抱歉，我无法在标题中更具体。
所以我得到了这个问题：
CREATE TABLE RecordPoints AS (
SELECT competitionId, personId, personCountryId, eventId, year, date,
if(regionalAverageRecord = 'WR',
(SELECT COUNT(DISTINCT personId) FROM ResultDates rd
WHERE rd.eventId=rd2.eventId AND rd.date <= rd2.date AND rd.average > 0), 0) wrAveragePoints,
if(regionalSingleRecord = 'WR',
(SELECT COUNT(DISTINCT personId) FROM ResultDates rd
WHERE rd.eventId=rd2.eventId AND rd.date <= rd2.date), 0) wrSinglePoints,
if(NOT regionalAverageRecord in('WR', 'NR'),
(SELECT COUNT(DISTINCT personId) FROM ResultDates rd
WHERE rd.eventId=rd2.eventId AND rd.date <= rd2.date AND average > 0 AND rd.personCountryId in
(SELECT Countries.id FROM Countries JOIN Continents on Countries.continentId=Continents.id where recordName = rd2.regionalAverageRecord)), 0) crAveragePoints,
if(NOT regionalAverageRecord in('WR', 'NR'),
(SELECT COUNT(DISTINCT personId) FROM ResultDates rd
WHERE rd.eventId=rd2.eventId AND rd.date <= rd2.date AND rd.personCountryId in
(SELECT Countries.id FROM Countries JOIN Continents on Countries.continentId=Continents.id where recordName = rd2.regionalSingleRecord)), 0) crSinglePoints,
if(regionalAverageRecord = 'NR',
(SELECT COUNT(DISTINCT personId) FROM ResultDates rd
WHERE rd.eventId=rd2.eventId AND rd.date <= rd2.date AND rd.personCountryId=rd2.personCountryId AND rd.average > 0 ), 0) nrAveragePoints,
if(regionalSingleRecord = 'NR',
(SELECT COUNT(DISTINCT personId) FROM ResultDates rd
WHERE rd.eventId=rd2.eventId AND rd.date <= rd2.date AND rd.personCountryId=rd2.personCountryId), 0) nrSinglePoints
FROM ResultDates rd2 WHERE (NOT regionalAverageRecord='' OR NOT regionalSingleRecord = ''));
花了9个小时才完成。为了打破它，我正在创建一个表，其中6列是完整的子查询，以计算一个personId出现在同一个表中的次数，然后我根据日期和一些事情看到的第一件事情发生了其他专栏。
使用CREATE INDEX date ON ResultDates (date)创建一个日期索引我认为加快了一点，但它仍然需要花费大量的时间。
ResultDates中的行看起来像
+------------+-----------------+---------------+---------+---------+-----+---------+----------------------+-----------------------+-------+-----+------+------------+
| personId | personCountryId | competitionId | eventId | roundId | pos | average | regionalSingleRecord | regionalAverageRecord | month | day | year | date |
+------------+-----------------+---------------+---------+---------+-----+---------+----------------------+-----------------------+-------+-----+------+------------+
| 1982THAI01 | USA | WC1982 | 333 | f | 1 | 0 | WR | | 6 | 5 | 1982 | 1982-06-05 |
+------------+-----------------+---------------+---------+---------+-----+---------+----------------------+-----------------------+-------+-----+------+------------+
其中regionalSingleRecord和regionalAverageRecord可以是这些“RecordNames”中的任何一个：WR，NR，大部分时间都没有，或AfR，AsR，ER，NAR，OcR或SAR然后我用来查找countryId基于那些recordNames连接到哪个大陆。
我创建了索引来将这些recordNames连接到洲际和大陆ID到countryIds，但不确定这有多少提高了速度。
运行EXPLAIN就可以了：
