首页 \ 问答 \ 在不相关的Django模型上执行SQL JOIN？(Perform a SQL JOIN on Django models that are not related?)

在不相关的Django模型上执行SQL JOIN？(Perform a SQL JOIN on Django models that are not related?)

 我有2个模型，用户（django.contrib.auth.models.User）和一个名为Log的模型。 两者都包含“电子邮件”字段。 Log没有指向User模型的ForeignKey。 我试图找出如何使用电子邮件字段作为通用性来对这两个表执行JOIN。  
 我希望能够执行基本上2个查询。 过滤的基本联接  
#Get all the User objects that have related Log objects with the level parameter set to 3.
User.objects.filter(log__level=3)
 
 我也想做一些聚合。  
User.objects.all().anotate(Count('log'))
 
 当然，能够反过来也是很好的。  
log = Log.objects.get(pk=3)
log.user...
 
 有没有办法用ORM做到这一点？ 也许我可以添加到模型的Meta类来“激活”关系？  
 谢谢！ 

I have 2 Models, User (django.contrib.auth.models.User) and a model named Log. Both contain an "email" field. Log does not have a ForeignKey pointing to the User model. I'm trying to figure out how I can perform a JOIN on these two tables using the email field as the commonality. 
There are basically 2 queries I want to be able to perform. A basic join for filtering 
#Get all the User objects that have related Log objects with the level parameter set to 3.
User.objects.filter(log__level=3)
 
I'd also like to do some aggregates. 
User.objects.all().anotate(Count('log'))
 
Of course, it would be nice to be able to do the reverse as well. 
log = Log.objects.get(pk=3)
log.user...
 
Is there a way to do this with the ORM? Maybe something I can add to the model's Meta class to "activate" the relation? 
Thanks!

原文：https://stackoverflow.com/questions/2328493

更新时间：2023-08-14 06:08

最满意答案

 设置数据：  
dd <- data.frame(x=c(1981,1990,2000:2013),
  y = c(3.262897,2.570096,7.098903,5.428424,6.056302,5.593942,
  10.869635,12.425793,5.601889,6.498187,6.967503,5.358961,3.519295,
  7.137202,19.121631,6.479928))
 
 问题是指数取代大于约709的任何数都会得到一个大于最大值的数字，可以存储为双精度浮点值（大约1e308），因此会导致数值溢出。 您可以通过移动x变量轻松解决此问题：  
lm(y~exp(x),data=dd) ## error
lm(y~exp(x-1981),data=dd) ## fine
 
 但是，您可以更轻松地绘制此模型的拟合值，如下所示：  
library(ggplot2); theme_set(theme_bw())
ggplot(dd,aes(x,y))+geom_point()+
   geom_smooth(method="glm",
            method.args=list(family=gaussian(link="log")))

Set up data: 
dd <- data.frame(x=c(1981,1990,2000:2013),
  y = c(3.262897,2.570096,7.098903,5.428424,6.056302,5.593942,
  10.869635,12.425793,5.601889,6.498187,6.967503,5.358961,3.519295,
  7.137202,19.121631,6.479928))
 
The problem is that exponentiating any number larger than about 709 gives a number greater than the maximum value storeable as a double-precision floating-point value (approx. 1e308), and hence leads to a numeric overflow. You can easily remedy this by shifting your x variable: 
lm(y~exp(x),data=dd) ## error
lm(y~exp(x-1981),data=dd) ## fine
 
However, you can plot the fitted value for this model more easily as follows: 
library(ggplot2); theme_set(theme_bw())
ggplot(dd,aes(x,y))+geom_point()+
   geom_smooth(method="glm",
            method.args=list(family=gaussian(link="log")))

在不相关的Django模型上执行SQL JOIN？(Perform a SQL JOIN on Django models that are not related?)

最满意答案

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