MySQL - 查询获取速度非常慢
[英]MySQL - query fetching very slow
问题描述
CREATE TABLE `offers` (
`id` mediumint(8) unsigned NOT NULL auto_increment,
`cap_id` varchar(255),
`deposit_value` varchar(255) default NULL,
`term` varchar(255) default NULL,
`annual_mileage` varchar(255) default NULL,
`finance_type` varchar(255) default NULL,
`monthly_payment` mediumint default NULL,
`stock` varchar(255) default NULL,
PRIMARY KEY (`id`)
) AUTO_INCREMENT=1;
INSERT INTO `offers` (`cap_id`,`deposit_value`,`term`,`annual_mileage`,`finance_type`,`monthly_payment`,`stock`) VALUES ("93897","6","24","12000","B",363,"0"),("90626","1","24","12000","P",810,"0"),("93607","6","36","25000","B",172,"1"),("35877","1","48","10000","B",678,"1"),("77134","3","24","10000","P",454,"0"),("47080","6","36","10000","B",737,"0"),("46392","3","24","30000","P",261,"0"),("21418","3","36","20000","B",390,"1"),("80175","9","48","10000","B",713,"1"),("91487","6","48","12000","P",778,"1");
INSERT INTO `offers` (`cap_id`,`deposit_value`,`term`,`annual_mileage`,`finance_type`,`monthly_payment`,`stock`) VALUES ("18311","9","24","20000","B",384,"0"),("41740","9","24","12000","P",674,"1"),("69030","9","24","10000","P",518,"0"),("53342","3","36","8000","B",746,"1"),("65566","6","36","30000","P",145,"1"),("63172","6","48","5000","P",698,"1"),("79712","1","48","30000","B",330,"1"),("90505","1","36","8000","B",458,"0"),("42393","1","36","20000","B",363,"1"),("70454","9","24","5000","B",673,"1");
INSERT INTO `offers` (`cap_id`,`deposit_value`,`term`,`annual_mileage`,`finance_type`,`monthly_payment`,`stock`) VALUES ("81215","1","48","8000","B",472,"0"),("76538","3","24","15000","B",226,"0"),("05094","9","36","12000","P",721,"1"),("57363","9","48","5000","B",777,"1"),("23233","1","48","12000","B",381,"0"),("40542","3","48","12000","P",610,"0"),("63824","3","24","12000","B",761,"1"),("17686","3","24","5000","P",893,"1"),("57669","9","48","30000","P",805,"0"),("21864","1","24","25000","P",530,"0");
INSERT INTO `offers` (`cap_id`,`deposit_value`,`term`,`annual_mileage`,`finance_type`,`monthly_payment`,`stock`) VALUES ("48360","3","48","12000","P",159,"0"),("88614","3","48","20000","B",730,"0"),("17693","1","24","10000","B",298,"0"),("34049","6","48","20000","B",728,"0"),("15038","9","24","10000","P",720,"1"),("31809","1","36","20000","P",237,"0"),("49277","9","48","25000","P",235,"1"),("54607","1","24","12000","P",661,"1"),("65098","1","48","20000","P",548,"1"),("76440","9","48","10000","P",495,"1");
CREATE TABLE `offers_lowest` (
`id` mediumint(8) unsigned NOT NULL auto_increment,
`date` DATE,
`cap_id` varchar(255),
`deposit` varchar(255) default NULL,
`term` varchar(255) default NULL,
`mileage` varchar(255) default NULL,
`finance_type` varchar(255) default NULL,
`lowest_price` mediumint default NULL,
`stock` varchar(255) default NULL,
PRIMARY KEY (`id`)
) AUTO_INCREMENT=1;
