name,@f:=0,@f:=1) f, @a:=created_time,@b:=item_name  
  from   
(select * from t_source order by created_time,item_name) t0) t1 where f=1;

这种方法用时13秒，查询计划如下：

mysql> explain select item_id,created_time,modified_time,item_name,other  
    ->   from   
    -> (select t0.*,if(@a=created_time and @b=item_name,@f:=0,@f:=1) f, @a:=created_time,@b:=item_name  
    ->   from   
    -> (select * from t_source order by created_time,item_name) t0) t1 where f=1; 
+----+-------------+------------+------------+------+---------------+-------------+---------+-------+--------+----------+----------------+
| id | select_type | table      | partitions | type | possible_keys | key         | key_len | ref   | rows   | filtered | Extra          |
+----+-------------+------------+------------+------+---------------+-------------+---------+-------+--------+----------+----------------+
|  1 | PRIMARY     | <derived2> | NULL       | ref  | <auto_key0>   | <auto_key0> | 4       | const |     10 |   100.00 | NULL           |
|  2 | DERIVED     | <derived3> | NULL       | ALL  | NULL          | NULL        | NULL    | NULL  | 997282 |   100.00 | NULL           |
|  3 | DERIVED     | t_source   | NULL       | ALL  | NULL          | NULL        | NULL    | NULL  | 997282 |   100.00 | Using filesort |
+----+-------------+------------+------------+------+---------------+-------------+---------+-------+--------+----------+----------------+
3 rows in set, 5 warnings (0.00 sec)

最内层的查询扫描t_source表的100万行，并使用文件排序，生成导出表derived3。
第二层查询要扫描derived3的100万行，生成导出表derived2，完成变量的比较和赋值，并自动创建一个导出列f上的索引auto_key0。
最外层使用auto_key0索引扫描derived2得到去重的结果行。

与上面方法2比较，总的扫描行数不变，都是200万行。只存在一点微小的差别，这次自动生成的索引是在常量列 f 上，而表关联自动生成的索引是在item_id列上，所以查询时间几乎相同。

至此，我们还没有在源表上创建任何索引。无论使用哪种写法，要查重都需要对created_time和item_name字段进行排序，因此很自然地想到，如果在这两个字段上建立联合索引，利用索引本身有序的特性消除额外排序，从而提高查询性能。

2. 建立created_time和item_name上的联合索引对比测试

-- 建立created_time和item_name字段的联合索引
create index idx_sort on t_source(created_time,item_name,item_id);  
analyze table t_source;

（1）使用相关子查询

truncate t_target;  
insert into t_target  
select distinct t1.* from t_source t1 where item_id in   
(select min(item_id) from t_source t2 where t1.created_time=t2.created_time and t1.item_name=t2.item_name);

本次用时19秒，查询计划如下：

mysql> explain select distinct t1.* from t_source t1 where item_id in   
    -> (select min(item_id) from t_source t2 where t1.created_time=t2.created_time and t1.item_name=t2.item_name);  
+----+--------------------+-------+------------+------+---------------+----------+---------+----------------------------------------+--------+----------+------------------------------+
| id | select_type        | table | partitions | type | possible_keys | key      | key_len | ref                                    | rows   | filtered | Extra                        |
+----+--------------------+-------+------------+------+---------------+----------+---------+----------------------------------------+--------+----------+------------------------------+
|  1 | PRIMARY            | t1    | NULL       | ALL  | NULL          | NULL     | NULL    | NULL                                   | 997281 |   100.00 | Using where; Using temporary |
|  2 | DEPENDENT SUBQUERY | t2    | NULL       | ref  | idx_sort      | idx_sort | 89      | test.t1.created_time,test.t1.item_name |      2 |   100.00 | Using index                  |
+----+--------------------+-------+------------+------+---------------+----------+---------+----------------------------------------+--------+----------+------------------------------+
2 rows in set, 3 warnings (0.00 sec)

