In my database, I have a user table with 2 columns: score & history :
I have another table called card containing some specific information & a card _id.
Each time a specific card is clicked, I would like the '/CardClicked' router to be called for :
Additional (increment) a specific number value (1.5) on score column
Add the cart _id value on the array of history column: (to save, which card the user clicked)
I wanted to have something like that in my database:
users:
{ "_id" : 1, "score" : 6.0, "history" : [ 62k6hf45b0af050fe, 69k5hf45b0af450fg, 65k5hf45b0af450fg, ]}
{ "_id" : 2, "score" : 4.5, "history" : [ 65k5hf45b0af450fg,] }
{ "_id" : 3, "score" : 10.0, "history" : [ 66k5hf45t0af930rp, 67k5hf45b0af450fg, 62k6hf45b0af050fe, 61y5hf884b0af450vb, ] }
But, idk why it doesn't update properly on columns, however i don't have any error, that my code i made it :
My UserSchema.js :
const mongoose = require('mongoose')
const UserSchema = new mongoose.Schema({
...
score: {
type: Number,
},
history: {
type: [String]
},
createdAt: {
type: Date,
default: Date.now,
},
})
module.exports = mongoose.model('User', UserSchema)
My router.js :
app.post('/CardClicked', async function (req, res){
console.log("User ID "+req.user._id, "Card ID : "+req.body._id);
try{
const condition = { _id: req.user._id };
//the req.body._id contain the card id from my front to send that to router
const putPostID = { $addToSet: { history: req.body._id }};
const additionalPoints = {$inc : { score : 1.5 }};
await User.findOneAndUpdate(condition, additionalPoints, putPostID, {upsert: true})
}catch(err){
res.status(500).send({error: err })
}
});
Every update operation must go into one object. In your case:
User.findOneAndUpdate(condition, {
$inc: { score: 1.5 },
$addToSet: { history: req.body._id },
},
{ upsert: true },
)
I'm trying to delete a project object from an array.
projects:
{
_id : ...,
userName: milad ,
projets : [
{ .. projectId, pName, location .... A },
{ .. projectId, pName, location .... B },
{ .. projectId, pName, location .... C },
]
}
How to delete Project B in MongoDB?
projects after remove Project B:
{
_id : ...,
userName: milad ,
projets : [
{ .. projectId, pName, location .... A },
{ .. projectId, pName, location .... C },
]
}
If you have received a document from your database, you can remove an object from your array - based on the location - in such a way:
const doc = {
_id: '507f191e810c19729de860e0',
userName: 'milad',
projects: [
{ _id: '507f191e810c19729de860ea', pName: 'projectA', location: 'a' },
{ _id: '507f191e810c19729de860eb', pName: 'projectB', location: 'b' },
{ _id: '507f191e810c19729de860ec', pName: 'projectC', location: 'c' }
]
}
// remove project b (one method, there are many more)
doc.projects = doc.projects.filter((project) => project.location !== 'b');
After the filter process you have to save it back to your mongoDB:
Mongoose: (example)
await doc.save();
another answer
db.collection('Profile').updateOne({ userName: username,
"Projects.projectId": projectId }, { $pull: { 'Projects': { projectId: projectId } }})
}
I am trying to insert a time object into the times array for a specific activity name for a specific user. For example, if the user was "someuser" and I wanted to add a time to the times for guitar I am unsure as to what to do.
{
username: "someuser",
activities: [
{
name: "guitar",
times: []
},
{
name: "code",
times: []
}
]
}, {
username: "anotheruser",
activities: []
}
This is currently the function that I have, I cannot figure out what I am doing wrong, any help would be greatly appreciated:
function appendActivityTime(user, activityName, newRange) {
User.updateOne(
{username: user, 'activities.name': activityName},
{ $push: {'activities.$.times': {newRange}},
function (err) {
if (err) {
console.log(err);
} else {
console.log("Successfully added time range: " + newRange);
}
}}
);
}
appendActivityTime("someuser", "guitar", rangeObject);
i've tried your attempt and it worked for me:
db.getCollection("test").updateOne(
{ username: "someuser", "activities.name": "guitar" },
{ $push: { "activities.$.times": { from: ISODate(), to: ISODate() } } } //Don't worry about ISODate() in node.js use date objects
)
results:
{
"_id" : ObjectId("5f6c384af49dcd4019982b2c"),
"username" : "someuser",
"activities" : [
{
"name" : "guitar",
"times" : [
{
"from" : ISODate("2020-09-24T06:15:03.578+0000"),
"to" : ISODate("2020-09-24T06:15:03.578+0000")
}
]
},
{
"name" : "code",
"times" : [
]
}
]
}
what i would suggest you using instead is arrayFilter, they are much more precise and when you get used to them, they became very handy
If you are not confident with updating nested documents, let mongoose make the query.
