Mongoose (and MongoDB for that matter) seem to prefer the dbref/population idiom rather than traditional SQL relationships.
While I respect the simplicity of the solution proposed here: How to show related subdocument properties in Meteor
The MongoDB docs speak to DBRefs here:
http://docs.mongodb.org/manual/reference/database-references/#dbref-explanation
and Mongoose Populations are documented here: http://mongoosejs.com/docs/populate.html
While pulling an item from 1 query and passing it into another query is definitely one option the syntactic sugar of being able to pull a single query that provides all of the required data in one pull has it's advantages.
What is the Meteor philosophy on this?
The closest thing I know of to mongoose's populations is collection helpers. It works on both the client and the server and allows you to automatically transform documents into objects with useful methods.
As you'll see in the documentation, you can use these methods to, among other things, establish relationships between documents. For example if you have Books and Authors collections you can define a helper like this:
Books.helpers({
author: function() {
return Authors.findOne(this.authorId);
}
});
Which lets your write code this:
Books.findOne().author().firstName;
You still need to go through the effort of writing the join yourself, but once written you can use it everywhere.
Related
I am using Knex.js to build SQL queries. It works well but I need to convert my query results into domain entities (a type representing an object from the domain) for my graphql resolvers. I used Knex to avoid using an ORM because a number of people online made it seem like an ORM will make queries more difficult. My current best idea is to follow the Repository pattern and have the ugly code for converting results to classes in the repo class. Better ideas are welcome :)
As I understood you want to just make a db-call based on GraphQL query (which is mean you already have db and want to use simple ORM instead of EF for example).
I don't know which platform do you have, but if you have .net, you can take a look NReco.GraphQL. It allows to set db-connection and define graphql schema in the json file (graphql schmea to db-table including relation between schemas), definately, it's worth take a look.
We are in the process of writing tests for our Node/MongoDB backend and I have a question about finding documents.
My understanding is it's preferable to use Mongoose to get your documents as opposed to the MongoDB driver. In other words, doing Customer.findOne().exec() instead of setting up a db connection and then doing db.collection("customers").findOne().
Other than the first option (using Mongoose to find the doc) being slightly less verbose, I'm curious what the other reasons are. Is a straight MongoDB lookup a bigger drag on the database?
One of the great feature of mongoose is the built in validation mechanism. Also the Populate method to get data from multiple collections is an awesome characteristic of Mongoose.
In terms of query performance, here is a good read:
https://medium.com/#bugwheels94/performance-difference-in-mongoose-vs-mongodb-60be831c69ad
Hope this helps :)
MongoDB is schemaless, which means a collection (table in relational DB) can contain documents (rows) of different structure - having different fields, for instance.
I'm new to Mongo, so I decided to use Mongoose which should make things a bit easier. Reading the guide:
Defining your schema
Everything in Mongoose starts with a Schema. Each schema maps to a
MongoDB collection and defines the shape of the documents within that
collection.
Notice at the last sentence. Doesn't it conflict with the schemaless philosophy of MongoDB? Or maybe it's that in 99% of cases, I want a collection of documents of the same structure, so in the introductory guide only that scenario is discussed? Does Mongoose even allow me to create schemaless collection?
MongoDB does not require a schema, but that confuses a lot of people from a standard SQL background so Mongoose is aimed at trying to bridge the gap between SQL and NoSQL. If you want to maintain a collection with different document types, than by all means do not use Mongoose.
If you're okay with the schemaless nature of MongoDB there is no reason to add additional abstractions and overhead to MongoDB which is what Mongoose surely applies.
The purpose of Mongoose is to use a Schema, there are other database drivers you can use to take advantage of MongoDBs Schemaless nature such as Mongoskin.
If you want to utilize the Mongoose's Schema Design and make an exeception you can use: Mongoose Strict.
According to the docs:
The strict option, (enabled by default), ensures that values passed to our model constructor that were not specified in our schema do not get saved to the db.
NoSQL doesn't mean no schema. It means, the database doesn't control schema. For instance, with MongoDB, you can look hard to find anything that determines a field in a document is a string, or a number or a date. The database doesn't care. You could store a number in a field in one document and in another document in the same collection, and in the same field, you could store a string. But, from a coding perspective, that can become quite hairy and would be bad practice. This is why you still have to define data types. So, you still need a schema of sorts and why Mongoose offers and, in fact, enforces this functionality.
Going a conceptual level higher now, the major concept of NoSQL is to put schema inside your code and not in some file of SQL commands i.e. not telling the DB what to expect in terms of data types and schema to be controlled by the database. So, instead of needing to have migration files/paths and versioning on database schema, you just have your code. ORMs, for example, try to bridge this issue too, where they often have automated migration systems.
ORMs also try to avoid the Object Relational Impedance Mismatch problem, which MongoDB avoids completely. Well, it doesn't have relationships per se, so the problem is avoided out of necessity.
