Develop Reference

Populate field then find - mongoose

I have a Cheques and a Payees collection, every cheque has its corresponding Payee ID.
What I'm trying to do is to write some queries on cheques, but I need to preform the searching after populating the payee (to get the name)
const search = req.query.search || "";
const cheques = await Cheque
.find({
isCancelled: false,
dueDate: { $gte: sinceDate, $lte: tillDate }
})
.select("_id serial dueDate value payee")
.skip(page * limit)
.limit(limit)
.sort({ dueDate: -1, serial: 1 })
.populate({
path: "payee",
select: "name"
})
I guess what I'm trying do is fit this somewhere in my code,
match: {
name: { $regex: search, $options: "i" }
},
I have tried to put the match within the populate, but then it will still find all cheques even if they don't satisfy the population match but populate as null.

I hate this answer and I hope someone is going to post a better one, but I've surfed the web for that with no luck.
The only method I was able to find is to use the $lookup method in aggregation.
So you'll have to change your code from calling .find() to .aggregate().
It's not the sad news, it's great, stable and no problems at all.
but I hated that because it's going to change some patterns you might be following in your code.
const search = req.query.search || "";
const cheques = await Cheque
.aggregate([
{
$lookup: { // similar to .populate() in mongoose
from: 'payees', // the other collection name
localField: 'payee', // the field referencing the other collection in the curent collection
foreignField: '_id', // the name of the column where the cell in the current collection can be found in the other collection
as: 'payee' // the field you want to place the db response in. this will overwrite payee id with the actual document in the response (it only writes to the response, not on the database, no worries)
},
{ // this is where you'll place your filter object you used to place inside .find()
$match: {
isCancelled: false,
dueDate: { $gte: sinceDate, $lte: tillDate }
'payee.branch': 'YOUR_FILTER', // this is how you access the actual object from the other collection after population, using the dot notation but inside a string.
}
},
{ // this is similar to .select()
$project: {_id: 1, serial: 1, dueDate: 1, value: 1, payee: 1}
},
{
$unwind: '$payee' // this picks the only object in the field payee: [ { payeeDoc } ] --> { payeeDoc }
}
])
.skip(page * limit)
.limit(limit)
.sort({ dueDate: -1, serial: 1 })
Notice how you can no longer chain .select() and .populate() on the model query the way you used to do it on .find(), because now you're using .aggregate() which returns a different class instance in mongoose.
you can call .projcet() instead of doing it inside the aggregation array if you want to, but as far as I know, you can't use the .select() method.
My opinion-based solution to this problem is to include the payee information you need for filtering in the Cheque collection.
In my senario, this happen when I was filtering for the sake of my users-roles and permissions, so someone can not see what another one is seeing.
It's up to you, but this makes it easier later when you want to generate the reports (I assume you're working on a payment service).

The populate() feature provided by mongoose first fetches all the Cheques with given conditions and then makes another query to payee with _ids to populate the fields you wanted.
https://mongoosejs.com/docs/api.html#query_Query-populate
by putting match in populate you're filtering which cheques need to be populated but not the cheques themselves.
A simple solution for this is to filter the cheques which are populated as null and return them for your use.
If you see more queries of this sort and/or the collection is huge, it's better you add the payee name in the Cheques collection itself if that fits your purpose.

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.

Using Meteor publish-with-relations package where each join cannot use the _id field

I am working to solve a problem not dissimilar to the discussion present at the following blog post. This is wishing to publish two related data sets in Meteor, with a 'reactive join' on the server side.
https://www.discovermeteor.com/blog/reactive-joins-in-meteor/
Unfortunately for me, however, the related collection I wish to join to, will not be joined using the "_id" field, but using another field. Normally in mongo and meteor I would create a 'filter' block where I could specify this query. However, as far as I can tell in the PWR package, there is an implicit assumption to join on '_id'.
If you review the example given on the 'publish-with-relations' github page (see below) you can see that both posts and comments are being joined to the Meteor.users '_id' field. But what if we needed to join to the Meteor.users 'address' field ?
https://github.com/svasva/meteor-publish-with-relations
In the short term I have specified my query 'upside down' (as luckily I m able to use the _id field when doing a reverse join), but I suspect this will result in an inefficient query as the datasets grow, so would rather be able to do a join in the direction planned.
The two collections we are joining can be thought of as like a conversation topic/header record, and a conversation message collection (i.e. one entry in the collection for each message in the conversation).
The conversation topic in my solution is using the _id field to join, the conversation messages have a "conversationKey" field to join with.
The following call works, but this is querying from the messages to the conversation, instead of vice versa, which would be more natural.
Meteor.publishWithRelations({
handle: this,
collection: conversationMessages,
filter: { "conversationKey" : requestedKey },
options : {sort: {msgTime: -1}},
mappings: [{
//reverse: true,
key: 'conversationKey',
collection: conversationTopics,
filter: { startTime: { $gt : (new Date().getTime() - aLongTimeAgo ) } },
options: {
sort: { createdAt: -1 }
},
}]
});

