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As of Mongo 3.2 the answers to this question are mostly no longer correct. The new $lookup operator added to the aggregation pipeline is essentially identical to a left outer join:

https://docs.mongodb.org/master/reference/operator/aggregation/lookup/#pipe._S_lookup

From the docs:

{

   $lookup:

     {

       from: <collection to join>,

       localField: <field from the input documents>,

       foreignField: <field from the documents of the "from" collection>,

       as: <output array field>

     }

}

Of course Mongo is not a relational database, and the devs are being careful to recommend specific use cases for $lookup, but at least as of 3.2 doing join is now possible with MongoDB.

This page on the official mongodb site addresses exactly this question:

http://docs.mongodb.org/ecosystem/tutorial/model-data-for-ruby-on-rails/

When we display our list of stories, we'll need to show the name of the user who posted the story. If we were using a relational database, we could perform a join on users and stores, and get all our objects in a single query. But MongoDB does not support joins and so, at times, requires bit of denormalization. Here, this means caching the 'username' attribute.

Relational purists may be feeling uneasy already, as if we were violating some universal law. But let’s bear in mind that MongoDB collections are not equivalent to relational tables; each serves a unique design objective. A normalized table provides an atomic, isolated chunk of data. A document, however, more closely represents an object as a whole. In the case of a social news site, it can be argued that a username is intrinsic to the story being posted.

We can merge/join all data inside only one collection with a easy function in few lines using the mongodb client console, and now we could be able of perform the desired query.
Below a complete example,

.- Authors:

db.authors.insert([

    {

        _id: 'a1',

        name: { first: 'orlando', last: 'becerra' },

        age: 27

    },

    {

        _id: 'a2',

        name: { first: 'mayra', last: 'sanchez' },

        age: 21

    }

]);

.- Categories:

db.categories.insert([

    {

        _id: 'c1',

        name: 'sci-fi'

    },

    {

        _id: 'c2',

        name: 'romance'

    }

]);

.- Books

db.books.insert([

    {

        _id: 'b1',

        name: 'Groovy Book',

        category: 'c1',

        authors: ['a1']

    },

    {

        _id: 'b2',

        name: 'Java Book',

        category: 'c2',

        authors: ['a1','a2']

    },

]);

.- Book lending

db.lendings.insert([

    {

        _id: 'l1',

        book: 'b1',

        date: new Date('01/01/11'),

        lendingBy: 'jose'

    },

    {

        _id: 'l2',

        book: 'b1',

        date: new Date('02/02/12'),

        lendingBy: 'maria'

    }

]);

.- The magic:

db.books.find().forEach(

    function (newBook) {

        newBook.category = db.categories.findOne( { "_id": newBook.category } );

        newBook.lendings = db.lendings.find( { "book": newBook._id  } ).toArray();

        newBook.authors = db.authors.find( { "_id": { $in: newBook.authors }  } ).toArray();

        db.booksReloaded.insert(newBook);

    }

);

.- Get the new collection data:

db.booksReloaded.find().pretty()

.- Response :)

{

    "_id" : "b1",

    "name" : "Groovy Book",

    "category" : {

        "_id" : "c1",

        "name" : "sci-fi"

    },

    "authors" : [

        {

            "_id" : "a1",

            "name" : {

                "first" : "orlando",

                "last" : "becerra"

            },

            "age" : 27

        }

    ],

    "lendings" : [

        {

            "_id" : "l1",

            "book" : "b1",

            "date" : ISODate("2011-01-01T00:00:00Z"),

            "lendingBy" : "jose"

        },

        {

            "_id" : "l2",

            "book" : "b1",

            "date" : ISODate("2012-02-02T00:00:00Z"),

            "lendingBy" : "maria"

        }

    ]

}

{

    "_id" : "b2",

    "name" : "Java Book",

    "category" : {

        "_id" : "c2",

        "name" : "romance"

    },

    "authors" : [

        {

            "_id" : "a1",

            "name" : {

                "first" : "orlando",

                "last" : "becerra"

            },

            "age" : 27

        },

        {

            "_id" : "a2",

            "name" : {

                "first" : "mayra",

                "last" : "sanchez"

            },

            "age" : 21

        }

    ],

    "lendings" : [ ]

}

I hope this lines can help you.

You have to do it the way you described. MongoDB is a non-relational database and doesn't support joins.

Here's an example of a "join" * Actors and Movies collections:

https://github.com/mongodb/cookbook/blob/master/content/patterns/pivot.txt

It makes use of .mapReduce() method

* join - an alternative to join in document-oriented databases

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

    }

});

It depends on what you're trying to do.

You currently have it set up as a normalized database, which is fine, and the way you are doing it is appropriate.

However, there are other ways of doing it.

You could have a posts collection that has imbedded comments for each post with references to the users that you can iteratively query to get. You could store the user's name with the comments, you could store them all in one document.