INSERT INTO `offers_lowest` (`date`,`cap_id`,`deposit`,`term`,`mileage`,`finance_type`,`lowest_price`,`stock`) VALUES ("2021-04-09","93897","6","24","12000","B",363,"0"),("2021-04-010","90626","1","24","12000","P",810,"0"),("2021-04-010","93607","6","36","25000","B",172,"1"),("2021-04-10","35877","1","48","10000","B",678,"1"),("2021-04-11","77134","3","24","10000","P",454,"0"),("2021-04-11","47080","6","36","10000","B",737,"0"),("2021-04-11","46392","3","24","30000","P",261,"0"),("2021-04-11","21418","3","36","20000","B",390,"1"),("2021-04-12","80175","9","48","10000","B",713,"1"),("2021-04-12","91487","6","48","12000","P",778,"1");
INSERT INTO `offers_lowest` (`date`,`cap_id`,`deposit`,`term`,`mileage`,`finance_type`,`lowest_price`,`stock`) VALUES ("2021-04-09","18311","9","24","20000","B",384,"0"),("2021-04-010","41740","9","24","12000","P",674,"1"),("2021-04-010","69030","9","24","10000","P",518,"0"),("2021-04-10","53342","3","36","8000","B",746,"1"),("2021-04-11","65566","6","36","30000","P",145,"1"),("2021-04-11","63172","6","48","5000","P",698,"1"),("2021-04-11","79712","1","48","30000","B",330,"1"),("2021-04-11","90505","1","36","8000","B",458,"0"),("2021-04-12","42393","1","36","20000","B",363,"1"),("2021-04-12","70454","9","24","5000","B",673,"1");
INSERT INTO `offers_lowest` (`date`,`cap_id`,`deposit`,`term`,`mileage`,`finance_type`,`lowest_price`,`stock`) VALUES ("2021-04-09","81215","1","48","8000","B",472,"0"),("2021-04-09","76538","3","24","15000","B",226,"0"),("2021-04-010","05094","9","36","12000","P",721,"1"),("2021-04-10","57363","9","48","5000","B",777,"1"),("2021-04-11","23233","1","48","12000","B",381,"0"),("2021-04-11","40542","3","48","12000","P",610,"0"),("2021-04-11","63824","3","24","12000","B",761,"1"),("2021-04-11","17686","3","24","5000","P",893,"1"),("2021-04-12","57669","9","48","30000","P",805,"0"),("2021-04-12","21864","1","24","25000","P",530,"0");
INSERT INTO `offers_lowest` (`date`,`cap_id`,`deposit`,`term`,`mileage`,`finance_type`,`lowest_price`,`stock`) VALUES ("2021-04-09","48360","3","48","12000","P",159,"0"),("2021-04-09","88614","3","48","20000","B",730,"0"),("2021-04-010","17693","1","24","10000","B",298,"0"),("2021-04-10","34049","6","48","20000","B",728,"0"),("2021-04-11","15038","9","24","10000","P",720,"1"),("2021-04-11","31809","1","36","20000","P",237,"0"),("2021-04-11","49277","9","48","25000","P",235,"1"),("2021-04-11","54607","1","24","12000","P",661,"1"),("2021-04-12","65098","1","48","20000","P",548,"1"),("2021-04-12","76440","9","48","10000","P",495,"1");
CREATE INDEX idx_profile_grouping ON offers (cap_id, deposit_value, term, annual_mileage);
CREATE INDEX idx_specials_query ON offers_lowest (cap_id, deposit, term, mileage);
So, I'm trying to join two tables - one a list of active offers on our platform (we're a car leasing comparison site) and a custom table which records, daily, the best price for every vehicle (cap_id), and every possible finance profile ie, 9 month deposit value, 24 month contract and 8,000 miles per year, etc. etc.
