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MYSQL数据库查询按日期分组统计详细代码

作者:嘉禾嘉宁papa

这篇文章主要给大家介绍了关于MYSQL数据库查询按日期分组统计的相关资料,按日期分组是指按照时间维度对数据进行分类汇总统计,常用于查询分析具有时间属性的数据,例如订单量、用户活跃等,需要的朋友可以参考下

一、按日分组

1.1、dayofyear、dayofmonth、dayofweek

查询语句

select 
	dayofmonth(transtime) as transDay,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-10-01 00:00:00' and '2016-10-31 23:59:59'
group by transDay
order by transDay asc;

查询结果

+----------+----------+
| transDay | transNum |
+----------+----------+
|        1 |     1704 |
|        2 |      985 |
|        3 |      723 |
|        4 |      606 |
|        5 |      581 |
|        6 |     1051 |
|        7 |     1257 |
|        8 |      637 |
|        9 |     1049 |
|       10 |      559 |
|       11 |      724 |
|       12 |      964 |
|       13 |     1139 |
|       14 |     2542 |
|       15 |     5957 |
|       16 |     3185 |
|       17 |      543 |
|       18 |      507 |
|       19 |      854 |
|       20 |      849 |
|       21 |     2216 |
|       22 |     3788 |
|       23 |     2498 |
|       24 |      693 |
|       25 |      597 |
|       26 |      756 |
|       27 |      854 |
|       28 |     1583 |
|       29 |     2180 |
|       30 |     1855 |
|       31 |      744 |
+----------+----------+
31 rows in set (0.05 sec)

  需要注意的是,如果是 dayofmonth 或者 dayofweek 时间跨月或者周,多月或者多周数据会合并到一起,如果希望分开,则可以采用下面的格式化方法。

1.2、格式化函数

查询语句

select 
	DATE_FORMAT(transtime, '%Y-%m-%d') as transDay,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-10-01 00:00:00' and '2016-10-31 23:59:59'
group by transDay
order by transDay asc;

查询结果

+------------+----------+
| transDay   | transNum |
+------------+----------+
| 2016-10-01 |     1704 |
| 2016-10-02 |      985 |
| 2016-10-03 |      723 |
| 2016-10-04 |      606 |
| 2016-10-05 |      581 |
| 2016-10-06 |     1051 |
| 2016-10-07 |     1257 |
| 2016-10-08 |      637 |
| 2016-10-09 |     1049 |
| 2016-10-10 |      559 |
| 2016-10-11 |      724 |
| 2016-10-12 |      964 |
| 2016-10-13 |     1139 |
| 2016-10-14 |     2542 |
| 2016-10-15 |     5957 |
| 2016-10-16 |     3185 |
| 2016-10-17 |      543 |
| 2016-10-18 |      507 |
| 2016-10-19 |      854 |
| 2016-10-20 |      849 |
| 2016-10-21 |     2216 |
| 2016-10-22 |     3788 |
| 2016-10-23 |     2498 |
| 2016-10-24 |      693 |
| 2016-10-25 |      597 |
| 2016-10-26 |      756 |
| 2016-10-27 |      854 |
| 2016-10-28 |     1583 |
| 2016-10-29 |     2180 |
| 2016-10-30 |     1855 |
| 2016-10-31 |      744 |
+------------+----------+
31 rows in set (0.08 sec)

二、按周分组

2.1、week函数

查询语句

select 
	week(transtime) as transWeek,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-01-01 00:00:00' and '2016-12-31 23:59:59'
group by transWeek
order by transWeek asc;