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: What you're doing is fine, and there are other ways of doing it. Check out the mongodb documentation's data model patterns for some great examples. http://docs.mongodb.org/manual/data-modeling/

You can join two collection in Mongo by using lookup which is offered in 3.2 version. In your case the query would be

db.comments.aggregate({

    $lookup:{

        from:"users",

        localField:"uid",

        foreignField:"uid",

        as:"users_comments"

    }

})

or you can also join with respect to users then there will be a little change as given below.

db.users.aggregate({

    $lookup:{

        from:"comments",

        localField:"uid",

        foreignField:"uid",

        as:"users_comments"

    }

})

It will work just same as left and right join in SQL.

There is a specification that a lot of drivers support that's called DBRef.

DBRef is a more formal specification for creating references between documents. DBRefs (generally) include a collection name as well as an object id. Most developers only use DBRefs if the collection can change from one document to the next. If your referenced collection will always be the same, the manual references outlined above are more efficient.

Taken from MongoDB Documentation: Data Models > Data Model Reference >
Database References

With right combination of $lookup, $project and $match, you can join mutiple tables on multiple parameters. This is because they can be chained multiple times.

Suppose we want to do following (reference)

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

Step 1: Link all tables

you can $lookup as many tables as you want.

$lookup - one for each table in query

$unwind - because data is denormalised correctly, else wrapped in arrays

Python code..

db.LeftTable.aggregate([

                        # connect all tables



                        {"$lookup": {

                          "from": "RightTable",

                          "localField": "ID",

                          "foreignField": "ID",

                          "as": "R"

                        }},

                        {"$unwind": "R"}



                        ])

Step 2: Define all conditionals

$project : define all conditional statements here, plus all the variables you'd like to select.

Python Code..

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

                        }}

                        ])

Step 3: Join all the conditionals

$match - join all conditions using OR or AND etc. There can be multiples of these.

$project: undefine all conditionals

Python Code..

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

                        }}



                        ])

Pretty much any combination of tables, conditionals and joins can be done in this manner.

Before 3.2.6, Mongodb does not support join query as like mysql. below solution which works for you.

 db.getCollection('comments').aggregate([

        {$match : {pid : 444}},

        {$lookup: {from: "users",localField: "uid",foreignField: "uid",as: "userData"}},

   ])

$lookup (aggregation)

Performs a left outer join to an unsharded collection in the same database to filter in documents from the “joined” collection for processing. To each input document, the $lookup stage adds a new array field whose elements are the matching documents from the “joined” collection. The $lookup stage passes these reshaped documents to the next stage.
The $lookup stage has the following syntaxes:

Equality Match

To perform an equality match between a field from the input documents with a field from the documents of the “joined” collection, the $lookup stage has the following syntax:

{

   $lookup:

     {

       from: <collection to join>,

       localField: <field from the input documents>,

       foreignField: <field from the documents of the "from" collection>,

       as: <output array field>

     }

}

The operation would correspond to the following pseudo-SQL statement:

SELECT *, <output array field>

FROM collection

WHERE <output array field> IN (SELECT <documents as determined from the pipeline>

                               FROM <collection to join>

                               WHERE <pipeline> );

Mongo URL

You can run SQL queries including join on MongoDB with mongo_fdw from Postgres.

MongoDB does not allow joins, but you can use plugins to handle that. Check the mongo-join plugin. It's the best and I have already used it. You can install it using npm directly like this npm install mongo-join. You can check out the full documentation with examples.

(++) really helpful tool when we need to join (N) collections

(--) we can apply conditions just on the top level of the query

Example

var Join = require('mongo-join').Join, mongodb = require('mongodb'), Db = mongodb.Db, Server = mongodb.Server;

db.open(function (err, Database) {

    Database.collection('Appoint', function (err, Appoints) {



        /* we can put conditions just on the top level */

        Appoints.find({_id_Doctor: id_doctor ,full_date :{ $gte: start_date },

            full_date :{ $lte: end_date }}, function (err, cursor) {

            var join = new Join(Database).on({

                field: '_id_Doctor', // <- field in Appoints document

                to: '_id',         // <- field in User doc. treated as ObjectID automatically.

                from: 'User'  // <- collection name for User doc

            }).on({

                field: '_id_Patient', // <- field in Appoints doc

                to: '_id',         // <- field in User doc. treated as ObjectID automatically.

                from: 'User'  // <- collection name for User doc

            })

            join.toArray(cursor, function (err, joinedDocs) {



                /* do what ever you want here */

                /* you can fetch the table and apply your own conditions */

                .....

                .....

                .....





                resp.status(200);

                resp.json({

                    "status": 200,

                    "message": "success",

                    "Appoints_Range": joinedDocs,





                });

                return resp;





            });



    });

You can do it using the aggregation pipeline, but it's a pain to write it yourself.

You can use mongo-join-query to create the aggregation pipeline automatically from your query.

This is how your query would look like:

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))

);

Your result would have the user object in the uid field and you can link as many levels deep as you want. You can populate the reference to the user, which makes reference to a Team, which makes reference to something else, etc..

Disclaimer: I wrote mongo-join-query to tackle this exact problem.

playORM can do it for you using S-SQL(Scalable SQL) which just adds partitioning such that you can do joins within partitions.