let document = await Model.findOne ({ });
document.activities = new_object;
await document.save();
I'm pretty new to Mongoose and MongoDB in general so I'm having a difficult time figuring out if something like this is possible:
Item = new Schema({
id: Schema.ObjectId,
dateCreated: { type: Date, default: Date.now },
title: { type: String, default: 'No Title' },
description: { type: String, default: 'No Description' },
tags: [ { type: Schema.ObjectId, ref: 'ItemTag' }]
});
ItemTag = new Schema({
id: Schema.ObjectId,
tagId: { type: Schema.ObjectId, ref: 'Tag' },
tagName: { type: String }
});
var query = Models.Item.find({});
query
.desc('dateCreated')
.populate('tags')
.where('tags.tagName').in(['funny', 'politics'])
.run(function(err, docs){
// docs is always empty
});
Is there a better way do this?
Edit
Apologies for any confusion. What I'm trying to do is get all Items that contain either the funny tag or politics tag.
Edit
Document without where clause:
[{
_id: 4fe90264e5caa33f04000012,
dislikes: 0,
likes: 0,
source: '/uploads/loldog.jpg',
comments: [],
tags: [{
itemId: 4fe90264e5caa33f04000012,
tagName: 'movies',
tagId: 4fe64219007e20e644000007,
_id: 4fe90270e5caa33f04000015,
dateCreated: Tue, 26 Jun 2012 00:29:36 GMT,
rating: 0,
dislikes: 0,
likes: 0
},
{
itemId: 4fe90264e5caa33f04000012,
tagName: 'funny',
tagId: 4fe64219007e20e644000002,
_id: 4fe90270e5caa33f04000017,
dateCreated: Tue, 26 Jun 2012 00:29:36 GMT,
rating: 0,
dislikes: 0,
likes: 0
}],
viewCount: 0,
rating: 0,
type: 'image',
description: null,
title: 'dogggg',
dateCreated: Tue, 26 Jun 2012 00:29:24 GMT
}, ... ]
With the where clause, I get an empty array.
With a modern MongoDB greater than 3.2 you can use $lookup as an alternate to .populate() in most cases. This also has the advantage of actually doing the join "on the server" as opposed to what .populate() does which is actually "multiple queries" to "emulate" a join.
So .populate() is not really a "join" in the sense of how a relational database does it. The $lookup operator on the other hand, actually does the work on the server, and is more or less analogous to a "LEFT JOIN":
Item.aggregate(
[
{ "$lookup": {
"from": ItemTags.collection.name,
"localField": "tags",
"foreignField": "_id",
"as": "tags"
}},
{ "$unwind": "$tags" },
{ "$match": { "tags.tagName": { "$in": [ "funny", "politics" ] } } },
{ "$group": {
"_id": "$_id",
"dateCreated": { "$first": "$dateCreated" },
"title": { "$first": "$title" },
"description": { "$first": "$description" },
"tags": { "$push": "$tags" }
}}
],
function(err, result) {
// "tags" is now filtered by condition and "joined"
}
)
N.B. The .collection.name here actually evaluates to the "string" that is the actual name of the MongoDB collection as assigned to the model. Since mongoose "pluralizes" collection names by default and $lookup needs the actual MongoDB collection name as an argument ( since it's a server operation ), then this is a handy trick to use in mongoose code, as opposed to "hard coding" the collection name directly.
Whilst we could also use $filter on arrays to remove the unwanted items, this is actually the most efficient form due to Aggregation Pipeline Optimization for the special condition of as $lookup followed by both an $unwind and a $match condition.
This actually results in the three pipeline stages being rolled into one:
{ "$lookup" : {
"from" : "itemtags",
"as" : "tags",
"localField" : "tags",
"foreignField" : "_id",
"unwinding" : {
"preserveNullAndEmptyArrays" : false
},
"matching" : {
"tagName" : {
"$in" : [
"funny",
"politics"
]
}
}
}}
This is highly optimal as the actual operation "filters the collection to join first", then it returns the results and "unwinds" the array. Both methods are employed so the results do not break the BSON limit of 16MB, which is a constraint that the client does not have.
