ORM分组操作示例（与SQL语句的比较）[数据库教程]

  • Z时代
  • 2024-01-10
  • 分类：综合

database

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 groupby 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 innerjoin dept2 on (employee2.dept_id=dept2.id) groupby 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)

 

ORM分组操作示例（与SQL语句的比较）

原文：https://www.cnblogs.com/zh-xiaoyuan/p/12786491.html

以上是 ORM分组操作示例（与SQL语句的比较）[数据库教程] 的全部内容， 来源链接： utcz.com/z/533352.html

回到顶部