外层查询的t_source表是驱动表，需要扫描100万行。
对于驱动表每行的item_id，通过idx_sort索引查询出两行数据。

（2）使用表连接

truncate t_target;  
insert into t_target  
select distinct t1.* from t_source t1,  
(select min(item_id) item_id,created_time,item_name from t_source group by created_time,item_name) t2  
where t1.item_id = t2.item_id;

本次用时13秒，查询计划如下：

mysql> explain select distinct t1.* from t_source t1,  
    -> (select min(item_id) item_id,created_time,item_name from t_source group by created_time,item_name) t2  
    -> where t1.item_id = t2.item_id;  
+----+-------------+------------+------------+-------+---------------+-------------+---------+-----------------+--------+----------+------------------------------+
| id | select_type | table      | partitions | type  | possible_keys | key         | key_len | ref             | rows   | filtered | Extra                        |
+----+-------------+------------+------------+-------+---------------+-------------+---------+-----------------+--------+----------+------------------------------+
|  1 | PRIMARY     | t1         | NULL       | ALL   | NULL          | NULL        | NULL    | NULL            | 997281 |   100.00 | Using where; Using temporary |
|  1 | PRIMARY     | <derived2> | NULL       | ref   | <auto_key0>   | <auto_key0> | 5       | test.t1.item_id |     10 |   100.00 | Distinct                     |
|  2 | DERIVED     | t_source   | NULL       | index | idx_sort      | idx_sort    | 94      | NULL            | 997281 |   100.00 | Using index                  |
+----+-------------+------------+------------+-------+---------------+-------------+---------+-----------------+--------+----------+------------------------------+
3 rows in set, 1 warning (0.00 sec)

和没有索引相比，子查询虽然从全表扫描变为了全索引扫描，但还是需要扫描100万行记录。因此查询性能提升并不是明显。

（3）使用变量

set @a:='1000-01-01 00:00:00';  
set @b:=' ';  
set @f:=0;  
truncate t_target;  
insert into t_target  
select item_id,created_time,modified_time,item_name,other  
  from   
(select t0.*,if(@a=created_time and @b=item_name,@f:=0,@f:=1) f, @a:=created_time,@b:=item_name  
  from   
(select * from t_source order by created_time,item_name) t0) t1 where f=1;

本次用时13秒，查询计划与没有索引时的完全相同。可见索引对这种写法没有作用。能不能消除嵌套，只用一层查询出结果呢？

（4）使用变量，并且消除嵌套查询

set @a:='1000-01-01 00:00:00';  
set @b:=' ';  
truncate t_target;  
insert into t_target  
select * from t_source force index (idx_sort)  
 where (@a!=created_time or @b!=item_name) and (@a:=created_time) is not null and (@b:=item_name) is not null  
 order by created_time,item_name;

本次用时12秒，查询计划如下：

mysql> explain select * from t_source force index (idx_sort)  
    ->  where (@a!=created_time or @b!=item_name) and (@a:=created_time) is not null and (@b:=item_name) is not null  
    ->  order by created_time,item_name;
+----+-------------+----------+------------+-------+---------------+----------+---------+------+--------+----------+-------------+
| id | select_type | table    | partitions | type  | possible_keys | key      | key_len | ref  | rows   | filtered | Extra       |
+----+-------------+----------+------------+-------+---------------+----------+---------+------+--------+----------+-------------+
|  1 | SIMPLE      | t_source | NULL       | index | NULL          | idx_sort | 94      | NULL | 997281 |    99.00 | Using where |
+----+-------------+----------+------------+-------+---------------+----------+---------+------+--------+----------+-------------+
1 row in set, 3 warnings (0.00 sec)

该语句具有以下特点：

消除了嵌套子查询，只需要对t_source表进行一次全索引扫描，查询计划已达最优。
无需distinct二次查重。
变量判断与赋值只出现在where子句中。
利用索引消除了filesort。