The only problem is that it seems "counter-intuitive" in some ways, particularly when you want the results in an array, but that is what the $group is for here, as it reconstructs to the original document form.
It's also unfortunate that we simply cannot at this time actually write $lookup in the same eventual syntax the server uses. IMHO, this is an oversight to be corrected. But for now, simply using the sequence will work and is the most viable option with the best performance and scalability.
Addendum - MongoDB 3.6 and upwards
Though the pattern shown here is fairly optimized due to how the other stages get rolled into the $lookup, it does have one failing in that the "LEFT JOIN" which is normally inherent to both $lookup and the actions of populate() is negated by the "optimal" usage of $unwind here which does not preserve empty arrays. You can add the preserveNullAndEmptyArrays option, but this negates the "optimized" sequence described above and essentially leaves all three stages intact which would normally be combined in the optimization.
MongoDB 3.6 expands with a "more expressive" form of $lookup allowing a "sub-pipeline" expression. Which not only meets the goal of retaining the "LEFT JOIN" but still allows an optimal query to reduce results returned and with a much simplified syntax:
Item.aggregate([
{ "$lookup": {
"from": ItemTags.collection.name,
"let": { "tags": "$tags" },
"pipeline": [
{ "$match": {
"tags": { "$in": [ "politics", "funny" ] },
"$expr": { "$in": [ "$_id", "$$tags" ] }
}}
]
}}
])
The $expr used in order to match the declared "local" value with the "foreign" value is actually what MongoDB does "internally" now with the original $lookup syntax. By expressing in this form we can tailor the initial $match expression within the "sub-pipeline" ourselves.
In fact, as a true "aggregation pipeline" you can do just about anything you can do with an aggregation pipeline within this "sub-pipeline" expression, including "nesting" the levels of $lookup to other related collections.
Further usage is a bit beyond the scope of what the question here asks, but in relation to even "nested population" then the new usage pattern of $lookup allows this to be much the same, and a "lot" more powerful in it's full usage.
Working Example
The following gives an example using a static method on the model. Once that static method is implemented the call simply becomes:
Item.lookup(
{
path: 'tags',
query: { 'tags.tagName' : { '$in': [ 'funny', 'politics' ] } }
},
callback
)
Or enhancing to be a bit more modern even becomes:
let results = await Item.lookup({
path: 'tags',
query: { 'tagName' : { '$in': [ 'funny', 'politics' ] } }
})
Making it very similar to .populate() in structure, but it's actually doing the join on the server instead. For completeness, the usage here casts the returned data back to mongoose document instances at according to both the parent and child cases.
It's fairly trivial and easy to adapt or just use as is for most common cases.
N.B The use of async here is just for brevity of running the enclosed example. The actual implementation is free of this dependency.
const async = require('async'),
mongoose = require('mongoose'),
Schema = mongoose.Schema;
mongoose.Promise = global.Promise;
mongoose.set('debug', true);
mongoose.connect('mongodb://localhost/looktest');
const itemTagSchema = new Schema({
tagName: String
});
const itemSchema = new Schema({
dateCreated: { type: Date, default: Date.now },
title: String,
description: String,
tags: [{ type: Schema.Types.ObjectId, ref: 'ItemTag' }]
});
itemSchema.statics.lookup = function(opt,callback) {
let rel =
mongoose.model(this.schema.path(opt.path).caster.options.ref);
let group = { "$group": { } };
this.schema.eachPath(p =>
group.$group[p] = (p === "_id") ? "$_id" :
(p === opt.path) ? { "$push": `$${p}` } : { "$first": `$${p}` });
let pipeline = [
{ "$lookup": {
"from": rel.collection.name,
"as": opt.path,
"localField": opt.path,
"foreignField": "_id"
}},
{ "$unwind": `$${opt.path}` },
{ "$match": opt.query },
group
];
this.aggregate(pipeline,(err,result) => {
if (err) callback(err);
result = result.map(m => {
m[opt.path] = m[opt.path].map(r => rel(r));
return this(m);
});
callback(err,result);
});
}
const Item = mongoose.model('Item', itemSchema);
const ItemTag = mongoose.model('ItemTag', itemTagSchema);
function log(body) {
