Given the following Schemata with an n-to-m relation:
var CampaignSchema = new Schema({
name: { type: String },
players: [{
type: Schema.ObjectId,
ref: 'Player'
}]
});
var PlayerSchema = new Schema({
name: { type: String },
campaigns: [{
type: Schema.ObjectId,
ref: 'Campaign'
}]
});
When I create/update a Campaign document, the Player documents should be kept in sync with the players array of the Campaign model.
I saw a lot of examples in the web using the pre save hook, updating the corresponding players to include/exclude this campaign from their campaigns array.
However, I would expect the post save hook to be a better place for this kind of updates, as I can be sure that the model was saved successfully before updating the player documents.
So my question(s) would be:
is there anything I have to consider when choosing between a pre/post save hook to keep my relations in sync, or is it irrelevant and just a matter of taste?
I had the same issue, after implementing a solution myself I ended up with circular dependency issues. Have a look on this package, it solved my problem easily.
Related
I'm currently learning some backend stuff using an Udemy course and I have an example website that lets you add campgrounds (campground name, picture, description, etc.) and review them. I'm using the Express framework for Node.js, and Mongoose to access the database.
My campground schema looks like:
const campgroundSchema = new mongoose.Schema({
name: String,
image: String,
description: String,
price: String,
comments: [
{
type: mongoose.Schema.Types.ObjectId,
ref: "Comment"
}
],
rating: {type: Number, default: 0}
});
And my comment/review schema looks like:
const commentSchema = new mongoose.Schema({
text: String,
rating: {
type: Number,
min: 1,
max: 5,
validate: {validator: Number.isInteger}
},
campground: {type: mongoose.Schema.Types.ObjectId, ref: "Campground"}
});
Campgrounds and Comments also have references to a User but I've left that out for simplicity.
I'm looking to know the best practice for updating and displaying the campground average rating.
The method used by the tutorial I'm following is to recalculate the average rating each time a comment is added, changed, or deleted. Here's how it would work for a new comment:
Campground.findById(campgroundId).populate("comments").exec(function(err, campground) {
Comment.create(newComment, function(err, comment) {
campground.comments.push(comment);
campground.rating = calculateRating(campground.comments);
campground.save();
});
});
"calculateRating" iterates through the comment array, gets the total sum, and returns the sum divided by the number of comments.
My gut instinct tells me that there should be a way to make the "rating" field of Campground perform the functionality of the "calculateRating" function, so that I don't have to update the rating every time a comment is added, changed, or removed. I've been poking around documentation for a while now, but since I'm pretty new to Mongoose and databases in general, I'm a bit lost on how to proceed.
In summary: I want to add functionality to my Campground model so that when I access its rating, it automatically accesses each comment referenced in the comments array, sums up their ratings, and returns the average.
My apologies if any of my terminology is incorrect. Any tips on how I would go about achieving this would be very much appreciated!
Love,
Cal
I think what you are trying to do is get a virtual property of the document that gets the average rating but it does not get persisted to the mongo database.
according to mongoosejs :- Virtuals are document properties that you can get and set but that do not get persisted to MongoDB. They are set on the schema.
You can do this:
CampgroundSchema.virtual('averageRating').get(function() {
let ratings = [];
this.comments.forEach((comment) => ratings.push(comment.rating));
return (ratings.reduce((a,b)=>a+b)/ratings.length).toFixed(2);
});
After that on your view engine after finding campgrounds or a campground, all you need to call is ; campground.averageRating;
Read more here : https://mongoosejs.com/docs/guide.html#virtuals
also note that you can not make any type of query on virtual properties.
I know there are a lot of similar questions, but they're too old and since Mongodb has evolved alot for last 5-6 years I am looking for a good schema design.
Goal: I want to have a post with comments by users.