Getting back to schema, with MongoDB and Mongoose, if you or one of your team make a change to the schema in the code, all your other team members need to do to get the database to work with it is pull in that new code. Voila, the schema is up-to-date and will work. No need to also pull in a copy of the newer migration file (to determine the new schema of the DB) to then have to run it on a (copy of the) db to update it too, just to continue programming. There is no need to make changes in schema in multiple places.
So, in the end, if you can imagine your schema is always in your code (only), making changes to an application with a database persisting state like MongoDB is a good bit simpler and even safer. (Safer, because code and schema can't get out of sync, as it's the one and the same.)
I'm new to mongoDB and in a node project i'm using mongoose to create a schema for my database. I have come to understand that in my case i should be using embedded data instead of by reference. (http://docs.mongodb.org/manual/core/data-modeling-introduction/)
The structure that i need to store in the database is something like this:
book
| title
| | chapter
| | | content (url to a html file)
Coming from a mySQL world, i'm trying to understand the noSQL database concepts and i was wondering how one would design the mongoose schema.
Thx,
You can nest documents, and document arrays in a MongoDB document:
db.books.findOne();
can return a JSON:
{
isbn:"253GHST78F6",
title:"some book",
author:"Earnest Hemmingway",
chapters:[
{title:"chapter 1",content:"http://api.com/chapters/1.html"},
{title:"chapter 2",content:"http://api.com/chapters/2.html"}
]
}
But, there are a few more things to keep in mind when modeling a collection in MongoDB:
Model data as close as possible to what will be asked for: There are no Joins in MongoDB, so, try to keep all such data together (pre joined), that is supposed be queried together in future.
Nesting is good for queries but nested update and search is bad:
Indexing and searching inside nested documents, especially arrays, will be expensive.
Avoid placing any data in nested arrays, that is very frequently updated.
There are no Foriegn Keys in MongoDB. So, if you have multiple copies of same document nested under different collections, keeping all of them updated is your responsibility.
First take a look at this Martin Fowlers excellent video:
I think there is no better authority that can explain nosql, then Fowler. Especially knowing his sql background.
Second, MongoDB follows json schema. If you know how to work with json, you will know how to work with nosql (mongodb). Basic thing that needs to be understand is that Mongo schema is expressed in a language that is using it.
So, if you are using NodeJS with Mongo, you still have objects and array to work with. In simple words, Mongo is not forcing any particular schema. It is on developer to create his scheme based on his language/mongo driver.
So how would you express you data logic in your language ? If it is in form of JS object, then move that form to db.
I really like MongoDB, becuse it can be combined with some great JS tools like Underscore.js for all kind of data manipulations.
Mongoose doesn't appear to support Mongo DBRefs. Apparently they released "DBRef" support but it was actually just plain references (no ability to reference documents from different collections). I've finally managed to craft a schema that allows me to hold an array of ObjectID references and populate them, which is great for certain parts of my schema, but it would be extremely convenient if I could use proper DBRefs to create an array that lets me refer to documents from a number of collections.
Luckily(?) there's a module that can monkey patch DBRef support into mongoose: https://github.com/goulash1971/mongoose-dbref
Unluckily, I can't make any sense of the documents. The best I can tell is that there is no ability to use DBRefs in an array (there is a 'fetch' method to dereference, but it takes a single dbref); 'populate' doesn't seem to be patched to fill in DBRefs, and I can't tell how I'm supposed to assign a DBRef given a source document [collection.items.push(?????)].
From the internet, it appears that I can assign an object of the form { $id: document._id, $ref: 'Collection' } -- when logging the result, it appears to have "taken" as a DBRef data type, but I am unsure if this is correct since I cannot seem to do anything useful with it (turn the ref back into a document).
What I really want is a way to represent an ordered list of items from multiple collections; any solution to this is fine by me, but so far DBRefs are the best I've got. Help?
A DBRef (as explained in detail here) is a tuple containing the ObjectId, collection name, and possibly the database container name of a referenced object in another collection.
Internally in the MongoDB server these serve no purpose and are just data within a document. The point is for use in some drivers and ODM implementations to allow for some sort of automatic expansion by issuing additional queries to the server in order to have the data that is elsewhere appear to be an ordinary sub-document part of the referencing document. This can be automatic or a lazy load depending on the implementation, but is always done over the wire and processed on the client side. The server will do nothing to traverse or join this data.
Additionally, MongoDB collections are schemaless, so there is nothing as in the relational sense that says all documents in a collection have to have the same structure.
In the case of Mongoose, there are built in functions to do this sort of loading for you as a convenience, and while not strictly a DBRef and utilizing documents with a different schema in the same collection is the same means as storing the documents external to the referencing document.
It is important to consider the data access patterns of your application and not to simply opt for the same sort of relational design you are used to. Keeping in mind that you are only ever reading from one collection at a time, it is most desirable to get at the data you need in a single read or write, without multiple operations over the wire, which will slow things down considerably.
In short, you should always consider embedding sub-documents first, and then use external references any your best supported form only when you absolutely have to. Your application users will thank you in the end.