将MySQL去重操作优化到极致的操作方法
人气:0•问题提出
源表t_source结构如下：
item_id int, created_time datetime, modified_time datetime, item_name varchar(20), other varchar(20)
要求：
1.源表中有100万条数据，其中有50万created_time和item_name重复。
2.要把去重后的50万数据写入到目标表。
3.重复created_time和item_name的多条数据，可以保留任意一条，不做规则限制。
•实验环境
Linux虚机：CentOS release 6.4；8G物理内存（MySQL配置4G）；100G机械硬盘；双物理CPU双核，共四个处理器；MySQL 8.0.16。
•建立测试表和数据
-- 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：
1.执行笛卡尔乘积（交叉连接）
2.应用ON筛选器（连接条件）
3.添加外部行（outer join）
4.应用where筛选器
5.分组
6.应用cube或rollup
7.应用having筛选器
8.处理select列表
9.应用distinct子句
10.应用order by子句
11.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。
总结
以上所述是小编给大家介绍的将MySQL去重操作优化到极致的操作方法,希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对网站的支持！
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