Django中prefetch_related()函數(shù)優(yōu)化實(shí)戰(zhàn)指南

更新時(shí)間：2022年11月02日 10:32:10 作者：wy0925

我們可以利用Django框架中select_related和prefetch_related函數(shù)對(duì)數(shù)據(jù)庫(kù)查詢(xún)優(yōu)化,這篇文章主要給大家介紹了關(guān)于Django中prefetch_related()函數(shù)優(yōu)化的相關(guān)資料,需要的朋友可以參考下

前言

對(duì)于多對(duì)多字段(ManyToManyField)和一對(duì)多字段, 可以使用prefetch_related()來(lái)進(jìn)行優(yōu)化

prefetch_related()和select_related()的設(shè)計(jì)目的很相似，都是為了減少SQL查詢(xún)的數(shù)量，但是實(shí)現(xiàn)的方式不一樣。后者是通過(guò)JOIN語(yǔ)句，在SQL查詢(xún)內(nèi)解決問(wèn)題。但是對(duì)于多對(duì)多關(guān)系，使用SQL語(yǔ)句解決就顯得有些不太明智，因?yàn)镴OIN得到的表將會(huì)很長(zhǎng)，會(huì)導(dǎo)致SQL語(yǔ)句運(yùn)行時(shí)間的增加和內(nèi)存占用的增加。若有n個(gè)對(duì)象，每個(gè)對(duì)象的多對(duì)多字段對(duì)應(yīng)Mi條，就會(huì)生成Σ(n)Mi 行的結(jié)果表。prefetch_related()的解決方法是，分別查詢(xún)每個(gè)表，然后用Python處理他們之間的關(guān)系。繼續(xù)以上邊的例子進(jìn)行說(shuō)明，如果我們要獲得張三所有去過(guò)的城市，使用prefetch_related()應(yīng)該是這么做：

zhangs = Person.objects.prefetch_related('visitation').get(firstname=u"張",lastname=u"三")
>>> for city in zhangs.visitation.all() :
...   print city

上述代碼觸發(fā)的SQL查詢(xún)?nèi)缦拢?/p>

SELECT `QSOptimize_person`.`id`, `QSOptimize_person`.`firstname`,
`QSOptimize_person`.`lastname`, `QSOptimize_person`.`hometown_id`, `QSOptimize_person`.`living_id`
FROM `QSOptimize_person`
WHERE (`QSOptimize_person`.`lastname` = '三'  AND `QSOptimize_person`.`firstname` = '張');
  
SELECT (`QSOptimize_person_visitation`.`person_id`) AS `_prefetch_related_val`, `QSOptimize_city`.`id`,
`QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE `QSOptimize_person_visitation`.`person_id` IN (1);

第一條SQL查詢(xún)僅僅是獲取張三的Person對(duì)象，第二條比較關(guān)鍵，它選取關(guān)系表`QSOptimize_person_visitation`中`person_id`為張三的行，然后和`city`表內(nèi)聯(lián)（INNER JOIN 也叫等值連接）得到結(jié)果表。

+----+-----------+----------+-------------+-----------+
| id | firstname | lastname | hometown_id | living_id |
+----+-----------+----------+-------------+-----------+
| 1 | 張 | 三 | 3 | 1 |
+----+-----------+----------+-------------+-----------+
1 row in set (0.00 sec)
 
+-----------------------+----+-----------+-------------+
| _prefetch_related_val | id | name | province_id |
+-----------------------+----+-----------+-------------+
| 1 | 1 | 武漢市 | 1 |
| 1 | 2 | 廣州市 | 2 |
| 1 | 3 | 十堰市 | 1 |
+-----------------------+----+-----------+-------------+
3 rows in set (0.00 sec)

顯然張三武漢、廣州、十堰都去過(guò)。

又或者，我們要獲得湖北的所有城市名，可以這樣：

>>> hb = Province.objects.prefetch_related('city_set').get(name__iexact=u"湖北省")
>>> for city in hb.city_set.all():
... city.name
...

