As others have pointed out you are trying to create a relational database from none relational database which you really don't want to do but anyways, if you have a case that you have to do this here is a solution you can use. We first do a foreach find on collection A( or in your case users) and then we get each item as an object then we use object property (in your case uid) to lookup in our second collection (in your case comments) if we can find it then we have a match and we can print or do something with it. Hope this helps you and good luck :)

db.users.find().forEach(
function (object) {
    var commonInBoth=db.comments.findOne({ "uid": object.uid} );
    if (commonInBoth != null) {
        printjson(commonInBoth) ;
        printjson(object) ;
    }else {
        // did not match so we don't care in this case
    }
});

您可以使用聚合管道来实现它，但是自己编写它很麻烦。

您可以使用mongo-join-query从您的查询自动创建聚合管道。

这是你的查询的样子:

const mongoose = require("mongoose");
const joinQuery = require("mongo-join-query");

joinQuery(
    mongoose.models.Comment,
    {
        find: { pid:444 },
        populate: ["uid"]
    },
    (err, res) => (err ? console.log("Error:", err) : console.log("Success:", res.results))
);

您的结果将在uid字段中有user对象，您可以链接任意多的层次。您可以填充对用户的引用，从而引用一个Team，再引用其他东西，等等。

免责声明:我编写了mongo-join-query来解决这个问题。

不，看起来你并没有做错。MongoDB连接是“客户端”。就像你说的

目前，我首先获取符合条件的注释，然后找出结果集中的所有uid，获取用户对象，并将它们与注释的结果合并。看来我做错了。

1) Select from the collection you're interested in.
2) From that collection pull out ID's you need
3) Select from other collections
4) Decorate your original results.

它不是一个“真正的”连接，但它实际上比SQL连接有用得多，因为您不必处理“多”面连接的重复行，而是修饰最初选择的集合。

这一页上有很多废话和FUD。结果5年后，MongoDB仍然存在。

这取决于你想做什么。

目前您已经将其设置为规范化数据库，这很好，而且您的操作方式也很合适。

然而，还有其他的方法。

您可以有一个帖子集合，其中为每个帖子嵌入了评论，并引用了您可以迭代查询以获得的用户。您可以将用户名与注释一起存储，您可以将它们都存储在一个文档中。

The thing with NoSQL is it's designed for flexible schemas and very fast reading and writing. In a typical Big Data farm the database is the biggest bottleneck, you have fewer database engines than you do application and front end servers...they're more expensive but more powerful, also hard drive space is very cheap comparatively. Normalization comes from the concept of trying to save space, but it comes with a cost at making your databases perform complicated Joins and verifying the integrity of relationships, performing cascading operations. All of which saves the developers some headaches if they designed the database properly.

With NoSQL, if you accept that redundancy and storage space aren't issues because of their cost (both in processor time required to do updates and hard drive costs to store extra data), denormalizing isn't an issue (for embedded arrays that become hundreds of thousands of items it can be a performance issue, but most of the time that's not a problem). Additionally you'll have several application and front end servers for every database cluster. Have them do the heavy lifting of the joins and let the database servers stick to reading and writing.

TL;DR:你现在做的很好，还有其他的方法。查看mongodb文档的数据模型模式以获得一些很棒的示例。http://docs.mongodb.org/manual/data-modeling/

你可以使用Postgres中的mongo_fdw在MongoDB上运行包括join在内的SQL查询。

通过正确组合$lookup， $project和$match，您可以在多个参数上连接多个表。这是因为它们可以被链接多次。

假设我们想做以下(引用)

SELECT S.* FROM LeftTable S
LEFT JOIN RightTable R ON S.ID = R.ID AND S.MID = R.MID  
WHERE R.TIM > 0 AND S.MOB IS NOT NULL

步骤1:链接所有表

您可以根据需要查找任意数量的表。

$lookup -查询中的每个表一个

$unwind -正确地反规格化数据，否则它将被包装在数组中

Python代码. .

db.LeftTable.aggregate([
                        # connect all tables

                        {"$lookup": {
                          "from": "RightTable",
                          "localField": "ID",
                          "foreignField": "ID",
                          "as": "R"
                        }},
                        {"$unwind": "R"}
                   
                        ])

步骤2:定义所有条件

$project:在这里定义所有的条件语句，加上所有你想选择的变量。

Python代码. .

db.LeftTable.aggregate([
                        # connect all tables

                        {"$lookup": {
                          "from": "RightTable",
                          "localField": "ID",
                          "foreignField": "ID",
                          "as": "R"
                        }},
                        {"$unwind": "R"},

                        # define conditionals + variables

                        {"$project": {
                          "midEq": {"$eq": ["$MID", "$R.MID"]},
                          "ID": 1, "MOB": 1, "MID": 1
                        }}
                        ])

第三步:连接所有的条件句

$match -使用OR或AND等连接所有条件可以有很多个。

$project:取消所有的条件

完整的Python代码。

db.LeftTable.aggregate([
                        # connect all tables

                        {"$lookup": {
                          "from": "RightTable",
                          "localField": "ID",
                          "foreignField": "ID",
                          "as": "R"
                        }},
                        {"$unwind": "$R"},

                        # define conditionals + variables

                        {"$project": {
                          "midEq": {"$eq": ["$MID", "$R.MID"]},
                          "ID": 1, "MOB": 1, "MID": 1
                        }},

                        # join all conditionals

                        {"$match": {
                          "$and": [
                            {"R.TIM": {"$gt": 0}}, 
                            {"MOB": {"$exists": True}},
                            {"midEq": {"$eq": True}}
                        ]}},

                        # undefine conditionals

                        {"$project": {
                          "midEq": 0
                        }}

                        ])

几乎任何表、条件和连接的组合都可以用这种方式完成。