查询结果

+-----------+----------+
| transWeek | transNum |
+-----------+----------+
|         0 |        2 |
|         1 |        8 |
|         2 |        9 |
|         3 |        1 |
|         4 |        4 |
|         5 |        7 |
|         6 |        4 |
|         7 |        9 |
|         8 |        7 |
|         9 |        2 |
|        10 |       21 |
|        11 |       18 |
|        12 |       19 |
|        13 |       34 |
|        14 |       31 |
|        15 |       17 |
|        16 |      130 |
|        17 |      261 |
|        18 |      230 |
|        19 |      494 |
|        20 |      452 |
|        21 |      485 |
|        22 |      590 |
|        23 |      684 |
|        24 |      580 |
|        25 |      620 |
|        26 |      370 |
|        27 |      155 |
|        28 |      721 |
|        29 |      747 |
|        30 |      659 |
|        31 |      775 |
|        32 |      843 |
|        33 |      897 |
|        34 |      926 |
|        35 |      975 |
|        36 |      975 |
|        37 |     1048 |
|        38 |      393 |
|        39 |     4145 |
|        40 |     5840 |
|        41 |    12934 |
|        42 |    11942 |
|        43 |     9161 |
|        44 |     9102 |
|        45 |     8284 |
|        46 |     6150 |
|        47 |     5825 |
|        48 |     6374 |
|        49 |     6929 |
|        50 |     4366 |
|        51 |     3858 |
|        52 |     5855 |
+-----------+----------+
53 rows in set (0.12 sec)

2.2、weekofyear函数

查询语句

select 
	weekofyear(transtime) as transWeek,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-01-01 00:00:00' and '2016-12-31 23:59:59'
group by transWeek
order by transWeek asc;

查询结果

+-----------+----------+
| transWeek | transNum |
+-----------+----------+
|         1 |       11 |
|         2 |        6 |
|         4 |        5 |
|         5 |        6 |
|         6 |        5 |
|         7 |        9 |
|         8 |        7 |
|         9 |        1 |
|        10 |       22 |
|        11 |       17 |
|        12 |       23 |
|        13 |       34 |
|        14 |       28 |
|        15 |       16 |
|        16 |      199 |
|        17 |      194 |
|        18 |      299 |
|        19 |      510 |
|        20 |      458 |
|        21 |      492 |
|        22 |      571 |
|        23 |      709 |
|        24 |      546 |
|        25 |      640 |
|        26 |      278 |
|        27 |      257 |
|        28 |      723 |
|        29 |      720 |
|        30 |      710 |
|        31 |      786 |
|        32 |      813 |
|        33 |      921 |
|        34 |      957 |
|        35 |     1002 |
|        36 |      932 |
|        37 |     1032 |
|        38 |      327 |
|        39 |     5064 |
|        40 |     5904 |
|        41 |    15070 |
|        42 |    11255 |
|        43 |     8518 |
|        44 |     9203 |
|        45 |     7836 |
|        46 |     5448 |
|        47 |     6608 |
|        48 |     5934 |
|        49 |     6639 |
|        50 |     4160 |
|        51 |     3887 |
|        52 |     5173 |
|        53 |        3 |
+-----------+----------+
52 rows in set (0.12 sec)

2.3、weekday函数

查询语句

select 
	weekday(transtime) as transWeek,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-10-01 00:00:00' and '2016-10-31 23:59:59'
group by transWeek
order by transWeek asc;

查询结果

+-----------+----------+
| transWeek | transNum |
+-----------+----------+
|         0 |     3262 |
|         1 |     2434 |
|         2 |     3155 |
|         3 |     3893 |
|         4 |     7598 |
|         5 |    14266 |
|         6 |     9572 |
+-----------+----------+
7 rows in set (0.05 sec)

同时如果觉得不直观,你想获取名称,可以使用 dayname(date) 替代 weekday(date) ,结果如下

2.4、dayname函数

查询语句

select 
	dayname(transtime) as transWeek,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-10-01 00:00:00' and '2016-10-31 23:59:59'
group by transWeek
order by transWeek asc;

查询结果

+-----------+----------+
| transWeek | transNum |
+-----------+----------+
| Friday    |     7598 |
| Monday    |     3262 |
| Saturday  |    14266 |
| Sunday    |     9572 |
| Thursday  |     3893 |
| Tuesday   |     2434 |
| Wednesday |     3155 |
+-----------+----------+
7 rows in set (0.08 sec)