觸發(fā)的SQL查詢(xún)：

SELECT `QSOptimize_province`.`id`, `QSOptimize_province`.`name`
FROM `QSOptimize_province`
WHERE `QSOptimize_province`.`name` LIKE '湖北省' ;
 
SELECT `QSOptimize_city`.`id`, `QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
WHERE `QSOptimize_city`.`province_id` IN (1);

得到的表：

+----+-----------+
| id | name      |
+----+-----------+
|  1 | 湖北省    |
+----+-----------+
1 row in set (0.00 sec)
 
+----+-----------+-------------+
| id | name      | province_id |
+----+-----------+-------------+
|  1 | 武漢市    |           1 |
|  3 | 十堰市    |           1 |
+----+-----------+-------------+
2 rows in set (0.00 sec)

我們可以看見(jiàn)，prefetch使用的是 IN 語(yǔ)句實(shí)現(xiàn)的。這樣，在QuerySet中的對(duì)象數(shù)量過(guò)多的時(shí)候，根據(jù)數(shù)據(jù)庫(kù)特性的不同有可能造成性能問(wèn)題。

使用方法

*lookups 參數(shù)

prefetch_related()在Django < 1.7 只有這一種用法。和select_related()一樣，prefetch_related()也支持深度查詢(xún)，例如要獲得所有姓張的人去過(guò)的?。?/p>

>>> zhangs = Person.objects.prefetch_related('visitation__province').filter(firstname__iexact=u'張')
>>> for i in zhangs:
... for city in i.visitation.all():
... print city.province
...

觸發(fā)的SQL：

SELECT `QSOptimize_person`.`id`, `QSOptimize_person`.`firstname`,
`QSOptimize_person`.`lastname`, `QSOptimize_person`.`hometown_id`, `QSOptimize_person`.`living_id`
FROM `QSOptimize_person`
WHERE `QSOptimize_person`.`firstname` LIKE '張' ;
 
SELECT (`QSOptimize_person_visitation`.`person_id`) AS `_prefetch_related_val`, `QSOptimize_city`.`id`,
`QSOptimize_city`.`name`, `QSOptimize_city`.`province_id` FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE `QSOptimize_person_visitation`.`person_id` IN (1, 4);
 
SELECT `QSOptimize_province`.`id`, `QSOptimize_province`.`name`
FROM `QSOptimize_province`
WHERE `QSOptimize_province`.`id` IN (1, 2);

獲得的結(jié)果：

+----+-----------+----------+-------------+-----------+
| id | firstname | lastname | hometown_id | living_id |
+----+-----------+----------+-------------+-----------+
|  1 | 張        | 三       |           3 |         1 |
|  4 | 張        | 六       |           2 |         2 |
+----+-----------+----------+-------------+-----------+
2 rows in set (0.00 sec)
 
+-----------------------+----+-----------+-------------+
| _prefetch_related_val | id | name      | province_id |
+-----------------------+----+-----------+-------------+
|                     1 |  1 | 武漢市    |           1 |
|                     1 |  2 | 廣州市    |           2 |
|                     4 |  2 | 廣州市    |           2 |
|                     1 |  3 | 十堰市    |           1 |
+-----------------------+----+-----------+-------------+
4 rows in set (0.00 sec)
 
+----+-----------+
| id | name      |
+----+-----------+
|  1 | 湖北省    |
|  2 | 廣東省    |
+----+-----------+
2 rows in set (0.00 sec)

值得一提的是，鏈?zhǔn)絧refetch_related會(huì)將這些查詢(xún)添加起來(lái)，就像1.7中的select_related那樣。

要注意的是，在使用QuerySet的時(shí)候，一旦在鏈?zhǔn)讲僮髦懈淖兞藬?shù)據(jù)庫(kù)請(qǐng)求，之前用prefetch_related緩存的數(shù)據(jù)將會(huì)被忽略掉。這會(huì)導(dǎo)致Django重新請(qǐng)求數(shù)據(jù)庫(kù)來(lái)獲得相應(yīng)的數(shù)據(jù)，從而造成性能問(wèn)題。這里提到的改變數(shù)據(jù)庫(kù)請(qǐng)求指各種filter()、exclude()等等最終會(huì)改變SQL代碼的操作。而all()并不會(huì)改變最終的數(shù)據(jù)庫(kù)請(qǐng)求，因此是不會(huì)導(dǎo)致重新請(qǐng)求數(shù)據(jù)庫(kù)的。

