Question

我有一个非常大的表（数亿行），它将测试结果以及日期时间和外键存储到名为“链接”的相关实体中，我需要按时间对行进行分组间隔10,15,20,30和60分钟以及按时间过滤和＆＃39; link_id＆＃39;我知道这可以通过这个查询完成，如[here] [1]所述：

SELECT time,AVG(RTT),MIN(RTT),MAX(RTT),COUNT(*) FROM  trace
WHERE link_id=1 AND time>='2015-01-01' AND time <= '2015-01-30'
GROUP BY UNIX_TIMESTAMP(time) DIV 600;

此解决方案有效，但速度非常慢（平均约为10），因此我尝试按时间间隔为每个组添加日期时间列＆＃39;例如行：

id | time                     | rtt        | link_id
1  | 2014-01-01 12:34:55.4034 | 154.3      | 2

成为：

id | time                     | rtt        | link_id | time_60                   |time_30 ...
1  | 2014-01-01 12:34:55.4034 | 154.3      | 2       | 2014-01-01 12:00:00.00    | 2014-01-01 12:30:00.00 ...

我得到以下查询的间隔：

SELECT time_10,AVG(RTT),MIN(RTT),MAX(RTT),COUNT(*) FROM  trace
WHERE link_id=1 AND time>='2015-01-01' AND time <= '2015-01-30'
GROUP BY time_10;

此查询至少快50％（平均约5秒），但仍然相当慢，我怎样才能更快地优化此查询？

解释查询输出：

+----+-------------+------------+------+------------------------------------------------------------------------+----------------------------------------------------+---------+-------+---------+----------------------------------------------+
| id | select_type | table      | type | possible_keys                                                          | key                                                | key_len | ref   | rows    | Extra                                        |
+----+-------------+------------+------+------------------------------------------------------------------------+----------------------------------------------------+---------+-------+---------+----------------------------------------------+
|  1 | SIMPLE      | main_trace | ref  | main_trace_link_id_c6febb11f84677f_fk_main_link_id,main_trace_e7549e3e | main_trace_link_id_c6febb11f84677f_fk_main_link_id | 4       | const | 1478359 | Using where; Using temporary; Using filesort |
+----+-------------+------------+------+------------------------------------------------------------------------+----------------------------------------------------+---------+-------+---------+----------------------------------------------+

这些是表索引：

+------------+------------+----------------------------------------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| Table      | Non_unique | Key_name                                           | Seq_in_index | Column_name | Collation | Cardinality | Sub_part | Packed | Null | Index_type | Comment | Index_comment |
+------------+------------+----------------------------------------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+
| main_trace |          0 | PRIMARY                                            |            1 | id          | A         |     2956718 |     NULL | NULL   |      | BTREE      |         |               |
| main_trace |          1 | main_trace_link_id_c6febb11f84677f_fk_main_link_id |            1 | link_id     | A         |           2 |     NULL | NULL   |      | BTREE      |         |               |
| main_trace |          1 | main_trace_07cc694b                                |            1 | time        | A         |     2956718 |     NULL | NULL   |      | BTREE      |         |               |
| main_trace |          1 | main_trace_e7549e3e                                |            1 | time_10     | A         |       22230 |     NULL | NULL   | YES  | BTREE      |         |               |
| main_trace |          1 | main_trace_01af8333                                |            1 | time_15     | A         |       14783 |     NULL | NULL   | YES  | BTREE      |         |               |
| main_trace |          1 | main_trace_1681ff94                                |            1 | time_20     | A         |       10870 |     NULL | NULL   | YES  | BTREE      |         |               |
| main_trace |          1 | main_trace_f7c28c93                                |            1 | time_30     | A         |        6399 |     NULL | NULL   | YES  | BTREE      |         |               |
| main_trace |          1 | main_trace_0f29fcc5                                |            1 | time_60     | A         |        3390 |     NULL | NULL   | YES  | BTREE      |         |               |
+------------+------------+----------------------------------------------------+--------------+-------------+-----------+-------------+----------+--------+------+------------+---------+---------------+

Answer 1

对于此查询：

SELECT time_10, AVG(RTT), MIN(RTT), MAX(RTT), COUNT(*)
FROM  trace
WHERE link_id = 1 AND time >= '2015-01-01' AND time <= '2015-01-30'
GROUP BY time_10;

最佳指数是覆盖指数：trace(link_id, time, time_10, rtt)。

Answer 2

（id，time）后面跟一个潜在的analyze table trace的复合索引会使它变得活泼。

这只是一个建议，我不是说这样做。分析表可能需要一些人来运行数百万行。

建议仅基于一个查询创建索引并不是一个好主意。假设是，您有其他疑问。而且它们会拖累插入/更新。

Answer 3

time <= '2015-01-30'排除了1月最后一天的大部分时间;你想要那个吗？这种模式运作良好，避免了许多终端（例如，leapyear）：

WHERE time >= '2015-01-01'
  AND time  < '2015-01-01' + INTERVAL 1 MONTH

如果这是静态数据（例如一次性写入数据仓库），则可以通过构建和维护Summary Tables来使查询更多更快。

Mysql Group按时间间隔优化

3 个答案: