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Resolve nested GraphQL queries that don't match DB schema

TLDR: How does one resolve nested GraphQL queries where the GQL Schema departs from the data stored inside of a MongoDB database?
We've got an application where each user in our DB has array of foreign keys that reference other documents, in this case "pets." These pets are in a separate collection.
{
"humans": [
{
"id": "1",
"name": "bob",
"pets": [
"jBWMVGjm50l5LGwepDoty1",
"jBWMVGjm50l5LGwepDoty12"
]
}
]
}
I've got a GraphQL API in front of this DB and I'm trying to write my resolvers to handle nested queries. The problem is that our GraphQL schema does not match the DB schema. Inside of the Human type, the pets field expects an array of pets, like this:
type Human {
id: ID!
name: String!
pets: [Pet!] # This is an array of pet objects...
gender: String!
hair: String!
favoriteNum: Int!
alive: Boolean!
createdAt: Int!
}
Currently, the humans resolver will query for a human by his/her ID, and return that human, like so:
human(parent, { input }, { models }) {
return models.Human.findOne({ id: input.id });
}
The issue here, obviously, is that the returned human from the DB does not conform to the GQL schema. The array of "pets" is not an array of objects, it's an array of IDs. What is the proper way of resolving a query like this?
We've tried adding another DB call for the human's pets inside of the humans resolver. The problem here is that if someone makes a query for just the human's name, our resolver would have to go and fetch all of the pets data, even though our user did not request it...
The same problem would crop up again, however, if our pets had foreign keys! How do we resolve this issue?

That looks like a design question. Graphql should always resolve a graph, so your query caller can be sure to receive all reference to this object. Let's take your human type and look at it as caller. When the caller sees there are pets in the human object he thinks he receives this object by doing a query like
query(
human(id) {
pets {
stuffOnlyPetshave
}
}
)
So one approach is to query the pets from your database in the human resolver. That should be default way to get the complete graph.
But if you want to avoid the database query on every human query, then you should do a conditional pet query in you frontend.

How to do a simple join in GraphQL?

I am very new in GraphQL and trying to do a simple join query. My sample tables look like below:
{
phones: [
{
id: 1,
brand: 'b1',
model: 'Galaxy S9 Plus',
price: 1000,
},
{
id: 2,
brand: 'b2',
model: 'OnePlus 6',
price: 900,
},
],
brands: [
{
id: 'b1',
name: 'Samsung'
},
{
id: 'b2',
name: 'OnePlus'
}
]
}
I would like to have a query to return a phone object with its brand name in it instead of the brand code.
E.g. If queried for the phone with id = 2, it should return:
{id: 2, brand: 'OnePlus', model: 'OnePlus 6', price: 900}