舉個(gè)例子，要獲取所有人訪問(wèn)過(guò)的城市中帶有“市”字的城市，這樣做會(huì)導(dǎo)致大量的SQL查詢(xún)：

plist =Person.objects.prefetch_related('visitation')
[p.visitation.filter(name__icontains=u"市")for p in plist]

因?yàn)閿?shù)據(jù)庫(kù)中有4人，導(dǎo)致了2+4次SQL查詢(xún)：

SELECT `QSOptimize_person`.`id`, `QSOptimize_person`.`firstname`, `QSOptimize_person`.`lastname`,
`QSOptimize_person`.`hometown_id`, `QSOptimize_person`.`living_id`
FROM `QSOptimize_person`;
 
SELECT (`QSOptimize_person_visitation`.`person_id`) AS `_prefetch_related_val`, `QSOptimize_city`.`id`,
`QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE `QSOptimize_person_visitation`.`person_id` IN (1, 2, 3, 4);
 
SELECT `QSOptimize_city`.`id`, `QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE(`QSOptimize_person_visitation`.`person_id` = 1 AND `QSOptimize_city`.`name` LIKE '%市%' );
 
SELECT `QSOptimize_city`.`id`, `QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE (`QSOptimize_person_visitation`.`person_id` = 2 AND `QSOptimize_city`.`name` LIKE '%市%' );
 
SELECT `QSOptimize_city`.`id`, `QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE (`QSOptimize_person_visitation`.`person_id` = 3 AND `QSOptimize_city`.`name` LIKE '%市%' );
 
SELECT `QSOptimize_city`.`id`, `QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE (`QSOptimize_person_visitation`.`person_id` = 4 AND `QSOptimize_city`.`name` LIKE '%市%' );

眾所周知，QuerySet是lazy的，要用的時(shí)候才會(huì)去訪問(wèn)數(shù)據(jù)庫(kù)。運(yùn)行到第二行Python代碼時(shí)，for循環(huán)將plist看做iterator，這會(huì)觸發(fā)數(shù)據(jù)庫(kù)查詢(xún)。最初的兩次SQL查詢(xún)就是prefetch_related導(dǎo)致的。

雖然已經(jīng)查詢(xún)結(jié)果中包含所有所需的city的信息，但因?yàn)樵谘h(huán)體中對(duì)Person.visitation進(jìn)行了filter操作，這顯然改變了數(shù)據(jù)庫(kù)請(qǐng)求。因此這些操作會(huì)忽略掉之前緩存到的數(shù)據(jù)，重新進(jìn)行SQL查詢(xún)。

但是如果有這樣的需求了應(yīng)該怎么辦呢？在Django >= 1.7，可以通過(guò)下一節(jié)的Prefetch對(duì)象來(lái)實(shí)現(xiàn)，如果你的環(huán)境是Django < 1.7，可以在Python中完成這部分操作。

plist = Person.objects.prefetch_related('visitation')
[[city for city in p.visitation.all() if u"市" in city.name] for p in plist]

Prefetch對(duì)象

在Django >= 1.7，可以用Prefetch對(duì)象來(lái)控制prefetch_related函數(shù)的行為。

1.一個(gè)Prefetch對(duì)象只能指定一項(xiàng)prefetch操作。

2.Prefetch對(duì)象對(duì)字段指定的方式和prefetch_related中的參數(shù)相同，都是通過(guò)雙下劃線連接的字段名完成的。

3.可以通過(guò) queryset 參數(shù)手動(dòng)指定prefetch使用的QuerySet。

4.可以通過(guò) to_attr 參數(shù)指定prefetch到的屬性名。

5.Prefetch對(duì)象和字符串形式指定的lookups參數(shù)可以混用。

最佳實(shí)踐

1.prefetch_related主要針一對(duì)多和多對(duì)多關(guān)系進(jìn)行優(yōu)化。

2.prefetch_related通過(guò)分別獲取各個(gè)表的內(nèi)容，然后用Python處理他們之間的關(guān)系來(lái)進(jìn)行優(yōu)化。

