將MySQL去重操作優化到極致的操作方法

這篇文章主要介紹瞭如何將MySQL去重操作優化到極致,本文給大家介紹的非常詳細，具有一定的參考借鑑價值，需要的朋友可以參考下

•問題提出

源表t_source結構如下：

item_id int,
 created_time datetime,
 modified_time datetime,
 item_name varchar(20),
 other varchar(20)

要求：

1.源表中有100萬條數據，其中有50萬created_time和item_name重複。
2.要把去重後的50萬數據寫入到目標表。
3.重複created_time和item_name的多條數據，可以保留任意一條，不做規則限制。

•實驗環境

Linux虛機：CentOS release 6.4；8G物理內存（MySQL配置4G）；100G機械硬盤；雙物理CPU雙核，共四個處理器；MySQL 8.0.16。

•建立測試表和數據

-- 建立源表
create table t_source 
( item_id int, 
 created_time datetime, 
 modified_time datetime, 
 item_name varchar(20), 
 other varchar(20) 
); 
-- 建立目標表
create table t_target like t_source; 
-- 生成100萬測試數據，其中有50萬created_time和item_name重複
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(); 

-- 源表沒有主鍵或唯一性約束，有可能存在兩條完全一樣的數據，所以再插入一條記錄模擬這種情況。
insert into t_source select * from t_source where item_id=1;

 源表中有1000001條記錄，去重後的目標表應該有500000條記錄。
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、巧用索引與變量

1. 無索引對比測試

（1）使用相關子查詢

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);

這個語句很長時間都出不來結果，只看一下執行計劃吧。

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)

主查詢和相關子查詢都是全表掃描，一共要掃描100萬*100萬數據行，難怪出不來結果。

（2）使用表連接

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;

這種方法用時14秒，查詢計劃如下：

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)

•內層查詢掃描t_source表的100萬行，建立臨時表，找出去重後的最小item_id，生成導出表derived2，此導出表有50萬行。
•MySQL會在導出表derived2上自動創建一個item_id字段的索引auto_key0。
•外層查詢也要掃描t_source表的100萬行數據，在與導出表做鏈接時，對t_source表每行的item_id，使用auto_key0索引查找導出表中匹配的行，並在此時優化distinct操作，在找到第一個匹配的行後即停止查找同樣值的動作。

（3）使用變量

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;

這種方法用時13秒，查詢計劃如下：

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)

•最內層的查詢掃描t_source表的100萬行，並使用文件排序，生成導出表derived3。
•第二層查詢要掃描derived3的100萬行，生成導出表derived2，完成變量的比較和賦值，並自動創建一個導出列f上的索引auto_key0。
•最外層使用auto_key0索引掃描derived2得到去重的結果行。

與上面方法2比較，總的掃描行數不變，都是200萬行。只存在一點微小的差別，這次自動生成的索引是在常量列 f 上，而表關聯自動生成的索引是在item_id列上，所以查詢時間幾乎相同。

至此，我們還沒有在源表上創建任何索引。無論使用哪種寫法，要查重都需要對created_time和item_name字段進行排序，因此很自然地想到，如果在這兩個字段上建立聯合索引，利用索引本身有序的特性消除額外排序，從而提高查詢性能。

2. 建立created_time和item_name上的聯合索引對比測試

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

（1）使用相關子查詢

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);

本次用時19秒，查詢計劃如下：

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)

•外層查詢的t_source表是驅動表，需要掃描100萬行。

•對於驅動表每行的item_id，通過idx_sort索引查詢出兩行數據。

（2）使用表連接

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;

本次用時13秒，查詢計劃如下：

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)

和沒有索引相比，子查詢雖然從全表掃描變爲了全索引掃描，但還是需要掃描100萬行記錄。因此查詢性能提升並不是明顯。

（3）使用變量

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; 

本次用時13秒，查詢計劃與沒有索引時的完全相同。可見索引對這種寫法沒有作用。能不能消除嵌套，只用一層查詢出結果呢？

（4）使用變量，並且消除嵌套查詢

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;