TL;DR
Yes, GraphQL does support a sort of pseudo-join. You can see the books and authors example below running in my demo project.
Example
Consider a simple database design for storing info about books:
create table Book ( id string, name string, pageCount string, authorId string );
create table Author ( id string, firstName string, lastName string );
Because we know that Author can write many Books that database model puts them in separate tables. Here is the GraphQL schema:
type Query {
bookById(id: ID): Book
}
type Book {
id: ID
title: String
pageCount: Int
author: Author
}
type Author {
id: ID
firstName: String
lastName: String
}
Notice there is no authorId on the Book type but a type Author. The database authorId column on the book table is not exposed to the outside world. It is an internal detail.
We can pull back a book and it's author using this GraphQL query:
{
bookById(id:"book-1"){
id
title
pageCount
author {
firstName
lastName
}
}
}
Here is a screenshot of it in action using my demo project:
The result nests the Author details:
{
"data": {
"book1": {
"id": "book-1",
"title": "Harry Potter and the Philosopher's Stone",
"pageCount": 223,
"author": {
"firstName": "Joanne",
"lastName": "Rowling"
}
}
}
}
The single GQL query resulted in two separate fetch-by-id calls into the database. When a single logical query turns into multiple physical queries we can quickly run into the infamous N+1 problem.
The N+1 Problem
In our case above a book can only have one author. If we only query one book by ID we only get a "read amplification" against our database of 2x. Imaging if you can query books with a title that starts with a prefix:
type Query {
booksByTitleStartsWith(titlePrefix: String): [Book]
}
Then we call it asking it to fetch the books with a title starting with "Harry":
{
booksByTitleStartsWith(titlePrefix:"Harry"){
id
title
pageCount
author {
firstName
lastName
}
}
}
In this GQL query we will fetch the books by a database query of title like 'Harry%' to get many books including the authorId of each book. It will then make an individual fetch by ID for every author of every book. This is a total of N+1 queries where the 1 query pulls back N records and we then make N separate fetches to build up the full picture.
The easy fix for that example is to not expose a field author on Book and force the person using your API to fetch all the authors in a separate query authorsByIds so we give them two queries:
type Query {
booksByTitleStartsWith(titlePrefix: String): [Book] /* <- single database call */
authorsByIds(authorIds: [ID]) [Author] /* <- single database call */
}
type Book {
id: ID
title: String
pageCount: Int
}
type Author {
id: ID
firstName: String
lastName: String
}
The key thing to note about that last example is that there is no way in that model to walk from one entity type to another. If the person using your API wants to load the books authors the same time they simple call both queries in single post:
query {
booksByTitleStartsWith(titlePrefix: "Harry") {
id
title
}
authorsByIds(authorIds: ["author-1","author-2","author-3") {
id
firstName
lastName
}
}
Here the person writing the query (perhaps using JavaScript in a web browser) sends a single GraphQL post to the server asking for both booksByTitleStartsWith and authorsByIds to be passed back at once. The server can now make two efficient database calls.
This approach shows that there is "no magic bullet" for how to map the "logical model" to the "physical model" when it comes to performance. This is known as the Object–relational impedance mismatch problem. More on that below.
Is Fetch-By-ID So Bad?
Note that the default behaviour of GraphQL is still very helpful. You can map GraphQL onto anything. You can map it onto internal REST APIs. You can map some types into a relational database and other types into a NoSQL database. These can be in the same schema and the same GraphQL end-point. There is no reason why you cannot have Author stored in Postgres and Book stored in MongoDB. This is because GraphQL doesn't by default "join in the datastore" it will fetch each type independently and build the response in memory to send back to the client. It may be the case that you can use a model that only joins to a small dataset that gets very good cache hits. You can then add caching into your system and not have a problem and benefit from all the advantages of GraphQL.
What About ORM?
There is a project called Join Monster which does look at your database schema, looks at the runtime GraphQL query, and tries to generate efficient database joins on-the-fly. That is a form of Object Relational Mapping which sometimes gets a lot of "OrmHate". This is mainly due to Object–relational impedance mismatch problem.
In my experience, any ORM works if you write the database model to exactly support your object API. In my experience, any ORM tends to fail when you have an existing database model that you try to map with an ORM framework.
IMHO, if the data model is optimised without thinking about ORM or queries then avoid ORM. For example, if the data model is optimised to conserve space in classical third normal form. My recommendation there is to avoid querying the main data model and use the CQRS pattern. See below for an example.
What Is Practical?
If you do want to use pseudo-joins in GraphQL but you hit an N+1 problem you can write code to map specific "field fetches" onto hand-written database queries. Carefully performance test using realist data whenever any fields return an array.
Even when you can put in hand written queries you may hit scenarios where those joins don't run fast enough. In which case consider the CQRS pattern and denormalise some of the data model to allow for fast lookups.
Update: GraphQL Java "Look-Ahead"
In our case we use graphql-java and use pure configuration files to map DataFetchers to database queries. There is a some generic logic that looks at the graph query being run and calls parameterized sql queries that are in a custom configuration file. We saw this article Building efficient data fetchers by looking ahead which explains that you can inspect at runtime the what the person who wrote the query selected to be returned. We can use that to "look-ahead" at what other entities we would be asked to fetch to satisfy the entire query. At which point we can join the data in the database and pull it all back efficiently in the a single database call. The graphql-java engine will still make N in-memory fetches to our code. The N requests to get the author of each book are satisfied by simply lookups in a hashmap that we loaded out of the single database call that joined the author table to the books table returning N complete rows efficiently.
Our approach might sound a little like ORM yet we did not make any attempt to make it intelligent. The developer creating the API and our custom configuration files has to decide which graphql queries will be mapped to what database queries. Our generic logic just "looks-ahead" at what the runtime graphql query actually selects in total to understand all the database columns that it needs to load out of each row returned by the SQL to build the hashmap. Our approach can only handle parent-child-grandchild style trees of data. Yet this is a very common use case for us. The developer making the API still needs to keep a careful eye on performance. They need to adapt both the API and the custom mapping files to avoid poor performance.