3.可以通過(guò)可變長(zhǎng)參數(shù)指定需要select_related的字段名。指定方式和特征與select_related是相同的。

4.在Django >= 1.7可以通過(guò)Prefetch對(duì)象來(lái)實(shí)現(xiàn)復(fù)雜查詢(xún)，但低版本的Django好像只能自己實(shí)現(xiàn)。

5.作為prefetch_related的參數(shù)，Prefetch對(duì)象和字符串可以混用。

6.prefetch_related的鏈?zhǔn)秸{(diào)用會(huì)將對(duì)應(yīng)的prefetch添加進(jìn)去，而非替換，似乎沒(méi)有基于不同版本上區(qū)別。

7.可以通過(guò)傳入None來(lái)清空之前的prefetch_related。

選擇哪個(gè)函數(shù)

如果我們想要獲得所有家鄉(xiāng)是湖北的人，最無(wú)腦的做法是先獲得湖北省，再獲得湖北的所有城市，最后獲得故鄉(xiāng)是這個(gè)城市的人。就像這樣：

>>> hb = Province.objects.get(name__iexact=u"湖北省")
>>> people = []
>>> for city in hb.city_set.all():
... people.extend(city.birth.all())
...

顯然這不是一個(gè)明智的選擇，因?yàn)檫@樣做會(huì)導(dǎo)致1+（湖北省城市數(shù)）次SQL查詢(xún)。反正是個(gè)反例，導(dǎo)致的查詢(xún)和獲得掉結(jié)果就不列出來(lái)了。prefetch_related() 或許是一個(gè)好的解決方法，讓我們來(lái)看看。

>>> hb = Province.objects.prefetch_related("city_set__birth").objects.get(name__iexact=u"湖北省")
>>> people = []
>>> for city in hb.city_set.all():
... people.extend(city.birth.all())
...

因?yàn)槭且粋€(gè)深度為2的prefetch，所以會(huì)導(dǎo)致3次SQL查詢(xún)：

SELECT `QSOptimize_province`.`id`, `QSOptimize_province`.`name`
FROM `QSOptimize_province`
WHERE `QSOptimize_province`.`name` LIKE '湖北省' ;
 
SELECT `QSOptimize_city`.`id`, `QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
WHERE `QSOptimize_city`.`province_id` IN (1);
 
SELECT `QSOptimize_person`.`id`, `QSOptimize_person`.`firstname`, `QSOptimize_person`.`lastname`,
`QSOptimize_person`.`hometown_id`, `QSOptimize_person`.`living_id`
FROM `QSOptimize_person`
WHERE `QSOptimize_person`.`hometown_id` IN (1, 3);

嗯…看上去不錯(cuò)，但是3次查詢(xún)么？倒過(guò)來(lái)查詢(xún)可能會(huì)更簡(jiǎn)單？

>>> people = list(Person.objects.select_related("hometown__province").filter(hometown__province__name__iexact=u"湖北省"))

SELECT `QSOptimize_person`.`id`, `QSOptimize_person`.`firstname`, `QSOptimize_person`.`lastname`,
`QSOptimize_person`.`hometown_id`, `QSOptimize_person`.`living_id`, `QSOptimize_city`.`id`,
`QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`, `QSOptimize_province`.`id`, `QSOptimize_province`.`name`
FROM `QSOptimize_person`
INNER JOIN `QSOptimize_city` ON (`QSOptimize_person`.`hometown_id` = `QSOptimize_city`.`id`)
INNER JOIN `QSOptimize_province` ON (`QSOptimize_city`.`province_id` = `QSOptimize_province`.`id`)
WHERE `QSOptimize_province`.`name` LIKE '湖北省';