console.log(JSON.stringify(body, undefined, 2))
}
async.series(
[
// Clean data
(callback) => async.each(mongoose.models,(model,callback) =>
model.remove({},callback),callback),
// Create tags and items
(callback) =>
async.waterfall(
[
(callback) =>
ItemTag.create([{ "tagName": "movies" }, { "tagName": "funny" }],
callback),
(tags, callback) =>
Item.create({ "title": "Something","description": "An item",
"tags": tags },callback)
],
callback
),
// Query with our static
(callback) =>
Item.lookup(
{
path: 'tags',
query: { 'tags.tagName' : { '$in': [ 'funny', 'politics' ] } }
},
callback
)
],
(err,results) => {
if (err) throw err;
let result = results.pop();
log(result);
mongoose.disconnect();
}
)
Or a little more modern for Node 8.x and above with async/await and no additional dependencies:
const { Schema } = mongoose = require('mongoose');
const uri = 'mongodb://localhost/looktest';
mongoose.Promise = global.Promise;
mongoose.set('debug', true);
const itemTagSchema = new Schema({
tagName: String
});
const itemSchema = new Schema({
dateCreated: { type: Date, default: Date.now },
title: String,
description: String,
tags: [{ type: Schema.Types.ObjectId, ref: 'ItemTag' }]
});
itemSchema.statics.lookup = function(opt) {
let rel =
mongoose.model(this.schema.path(opt.path).caster.options.ref);
let group = { "$group": { } };
this.schema.eachPath(p =>
group.$group[p] = (p === "_id") ? "$_id" :
(p === opt.path) ? { "$push": `$${p}` } : { "$first": `$${p}` });
let pipeline = [
{ "$lookup": {
"from": rel.collection.name,
"as": opt.path,
"localField": opt.path,
"foreignField": "_id"
}},
{ "$unwind": `$${opt.path}` },
{ "$match": opt.query },
group
];
return this.aggregate(pipeline).exec().then(r => r.map(m =>
this({ ...m, [opt.path]: m[opt.path].map(r => rel(r)) })
));
}
const Item = mongoose.model('Item', itemSchema);
const ItemTag = mongoose.model('ItemTag', itemTagSchema);
const log = body => console.log(JSON.stringify(body, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri);
// Clean data
await Promise.all(Object.entries(conn.models).map(([k,m]) => m.remove()));
// Create tags and items
const tags = await ItemTag.create(
["movies", "funny"].map(tagName =>({ tagName }))
);
const item = await Item.create({
"title": "Something",
"description": "An item",
tags
});
// Query with our static
const result = (await Item.lookup({
path: 'tags',
query: { 'tags.tagName' : { '$in': [ 'funny', 'politics' ] } }
})).pop();
log(result);
mongoose.disconnect();
} catch (e) {
console.error(e);
} finally {
process.exit()
}
})()
And from MongoDB 3.6 and upward, even without the $unwind and $group building:
const { Schema, Types: { ObjectId } } = mongoose = require('mongoose');
const uri = 'mongodb://localhost/looktest';
mongoose.Promise = global.Promise;
mongoose.set('debug', true);
const itemTagSchema = new Schema({
tagName: String
});
const itemSchema = new Schema({
title: String,
description: String,
tags: [{ type: Schema.Types.ObjectId, ref: 'ItemTag' }]
},{ timestamps: true });
itemSchema.statics.lookup = function({ path, query }) {
let rel =
mongoose.model(this.schema.path(path).caster.options.ref);
// MongoDB 3.6 and up $lookup with sub-pipeline
let pipeline = [
{ "$lookup": {
"from": rel.collection.name,
"as": path,
"let": { [path]: `$${path}` },
"pipeline": [
{ "$match": {
...query,
"$expr": { "$in": [ "$_id", `$$${path}` ] }
}}
]
}}
];
return this.aggregate(pipeline).exec().then(r => r.map(m =>
this({ ...m, [path]: m[path].map(r => rel(r)) })
));
};
const Item = mongoose.model('Item', itemSchema);
const ItemTag = mongoose.model('ItemTag', itemTagSchema);
const log = body => console.log(JSON.stringify(body, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri);
// Clean data
await Promise.all(Object.entries(conn.models).map(([k,m]) => m.remove()));
// Create tags and items
const tags = await ItemTag.insertMany(
["movies", "funny"].map(tagName => ({ tagName }))
);
const item = await Item.create({
"title": "Something",
"description": "An item",
tags
});
// Query with our static
let result = (await Item.lookup({
path: 'tags',
query: { 'tagName': { '$in': [ 'funny', 'politics' ] } }
})).pop();
log(result);
await mongoose.disconnect();
} catch(e) {
console.error(e)
} finally {
process.exit()
}
})()
what you are asking for isn't directly supported but can be achieved by adding another filter step after the query returns.
