mysql - 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，该系统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行程时间调查数据。我有以下执行计算的 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

生成以下格式的表格:

+------------+---------------+---------------------+---------------+---------------------+---------+-----------------------------+-----------------------------+---------------------+
| 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 10 分钟的结果示例。我对此很满意，结果如何，但是我希望能够将这些结果分组为 5,10 或 15 分钟的聚合期

我在本网站和其他网站上寻找其他可能的解决方案，最值得注意的是建议使用 GROUP BY UNIX_TIMESTAMP(<time_stamp>) DIV <time>产生聚合。我试过了，但它不适用于此查询。

我该怎么做才能有效地聚合这些数据？可以用我生成的查询来完成吗，还是需要重写？

更新

我设法通过以下 SQL 代码使计算大致按照我的要求进行:

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;

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           |
+------------+-----------+---------------+---------------+---------------------------------+

然而，如图所示，这会在本应有数据的地方生成大量 NULL 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() 解决了这个问题命令而不是手动计算。我不确定我现在可以用它做什么来“消除”这些结果。

关于我现在可以在这里做什么有什么建议吗？

Answer 1

您需要对除法结果进行四舍五入。用地板、天花板或圆形做这个

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

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

特别是；

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