在MySQL 8之前，该语句是单线程去重的最佳解决方案。仔细分析这条语句，发现它巧妙地利用了SQL语句的逻辑查询处理步骤和索引特性。一条SQL查询的逻辑步骤为：

执行笛卡尔乘积（交叉连接）
应用ON筛选器（连接条件）
添加外部行（outer join）
应用where筛选器
分组
应用cube或rollup
应用having筛选器
处理select列表
应用distinct子句
应用order by子句
应用limit子句

每条查询语句的逻辑执行步骤都是这11步的子集。拿这条查询语句来说，其执行顺序为：强制通过索引idx_sort查找数据行 -> 应用where筛选器 -> 处理select列表 -> 应用order by子句。

为了使变量能够按照created_time和item_name的排序顺序进行赋值和比较，必须按照索引顺序查找数据行。这里的force index (idx_sort)提示就起到了这个作用，必须这样写才能使整条查重语句成立。否则，因为先扫描表才处理排序，因此不能保证变量赋值的顺序，也就不能确保查询结果的正确性。order by子句同样不可忽略，否则即使有force index提示，MySQL也会使用全表扫描而不是全索引扫描，从而使结果错误。索引同时保证了created_time,item_name的顺序，避免了文件排序。force index (idx_sort)提示和order by子句缺一不可，索引idx_sort在这里可谓恰到好处、一举两得。

查询语句开始前，先给变量初始化为数据中不可能出现的值，然后进入where子句从左向右判断。先比较变量和字段的值，再将本行created_time和item_name的值赋给变量，按created_time、item_name的顺序逐行处理。item_name是字符串类型，(@b:=item_name)不是有效的布尔表达式，因此要写成(@b:=item_name) is not null。

最后补充一句，这里忽略了“insert into t_target select * from t_source group by created_time,item_name;”的写法，因为它受“sql_mode='ONLY_FULL_GROUP_BY'”的限制。

二、利用窗口函数

MySQL 8中新增的窗口函数使得原来麻烦的去重操作变得很简单。

truncate t_target;  
insert into t_target 
select item_id, created_time, modified_time, item_name, other
  from (select *, row_number() over(partition by created_time,item_name) as rn
          from t_source) t where rn=1;

这个语句执行只需要12秒，而且写法清晰易懂，其查询计划如下：

mysql> explain select item_id, created_time, modified_time, item_name, other
    ->   from (select *, row_number() over(partition by created_time,item_name) as rn
    ->           from t_source) t where rn=1;
+----+-------------+------------+------------+------+---------------+-------------+---------+-------+--------+----------+----------------+
| id | select_type | table      | partitions | type | possible_keys | key         | key_len | ref   | rows   | filtered | Extra          |
+----+-------------+------------+------------+------+---------------+-------------+---------+-------+--------+----------+----------------+
|  1 | PRIMARY     | <derived2> | NULL       | ref  | <auto_key0>   | <auto_key0> | 8       | const |     10 |   100.00 | NULL           |
|  2 | DERIVED     | t_source   | NULL       | ALL  | NULL          | NULL        | NULL    | NULL  | 997281 |   100.00 | Using filesort |
+----+-------------+------------+------------+------+---------------+-------------+---------+-------+--------+----------+----------------+
2 rows in set, 2 warnings (0.00 sec)

该查询对t_source表进行了一次全表扫描，同时用filesort对表按分区字段created_time、item_name进行了排序。外层查询从每个分区中保留一条数据。因为重复created_time和item_name的多条数据中可以保留任意一条，所以oevr中不需要使用order by子句。

从执行计划看，窗口函数去重语句似乎没有消除嵌套查询的变量去重好，但此方法实际执行是最快的。

MySQL窗口函数说明参见“https://dev.mysql.com/doc/refman/8.0/en/window-functions.html”。

三、多线程并行执行

前面已经将单条查重语句调整到最优，但还是以单线程方式执行。能否利用多处理器，让去重操作多线程并行执行，从而进一步提高速度呢？比如我的实验环境是4处理器，如果使用4个线程同时执行查重SQL，理论上应该接近4倍的性能提升。