first, .populate( 'tags', null, { tagName: { $in: ['funny', 'politics'] } } ) is definitely what you need to do to filter the tags documents. then, after the query returns you'll need to manually filter out documents that don't have any tags docs that matched the populate criteria. something like:
query....
.exec(function(err, docs){
docs = docs.filter(function(doc){
return doc.tags.length;
})
// do stuff with docs
});
Try replacing
.populate('tags').where('tags.tagName').in(['funny', 'politics'])
by
.populate( 'tags', null, { tagName: { $in: ['funny', 'politics'] } } )
Update: Please take a look at the comments - this answer does not correctly match to the question, but maybe it answers other questions of users which came across (I think that because of the upvotes) so I will not delete this "answer":
First: I know this question is really outdated, but I searched for exactly this problem and this SO post was the Google entry #1. So I implemented the docs.filter version (accepted answer) but as I read in the mongoose v4.6.0 docs we can now simply use:
Item.find({}).populate({
path: 'tags',
match: { tagName: { $in: ['funny', 'politics'] }}
}).exec((err, items) => {
console.log(items.tags)
// contains only tags where tagName is 'funny' or 'politics'
})
Hope this helps future search machine users.
After having the same problem myself recently, I've come up with the following solution:
First, find all ItemTags where tagName is either 'funny' or 'politics' and return an array of ItemTag _ids.
Then, find Items which contain all ItemTag _ids in the tags array
ItemTag
.find({ tagName : { $in : ['funny','politics'] } })
.lean()
.distinct('_id')
.exec((err, itemTagIds) => {
if (err) { console.error(err); }
Item.find({ tag: { $all: itemTagIds} }, (err, items) => {
console.log(items); // Items filtered by tagName
});
});
#aaronheckmann 's answer worked for me but I had to replace return doc.tags.length; to return doc.tags != null; because that field contain null if it doesn't match with the conditions written inside populate.
So the final code:
query....
.exec(function(err, docs){
docs = docs.filter(function(doc){
return doc.tags != null;
})
// do stuff with docs
});
My problem is reading properties of nested object, which is inside other nested object.
GraphQL
type Mapping {
id: ID!
partnerSegmentId: ID!
ctSegmentId: CtSegment!
}
type PartnerSegment {
id: ID!
name: String!
platformId: Int!
partner: Partner!
}
type Partner {
id: ID!
name: String!
}
Once I try to query it like:
{
allMappings {
partnerSegmentId {
id
name
partner {
id
}
}
}
}
I recieve:
{
"data": {
"allMappings": [
null
]
},
"errors": [
{
"message": "Cannot return null for non-nullable field Partner.name.",
"locations": [
{
"line": 8,
"column": 9
}
],
"path": [
"allMappings",
0,
"partnerSegmentId",
"partner",
"name"
]
}
]
}
Mapping schema
const mappingSchema = new mongoose.Schema(
{
partnerSegmentId: {
type: mongoose.Schema.Types.ObjectId,
ref: 'PartnerSegment',
required: [true, 'Mapping must have partner segment id.']
},
ctSegmentId: {
type: mongoose.Schema.Types.ObjectId,
ref: 'CtSegment',
required: [true, 'Mapping must have CT segment id.']
}
},
{ timestamps: true }
);
I tried to read separately Partner, PartnerSegment and Mapping models. All works fine. Any idea where i should search source of the problem? I've checked mongodb docs and ids looks okay. I suppose it's fault of my model.
If you would like to take a closer look it's project repo.
SOLUTION:
Garbage Id in the return value was caused by not working populate in the nested entity. The way I managed to solve the problem:
const allMappings = () =>
Mapping.find({})
.populate('user')
.populate('ctSegment')
.populate({
path: 'partnerSegment',
populate: {
path: 'partner'
}
})
.exec();