+----+--------------------+------------+------------+------+-------------------+--------------+---------+----------------------------------+--------+----------+---------------------------------------------------------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+--------------------+------------+------------+------+-------------------+--------------+---------+----------------------------------+--------+----------+---------------------------------------------------------------+
| 1 | PRIMARY | rd2 | NULL | ref | idx_personId | idx_personId | 32 | const | 567 | 99.00 | Using where |
| 9 | DEPENDENT SUBQUERY | rd | NULL | ALL | date,idx_personId | NULL | NULL | NULL | 992294 | 0.33 | Range checked for each record (index map: 0x3) |
| 8 | DEPENDENT SUBQUERY | rd | NULL | ALL | date,idx_personId | NULL | NULL | NULL | 992294 | 0.11 | Range checked for each record (index map: 0x3) |
| 6 | DEPENDENT SUBQUERY | Continents | NULL | ref | P_id,recordIndex | recordIndex | 9 | cubing.rd2.regionalSingleRecord | 1 | 100.00 | Using index; Start temporary |
| 6 | DEPENDENT SUBQUERY | Countries | NULL | ALL | NULL | NULL | NULL | NULL | 203 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| 6 | DEPENDENT SUBQUERY | rd | NULL | ALL | date | NULL | NULL | NULL | 992294 | 0.33 | Range checked for each record (index map: 0x1); End temporary |
| 4 | DEPENDENT SUBQUERY | Continents | NULL | ref | P_id,recordIndex | recordIndex | 9 | cubing.rd2.regionalAverageRecord | 1 | 100.00 | Using index; Start temporary |
| 4 | DEPENDENT SUBQUERY | Countries | NULL | ALL | NULL | NULL | NULL | NULL | 203 | 10.00 | Using where; Using join buffer (Block Nested Loop) |
| 4 | DEPENDENT SUBQUERY | rd | NULL | ALL | date | NULL | NULL | NULL | 992294 | 0.11 | Range checked for each record (index map: 0x1); End temporary |
| 3 | DEPENDENT SUBQUERY | rd | NULL | ALL | date,idx_personId | NULL | NULL | NULL | 992294 | 3.33 | Range checked for each record (index map: 0x3) |
| 2 | DEPENDENT SUBQUERY | rd | NULL | ALL | date,idx_personId | NULL | NULL | NULL | 992294 | 1.11 | Range checked for each record (index map: 0x3) |
+----+--------------------+------------+------------+------+-------------------+--------------+---------+----------------------------------+--------+----------+---------------------------------------------------------------+
我一直在谷歌搜索如何提高它的速度。根据我的谷歌搜索，我知道它看起来不太好。特别是我正在查看的初始表中的992294行。
我的问题是，我不知道如何进行优化以使所有这些更快。我已经读过精心设计的索引可以提高速度，所以我很好奇这里可以使用哪种索引。
答案 0 :(得分：0)
select子句中的子查询可能非常昂贵。相关的子查询通常表现不佳，通常有更好的选择。
我没有时间给出一个彻底的答案，但是我通过略读查询的一般印象是，您可以在主查询中将其重构为JOIN ResultDates到一次;然后在SELECT子句中使用条件聚合。像这样......
SELECT rd.competitionId, rd.personId, rd.personCountryId, rd.eventId
, rd.year, rd.date
, COUNT(DISTINCT IF(rd.regionalAverageRecord = 'WR' AND rdPrev.average > 0, rdPrev.person_id, NULL) AS wrAveragePoints
, COUNT(DISTINCT IF(regionalSingleRecord = 'WR', rdPrev.person_id, NULL) AS wrSinglePoints
, [etc....]
FROM ResultDates AS rd
LEFT JOIN ResultDates AS rdPrev
ON rd.eventId=rdPrev.eventId
AND rdPrev.date <= rd.date
WHERE (NOT rd.regionalAverageRecord='' OR NOT rd.regionalSingleRecord = '')
;
编辑：对于涉及Countries和Continents表的子查询/字段，您也可以只LEFT JOIN这些表，并以类似的方式使用连接值至于我如何在rdPrev.average计算中使用wrAveragePoints。
注意：COUNT()和大多数其他聚合函数忽略NULL值。