按时间间隔聚合对查询结果进行分组

Question

我正在开发一个MySQL数据库系统，它将使我的公司能够在目前所需时间的一小部分内处理我们的旅行时间调查数据。我得到了执行计算的以下MySQL命令：

select anpr_1_unique.date as "Date",
anpr_1_unique.NETBIOSNAME as "ID for site A",
anpr_1_unique.time as "Timestamp at site A",
anpr_3_unique.NETBIOSNAME as "ID for site B",
anpr_3_unique.time as "Timestamp at site B",
anpr_1_unique.plate as "Plate",
if (timediff(anpr_3_unique.time,anpr_1_unique.time) like "%-%", null,timediff(anpr_3_unique.time,anpr_1_unique.time)) as "Journey time in direction 1",
if (timediff(anpr_1_unique.time,anpr_3_unique.time) like "%-%", null,timediff(anpr_1_unique.time,anpr_3_unique.time)) as "Journey time in direction 1",
if (timediff(anpr_1_unique.time,anpr_3_unique.time) like "%-%", "A->B","B->A") as "Direction of travel"
from anpr_1_unique inner join anpr_3_unique on anpr_1_unique.plate=anpr_3_unique.plate 
where anpr_1_unique.date = "2013-02-26" and anpr_3_unique.date="2013-02-26" and anpr_1_unique.time like "%06:%%:%%%" order by anpr_1_unique.time

它会生成以下格式的表：

+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
| Date       | ID for site A | Timestamp at site A | ID for site B | Timestamp at site B | Plate   | Journey time in direction 1 | Journey time in direction 1 | Direction of travel |
+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
| 2013-02-26 | Farnham N 1   | 06:50:52            | Farnham S     | 06:54:32            | LS60JAO | 00:03:40                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:51:33            | Farnham S     | 06:53:32            | FH53WGW | 00:01:59                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:51:51            | Farnham S     | 06:54:23            | V987USD | 00:02:32                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:52:17            | Farnham S     | 06:54:28            | BK61RNY | 00:02:11                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:52:30            | Farnham S     | 06:54:35            | WU59SXP | 00:02:05                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:53:02            | Farnham S     | 12:23:33            | NA08UKV | 05:30:31                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:53:44            | Farnham S     | 06:56:09            | KC04CTF | 00:02:25                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:53:46            | Farnham S     | 06:56:11            | LL58YAJ | 00:02:25                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:54:41            | Farnham S     | 06:57:44            | AP57CWE | 00:03:03                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:54:46            | Farnham S     | 14:55:29            | EU55LRF | 08:00:43                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:55:13            | Farnham S     | 06:58:08            | AJ60KVK | 00:02:55                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:55:19            | Farnham S     | 06:58:44            | T96ALO  | 00:03:25                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:55:57            | Farnham S     | 06:58:46            | F604WNV | 00:02:49                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:56:22            | Farnham S     | 06:58:50            | S905AAP | 00:02:28                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:56:52            | Farnham S     | 06:59:35            | LO08SKV | 00:02:43                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:56:55            | Farnham S     | 06:59:37            | KT53TNK | 00:02:42                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:57:45            | Farnham S     | 07:00:33            | LC60EOR | 00:02:48                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:58:44            | Farnham S     | 07:06:15            | S278VNA | 00:07:31                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:58:49            | Farnham S     | 07:00:46            | X906GVT | 00:01:57                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:59:15            | Farnham S     | 07:01:35            | YS11AWP | 00:02:20                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:59:44            | Farnham S     | 07:01:46            | LP12NWA | 00:02:02                    | NULL                        | A->B                |
| 2013-02-26 | Farnham N 1   | 06:59:59            | Farnham S     | 07:02:29            | RJ11BUA | 00:02:30                    | NULL                        | A->B                |
+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
22 rows in set (0.09 sec)

这只是一个10分钟的结果样本。我对此很满意，也很满意结果，但是我希望能够将这些结果分组为5分钟、10分钟或15分钟的聚合周期

我在这个网站和其他网站上四处寻找其他可能的解决方案，最著名的是建议使用GROUP BY UNIX_TIMESTAMP(<time_stamp>) DIV <time>来生成聚合。我已经尝试过了，但它不适用于这个查询。