1. 数据分片

在生成测试数据时，created_time采用每条记录加一秒的方式，也就是最大和在最小的时间差为50万秒，而且数据均匀分布，因此先把数据平均分成4份。

（1）查询出4份数据的created_time边界值

mysql> select date_add('2017-01-01',interval 125000 second) dt1,
    ->        date_add('2017-01-01',interval 2*125000 second) dt2,
    ->        date_add('2017-01-01',interval 3*125000 second) dt3,
    ->        max(created_time) dt4
    ->   from t_source;
+---------------------+---------------------+---------------------+---------------------+
| dt1                 | dt2                 | dt3                 | dt4                 |
+---------------------+---------------------+---------------------+---------------------+
| 2017-01-02 10:43:20 | 2017-01-03 21:26:40 | 2017-01-05 08:10:00 | 2017-01-06 18:53:20 |
+---------------------+---------------------+---------------------+---------------------+
1 row in set (0.00 sec)

（2）查看每份数据的记录数，确认数据平均分布

mysql> select case when created_time >= '2017-01-01' 
    ->              and created_time < '2017-01-02 10:43:20'
    ->             then '2017-01-01'
    ->             when created_time >= '2017-01-02 10:43:20'
    ->              and created_time < '2017-01-03 21:26:40'
    ->             then '2017-01-02 10:43:20'
    ->             when created_time >= '2017-01-03 21:26:40' 
    ->              and created_time < '2017-01-05 08:10:00'
    ->             then '2017-01-03 21:26:40' 
    ->             else '2017-01-05 08:10:00'
    ->         end min_dt,
    ->        case when created_time >= '2017-01-01' 
    ->              and created_time < '2017-01-02 10:43:20'
    ->             then '2017-01-02 10:43:20'
    ->             when created_time >= '2017-01-02 10:43:20'
    ->              and created_time < '2017-01-03 21:26:40'
    ->             then '2017-01-03 21:26:40'
    ->             when created_time >= '2017-01-03 21:26:40' 
    ->              and created_time < '2017-01-05 08:10:00'
    ->             then '2017-01-05 08:10:00'
    ->             else '2017-01-06 18:53:20'
    ->         end max_dt,
    ->        count(*)
    ->   from t_source
    ->  group by case when created_time >= '2017-01-01' 
    ->              and created_time < '2017-01-02 10:43:20'
    ->             then '2017-01-01'
    ->             when created_time >= '2017-01-02 10:43:20'
    ->              and created_time < '2017-01-03 21:26:40'
    ->             then '2017-01-02 10:43:20'
    ->             when created_time >= '2017-01-03 21:26:40' 
    ->              and created_time < '2017-01-05 08:10:00'
    ->             then '2017-01-03 21:26:40' 
    ->             else '2017-01-05 08:10:00'
    ->         end,
    ->        case when created_time >= '2017-01-01' 
    ->              and created_time < '2017-01-02 10:43:20'
    ->             then '2017-01-02 10:43:20'
    ->             when created_time >= '2017-01-02 10:43:20'
    ->              and created_time < '2017-01-03 21:26:40'
    ->             then '2017-01-03 21:26:40'
    ->             when created_time >= '2017-01-03 21:26:40' 
    ->              and created_time < '2017-01-05 08:10:00'
    ->             then '2017-01-05 08:10:00'
    ->             else '2017-01-06 18:53:20'
    ->         end;
+---------------------+---------------------+----------+
| min_dt              | max_dt              | count(*) |
+---------------------+---------------------+----------+
| 2017-01-01          | 2017-01-02 10:43:20 |   249999 |
| 2017-01-02 10:43:20 | 2017-01-03 21:26:40 |   250000 |
| 2017-01-03 21:26:40 | 2017-01-05 08:10:00 |   250000 |
| 2017-01-05 08:10:00 | 2017-01-06 18:53:20 |   250002 |
+---------------------+---------------------+----------+
4 rows in set (4.86 sec)

4份数据的并集应该覆盖整个源数据集，并且数据之间是不重复的。也就是说4份数据的created_time要连续且互斥，连续保证处理全部数据，互斥确保了不需要二次查重。实际上这和时间范围分区的概念类似，或许用分区表更好些，只是这里省略了重建表的步骤。