所以，我正在尝试加入两个表 - 一个是我们平台上的活动报价列表（我们是一个汽车租赁比较网站），另一个是自定义表，它每天记录每辆车的最佳价格（cap_id），以及每个可能的财务状况，即 9 个月的存款价值、24 个月的合同和每年 8,000 英里等。
This is with the end goal of identifying what a 'good' price is, with the output containing both MIN(offers.monthly_payment) and MIN(offers_lowest.lowest_price) , and a calculated percentage difference, with the historical price data taken from a 7-day date range not inclusive of the current day.这是为了确定什么是“好”价格的最终目标，output 包含MIN(offers.monthly_payment)和MIN(offers_lowest.lowest_price)以及计算的百分比差异，历史价格数据取自 7 -day 日期范围不包括当天。
offers_lowest is aggregated from offers daily, finding the minimum monthly_payment grouped by: cap_id, deposit_value, term, annual_mileage, finance_type offers_lowest是从每天的offers中汇总的，找到按以下条件分组的最低monthly_payment付款： cap_id, deposit_value, term, annual_mileage, finance_type
This is what I've come up with so far:到目前为止，这是我想出的：
SELECT
s.cap_id,
s.deposit,
s.term,
s.mileage,
s.best_price,
f.previous_best,
(( s.best_price - f.previous_best ) / f.previous_best ) * 100 AS difference,
s.stock
FROM
(SELECT
o.cap_id,
o.deposit_value as deposit,
o.term,
o.annual_mileage as mileage,
o.finance_type,
MIN(o.monthly_payment) AS best_price,
o.stock,
o.brand_id
FROM
offers o USE INDEX(idx_profile_grouping)
WHERE o.finance_type = 'P'
GROUP BY o.cap_id, o.deposit_value, o.term, o.annual_mileage
) s
INNER JOIN
(SELECT
ol.cap_id,
ol.deposit,
ol.term,
ol.mileage,
MIN(lowest_price) as previous_best
FROM
offers_lowest ol USE INDEX(idx_specials_query)
WHERE finance_type = 'P'
AND ol.date > CURDATE() - INTERVAL 7 DAY
AND ol.date <= CURDATE() - INTERVAL 1 DAY
GROUP BY ol.cap_id, ol.deposit, ol.term, ol.mileage
) f ON s.cap_id = f.cap_id AND s.deposit = f.deposit AND s.term = f.term AND s.mileage = f.mileage
GROUP BY s.cap_id, s.deposit, s.term, s.mileage
The issue is with speed.问题在于速度。 When I run either one of the subqueries individually, it takes <0.1s to run, then around 75 seconds to fetch.当我单独运行任一子查询时，运行时间 <0.1 秒，然后获取大约 75 秒。 When I run the whole thing, it runs until I stop it (>10 minutes).当我运行整个过程时，它会一直运行直到我停止它（>10 分钟）。
The indexes are ones I created over the cap_id, deposit, term and mileage fields (both tables) in an attempt to speed things up.这些索引是我在 cap_id、deposit、term 和 mileage 字段（两个表）上创建的，以试图加快速度。
EXPLAIN returns this:解释返回这个：
| # id ＃ ID |
select_type选择类型 |
table桌子 |
partitions分区 |
type类型 |
possible_keys可能的键 |
key钥匙 |
key_len key_len |
ref参考 |
rows行 |
filtered过滤 |
Extra额外的 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 1 |
PRIMARY基本的 |
ALL全部 |
965741 965741 |
100.00 100.00 |
Using where;使用哪里； Using temporary;使用临时的； Using filesort使用文件排序 |
||||||
| 1 1 |
PRIMARY基本的 |
ref参考 |
<auto_key0> <auto_key0> |
<auto_key0> <auto_key0> |
10 10 |
f.cap_id f.cap_id |
f.deposit f.存款 |
f.term f.术语 |
f.mileage f.里程 |
||
| 3 3 |
DERIVED衍生的 |
ol奥尔 |
index指数 |
idx_specials_query idx_specials_query |
idx_specials_query idx_specials_query |
13 13 |
17386818 17386818 |
5.55 5.55 |
Using where使用哪里 |
||
| 2 2 |
DERIVED衍生的 |
o ○ |
index指数 |
idx_profile_grouping idx_profile_grouping |
idx_profile_grouping idx_profile_grouping |
10 10 |
4800964 4800964 |
50.00 50.00 |
Using where使用哪里 |
And SHOW PROFILE returns this:并且 SHOW PROFILE 返回这个：
| Status地位 |
Duration期间 |
|---|---|
| starting开始 |
0.000017 0.000017 |
| checking query cache for query检查查询缓存以进行查询 |
0.000171 0.000171 |
| checking permissions检查权限 |
0.000006 0.000006 |
| checking permissions检查权限 |
0.000005 0.000005 |
| Opening tables打开表 |
0.000020 0.000020 |
| init在里面 |
0.000109 0.000109 |