What I have designed so far is:
Separate post model:
const projectSchema = new mongoose.Schema({
user: { type: mongoose.Schema.Types.ObjectId, required: true, ref: 'User' },
title: { type: String, required: true },
image: { type: String, default: undefined },
description: { type: String, required: true, minLength: 200, maxlength: 500 },
comments: [{
type: mongoose.Schema.Types.ObjectId, ref: 'Comment'
}],
state: { type: Boolean, default: true },
collaborators: { type: Array, default: [] },
likes: { type: Array, default: [] }
})
And a separate comments model:
const commentSchema = new mongoose.Schema({
comment: { type: String, required: true },
project: { type: String, required: true, ref: 'Project' },
user: { type: String, required: true, ref: 'User' }
})
The reason I am going for the relational approach is because if the comments increase to say 10,000 in number, it will increase the size of schema by alot.
This way, no matter how many comments we can populate them using their IDs, also, we will have different collection for comments iself.
Reference : one-to-many
Is this a good approach for my project?
The way I am querying the comments from one single post:
const project = await Project.findById(
new mongoose.Types.ObjectId(req.params.projectId)
).populate({
path: 'comments',
populate: { path: 'user' }
}).lean()
Whether it's a good design depends how many comments per post do you expect, and what query will be performed on your app.
There's a good blog from mongodb.com on how to design your database schema
The common design is:
One to Few (Use embed)
One to Many (Use embed reference)
One to squillions (The usual relational database one-to-many approach)
Summary is:
So, even at this basic level, there is more to think about when designing a MongoDB schema than when designing a comparable relational schema. You need to consider two factors:
Will the entities on the “N” side of the One-to-N ever need to stand alone?
What is the cardinality of the relationship: is it one-to-few; one-to-many; or one-to-squillions?
Based on these factors, you can pick one of the three basic One-to-N schema designs:
Embed the N side if the cardinality is one-to-few and there is no need to access the embedded object outside the context of the parent object
Use an array of references to the N-side objects if the cardinality is one-to-many or if the N-side objects should stand alone for any reasons
Use a reference to the One-side in the N-side objects if the cardinality is one-to-squillions
There is also a blog about advanced schema design which is worth the read.
You seems to be using the two-way referencing approach.
The difference between yours and one-to-squillions is you are not only storing post id reference on comment document, but also storing comment ids as reference in post document, while one-to-squillions will only stores project id reference in comment document.
Using your approach will be better if you need to get comment ids of a post. But the disadvantage is you need to run two queries when deleting or creating a comment, one to delete / create comment id from post, and the other one to delete / create the comment document it self. It's also will be slower to find "which post belongs to given comment id".
While using one-to-squillions would gives you worse performance when performing a query to get comments by post id. But you can mitigate this by properly indexing your comment collection.
Here is my current Schema
Brand:
var mongoose = require('mongoose');
var Schema = mongoose.Schema;
var BrandSchema = new mongoose.Schema({
name: { type: String, lowercase: true , unique: true, required: true },
photo: { type: String , trim: true},
email: { type: String , lowercase: true},
year: { type: Number},
timestamp: { type : Date, default: Date.now },
description: { type: String},
location: { },
social: {
website: {type: String},
facebook: {type: String },
twitter: {type: String },
instagram: {type: String }
}
});
Style:
var mongoose = require('mongoose');
var Schema = mongoose.Schema;
var StyleSchema = new mongoose.Schema({
name: { type: String, lowercase: true , required: true},
});
Product
var mongoose = require('mongoose');
var Schema = mongoose.Schema;
var ProductSchema = new mongoose.Schema({
name: { type: String, lowercase: true , required: true},
brandId : {type: mongoose.Schema.ObjectId, ref: 'Brand'},
styleId: {type: mongoose.Schema.ObjectId, ref: 'Style'},
year: { type: Number },
avgRating: {type: Number}
});
Post:
var mongoose = require('mongoose');
var Schema = mongoose.Schema;
var PostSchema = new mongoose.Schema({
rating: { type: Number},
upVote: {type: Number},
brandId : {type: mongoose.Schema.ObjectId, ref: 'Brand'},
comment: {type: String},
productId: {type: mongoose.Schema.ObjectId, ref: 'Style'},
styleId: {type: mongoose.Schema.ObjectId, ref: 'Style'},
photo: {type: String}
});
I'm currently making use of the mongoose populate feature:
exports.productsByBrand = function(req, res){
Product.find({product: req.params.id}).populate('style').exec(function(err, products){
res.send({products:products});
});
};
This works, however, being a noob --- i've started reading about performance issues with the mongoose populate, since it's really just adding an additional query.