2. 建立查重的存储过程

有了以上信息我们就可以写出4条语句处理全部数据。为了调用接口尽量简单，建立下面的存储过程。

delimiter //
create procedure sp_unique(i smallint)    
begin     
    set @a:='1000-01-01 00:00:00';  
    set @b:=' ';  
    if (i<4) then
        insert into t_target  
        select * from t_source force index (idx_sort)  
         where created_time >= date_add('2017-01-01',interval (i-1)*125000 second) 
           and created_time < date_add('2017-01-01',interval i*125000 second) 
           and (@a!=created_time or @b!=item_name) 
           and (@a:=created_time) is not null 
           and (@b:=item_name) is not null  
         order by created_time,item_name;  
    else 
    insert into t_target  
        select * from t_source force index (idx_sort)  
         where created_time >= date_add('2017-01-01',interval (i-1)*125000 second) 
           and created_time <= date_add('2017-01-01',interval i*125000 second) 
           and (@a!=created_time or @b!=item_name) 
           and (@a:=created_time) is not null 
           and (@b:=item_name) is not null  
         order by created_time,item_name;  
    end if;    
end     
//

查询语句的执行计划如下：

mysql> explain select * from t_source force index (idx_sort)  
    ->          where created_time >= date_add('2017-01-01',interval (1-1)*125000 second) 
    ->            and created_time < date_add('2017-01-01',interval 1*125000 second) 
    ->            and (@a!=created_time or @b!=item_name) 
    ->            and (@a:=created_time) is not null 
    ->            and (@b:=item_name) is not null  
    ->          order by created_time,item_name; 
+----+-------------+----------+------------+-------+---------------+----------+---------+------+--------+----------+-----------------------+
| id | select_type | table    | partitions | type  | possible_keys | key      | key_len | ref  | rows   | filtered | Extra                 |
+----+-------------+----------+------------+-------+---------------+----------+---------+------+--------+----------+-----------------------+
|  1 | SIMPLE      | t_source | NULL       | range | idx_sort      | idx_sort | 6       | NULL | 498640 |   100.00 | Using index condition |
+----+-------------+----------+------------+-------+---------------+----------+---------+------+--------+----------+-----------------------+
1 row in set, 3 warnings (0.00 sec)

MySQL优化器进行索引范围扫描，并且使用索引条件下推（ICP）优化查询。

3. 并行执行

下面分别使用shell后台进程和MySQL Schedule Event实现并行。

（1）shell后台进程

建立duplicate_removal.sh文件，内容如下：

#!/bin/bash
mysql -vvv -u root -p123456 test -e "truncate t_target" &>/dev/null 
date '+%H:%M:%S'
for y in {1..4}
do
  sql="call sp_unique($y)"
  mysql -vvv -u root -p123456 test -e "$sql" &>par_sql1_$y.log &
done
wait
date '+%H:%M:%S'

执行脚本文件

./duplicate_removal.sh

执行输出如下：

[mysql@hdp2~]$./duplicate_removal.sh
14:27:30
14:27:35

这种方法用时5秒，并行执行的4个过程调用分别用时为4.87秒、4.88秒、4.91秒、4.73秒：

[mysql@hdp2~]$cat par_sql1_1.log | sed '/^$/d'
mysql: [Warning] Using a password on the command line interface can be insecure.
--------------
call sp_unique(1)
--------------
Query OK, 124999 rows affected (4.87 sec)
Bye
[mysql@hdp2~]$cat par_sql1_2.log | sed '/^$/d'
mysql: [Warning] Using a password on the command line interface can be insecure.
--------------
call sp_unique(2)
--------------
Query OK, 125000 rows affected (4.88 sec)
Bye
[mysql@hdp2~]$cat par_sql1_3.log | sed '/^$/d'
mysql: [Warning] Using a password on the command line interface can be insecure.
--------------
call sp_unique(3)
--------------
Query OK, 125000 rows affected (4.91 sec)
Bye
[mysql@hdp2~]$cat par_sql1_4.log | sed '/^$/d'
mysql: [Warning] Using a password on the command line interface can be insecure.
--------------
call sp_unique(4)
--------------
Query OK, 125001 rows affected (4.73 sec)
Bye
[mysql@hdp2~]$