GraphQL as a query language on the front-end does not support 'joins' in the classic SQL sense.
Rather, it allows you to pick and choose which fields in a particular model you want to fetch for your component.
To query all phones in your dataset, your query would look like this:
query myComponentQuery {
phone {
id
brand
model
price
}
}
The GraphQL server that your front-end is querying would then have individual field resolvers - telling GraphQL where to fetch id, brand, model etc.
The server-side resolver would look something like this:
Phone: {
id(root, args, context) {
pg.query('Select * from Phones where name = ?', ['blah']).then(d => {/*doStuff*/})
//OR
fetch(context.upstream_url + '/thing/' + args.id).then(d => {/*doStuff*/})
return {/*the result of either of those calls here*/}
},
price(root, args, context) {
return 9001
},
},

Angular.js accessing and displaying nested models efficiently

I'm building a site at the moment where there are many relational links between data. As an example, users can make bookings, which will have booker and bookee, along with an array of messages which can be attached to a booking.
An example json would be...
booking = {
id: 1,
location: 'POST CDE',
desc: "Awesome stackoverflow description."
booker: {
id: 1, fname: 'Lawrence', lname: 'Jones',
},
bookee: {
id: 2, fname: 'Stack', lname: 'Overflow',
},
messages: [
{ id: 1, mssg: 'For illustration only' }
]
}
Now my question is, how would you model this data in your angular app? And, while very much related, how would you pull it from the server?
As I can see it I have a few options.
Pull everything from the server at once
Here I would rely on the server to serialize the nested data and just use the given json object. Downsides are that I don't know what users will be involved when requesting a booking or similar object, so I can't cache them and I'll therefore be pulling a large chunk of data every time I request.
Pull the booking with booker/bookee as user ids
For this I would use promises for my data models, and have the server return an object such as...
booking = {
id: 1,
location: 'POST CDE',
desc: "Awesome stackoverflow description."
booker: 1, bookee: 2,
messages: [1]
}
Which I would then pass to a Booking constructor, which would resolve the relevant (booker,bookee and message) ids into data objects via their respective factories.
The disadvantages here are that many ajax requests are used for a single booking request, though it gives me the ability to cache user/message information.
In summary, is it better practise to rely on a single ajax request to collect all the nested information at once, or rely on various requests to 'flesh out' the initial response after the fact.
I'm using Rails 4 if that helps (maybe Rails would be more suited to a single request?)

I'm going to use a system where I can hopefully have the best of both worlds, by creating a base class for all my resources that will be given a custom resolve function, that will know what fields in that particular class may require resolving. A sample resource function would look like this...
class Booking
# other methods...
resolve: ->
booking = this
User
.query(booking.booker, booking.bookee)
.then (users) ->
[booking.booker, booking.bookee] = users
Where it will pass the value of the booker and bookee fields to the User factory, which will have a constructor like so...
class User
# other methods
constructor: (data) ->
user = this
if not isNaN(id = parseInt data, 10)
User.get(data).then (data) ->
angular.extend user, data
else angular.extend this, data
If I have passed the User constructor a value that cannot be parsed into a number (so this will happily take string ids as well as numerical) then it will use the User factorys get function to retrieve the data from the server (or through a caching system, implementation is obviously inside the get function itself). If however the value is detected to be non-NaN, then I'll assume that the User has already been serialized and just extend this with the value.
So it's invisible in how it caches and is independent of how the server returns the nested objects. Allows for modular ajax requests and avoids having to redownload unnecessary data via its caching system.
Once everything is up and running I'll write some tests to see whether the application would be better served with larger, chunked ajax requests or smaller modular ones like above. Either way this lets you pass all model data through your angular factories, so you can rely on every record having inherited any prototype methods you may want to use.