我如何才能有效地聚合这些数据？它可以用我生成的查询来完成吗，或者需要重写吗？

更新

使用以下SQL代码，我成功地完成了大致所需的计算：

select 
anpr_1_unique.date as "Date", 
str_to_date(concat(date_format(anpr_1_unique.time, '%H'),':',(floor(date_format(anpr_1_unique.time, '%i')/30)*30), ':00'), '%H:%i:%s') as starttime,
anpr_1_unique.NETBIOSNAME as "ID for site A",
anpr_3_unique.NETBIOSNAME as "ID for site B",
if (avg(timediff(anpr_3_unique.time,anpr_1_unique.time) like "%-%"), null,time(avg(timediff(anpr_3_unique.time,anpr_1_unique.time)))) as "Journey time in direction  A->B"
from anpr_1_unique 
inner join anpr_3_unique on anpr_1_unique.plate=anpr_3_unique.plate 
where anpr_1_unique.date = "2013-02-26" and anpr_3_unique.date="2013-02-26" 
and timediff(anpr_3_unique.time,anpr_1_unique.time) between "00:00:00" and "00:30:00"
GROUP BY   starttime; 
show warnings;

它会产生这样的数据：

+------------+-----------+---------------+---------------+---------------------------------+
| Date       | starttime | ID for site A | ID for site B | Journey time in direction  A->B |
+------------+-----------+---------------+---------------+---------------------------------+
| 2013-02-26 | 06:30:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 07:00:00  | Farnham N 1   | Farnham S     | 00:03:46.3276  |
| 2013-02-26 | 07:30:00  | Farnham N 1   | Farnham S     | 00:04:51.5588  |
| 2013-02-26 | 08:00:00  | Farnham N 1   | Farnham S     | 00:11:10.8462  |
| 2013-02-26 | 08:30:00  | Farnham N 1   | Farnham S     | 00:11:36.6410  |
| 2013-02-26 | 09:00:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 09:30:00  | Farnham N 1   | Farnham S     | 00:05:39.0714  |
| 2013-02-26 | 10:00:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 10:30:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 11:00:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 11:30:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 12:00:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 12:30:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 13:00:00  | Farnham N 1   | Farnham S     | 00:05:17.1250  |
| 2013-02-26 | 13:30:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 14:00:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 14:30:00  | Farnham N 1   | Farnham S     | 00:05:06.8864  |
| 2013-02-26 | 15:00:00  | Farnham N 1   | Farnham S     | 00:09:07.2308  |
| 2013-02-26 | 15:30:00  | Farnham N 1   | Farnham S     | 00:13:02.2558  |
| 2013-02-26 | 16:00:00  | Farnham N 1   | Farnham S     | NULL           |
| 2013-02-26 | 16:30:00  | Farnham N 1   | Farnham S     | 00:26:45.7143  |
| 2013-02-26 | 17:30:00  | Farnham N 1   | Farnham S     | 00:28:04.0000  |
| 2013-02-26 | 18:00:00  | Farnham N 1   | Farnham S     | 00:22:56.0667  |
| 2013-02-26 | 18:30:00  | Farnham N 1   | Farnham S     | NULL           |
+------------+-----------+---------------+---------------+---------------------------------+

但是，如图所示，这会在应该有数据的地方生成许多空结果。警告显示相同数量的“| Warning | 1292 | Truncated incorrect time value: '(number)' |”消息。我在计算时遇到了类似的问题，我使用timediff()命令解决了这个问题，而不是手动计算。我不确定我现在能做些什么来“去空”这些结果。

有什么建议吗，我可以在这里做，现在？

Answer 1

您需要对除法结果进行舍入。使用地板、天花板或圆形可以做到这一点

 GROUP BY    floor(UNIX_TIMESTAMP(<time_stamp>)/(seconds*minutes))

GROUP BY    floor( UNIX_TIMESTAMP(<time_stamp>)/(60*15) ) - group into 15 minute intervals

具体地说；

 GROUP BY    floor( UNIX_TIMESTAMP(CONCAT(anpr_1_unique.date, ' ', npr_1_unique.time))/(60*15) )