Develop Reference

How to use my JSON file to build a NoSQL database?

I have a JSON file which is 3000+ lines. What I'd like to do is create a NoSQL database with the same structure (it has embedded documents between 3-5 levels deep). But I want to add information to each level and create a schema for each item, so that I can go back at a later stage and update the information fields, and even have users log-in and change their own values.
I am using JavaScript to write a script that will iterate through the file and upload to MongoDB the schema that I want, based on the information at each level. But I'm struggling to write the code that does this efficiently. At this stage, I'm just wasting too much time trying this and that, and want to move on to the next step of my site.
Below is an example of the file. Basically, it's a bunch of embedded documents, and then at the final level (which will be at a different depth depending on which document it's in), there is an array where each of the fields is a string.
How can I use this data to create a MongoDB database while adding a schema to each item, but keeping the hierarchical nature of the documents? I want all of the documents to have one schema, and then each of the strings at the final depth to have their own, separate schema as well. I can't think of an efficient way to iterate through.
Example from the JSON file:
{
"Applied Sciences": {
"Agriculture": {
"Agricultural Economics": [
"Agricultural Environment And Natural Resources",
"Developmental Economics",
"Food And Consumer Economics",
"Production Economics And Farm Management"
],
"Agronomy": [
"Agroecology",
"Biotechnology",
"Plant Breeding",
"Soil Conservation",
"Soil Science",
"Theoretical Modeling"
],
Here's my schema for all but the strings at the end:
name: String,
completed: Boolean,
category: "Field",
items: {
type: Array
},
description: String,
resources: {
type: Array
}
};
And my rough code which at this stage just iterates through. I'm trying to use the same function call to create the Arrays in the schema, but I'm just not up to that stage yet because I can't even iterate properly through:
function createDatabase(data){
for (field in data){
items = {};
for (field in data){
if (typeof data[field] == "object");
items[field] = createDatabase(data[field]);
};
return items;
}

Pass query criteria to mongoDB aggregation

our current setup is: SPA frontend, Azure functions with mongoose middleware, MongoDB
(Maybe first read the question***)
Since we have a lot of documents in our DB and our customer wants to query them we are facing the following problem:
The user is assigned to his organization. He wants to search for Doc1s he has not responded to.
Doc1
{
_id
organization -> partitionKey
content
}
By creating doc2 with reference to doc1 he can respond.
Doc2
{
_id
organization -> partitionKey
Doc1ref
content
}
We have a 1:n relationship.
At the moment we filter just by query criteria of doc1 with limit and skip options.
But the new requirement is to filter the same way by referring doc2s.
I was thinking of:
Doing it in my code => Problem: after we have read with limit=100 and I filter it by my code, the result is not 100 anymore.
Extending doc1 by doc2 arrays => Must be the last option
Dynamic aggregation, Prepared in the code and executed at runtime => Don't want to user dynamic aggregations and the benefits of mongoose are almost lost.
Create a MongoDB view with lookup aggregation (populating doc1 by doc1.respondedOrganizations) => Problem is see here is the performance. When searching a lot of documents and then joining them by a non partitionKey.
*** So, I come to my question:
Is it possible to pass a virtual (not existing) query criteria...
doc1.find({ alreadyResponded : my.organization } )
...and use it as input variable in an aggregation
{
$lookup: {
from: Doc2s,
localField: _id,
foreignField: Doc1ref,
as: < output array field >
pipeline: [{
$match: {
$organization: {
$eq: $$alreadyResponded
}]
}
}
It would reduce query performance extremly.
Thanks

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
},
},

AngularFire2 with Firebase Realtime DB - Nested Data Query Angular 6 [duplicate]

The structure of the table is:
chats
--> randomId
-->--> participants
-->-->--> 0: 'name1'
-->-->--> 1: 'name2'
-->--> chatItems
etc
What I am trying to do is query the chats table to find all the chats that hold a participant by a passed in username string.
Here is what I have so far:
subscribeChats(username: string) {
return this.af.database.list('chats', {
query: {
orderByChild: 'participants',
equalTo: username, // How to check if participants contain username
}
});
}

