Mongoid simulate join by inject or mapreduce? - ruby-on-rails

Question

Disclaimer: I'm a novice.
I want to simulate a join for my mongodb embedded document. If I have an embedded list:
{
_id: ObjectId("5320f6c34b6576d373000000"),
user_id: "52f581096b657612fe020000",
list: "52f4fd9f52e39bc0c15674ea"
{
player_1: "52f4fd9f52e39bc0c15674ex",
player_2: "52f4fd9f52e39bc0c15674ey",
player_3: "52f4fd9f52e39bc0c15674ez"
}
}
And a player collection with each player being something like:
{
_id: ObjectId("52f4fd9f52e39bc0c15674ex"),
college: "Louisville",
headshot: "player.png",
height: "6'2",
name: "Wayne Brady",
position: "QB",
weight: 205
}
I want to end up with:
{
_id: ObjectId("5320f6c34b6576d373000000"),
user_id: "52f581096b657612fe020000",
list: "52f4fd9f52e39bc0c15674ea"
{
player_1:
{
_id: ObjectId("52f4fd9f52e39bc0c15674ex"),
college: "Louisville",
headshot: "player.png",
height: "6'2",
name: "Wayne Brady",
position: "QB",
weight: 205
},
etc...
}
}
So I can call User.lists.first.player_1.name.
This is what makes sense in my mind since I'm new to rails...and I don't want to embed players in each user's list because I'd have so many redundancies...
Advice? Is this possible, if so how? Is it a good idea, or is there a better way?

Answer 1

So have have a typical relational model, let's call it "one to many", which you have users or "user teams" and a whole pool of players. And in typical modelling form you want to "de-normalize" this to avoid duplication.
But here's the thing, MongoDB does not do joins. Joins are not "webscale" in the current parlance. So it leaves you thinking what to do. Or does MongoDB do joins?
db.eval(function() {
var user = db.user.findOne({ "user_id": "52f581096b657612fe020000" });
for ( k in user.list ) {
var player = db.player.findOne({ "_id": user.list[k] });
user.list[k] = player;
}
return user;
});
Which "arguably" is "kind of a join". And it was all done on the server, right?
But DO NOT DO THAT. While db.eval() has uses, something that you are going to query regularly is not one of the practical uses. Read the documentation, which shows the warnings. In particular, all JavaScript is running in a single thread so that will lock things up very quickly.
Now client side, you are more or less doing the same thing. And you ODM of choice is likely again doing "the same thing", though it is usually hiding it away in some manner so you don't see that part. Likewise the same could likely be said of your SQL ORM, which was also "sneaking off behind your back" and querying the database while you just accessed the objects in your code.
As for mapReduce. Well the problem with the data you present is that there is nothing to "reduce". There is a technique known as in "incremental mapReduce" but it would not be well suited to this type of data. A topic in itself, but you would basically need all the "users" associated to the "players" as well, stored in the "player data" to make that any kind of viability. And it's ultimately just another way of "cheating" joins.
This is the space in which MongoDB exists.
So rather than going and doing all this fetching or joining, it allows the concept of being able to "pre-join" your data as it were. And the point of this is to allow faster, and more atomic reads and writes. And this is known as embedding.
Looking at your data, there should not be a problem with embedding at all. Consider the points:
Presumably you are modelling "fantasy teams" for a given user. It would be fair to day that a "team" does not consist of an infinite number of players.
Aside from other things your "A1" usage is likely to be "displaying" the players associated with that "user team". And in so much as, you want to "display" as much information as possible, and keep that to a single read operation. You also want to easily add "players" to the "user team".
While a "player" may have "extended information", and possibly even some global statistics or scores, that information may well be not what you want to access, while associated to the "user team" that often. It can probably be written independently, and only read when looking at the "player detail".
Those are three good cases to support embedding. Sure you would be duplicating information stored against each user team, opposed to just a small "key" reference. And sure that information is likely to exist elsewhere in the full "player detail" and that would be duplication as well.
But the point of the "duplication" here is to optimize. So here it would seem valid to embed "some of the data", not all, but what you regularly use in your main operations. Considering the "player's" name, position, height and weight are not likely to change on a regular basis or not even at all in the context, then that seems a reasonable trade-off.
{
"_id": ObjectId("5320f6c34b6576d373000000"),
"user_id": ObjectId("52f581096b657612fe020000"),
"list": [
{
"_id": ObjectId("52f4fd9f52e39bc0c15674ex"),
"label": "Player1",
"college": "Louisville",
"headshot": "player.png",
"height": "6'2",
"name": "Wayne Brady",
"position": "QB",
"weight": 205
},
{
"label": "Player2",
(...)
}
]
},
That's not that bad. And it would take a lot to break the 16MB limit. And considering this seems to be a "user team" then it could probably do with some information from the "user" as well.
You also get a lot of power out of this when data is kept together like this, to find the top "player" picked by each user:
db.userteams.aggregate([
// Unwind the array
{ "$unwind": "$list" },
// Group and use the player name
{ "$group": {
"_id": {
"user_id": "$user_id",
"player": "$list.name",
},
"count": { "$sum": 1 }
}},
// Sort the results descending by popularity
{ "$sort": { "_id.user_id": 1, "count": -1 } },
// Group to limit the first one
{ "$group": {
"_id": "$_id.user_id",
"player": { "$first": "$_id.player" },
"picks": { "$first:" "$count" }
}}
])
Which admittedly is a reasonably trivial use of a name in this case, but it is an example of using information that has become available by the use of some embedding.
Of course you really believe that you need everything to be properly normalized, then do it that way, and live with the patterns you would need to access it. But this offers a perspective of doing this another way.
So don't over-concern yourself with embedding everything, and lose a little fear on embedding some things. There are no "get out of jail free cards" for using something not suited to relational modeling in a standard relational way. Choose something that suits your needs.