For my post , especially, it seems that could be taxing. The intent for the post is to be a live twitter / instagram-like feed. It seems that could be a lot of queries, which could greatly slow my app down.
also, I want to be able to search prodcuts / post / brand by fields at some point.
Should i consider nesting / embedding this data (products nested / embedded in brands)?
What's the most efficient schema design or would my setup be alright -- given what i've specified I want to use it for?
User story:
There will be an Admin User.
The admin will be able to add the Brand with the specific fields in the Brand Schema.
Brands will have associated Products, each Product will have a Style / category.
Search:
Users will be able to search Brands by name and location (i'm looking into doing this with angular filtering / tags).
Users will be able to search Products by fields (name, style, etc).
Users will be able to search Post by Brand Product and Style.
Post:
Users will be able to Post into a feed. When making a Post, they will choose a Brand and a Product to associate the Post with. The Post will display the Brand name, Product name, and Style -- along with newly entered Post fields (photo, comment, and rating).
Other users can click on the Brand name to link to the Brand show page. They can click on the Product name to link to a Product show page.
Product show page:
Will show Product fields from the above Schema -- including associated Style name from Style schema. It will also display Post pertaining to the specific Product.
Brand show page:
Will simply show Brand fields and associated products.
My main worry is the Post, which will have to populate / query for the Brand , Product, and Style within a feed.
Again, I'm contemplating if I should embed the Products within the Brand -- then would I be able to associate the Brand Product and Style with the Post for later queries? Or, possibly $lookup or other aggregate features.
Mongodb itself does not support joins. So, mongoose populate is an attempt at external reference resolution. The thing with mongodb is that you need to design your data so that:
most of you queries need not to refer multiple collections.
after getting data from query, you need not to transform it too much.
Consider the entities involved, and their relations:
Brand is brand. Doesn't depend on anything else.
Every Product belongs to a Brand.
Every Product is associated with a Style.
Every Post is associated with a Product.
Indirectly, every Post is associated to a Brand and Style, via product.
Now about the use cases:
Refer: If you are looking up one entity by id, then fetching 1-2 related entities is not really a big overhead.
List: It is when you have to return a large set of objects and each object needs an additional query to get associated objects. This is a performance issue. This is usually reduced by processing "pages" of result set at a time, say 20 records per request. Lets suppose you query 20 products (using skip and limit). For 20 products you extract two id arrays, one of referred styles, and other of referred brands. You do 2 additional queries using $in:[ids], get brands and styles object and place them in result set. That's 3 queries per page. Users can request next page as they scroll down, and so on.
Search: You want to search for products, but also want to specify brand name and style name. Sadly, product model only holds ids for style and brand. Same issue with searching Posts with brand and product. Popular solution is to maintain a separate "search index", a sort of table, that stores data exactly the way it will be searched for, with all searchable fields (like brand name, style name) at one place. Maintaining such search collections in mongodb manually can be a pain. This is where ElasticSearch comes in. Since you are already using mongoose, you can simply add mongoosastic to your models. ElasticSearch's search capabilities are far greater than a DB Storage engine will offer you.
Extra Speed: There is still some room for speeding things up: Caching. Attach mongoose-redis-cache and have frequent repeated queries served, in-memory from Redis, reducing load on mongodb.