可以看到，每个过程的执行时间均4.85，因为是并行执行，总的过程执行时间为最慢的4.91秒，比单线程速度提高了2.5倍。

（2）MySQL Schedule Event

建立事件历史日志表

-- 用于查看事件执行时间等信息
create table t_event_history  (  
   dbname  varchar(128) not null default '',  
   eventname  varchar(128) not null default '',  
   starttime  datetime(3) not null default '1000-01-01 00:00:00',  
   endtime  datetime(3) default null,  
   issuccess  int(11) default null,  
   duration  int(11) default null,  
   errormessage  varchar(512) default null,  
   randno  int(11) default null
);

为每个并发线程创建一个事件

delimiter //
create event ev1 on schedule at current_timestamp + interval 1 hour on completion preserve disable do 
begin
    declare r_code char(5) default '00000';  
    declare r_msg text;  
    declare v_error integer;  
    declare v_starttime datetime default now(3);  
    declare v_randno integer default floor(rand()*100001);  
      
    insert into t_event_history (dbname,eventname,starttime,randno) 
    #作业名    
    values(database(),'ev1', v_starttime,v_randno);    
     
    begin  
        #异常处理段  
        declare continue handler for sqlexception    
        begin  
            set v_error = 1;  
            get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text;  
        end;  
          
        #此处为实际调用的用户程序过程  
        call sp_unique(1);  
    end;  
      
    update t_event_history set endtime=now(3),issuccess=isnull(v_error),duration=timestampdiff(microsecond,starttime,now(3)), errormessage=concat('error=',r_code,', message=',r_msg),randno=null where starttime=v_starttime and randno=v_randno;  
      
end
//     
 
create event ev2 on schedule at current_timestamp + interval 1 hour on completion preserve disable do 
begin
    declare r_code char(5) default '00000';  
    declare r_msg text;  
    declare v_error integer;  
    declare v_starttime datetime default now(3);  
    declare v_randno integer default floor(rand()*100001);  
      
    insert into t_event_history (dbname,eventname,starttime,randno) 
    #作业名    
    values(database(),'ev2', v_starttime,v_randno);    
     
    begin  
        #异常处理段  
        declare continue handler for sqlexception    
        begin  
            set v_error = 1;  
            get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text;  
        end;  
          
        #此处为实际调用的用户程序过程  
        call sp_unique(2);  
    end;  
      
    update t_event_history set endtime=now(3),issuccess=isnull(v_error),duration=timestampdiff(microsecond,starttime,now(3)), errormessage=concat('error=',r_code,', message=',r_msg),randno=null where starttime=v_starttime and randno=v_randno;  
      
end
//  
 
create event ev3 on schedule at current_timestamp + interval 1 hour on completion preserve disable do 
begin
    declare r_code char(5) default '00000';  
    declare r_msg text;  
    declare v_error integer;  
    declare v_starttime datetime default now(3);  
    declare v_randno integer default floor(rand()*100001);  
      
    insert into t_event_history (dbname,eventname,starttime,randno) 
    #作业名    
    values(database(),'ev3', v_starttime,v_randno);    
     
    begin  
        #异常处理段  
        declare continue handler for sqlexception    
        begin  
            set v_error = 1;  
            get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text;  
        end;  
          