三、按月分组

3.1、month函数

查询语句

select 
	month(transtime) as transMonth,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-01-01 00:00:00' and '2016-12-31 23:59:59'
group by transMonth
order by transMonth asc;

查询结果

+------------+----------+
| transMonth | transNum |
+------------+----------+
|          1 |       25 |
|          2 |       27 |
|          3 |       83 |
|          4 |      449 |
|          5 |     1893 |
|          6 |     2611 |
|          7 |     2411 |
|          8 |     3811 |
|          9 |     5334 |
|         10 |    44180 |
|         11 |    30140 |
|         12 |    24004 |
+------------+----------+
12 rows in set (0.12 sec)

同时如果你想获取名字,可以使用 MONTHNAME(date) 替代 month(date) ,结果如下:

+------------+----------+
| transMonth | transNum |
+------------+----------+
| April      |      449 |
| August     |     3811 |
| December   |    24004 |
| February   |       27 |
| January    |       25 |
| July       |     2411 |
| June       |     2611 |
| March      |       83 |
| May        |     1893 |
| November   |    30140 |
| October    |    44180 |
| September  |     5334 |
+------------+----------+
12 rows in set (0.20 sec)

需要注意的是,如果是跨年了,多年的数据会合并到一起,就不知道每一年的某月是多少了,如果希望分开,则可以采用下面的格式化方法。

3.2、DATE_FORMAT函数

查询语句

select 
	DATE_FORMAT(transtime, '%Y-%m') as transMonth,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-01-01 00:00:00' and '2016-12-31 23:59:59'
group by transMonth
order by transMonth asc;

查询结果

+------------+----------+
| transMonth | transNum |
+------------+----------+
| 2016-01    |       25 |
| 2016-02    |       27 |
| 2016-03    |       83 |
| 2016-04    |      449 |
| 2016-05    |     1893 |
| 2016-06    |     2611 |
| 2016-07    |     2411 |
| 2016-08    |     3811 |
| 2016-09    |     5334 |
| 2016-10    |    44180 |
| 2016-11    |    30140 |
| 2016-12    |    24004 |
+------------+----------+
12 rows in set (0.20 sec)

四、按季分组

4.1、quarter函数

查询语句

select 
	quarter(transtime) as quarterNo,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2016-01-01 00:00:00' and '2016-12-31 23:59:59'
group by quarterNo
order by quarterNo asc;

查询结果

+-----------+----------+
| quarterNo | transNum |
+-----------+----------+
|         1 |      135 |
|         2 |     4953 |
|         3 |    11556 |
|         4 |    98324 |
+-----------+----------+
4 rows in set (0.12 sec)

五、按年分组

5.1、year函数

查询语句

select 
	year(transtime) as transYear,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2015-01-01 00:00:00' and '2022-12-31 23:59:59'
group by transYear
order by transYear asc;

查询结果

+-----------+----------+
| transYear | transNum |
+-----------+----------+
|      2015 |        6 |
|      2016 |   114968 |
|      2017 |    66703 |
|      2018 |     2738 |
|      2019 |     1853 |
|      2020 |      651 |
|      2021 |       40 |
+-----------+----------+
7 rows in set (0.19 sec)

5.2、DATE_FORMAT函数

查询语句

select 
	DATE_FORMAT(transtime, '%Y') as transYear,
	count(*) as transNum
from tb_inf_otherbiz 
where transtime between '2015-01-01 00:00:00' and '2022-12-31 23:59:59'
group by transYear
order by transYear asc;

查询结果

+-----------+----------+
| transYear | transNum |
+-----------+----------+
|      2015 |        6 |
|      2016 |   114968 |
|      2017 |    66703 |
|      2018 |     2738 |
|      2019 |     1853 |
|      2020 |      651 |
|      2021 |       40 |
+-----------+----------+
7 rows in set (0.19 sec)

其他

格式转换

select from_unixtime(create_time / 1000, '%Y-%m-%d %H:%i:%S') create_time
from t_content

结语

本文的操作都是基于mysql8.0的版本,搞懂mysql的函数这些查询都会变得简单。

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