Your current data structure is great to look up the participants of a specific chat. It is however not a very good structure for looking up the inverse: the chats that a user participates in.
A few problems here:
you're storing a set as an array
you can only index on fixed paths
Set vs array
A chat can have multiple participants, so you modelled this as an array. But this actually is not the ideal data structure. Likely each participant can only be in the chat once. But by using an array, I could have:
participants: ["puf", "puf"]
That is clearly not what you have in mind, but the data structure allows it. You can try to secure this in code and security rules, but it would be easier if you start with a data structure that implicitly matches your model better.
My rule of thumb: if you find yourself writing array.contains(), you should be using a set.
A set is a structure where each child can be present at most once, so it naturally protects against duplicates. In Firebase you'd model a set as:
participants: {
"puf": true
}
The true here is really just a dummy value: the important thing is that we've moved the name to the key. Now if I'd try to join this chat again, it would be a noop:
participants: {
"puf": true
}
And when you'd join:
participants: {
"john": true,
"puf": true
}
This is the most direct representation of your requirement: a collection that can only contain each participant once.
You can only index known properties
With the above structure, you could query for chats that you are in with:
ref.child("chats").orderByChild("participants/john").equalTo(true)
The problem is that this requires you to define an index on `participants/john":
{
"rules": {
"chats": {
"$chatid": {
"participants": {
".indexOn": ["john", "puf"]
}
}
}
}
}
This will work and perform great. But now each time someone new joins the chat app, you'll need to add another index. That's clearly not a scaleable model. We'll need to change our data structure to allow the query you want.
Invert the index - pull categories up, flattening the tree
Second rule of thumb: model your data to reflect what you show in your app.
Since you are looking to show a list of chat rooms for a user, store the chat rooms for each user:
userChatrooms: {
john: {
chatRoom1: true,
chatRoom2: true
},
puf: {
chatRoom1: true,
chatRoom3: true
}
}
Now you can simply determine your list of chat rooms with:
ref.child("userChatrooms").child("john")
And then loop over the keys to get each room.
You'll like have two relevant lists in your app:
the list of chat rooms for a specific user
the list of participants in a specific chat room
In that case you'll also have both lists in the database.
chatroomUsers
chatroom1
user1: true
user2: true
chatroom2
user1: true
user3: true
userChatrooms
user1:
chatroom1: true
chatroom2: true
user2:
chatroom1: true
user2:
chatroom2: true
I've pulled both lists to the top-level of the tree, since Firebase recommends against nesting data.
Having both lists is completely normal in NoSQL solutions. In the example above we'd refer to userChatrooms as the inverted index of chatroomsUsers.
Cloud Firestore
This is one of the cases where Cloud Firestore has better support for this type of query. Its array-contains operator allows filter documents that have a certain value in an array, while arrayRemove allows you to treat an array as a set. For more on this, see Better Arrays in Cloud Firestore.

Find text with mongo in several referenced collections

As mongodb does not support join and I have the need to search in various business collections, services, and users, I have come up with a solution but I need it to be validated and / or improved.
The flow of the scheme would look like this:
Business = Schema({
name:String
services:[{
type:ObjectId,
ref:'Services'
}],
specialist:[{
type:ObjectId,
ref:'User'
}]
})
User = Schema({
full_name:String
businesses:[{
_id:{
type:ObjectId,
ref:'Business'
},
name:String,
role:String,
is_owner:Boolean
}]
})
Service = Schema({
name:String,
business:{
type:ObjectId,
ref:'Business'
},
specialist:[{
type:ObjectId,
ref:'User'
}]
})
Search = Schema({
text:{
type:String,
index:'text'
},
business:{
_id:{
type:ObjectId,
ref:'Business'
},
name:String
},
services:[{
_id:{
type:ObjectId,
ref:'Service'
},
name:
}],
specialist:[{
_id:{
type:ObjectId,
ref:'User'
},
full_name:String
}]
})
Businesses offer services, services are performed by specialists, and every time a business adds a specialist, the user will have a new business in their data with a specific role.
The idea is that when you create a business, you create a document in Search, and when the business creates services you assign them to the specialists, they will be added to the Search collection. In each modification of this document the text field will be updated with a concatenation of the business name, services and specialists, with this they would have a single collection where to do the searches indexed of text. Any field you are looking for will result in business.
Who has suggestions, both in model and in the solution of the common collection to search.

Check out $lookup (aggregation) new in version 3.2.
Performs a left outer join to an unsharded collection in the same
database to filter in documents from the “joined” collection for
processing. The $lookup stage does an equality match between a field
from the input documents with a field from the documents of the
“joined” collection.
By the way,the advantage of a document database is that it eliminates lots of Joins. Related information is stored together for fast query access through the MongoDB query language. This data model give you the ability to represent hierarchical relationships, to store arrays, and other more complex structures easily. So I suggest when you design a MongoDB schema you should place as much in a single document as you can.
You can see more details from MongoDB relationships: embed or reference?.
Hope this helps.