本次用時12秒，查詢計劃如下：

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)

該語句具有以下特點：

•消除了嵌套子查詢，只需要對t_source表進行一次全索引掃描，查詢計劃已達最優。
•無需distinct二次查重。
•變量判斷與賦值只出現在where子句中。
•利用索引消除了filesort。

在MySQL 8之前，該語句是單線程去重的最佳解決方案。仔細分析這條語句，發現它巧妙地利用了SQL語句的邏輯查詢處理步驟和索引特性。一條SQL查詢的邏輯步驟爲：

1.執行笛卡爾乘積（交叉連接）
2.應用ON篩選器（連接條件）
3.添加外部行（outer join）
4.應用where篩選器
5.分組
6.應用cube或rollup
7.應用having篩選器
8.處理select列表
9.應用distinct子句
10.應用order by子句
11.應用limit子句

每條查詢語句的邏輯執行步驟都是這11步的子集。拿這條查詢語句來說，其執行順序爲：強制通過索引idx_sort查找數據行 -> 應用where篩選器 -> 處理select列表 -> 應用order by子句。

爲了使變量能夠按照created_time和item_name的排序順序進行賦值和比較，必須按照索引順序查找數據行。這裏的force index (idx_sort)提示就起到了這個作用，必須這樣寫才能使整條查重語句成立。否則，因爲先掃描表才處理排序，因此不能保證變量賦值的順序，也就不能確保查詢結果的正確性。order by子句同樣不可忽略，否則即使有force index提示，MySQL也會使用全表掃描而不是全索引掃描，從而使結果錯誤。索引同時保證了created_time,item_name的順序，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在這裏可謂恰到好處、一舉兩得。

查詢語句開始前，先給變量初始化爲數據中不可能出現的值，然後進入where子句從左向右判斷。先比較變量和字段的值，再將本行created_time和item_name的值賦給變量，按created_time、item_name的順序逐行處理。item_name是字符串類型，(@b:=item_name)不是有效的布爾表達式，因此要寫成(@b:=item_name) is not null。

最後補充一句，這裏忽略了“insert into t_target select * from t_source group by created_time,item_name;”的寫法，因爲它受“sql_mode='ONLY_FULL_GROUP_BY'”的限制。

二、利用窗口函數

MySQL 8中新增的窗口函數使得原來麻煩的去重操作變得很簡單。

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;

這個語句執行只需要12秒，而且寫法清晰易懂，其查詢計劃如下：

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)

該查詢對t_source表進行了一次全表掃描，同時用filesort對錶按分區字段created_time、item_name進行了排序。外層查詢從每個分區中保留一條數據。因爲重複created_time和item_name的多條數據中可以保留任意一條，所以oevr中不需要使用order by子句。

從執行計劃看，窗口函數去重語句似乎沒有消除嵌套查詢的變量去重好，但此方法實際執行是最快的。

MySQL窗口函數說明參見“https://dev.mysql.com/doc/refman/8.0/en/window-functions.html”。

三、多線程並行執行

前面已經將單條查重語句調整到最優，但還是以單線程方式執行。能否利用多處理器，讓去重操作多線程並行執行，從而進一步提高速度呢？比如我的實驗環境是4處理器，如果使用4個線程同時執行查重SQL，理論上應該接近4倍的性能提升。

1. 數據分片

在生成測試數據時，created_time採用每條記錄加一秒的方式，也就是最大和在最小的時間差爲50萬秒，而且數據均勻分佈，因此先把數據平均分成4份。

（1）查詢出4份數據的created_time邊界值

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）查看每份數據的記錄數，確認數據平均分佈

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份數據的並集應該覆蓋整個源數據集，並且數據之間是不重複的。也就是說4份數據的created_time要連續且互斥，連續保證處理全部數據，互斥確保了不需要二次查重。實際上這和時間範圍分區的概念類似，或許用分區表更好些，只是這裏省略了重建表的步驟。