Twitter like Feeds: Now if all Posts are public then listing them up for users in chronological order is a trivial query. However things change when you introduce "social networking" features. Then you need to list "activity feeds" of friends and followers. There's some wisdom about social inboxes and Fan-out lists in mongodb blog.
Moral of the story is that not all use cases have only "db schema query" solutions. Scalability is one of such cases. That's why other tools exist.
In my application I have a User Collection. Many of my other collections have an Author (an author contains ONLY the user._id and the user.name), for example my Post Collection. Since I normally only need the _id and the name to display e.g. my posts on the UI.
This works fine, and seems like a good approach, since now everytime I deal with posts I don`t have to load the whole user Object from the database - I can only load my post.author.userId/post.author.name.
Now my problem: A user changes his or her name. Obviously all my Author Objects scattered around in my database still have the old author.
Questions:
is my approuch solid, or should I only reference the userId everywhere I need it?
If I'd go for this solution I'd remove my Author Model and would need to make a User database call everytime I want to display the current Users`s name.
If I leave my Author as is, what would be a good way to implement a solution for situations like the user.name change?
I could write a service which checks every model which has Authors of the current user._id and updates them of course, but this sounds very tedious. Although I'm not sure there's a better solution.
Any pro tipps on how I should deal with problems like this in the future?
Yes, sometime database are good to recorded at modular style. But You shouldn't do separating collection for user/author such as
At that time if you use mongoose as driver you can use populate to get user schema data.
Example, I modeling user, author, post that.
var UserSchema = new mongoose.Schema({
type: { type: String, default: "user", enum: ["user", "author"], required: true },
name: { type: String },
// Author specific values
joinedAt: { type: Date }
});
var User = mongoose.model("User", UserSchema);
var PostSchema = new mongoose.Schema({
author: { type: mongoose.Scheam.Types.ObjectId, ref: "User" },
content: { type: String }
});
var Post = mongoose.model("Post", PostSchema);
In this style, Post are separated model and have to save like that. Something like if you want to query a post including author's name, you can use populate at mongoose.
Post.findOne().populate("author").exce(function(err, post) {
if(err)
// do error handling
if(post){
console.log(post.author.type) // author
}
});
One solution is save only id in Author collection, using Ref on the User collection, and populate each time to get user's name from the User collection.
var User = {
name: String,
//other fields
}
var Author = {
userId: {
type: String,
ref: "User"
}
}
Another solution is when updating name in User collection, update all names in Author collection.
I think first solution will be better.
I have a model "Category". Collection categories contains several objects.
I also a have model "Post". Collection posts may contain a lot of objects with users' posts. "Post" object may relate to 1+ categories. How to link "Post" object to 1+ "Category"-objects without placing "Post"-object inside "Category"-object as subdocument? Certainly, I need to have an option to find all posts related to certain category.
One of the ways I can imagine is to store in "Post"-object obj_id of all categories which it's related to. Smth like this:
var postSchema = mongoose.Schema({
title: String,
description: String,
category: [ObjectId],
created_time: Number,
})
and add category later...
post.category.push(obj_id);
but is it really a mongoose-way? Which way is correct? Thanks.
P.S. I've also read about population methods in mongoose docs, may it be useful in my case? Still not completely clear for me what is this.
Populate is a better tool for this since you are creating a many to many relationship between posts and categories. Subdocuments are appropriate when they belong exclusively to the parent object. You will need to change your postSchema to use a reference:
var postSchema = mongoose.Schema({
title: String,
description: String,
category: [{ type: Schema.Types.ObjectId, ref: 'Category' }],
created_time: Number,
});
You can add categories by pushing documents onto the array:
post.category.push(category1);
post.save(callback);
Then rehydrate them during query using populate:
Post.findOne({ title: 'Test' })
.populate('category')
.exec(function (err, post) {
if (err) return handleError(err);
console.log(post.category);
});