这是本系列的第二篇,内容是 prefetch_related() 函数的用途、实现途径、以及使用方法。
3. prefetch_related()
对于多对多字段(ManyToManyField)和一对多字段,可以使用prefetch_related()来进行优化。或许你会说,没有一个叫OneToManyField的东西啊。实际上 ,ForeignKey就是一个多对一的字段,而被ForeignKey关联的字段就是一对多字段了。
作用和方法
prefetch_related()和select_related()的设计目的很相似,都是为了减少SQL查询的数量,但是实现的方式不一样。后者是通过JOIN语句,在SQL查询内解决问题。但是对于多对多关系,使用SQL语句解决就显得有些不太明智,因为JOIN得到的表将会很长,会导致SQL语句运行时间的增加和内存占用的增加。若有n个对象,每个对象的多对多字段对应Mi条,就会生成Σ(n)Mi 行的结果表。
prefetch_related()的解决方法是,分别查询每个表,然后用Python处理他们之间的关系。继续以上边的例子进行说明,如果我们要获得张三所有去过的城市,使用prefetch_related()应该是这么做:
>>> zhangs
=
Person.objects.prefetch_related(
'visitation'
).get(firstname
=
u
"张"
,lastname
=
u
"三"
)
>>>
for
city
in
zhangs.visitation.
all
() :
...??
print
city
...
上述代码触发的SQL查询如下:
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` =
'张'
);
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);
第一条SQL查询仅仅是获取张三的Person对象,第二条比较关键,它选取关系表`QSOptimize_person_visitation`中`person_id`为张三的行,然后和`city`表内联(INNER JOIN 也叫等值连接)得到结果表。
+----+-----------+----------+-------------+-----------+
| id | firstname | lastname | hometown_id | living_id |
+----+-----------+----------+-------------+-----------+
|? 1 | 张??????? | 三?????? |?????????? 3 |???????? 1 |
+----+-----------+----------+-------------+-----------+
1 row in set (0.00 sec)
+-----------------------+----+-----------+-------------+
| _prefetch_related_val | id | name????? | province_id |
+-----------------------+----+-----------+-------------+
|???????????????????? 1 |? 1 | 武汉市??? |?????????? 1 |
|???????????????????? 1 |? 2 | 广州市??? |?????????? 2 |
|???????????????????? 1 |? 3 | 十堰市??? |?????????? 1 |
+-----------------------+----+-----------+-------------+
3 rows in set (0.00 sec)
显然张三武汉、广州、十堰都去过。
又或者,我们要获得湖北的所有城市名,可以这样:
>>> hb
=
Province.objects.prefetch_related(
'city_set'
).get(name__iexact
=
u
"湖北省"
)
>>>
for
city
in
hb.city_set.
all
():
...?? city.name
...
触发的SQL查询:
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 | 武汉市??? |?????????? 1 |
|? 3 | 十堰市??? |?????????? 1 |
+----+-----------+-------------+
2 rows in set (0.00 sec)
我们可以看见,prefetch使用的是 IN 语句实现的。这样,在QuerySet中的对象数量过多的时候,根据数据库特性的不同有可能造成性能问题。
使用方法*lookups 参数
prefetch_related()在Django < 1.7 只有这一种用法。和select_related()一样,prefetch_related()也支持深度查询,例如要获得所有姓张的人去过的省:
>>> zhangs
=
Person.objects.prefetch_related(
'visitation__province'
).
filter
(firstname__iexact
=
u
'张'
)
>>>
for
i
in
zhangs:
...??
for
city
in
i.visitation.
all
():
...????
print
city.province
...
触发的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
'张'
;
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);
获得的结果:
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
|
id
| firstname | lastname | hometown_id | living_id |
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
|?
1
| 张??????? | 三?????? |??????????
3
|????????
1
|
|?
4
| 张??????? | 六?????? |??????????
2
|????????
2
|
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
2
rows
in
set
(
0.00
sec)
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
+
| _prefetch_related_val |
id
| name????? | province_id |
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
+
|????????????????????
1
|?
1
| 武汉市??? |??????????
1
|
|????????????????????
1
|?
2
| 广州市??? |??????????
2
|
|????????????????????
4
|?
2
| 广州市??? |??????????
2
|
|????????????????????