Get last documents with a distinct criteria

Situation
I'm having trouble coming up with a good way to do a certain MongoDb query. First, here's what kind of query I want to do. Assume a simple database which logs entry and exit events (and possibly other actions, doesn't matter) by electronic card swipe. So there's a collection called swipelog with simple documents which look like this:
{
_id: ObjectId("524ab4790a4c0e402200052c")
name: "John Doe",
action: "entry",
timestamp: ISODate("2013-10-01T1:32:12.112Z")
}
Now I want to list names and their last entry times (and any other fields I may want, but example below uses just these two fields).
Current solution
Here is what I have now, as a "one-liner" for MongoDb JavaScript console:
db.swipelog.distinct('name')
.forEach( function(name) {
db.swipelog.find( { name: name, action:"entry" } )
.sort( { $natural:-1 } )
.limit(1)
.forEach( function(entry) {
printjson( [ entry.name, entry.timestamp ] )
})
})
Which prints something like:
[ "John Doe", ISODate("2013-10-01T1:32:12.112Z")]
[ "Jane Deo", ISODate("2013-10-01T1:36:12.112Z")]
...
Question
I think above has the obvious scaling problem. If there are a hundred names, then 1+100 queries will be made to the database. So what is a good/correct way to get "last timestamp of every distinct name" ? Changing database structure or adding some collections is ok, if it makes this easier.

You can use aggregation framework to achieve this:
db.collection.aggregate(
[
{$match:
{action:'entry'}
},
{$group:
{_id:'$name',
first:
{$max:'$timestamp'}
}
}
])
If you likely to include other fields in the results, you can use the $first operator
db.collection.aggregate(
[
{$match:
{action:'entry'}
},
{$sort:
{name:1, timestamp:-1}
},
{$group:
{_id:'$name',
timestamp: {$first:'$timestamp'},
otherField: {$first:'$otherField'},
}
}
])

This answer should be a comment on attish's answer above, but I don't have sufficient rep here to comment
Keep in mind that the aggregation framework cannot return more than 16MB of data. If you have a very large number of users, you may run into this limitation on your production system.
MongoDB 2.6 adds new features to the aggregation framework to deal with this:
db.collection.aggregateCursor() (temporary name) is identical to db.collection.aggregate() except that it returns a cursor instead of a document. This avoids the 16MB limitation
$out is a new pipeline phase that directs the pipeline's output to a collection. This allows you to run aggregation jobs
$sort has been improved to remove its RAM limitations and increase speed
If query performance is more important than data age, you could schedule a regular aggregate command that stores its results in collection like db.last_swipe, then have your application simply query db.last_swipe for the relevant user.
Conclusion: I agree that attish has the right approach. However, you may run into trouble scaling it on the current MongoDB release and should look into Mongo 2.6.

syntax for linking documents in mongodb

If I have two objects in a user collection:
{_id: 1, name: 'foo', workItems: []}
{_id: 2, name: 'bar', workItems: []}
how would I add links to objects in a workItem collection into the workItems array for each user?
I understand direct embedding but some workItems will be assigned to multiple users so I don't want to duplicate data. I have looked on mongodb.org but I can't find any examples of linking.

Sometimes it is just better to duplicate the data. MongoDB is a non relational Database. Some ways of doing stuffs are bad practices with relational databases but intended with non relational one. This really is not the same way of thinking even though there are obvious common points.
At my work, we use it in production and found it both easier and faster for read operations to duplicate the data. This is precisely where the power of MongoDB stands.
Of course, when a workitem is modified, this requires your application to update all the places where it appears... This may not be a good solution for systems that are write intensive.
Another point is that joints are not handled by the engine so that you will have to issue at least a second request. You will then have to do the joint manually on the application side. Either way, you will have to move logic from the database to the client application.

You can do a DBRef like this:
{ $ref : <name of collection where reference is>, $id : <_id of document>, $db : <optional argument for specifying the databse the document is at> }
So your document would look like this:
{_id: 1, name: 'foo', workItems: {$ref: "blarg", $id: "1"}}