        #此处为实际调用的用户程序过程  
        call sp_unique(3);  
    end;  
      
    update t_event_history set endtime=now(3),issuccess=isnull(v_error),duration=timestampdiff(microsecond,starttime,now(3)), errormessage=concat('error=',r_code,', message=',r_msg),randno=null where starttime=v_starttime and randno=v_randno;  
      
end
//  
 
create event ev4 on schedule at current_timestamp + interval 1 hour on completion preserve disable do 
begin
    declare r_code char(5) default '00000';  
    declare r_msg text;  
    declare v_error integer;  
    declare v_starttime datetime default now(3);  
    declare v_randno integer default floor(rand()*100001);  
      
    insert into t_event_history (dbname,eventname,starttime,randno) 
    #作业名    
    values(database(),'ev4', v_starttime,v_randno);    
     
    begin  
        #异常处理段  
        declare continue handler for sqlexception    
        begin  
            set v_error = 1;  
            get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text;  
        end;  
          
        #此处为实际调用的用户程序过程  
        call sp_unique(4);  
    end;  
      
    update t_event_history set endtime=now(3),issuccess=isnull(v_error),duration=timestampdiff(microsecond,starttime,now(3)), errormessage=concat('error=',r_code,', message=',r_msg),randno=null where starttime=v_starttime and randno=v_randno;  
      
end
//

为了记录每个事件执行的时间，在事件定义中增加了操作日志表的逻辑，因为每个事件中只多执行了一条insert，一条update，4个事件总共多执行8条很简单的语句，对测试的影响可以忽略不计。执行时间精确到毫秒。

触发事件执行

mysql -vvv -u root -p123456 test -e "truncate t_target;alter event ev1 on schedule at current_timestamp enable;alter event ev2 on schedule at current_timestamp enable;alter event ev3 on schedule at current_timestamp enable;alter event ev4 on schedule at current_timestamp enable;"

该命令行顺序触发了4个事件，但不会等前一个执行完才执行下一个，而是立即向下执行。这可从命令的输出可以清除看到：

[mysql@hdp2~]$mysql -vvv -u root -p123456 test -e "truncate t_target;alter event ev1 on schedule at current_timestamp enable;alter event ev2 on schedule at current_timestamp enable;alter event ev3 on schedule at current_timestamp enable;alter event ev4 on schedule at current_timestamp enable;"
mysql: [Warning] Using a password on the command line interface can be insecure.
--------------
truncate t_target
--------------

Query OK, 0 rows affected (0.06 sec)

--------------
alter event ev1 on schedule at current_timestamp enable
--------------

Query OK, 0 rows affected (0.02 sec)

--------------
alter event ev2 on schedule at current_timestamp enable
--------------

Query OK, 0 rows affected (0.00 sec)

--------------
alter event ev3 on schedule at current_timestamp enable
--------------

Query OK, 0 rows affected (0.02 sec)

--------------
alter event ev4 on schedule at current_timestamp enable
--------------

Query OK, 0 rows affected (0.00 sec)

Bye
[mysql@hdp2~]$

查看事件执行日志

mysql> select * from test.t_event_history;
+--------+-----------+-------------------------+-------------------------+-----------+----------+--------------+--------+
| dbname | eventname | starttime               | endtime                 | issuccess | duration | errormessage | randno |
+--------+-----------+-------------------------+-------------------------+-----------+----------+--------------+--------+
| test   | ev1       | 2019-07-31 14:38:04.000 | 2019-07-31 14:38:09.389 |         1 |  5389000 | NULL         |   NULL |
| test   | ev2       | 2019-07-31 14:38:04.000 | 2019-07-31 14:38:09.344 |         1 |  5344000 | NULL         |   NULL |
| test   | ev3       | 2019-07-31 14:38:05.000 | 2019-07-31 14:38:09.230 |         1 |  4230000 | NULL         |   NULL |
| test   | ev4       | 2019-07-31 14:38:05.000 | 2019-07-31 14:38:09.344 |         1 |  4344000 | NULL         |   NULL |
+--------+-----------+-------------------------+-------------------------+-----------+----------+--------------+--------+
4 rows in set (0.00 sec)

可以看到，每个过程的执行均为4.83秒，又因为是并行执行的，因此总的执行之间为最慢的5.3秒，优化效果和shell后台进程方式几乎相同。

本文参与腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。

原始发表：2019年07月31日，如有侵权请联系 cloudcommunity@tencent.com 删除

云数据库 SQL Server