I want to create two differents users for my meanjs app, a candidate and recruiter, they will have similar and different fields, so how can I extend the user Schema, to avoid duplicating code.
I highly recommend you to use mongoose discriminators. With discriminator you will have 3 schemas, a user Base schema where you will write all the similar code, and then recruiter and candidate schemas that will implement discriminators key. Here's some info about it:
Mongoose discriminators
Guide to Mongoose Discriminators
When looking at tutorials there is often a delineation between a schema and a model, particularly when dealing with mongoose/mongodb.
This makes porting over to postgresql somewhat confusing, as 'models' don't seem to exist under that system. What is the difference the two approaches?
For example, what would be a postgres/sql ORM equivalent of this line?
(mongoose and express.js):
var userSchema = schema.define('local', {
username: String,
password: String,
});
module.exports = mongoose.model('User', userSchema);
In mongoose, a schema represents the structure of a particular document, either completely or just a portion of the document. It's a way to express expected properties and values as well as constraints and indexes. A model defines a programming interface for interacting with the database (read, insert, update, etc). So a schema answers "what will the data in this collection look like?" and a model provides functionality like "Are there any records matching this query?" or "Add a new document to the collection".
In straight RDBMS, the schema is implemented by DDL statements (create table, alter table, etc), whereas there's no direct concept of a model, just SQL statements that can do highly flexible queries (select statements) as well as basic insert, update, delete operations.
Another way to think of it is the nature of SQL allows you to define a "model" for each query by selecting only particular fields as well as joining records from related tables together.
In other ORM systems like Ruby on Rails, the schema is defined via ActiveRecord mechanisms and the model is the extra methods your Model subclass adds that define additional business logic.
A schema is fundamentally describing the data construct of a
document (in MongoDB collection). This schema defines the name of each item of data, and the type of data, whether it is a string, number, date, Boolean, and so on.
A model is a compiled version of the schema. One instance of the model will map to one document in the database.
It is the model that handles the reading, creating, updating, and deleting of documents.
A document in a Mongoose collection is a single instance of a model. So it makes sense that if we're going to work with our data then it will be through the model.
A single instance of a model (like a User instance in var User = mongoose.model('User', userSchema);) maps directly to a single document in the database.
With this 1:1 relationship, it is the model that handles all document interaction - creating, reading, saving, and deleting. This makes the model a very powerful tool.
Taken from "Mongoose for Application Development", by Simon Holmes, 2013
I imagine models as classes created from a schema (maybe I am mistaken).
MongoDB stores everything in BSON , which is a binary format. A simple Hello World BSON document might look like this internally:
\x16\x00\x00\x00\x02hello\x00\x06\x00\x00\x00world\x00\x00. A computer can deal with all that mumbo-jumbo, but that's hard to read for humans. We want something we can easily understand, which is why developers have created the concept of a database model. A model is a representation of a database record as a nice object in your programming language of choice. In this case, our models will be JavaScript objects. Models can serve as simple objects that store database values, but they often have things like data validation, extra methods, and more. As you’ll see, Mongoose has a lot
of those features.
Taken from "Express in Action", by Evan Hahn, 2016
In Short:
A Mongoose model is a wrapper on the Mongoose schema. A Mongoose schema defines the structure of the document, default values, validators, etc., whereas a Mongoose model provides an interface to the database for creating, querying, updating, deleting records, etc.
Reference: Introduction to Mongoose for MongoDB - FCC
I have a well-defined schema, in the sense that there are no Mixed fields that require #markModified().
Per my understanding of the Mongoose docs, Mongoose should detect which fields/subdocuments changed, and only submit the modified fields for persistence among update(). Is this correct? Because it appears that Mongoose is submitting unmodified fields and subdocuments on update (or, eh, upsert) as well.
If I define a nested model or a relation in a mongoose schema, does it have a mechanism to autoload everything when re-hydrating instances, or do I simply have to ensure I've always loaded the appropriate models?
Nested, embedded model docs are auto-loaded, but if it's an ObjectId based relation, then you need to use Mongoose's populate support to explicitly load the related docs.
Mongoose performs all these actions based on the schemas and models that your code defines, typically during your app's startup.
Looking at the Mongoose ODM docs, it doesn't really say much about what are ObjectId's and how they can be used. I think its something like foreign keys in MongoDB?
If so, Embedded Documents seem to achieve the same purpose, when do I use which?
It would be very worthwhile to read the MongoDB documentation or a quick MongoDB intro such as The Little MongoDB Book (it's free) for some background on MongoDB concepts.
To answer your question:
An ObjectID is a unique 12-byte identifier which can be generated by MongoDB as the primary key (_id) for a collection. There is a specification for the ObjectID.
A DBRef (database reference) is an ObjectID referencing an object in another collection. A DBRef does require require another query to fetch the related object, and is a convention supported by the client drivers rather than MongoDB server. The Mongoid equivalent is called referenced relations.
Embedded documents are nested arrays or subdocuments within a document. In Mongoid these are embedded relations.
The approach to data modelling and schema design in MongoDB is very different from relational databases. There are (intentionally) no joins or foreign keys, but the document-oriented approach allows large amounts of related data to be stored and fetched in a single document. Depending on how you plan to query and update your data, embedding or linking may be a more suitable choice. The schema design page on the MongoDB wiki has some helpful tips to get you started.