+----+-----------+----------+-------------+-----------+----+--------+-------------+----+--------+
| id | firstname | lastname | hometown_id | living_id | id | name | province_id | id | name |
+----+-----------+----------+-------------+-----------+----+--------+-------------+----+--------+
| 1 | 張 | 三 | 3 | 1 | 3 | 十堰市 | 1 | 1 | 湖北省 |
| 2 | 李 | 四 | 1 | 3 | 1 | 武漢市 | 1 | 1 | 湖北省 |
| 3 | 王 | 麻子 | 3 | 2 | 3 | 十堰市 | 1 | 1 | 湖北省 |
+----+-----------+----------+-------------+-----------+----+--------+-------------+----+--------+
3 rows in set (0.00 sec)

完全沒(méi)問(wèn)題。不僅SQL查詢(xún)的數(shù)量減少了，python程序上也精簡(jiǎn)了。select_related()的效率要高于prefetch_related()。因此，最好在能用select_related()的地方盡量使用它，也就是說(shuō)，對(duì)于ForeignKey字段，避免使用prefetch_related()。對(duì)于同一個(gè)QuerySet，你可以同時(shí)使用這兩個(gè)函數(shù)。在我們一直使用的例子上加一個(gè)model：Order （訂單）

class Order(models.Model):
customer = models.ForeignKey(Person)
orderinfo = models.CharField(max_length=50)
time = models.DateTimeField(auto_now_add = True)
def __unicode__(self):
return self.orderinfo

如果我們拿到了一個(gè)訂單的id 我們要知道這個(gè)訂單的客戶去過(guò)的省份。因?yàn)橛蠱anyToManyField顯然必須要用prefetch_related()。如果只用prefetch_related()會(huì)怎樣呢？

>>> plist = Order.objects.prefetch_related('customer__visitation__province').get(id=1)
>>> for city in plist.customer.visitation.all():
... print city.province.name
...

顯然，關(guān)系到了4個(gè)表：Order、Person、City、Province，根據(jù)prefetch_related()的特性就得有4次SQL查詢(xún)

SELECT `QSOptimize_order`.`id`, `QSOptimize_order`.`customer_id`, `QSOptimize_order`.`orderinfo`, `QSOptimize_order`.`time`
FROM `QSOptimize_order`
WHERE `QSOptimize_order`.`id` = 1 ;
 
SELECT `QSOptimize_person`.`id`, `QSOptimize_person`.`firstname`, `QSOptimize_person`.`lastname`, `QSOptimize_person`.`hometown_id`, `QSOptimize_person`.`living_id`
FROM `QSOptimize_person`
WHERE `QSOptimize_person`.`id` IN (1);
 
SELECT (`QSOptimize_person_visitation`.`person_id`) AS `_prefetch_related_val`, `QSOptimize_city`.`id`,
`QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE `QSOptimize_person_visitation`.`person_id` IN (1);
 
SELECT `QSOptimize_province`.`id`, `QSOptimize_province`.`name`
FROM `QSOptimize_province`
WHERE `QSOptimize_province`.`id` IN (1, 2);

+----+-------------+---------------+---------------------+
| id | customer_id | orderinfo     | time                |
+----+-------------+---------------+---------------------+
|  1 |           1 | Info of Order | 2014-08-10 17:05:48 |
+----+-------------+---------------+---------------------+
1 row in set (0.00 sec)
 
+----+-----------+----------+-------------+-----------+
| id | firstname | lastname | hometown_id | living_id |
+----+-----------+----------+-------------+-----------+
|  1 | 張        | 三       |           3 |         1 |
+----+-----------+----------+-------------+-----------+
1 row in set (0.00 sec)
 
+-----------------------+----+--------+-------------+
| _prefetch_related_val | id | name   | province_id |
+-----------------------+----+--------+-------------+
|                     1 |  1 | 武漢市 |           1 |
|                     1 |  2 | 廣州市 |           2 |
|                     1 |  3 | 十堰市 |           1 |
+-----------------------+----+--------+-------------+
3 rows in set (0.00 sec)
 
+----+--------+
| id | name   |
+----+--------+
|  1 | 湖北省 |
|  2 | 廣東省 |
+----+--------+
2 rows in set (0.00 sec)

更好的辦法是先調(diào)用一次select_related()再調(diào)用prefetch_related()，最后再select_related()后面的表

>>> plist = Order.objects.select_related('customer').prefetch_related('customer__visitation__province').get(id=1)
>>> for city in plist.customer.visitation.all():
... print city.province.name
...