2. 建立查重的存儲過程

有了以上信息我們就可以寫出4條語句處理全部數據。爲了調用接口儘量簡單，建立下面的存儲過程。

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 
//

查詢語句的執行計劃如下：

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優化器進行索引範圍掃描，並且使用索引條件下推（ICP）優化查詢。

3. 並行執行

下面分別使用shell後臺進程和MySQL Schedule Event實現並行。

（1）shell後臺進程

•建立duplicate_removal.sh文件，內容如下：

#!/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'

•執行腳本文件

./duplicate_removal.sh

執行輸出如下：

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

這種方法用時5秒，並行執行的4個過程調用分別用時爲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，每個過程的執行時間均4.85，因爲是並行執行，總的過程執行時間爲最慢的4.91秒，比單線程速度提高了2.5倍。

（2）MySQL Schedule Event

•建立事件歷史日誌表

-- 用於查看事件執行時間等信息
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
);

•爲每個併發線程創建一個事件

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) 
 #作業名 
 values(database(),'ev1', v_starttime,v_randno); 
 begin 
 #異常處理段 
 declare continue handler for sqlexception 
 begin 
  set v_error = 1; 
  get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text; 
 end; 
 #此處爲實際調用的用戶程序過程 
 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) 
 #作業名 
 values(database(),'ev2', v_starttime,v_randno); 
 begin 
 #異常處理段 
 declare continue handler for sqlexception 
 begin 
  set v_error = 1; 
  get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text; 
 end; 
 #此處爲實際調用的用戶程序過程 
 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) 
 #作業名 
 values(database(),'ev3', v_starttime,v_randno); 
 begin 
 #異常處理段 
 declare continue handler for sqlexception 
 begin 
  set v_error = 1; 
  get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text; 
 end; 
 #此處爲實際調用的用戶程序過程 
 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) 
 #作業名 
 values(database(),'ev4', v_starttime,v_randno); 
 begin 
 #異常處理段 
 declare continue handler for sqlexception 
 begin 
  set v_error = 1; 
  get diagnostics condition 1 r_code = returned_sqlstate , r_msg = message_text; 
 end; 
 #此處爲實際調用的用戶程序過程 
 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
//

爲了記錄每個事件執行的時間，在事件定義中增加了操作日誌表的邏輯，因爲每個事件中只多執行了一條insert，一條update，4個事件總共多執行8條很簡單的語句，對測試的影響可以忽略不計。執行時間精確到毫秒。

•觸發事件執行

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;"

該命令行順序觸發了4個事件，但不會等前一個執行完才執行下一個，而是立即向下執行。這可從命令的輸出可以清除看到：

[mysql@hdp2~]$mysql -vvv -u root -p123456 test -e "truncate t_target;alter event ev1 on schedule at current_timestamp enable;alter event ev2 on schedule at current_timestamp enable;alter event ev3 on schedule at current_timestamp enable;alter event ev4 on schedule at current_timestamp enable;"
mysql: [Warning] Using a password on the command line interface can be insecure.
--------------
truncate t_target
--------------
Query OK, 0 rows affected (0.06 sec)
--------------
alter event ev1 on schedule at current_timestamp enable
--------------
Query OK, 0 rows affected (0.02 sec)
--------------
alter event ev2 on schedule at current_timestamp enable
--------------
Query OK, 0 rows affected (0.00 sec)
--------------
alter event ev3 on schedule at current_timestamp enable
--------------
Query OK, 0 rows affected (0.02 sec)
--------------
alter event ev4 on schedule at current_timestamp enable
--------------
Query OK, 0 rows affected (0.00 sec)
Bye
[mysql@hdp2~]$

•查看事件執行日誌

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，每個過程的執行均爲4.83秒，又因爲是並行執行的，因此總的執行之間爲最慢的5.3秒，優化效果和shell後臺進程方式幾乎相同。

總結

以上所述是小編給大家介紹的將MySQL去重操作優化到極致的操作方法,希望對大家有所幫助，如果大家有任何疑問請給我留言，小編會及時回覆大家的。在此也非常感謝大家對神馬文庫網站的支持！
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