1
|?
3
| 十堰市??? |??????????
1
|
+
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
-
-
+
4
rows
in
set
(
0.00
sec)
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
|
id
| name????? |
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
|?
1
| 湖北省??? |
|?
2
| 广东省??? |
+
-
-
-
-
+
-
-
-
-
-
-
-
-
-
-
-
+
2
rows
in
set
(
0.00
sec)
值得一提的是,链式prefetch_related会将这些查询添加起来,就像1.7中的select_related那样。
要注意的是,在使用QuerySet的时候,一旦在链式操作中改变了数据库请求,之前用prefetch_related缓存的数据将会被忽略掉。这会导致Django重新请求数据库来获得相应的数据,从而造成性能问题。这里提到的改变数据库请求指各种filter()、exclude()等等最终会改变SQL代码的操作。而all()并不会改变最终的数据库请求,因此是不会导致重新请求数据库的。
举个例子,要获取所有人访问过的城市中带有“市”字的城市,这样做会导致大量的SQL查询:
plist
=
Person.objects.prefetch_related(
'visitation'
)
[p.visitation.
filter
(name__icontains
=
u
"市"
)
for
p
in
plist]
因为数据库中有4人,导致了2+4次SQL查询:
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%'
);
详细分析一下这些请求事件。
众所周知,QuerySet是lazy的,要用的时候才会去访问数据库。运行到第二行Python代码时,for循环将plist看做iterator,这会触发数据库查询。最初的两次SQL查询就是prefetch_related导致的。
虽然已经查询结果中包含所有所需的city的信息,但因为在循环体中对Person.visitation进行了filter操作,这显然改变了数据库请求。因此这些操作会忽略掉之前缓存到的数据,重新进行SQL查询。
但是如果有这样的需求了应该怎么办呢?在Django >= 1.7,可以通过下一节的Prefetch对象来实现,如果你的环境是Django < 1.7,可以在Python中完成这部分操作。
plist
=
Person.objects.prefetch_related(
'visitation'
)
[[city
for
city
in
p.visitation.
all
()
if
u
"市"
in
city.name]
for
p
in
plist]
Prefetch 对象
在Django >= 1.7,可以用Prefetch对象来控制prefetch_related函数的行为。
注:由于我没有安装1.7版本的Django环境,本节内容是参考Django文档写的,没有进行实际的测试。
Prefetch对象的特征:
- 一个Prefetch对象只能指定一项prefetch操作。Prefetch对象对字段指定的方式和prefetch_related中的参数相同,都是通过双下划线连接的字段名完成的。可以通过 queryset 参数手动指定prefetch使用的QuerySet。可以通过 to_attr 参数指定prefetch到的属性名。Prefetch对象和字符串形式指定的lookups参数可以混用。
继续上面的例子,获取所有人访问过的城市中带有“武”字和“州”的城市:
wus
=
City.objects.
filter
(name__icontains
=
u
"武"
)
zhous
=
City.objects.
filter
(name__icontains
=
u
"州"
)
plist
=
Person.objects.prefetch_related(
????
Prefetch(
'visitation'
, queryset
=
wus, to_attr
=
"wu_city"
),
????
Prefetch(
'visitation'
, queryset
=
zhous, to_attr
=
"zhou_city"
),)
[p.wu_city
for
p
in
plist]
[p.zhou_city
for
p
in
plist]
注:这段代码没有在实际环境中测试过,若有不正确的地方请指正。
顺带一提,Prefetch对象和字符串参数可以混用。
None
可以通过传入一个None来清空之前的prefetch_related。就像这样:
>>> prefetch_cleared_qset
=
qset.prefetch_related(
None
)
小结
- prefetch_related主要针一对多和多对多关系进行优化。prefetch_related通过分别获取各个表的内容,然后用Python处理他们之间的关系来进行优化。可以通过可变长参数指定需要select_related的字段名。指定方式和特征与select_related是相同的。在Django >= 1.7可以通过Prefetch对象来实现复杂查询,但低版本的Django好像只能自己实现。作为prefetch_related的参数,Prefetch对象和字符串可以混用。prefetch_related的链式调用会将对应的prefetch添加进去,而非替换,似乎没有基于不同版本上区别。可以通过传入None来清空之前的prefetch_related。