轉 實例詳解Django的 select_related 和 prefetch_related 函數對 QuerySet 查詢的優化（三）

這是本系列的最後一篇，主要是select_related() 和 prefetch_related() 的最佳實踐。

第一篇在這裏 講例子和select_related()

第二篇在這裏 講prefetch_related()

 

4. 一些實例

選擇哪一個函數
若是咱們想要得到全部家鄉是湖北的人，最無腦的作法是先得到湖北省，再得到湖北的全部城市，最後得到故鄉是這個城市的人。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())
...
顯然這不是一個明智的選擇，由於這樣作會致使1+（湖北省城市數）次SQL查詢。反正是個反例，致使的查詢和得到掉結果就不列出來了。

prefetch_related() 或許是一個好的解決方法，讓咱們來看看。
>>> 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())
...
由於是一個深度爲2的prefetch，因此會致使3次SQL查詢：
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，可是3次查詢麼？倒過來查詢可能會更簡單？
>>> 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)
徹底沒問題。不只SQL查詢的數量減小了，python程序上也精簡了。

select_related()的效率要高於prefetch_related()。所以，最好在能用select_related()的地方儘可能使用它，也就是說，對於ForeignKey字段，避免使用prefetch_related()。

 

聯用
對於同一個QuerySet，你能夠同時使用這兩個函數。
在咱們一直使用的例子上加一個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
若是咱們拿到了一個訂單的id 咱們要知道這個訂單的客戶去過的省份。由於有ManyToManyField顯然必需要用prefetch_related()。若是隻用prefetch_related()會怎樣呢？
>>> plist = Order.objects.prefetch_related('customer__visitation__province').get(id=1)
>>> for city in plist.customer.visitation.all():
... print city.province.name
...
顯然，關係到了4個表：Order、Person、City、Province，根據prefetch_related()的特性就得有4次SQL查詢
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)

更好的辦法是先調用一次select_related()再調用prefetch_related()，最後再select_related()後面的表
>>> 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
...
這樣只會有3次SQL查詢，Django會先作select_related，以後prefetch_related的時候會利用以前緩存的數據，從而避免了1次額外的SQL查詢：
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，能夠在調用prefetch_related以前調用select_related，而且Django會按照你想的去作：先select_related，而後利用緩存到的數據prefetch_related。然而一旦prefetch_related已經調用，select_related將不起做用。

 

小結
由於select_related()老是在單次SQL查詢中解決問題，而prefetch_related()會對每一個相關表進行SQL查詢，所以select_related()的效率一般比後者高。
鑑於第一條，儘量的用select_related()解決問題。只有在select_related()不能解決問題的時候再去想prefetch_related()。
你能夠在一個QuerySet中同時使用select_related()和prefetch_related()，從而減小SQL查詢的次數。
只有prefetch_related()以前的select_related()是有效的，以後的將會被無視掉。

 

關於這兩個函數，我能想到的東西目前只有這麼多。不過基於一些我的緣由，寫第三篇時間比較短，寫的有些倉促。若是何時又想起了什麼，我會在這篇博文中添加。--------------------- 做者：CuGBabyBeaR 來源：CSDN 原文：https://blog.csdn.net/cugbabybear/article/details/38460877 版權聲明：本文爲博主原創文章，轉載請附上博文連接！