| System lock系统锁 |
0.000009 0.000009 |
| optimizing优化 |
0.000005 0.000005 |
| optimizing优化 |
0.000011 0.000011 |
| statistics统计数据 |
0.000033 0.000033 |
| preparing准备 |
0.000037 0.000037 |
| Sorting result排序结果 |
0.000007 0.000007 |
| optimizing优化 |
0.000015 0.000015 |
| statistics统计数据 |
0.000017 0.000017 |
| preparing准备 |
0.000017 0.000017 |
| Sorting result排序结果 |
0.000012 0.000012 |
| statistics统计数据 |
0.000046 0.000046 |
| preparing准备 |
0.000021 0.000021 |
| Creating tmp table创建 tmp 表 |
0.000020 0.000020 |
| Sorting result排序结果 |
0.000007 0.000007 |
| executing执行 |
0.000013 0.000013 |
| Sending data发送数据 |
0.000012 0.000012 |
| executing执行 |
0.000004 0.000004 |
| Sending data发送数据 |
68.205032 68.205032 |
| converting HEAP to ondisk将 HEAP 转换为磁盘 |
0.542756 0.542756 |
| Sending data发送数据 |
3.132642 3.132642 |
| executing执行 |
0.000019 0.000019 |
| Sending data发送数据 |
7.968966 7.968966 |
| converting HEAP to ondisk将 HEAP 转换为磁盘 |
0.746200 0.746200 |
| Sending data发送数据 |
22.506716 22.506716 |
| converting HEAP to ondisk将 HEAP 转换为磁盘 |
0.567816 0.567816 |
| Sending data发送数据 |
4.886870 4.886870 |
| Creating sort index创建排序索引 |
2.151519 2.151519 |
| end结尾 |
0.000019 0.000019 |
| query end查询结束 |
0.000010 0.000010 |
| removing tmp table删除 tmp 表 |
0.002051 0.002051 |
| query end查询结束 |
0.000019 0.000019 |
| closing tables收盘 |
0.000005 0.000005 |
| removing tmp table删除 tmp 表 |
0.001385 0.001385 |
| closing tables收盘 |
0.000009 0.000009 |
| removing tmp table删除 tmp 表 |
0.002780 0.002780 |
| closing tables收盘 |
0.000017 0.000017 |
| freeing items释放物品 |
0.000069 0.000069 |
| cleaned up清理干净 |
0.000007 0.000007 |
| cleaning up打扫干净 |
0.000021 0.000021 |
It seems from this that I've not written the most efficient query..由此看来，我还没有写出最有效的查询..
We use an AWS RDS instance, running MySQL 5.7.12.我们使用 AWS RDS 实例，运行 MySQL 5.7.12。
If there's anything additional I can provide to add background, please let me know.如果我可以提供任何其他内容来添加背景，请告诉我。
1 个解决方案
解决方案1
1 2021-04-12 16:40:09
FROM ( SELECT... ) JOIN ( SELECT... )is inefficient.FROM ( SELECT... ) JOIN ( SELECT... )效率低下。 Try to move at least one of them into the outerSELECT.尝试将其中至少一个移到外部SELECT中。 (This may eliminate "Creating sort index" if you add a suitable index for theJOIN. It may also eliminate most of the cryptic "sending data".)（如果您为JOIN添加合适的索引，这可能会消除“创建排序索引”。它也可能会消除大多数神秘的“发送数据”。）- That date range includes only 6 DATEs.该日期范围仅包括 6 个 DATE。
- What if one row has MIN(offers.monthly_payment) and a different row has MIN(offers_lowest.lowest_price)?如果一行有 MIN(offers.monthly_payment) 而另一行有 MIN(offers_lowest.lowest_price) 怎么办？
- Don't use VARCHAR(255) for numeric values.不要将 VARCHAR(255) 用于数值。 Eg, MEDIUMINT for mileage.例如，MEDIUMINT 表示里程。 (This may eliminate "converting HEAP to ondisk".) （这可能会消除“将 HEAP 转换为磁盘”。）
- Note how useless PROFILE is;注意 PROFILE 是多么无用； it likes to say "sending data" for most of the time (95% of total).大多数时候它喜欢说“发送数据”（占总数的 95%）。 I could identify 4% and recommend fixes, see above.我可以确定 4% 并建议修复，见上文。 The other 1% is unavoidable noise.另外 1% 是不可避免的噪音。
- The first
GROUP BYis inappropriate since it does not say which value to give for stock or brand_id.第一个GROUP BY是不合适的，因为它没有说明为 stock 或 brand_id 赋予哪个值。 If necessary, grab them with an extraJOINafter finding the rest of the info.如有必要，在找到信息的 rest 后，通过额外的JOIN获取它们。 -
offers_lowestneedsINDEX(finance_type, date)offers_lowest需求INDEX(finance_type, date) - Get rid of "index hints" (
USE INDEX...)摆脱“索引提示”（ USE INDEX...）
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