单表操作
建表：
class Employee(models.Model): name = models.CharField(max_length=16) age = models.IntegerField() salary = models.IntegerField() province = models.CharField(max_length=32) dept = models.CharField(max_length=16) def __str__(self): return self.name class Meta: db_table = "employee"
操作：
我们使用原生SQL语句，按照部分分组求平均工资：
select dept,AVG(salary) from employee group by dept;
ORM语句与SQL语句对应关系：
ORM查询：
ret = models.Employee.objects.all() print(ret)#<QuerySet [<Employee: 小黑>, <Employee: 小白>, <Employee: 赵导>, <Employee: 化工哥>]> #(0.003) SELECT `employee`.`id`, `employee`.`name`, `employee`.`age`, `employee`.`salary`, `employee`.`province`, `employee`.`dept`
FROM `employee` LIMIT 21; args=()
ret = models.Employee.objects.values("dept") print(ret) # (0.002) SELECT `employee`.`dept` FROM `employee` LIMIT 21; args = () # < QuerySet[{‘dept‘: ‘保安部‘}, {‘dept‘: ‘影视部‘}, {‘dept‘: ‘影视部‘}, {‘dept‘: ‘福利部‘}] >
ret = models.Employee.objects.values("dept").annotate(avg=Avg("salary")).values("dept","avg") print(ret) #(0.068) SELECT `employee`.`dept`, AVG(`employee`.`salary`) AS `avg` FROM `employee` GROUP BY `employee`.`dept` ORDER BY NULL LIMIT 21; #<QuerySet [{‘dept‘: ‘保安部‘, ‘avg‘: 2000.0}, {‘dept‘: ‘影视部‘, ‘avg‘: 6500.0}, {‘dept‘: ‘福利部‘, ‘avg‘: 8000.0}]>
多表操作
建表：
class Employee2(models.Model): name = models.CharField(max_length=16) age = models.IntegerField() salary = models.IntegerField() province = models.CharField(max_length=32) dept = models.ForeignKey(to="Dept") def __str__(self): return self.name class Meta: db_table = "employee2" class Dept(models.Model): name = models.CharField(max_length=16, unique=True) def __str__(self): return self.name class Meta: db_table = "dept2"
SQL查询：
select dept2.name,AVG(salary) from employee2 inner join dept2 on (employee2.dept_id=dept2.id) group by dept_id;
ORM查询：
from django.db.models import Avg ret = models.Employee2.objects.values("dept_id").annotate(avg=Avg("salary")).values("dept__name","avg") print(ret) # < QuerySet[{‘dept__name‘: ‘保安部‘, ‘avg‘: 2000.0}, {‘dept__name‘: ‘影视部‘, ‘avg‘: 6500.0}, {‘dept__name‘: ‘福利部‘, ‘avg‘: 8000.0}] > # (0.089) SELECT `dept2`.`name`,AVG(`employee2`.`salary`) AS `avg` FROM `employee2` INNER JOIN `dept2` ON(`employee2`.`dept_id` = `dept2`.id`)
GROUP BY `employee2`.`dept_id`,`dept2`.`name` ORDER BY NULL LIMIT 21;args = ()
# 查所有的员工和部门名称 ret = models.Employee2.objects.values("name", "dept__name") print(ret) #(0.012) SELECT `employee2`.`name`, `dept2`.`name` FROM `employee2` INNER JOIN `dept2` ON (`employee2`.`dept_id` = `dept2`.`id`) LIMIT 21; #<QuerySet [{‘name‘: ‘小黑‘, ‘dept__name‘: ‘保安部‘}, {‘name‘: ‘小白‘, ‘dept__name‘: ‘影视部‘}, {‘name‘: ‘赵导‘, ‘dept__name‘: ‘影视部‘},
{‘name‘: ‘化工哥‘, ‘dept__name‘: ‘福利部‘}]>
select_related 和 prefetch_related 的使用
def select_related(self, *fields) 性能相关：表之间进行join连表操作，一次性获取关联的数据。 总结： 1. select_related主要针一对一和多对一关系进行优化。 2. select_related使用SQL的JOIN语句进行优化，通过减少SQL查询的次数来进行优化、提高性能。 def prefetch_related(self, *lookups) 性能相关：多表连表操作时速度会慢，使用其执行多次SQL查询在Python代码中实现连表操作。 总结： 1. 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。 2. prefetch_related()的优化方式是分别查询每个表，然后用Python处理他们之间的关系。
select_related的使用示例
#select_related的使用：表之间进行join连表操作，一次性获取关联的数据。 ret = models.Employee2.objects.select_related() print(ret) #(0.019) SELECT `employee2`.`id`, `employee2`.`name`, `employee2`.`age`, `employee2`.`salary`, `employee2`.`province`, `employee2`.`dept_id`,
`dept2`.`id`, `dept2`.`name` FROM `employee2` INNER JOIN `dept2` ON (`employee2`.`dept_id` = `dept2`.`id`) LIMIT 21; args=() #<QuerySet [<Employee2: 小黑>, <Employee2: 小白>, <Employee2: 赵导>, <Employee2: 化工哥>]> ret = models.Employee2.objects.select_related().values("name","dept__name") print(ret) #(0.020) SELECT `employee2`.`name`, `dept2`.`name` FROM `employee2` INNER JOIN `dept2` ON (`employee2`.`dept_id` = `dept2`.`id`) LIMIT 21; #<QuerySet [{‘name‘: ‘小黑‘, ‘dept__name‘: ‘保安部‘}, {‘name‘: ‘小白‘, ‘dept__name‘: ‘影视部‘}, {‘name‘: ‘赵导‘, ‘dept__name‘: ‘影视部‘},
{‘name‘: ‘化工哥‘, ‘dept__name‘: ‘福利部‘}]>
建立多对多关系表：
class Author(models.Model): name = models.CharField(max_length=32) books = models.ManyToManyField(to="Book") def __str__(self): return self.name class Meta: db_table = "author" class Book(models.Model): title = models.CharField(max_length=32) def __str__(self): return self.title class Meta: db_table = "book"
ret = models.Author.objects.select_related("books__title").values("name", "books__title") print(ret) #(0.014) SELECT `author`.`name`, `book`.`title` FROM `author` LEFT OUTER JOIN `author_books` ON (`author`.`id` = `author_books`.`author_id`)
LEFT OUTER JOIN `book` ON (`author_books`.`book_id` = `book`.`id`) LIMIT 21; args=() #<QuerySet [{‘name‘: ‘小黑‘, ‘books__title‘: ‘沙河出版社‘}, {‘name‘: ‘小白‘, ‘books__title‘: ‘沙河出版社‘}, {‘name‘: ‘小黑‘,
‘books__title‘: ‘光子出版社‘}, {‘name‘: ‘小黄‘, ‘books__title‘: ‘光子出版社‘}, {‘name‘: ‘小黑‘, ‘books__title‘: ‘番茄物语‘},
{‘name‘: ‘小白‘, ‘books__title‘: ‘番茄物语‘}, {‘name‘: ‘小黄‘, ‘books__title‘: ‘番茄物语‘}]>
批量操作
def bulk_create(self, objs, batch_size=None): # 批量插入 # batch_size表示一次插入的个数 objs = [ models.DDD(name=‘r11‘), models.DDD(name=‘r22‘) ] models.DDD.objects.bulk_create(objs, 10)
示例：
# 批量创建 # 有100个书籍对象 objs = [models.Book(title="沙河{}".format(i)) for i in range(6)] # # 在数据库中批量创建, 2次一提交 models.Book.objects.bulk_create(objs, 2)