這樣只會(huì)有3次SQL查詢(xún)，Django會(huì)先做select_related，之后prefetch_related的時(shí)候會(huì)利用之前緩存的數(shù)據(jù)，從而避免了1次額外的SQL查詢(xún)：

SELECT `QSOptimize_order`.`id`, `QSOptimize_order`.`customer_id`, `QSOptimize_order`.`orderinfo`,
`QSOptimize_order`.`time`, `QSOptimize_person`.`id`, `QSOptimize_person`.`firstname`,
`QSOptimize_person`.`lastname`, `QSOptimize_person`.`hometown_id`, `QSOptimize_person`.`living_id`
FROM `QSOptimize_order`
INNER JOIN `QSOptimize_person` ON (`QSOptimize_order`.`customer_id` = `QSOptimize_person`.`id`)
WHERE `QSOptimize_order`.`id` = 1 ;
 
SELECT (`QSOptimize_person_visitation`.`person_id`) AS `_prefetch_related_val`, `QSOptimize_city`.`id`,
`QSOptimize_city`.`name`, `QSOptimize_city`.`province_id`
FROM `QSOptimize_city`
INNER JOIN `QSOptimize_person_visitation` ON (`QSOptimize_city`.`id` = `QSOptimize_person_visitation`.`city_id`)
WHERE `QSOptimize_person_visitation`.`person_id` IN (1);
 
SELECT `QSOptimize_province`.`id`, `QSOptimize_province`.`name`
FROM `QSOptimize_province`
WHERE `QSOptimize_province`.`id` IN (1, 2);

+----+-------------+---------------+---------------------+----+-----------+----------+-------------+-----------+
| id | customer_id | orderinfo     | time                | id | firstname | lastname | hometown_id | living_id |
+----+-------------+---------------+---------------------+----+-----------+----------+-------------+-----------+
|  1 |           1 | Info of Order | 2014-08-10 17:05:48 |  1 | 張        | 三       |           3 |         1 |
+----+-------------+---------------+---------------------+----+-----------+----------+-------------+-----------+
1 row in set (0.00 sec)
 
+-----------------------+----+--------+-------------+
| _prefetch_related_val | id | name   | province_id |
+-----------------------+----+--------+-------------+
|                     1 |  1 | 武漢市 |           1 |
|                     1 |  2 | 廣州市 |           2 |
|                     1 |  3 | 十堰市 |           1 |
+-----------------------+----+--------+-------------+
3 rows in set (0.00 sec)
 
+----+--------+
| id | name   |
+----+--------+
|  1 | 湖北省 |
|  2 | 廣東省 |
+----+--------+
2 rows in set (0.00 sec)

值得注意的是，可以在調(diào)用prefetch_related之前調(diào)用select_related，并且Django會(huì)按照你想的去做：先select_related，然后利用緩存到的數(shù)據(jù)prefetch_related。然而一旦prefetch_related已經(jīng)調(diào)用，select_related將不起作用。

小結(jié)

因?yàn)閟elect_related()總是在單次SQL查詢(xún)中解決問(wèn)題，而prefetch_related()會(huì)對(duì)每個(gè)相關(guān)表進(jìn)行SQL查詢(xún)，因此select_related()的效率通常比后者高。
鑒于第一條，盡可能的用select_related()解決問(wèn)題。只有在select_related()不能解決問(wèn)題的時(shí)候再去想prefetch_related()。
你可以在一個(gè)QuerySet中同時(shí)使用select_related()和prefetch_related()，從而減少SQL查詢(xún)的次數(shù)。
只有prefetch_related()之前的select_related()是有效的，之后的將會(huì)被無(wú)視掉。

總結(jié)

到此這篇關(guān)于Django中prefetch_related()函數(shù)優(yōu)化實(shí)戰(zhàn)指南的文章就介紹到這了,更多相關(guān)Django prefetch_related()函數(shù)優(yōu)化內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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