Can you do a join without an _id?
No, not with PWR. Joining with a foreign key which is the id in another table/collection is nearly always how relational data is queried. PWR is making that assumption to reduce the complexity of an already tricky implementation.
How can this publish be improved?
You don't actually need a reactive join here because one query does not depend on the result of another. It would if each conversation topic held an array of conversation message ids. Because both collections can be queried independently, you can return an array of cursors instead:
Meteor.publish('conversations', function(requestedKey) {
check(requestedKey, String);
var aLongTimeAgo = 864000000;
var filter = {startTime: {$gt: new Date().getTime() - aLongTimeAgo}};
return [
conversationMessages.find({conversationKey: requestedKey}),
conversationTopics.find(requestedKey, {filter: filter})
];
});
Notes
Sorting in your publish function isn't useful unless you are using a limit.
Be sure to use a forked version of PWR like this one which includes Tom's memory leak fix.
Instead of conversationKey I would call it conversationTopicId to be more clear.

I think this could be now much easier solved with the reactive-publish package (I am one of authors). You can make any query now inside an autorun and then use the results of that to publish the query you want to push to the client. I would write you an example code, but I do not really understand what exactly do you need. For example, you mention you would like to limit topics, but you do not explain why would they be limited if you are providing requestedKey which is an ID of a document anyway? So only one result is available?

Modelling blogs and ratings in mongodb and nodejs

I have a blogs collection that contains title, body and agrregate rating that the users have given to them. Another collection 'Ratings' whose schema has reference to the blog, user who rated(if at all he rates them) it in the form of their ObjectIds and the rating they have given ie., +1 or -1.
When a particular user browses through blogs in the 'latest first' order (say 40 of them per page. Call them an array of blogs[0] to blogs[39]) I have to retrieve the rating documents related to this particular user and those 40 blogs if at all the user rated them and notify him of what ratings he has given those blogs.
I tried to extract all rating documents of a particular user in which blog reference objectIds lie between blogs[0]._id and blogs[39]._id which returns empty list in my case. May be objectIds cant be compared using $lt and $gt queries. In that case how should I go about it? Should I redesign my schemas to fit to this scenario?
I am using mongoosejs driver for this case. Here are the relevant parts of the code which differ a bit in execution but youu get the idea.
Schemas:
Client= new mongoose.Schema({
ip:String
})
Rates = new mongoose.Schema({
client:ObjectId,
newsid:ObjectId,
rate:Number
})
News = new mongoose.Schema({
title: String,
body: String,
likes:{type:Number,default:0},
dislikes:{type:Number,default:0},
created:Date,
// tag:String,
client:ObjectId,
tag:String,
ff:{type:Number,default:20}
});
models:
var newsm=mongoose.model('News', News);
var clientm=mongoose.model('Client', Client);
var ratesm=mongoose.model('Rates', Rates);
Logic:
newsm.find({tag:tag[req.params.tag_id]},[],{ sort:{created:-1},limit: buffer+1 },function(err,news){
ratesm.find({client:client._id,newsid:{$lte:news[0]._id,$gte:news.slice(-1)[0]._id}},function(err,ratings){
})
})
Edit:
While implementing the below said schema, I had to do this query in mongoose.js
> db.blogposts.findOne()
{ title : "My First Post", author: "Jane",
comments : [{ by: "Abe", text: "First" },
{ by : "Ada", text : "Good post" } ]
}
> db.blogposts.find( { "comments.by" : "Ada" } )
How do I do this query in mongoose?

A good practice with MongoDB (and other non-relational data stores) is to model your data so it is easy to use/query in your application. In your case, you might consider denormalizing the structure a bit and store the rating right in the blog collection, so a blog might look something like this:
{
title: "My New Post",
body: "Here's my new post. It is great. ...",
likes: 20,
dislikes: 5,
...
rates: [
{ client_id: (id of client), rate: 5 },
{ client_id: (id of another client), rate: 3 },
{ client_id: (id of a third client), rate: 10 }
]
}
The idea being that the objects in the rates array contains all the data you'll need to display the blog entry, complete with ratings, right in the single document. If you also need to query the rates in another way (e.g. find all the ratings made by user X), and the site is read-heavy, you may consider also storing the data in a Rates collection as you're doing now. Sure, the data is in two places, and it's harder to update, but it may be an overall win after you analyze your app and how it accesses your data.
Note that you can apply indexes deep into a document's structure, so for example you can index News.rates.client_id, and then you can quickly find any documents in the News collection that a particular user has rated.