On my backend I use mongoDB with nodejs and mongoose
I have many records in mongodb with this structure:
{
..fields
type: 'out',
user: 'id1', <--mongodb objectID,
orderPayment: [
{
_id: 'id1',
paid: true,
paymentSum: 40
},
{
_id: 'id2',
paid: true,
paymentSum: 60,
},
{
_id: 'id3',
paid: false,
paymentSum: 50,
}
]
},
{
..fields
type: 'in',
user: 'id1', <--mongodb objectID
orderPayment: [
{
_id: 'id1',
paid: true,
paymentSum: 10
},
{
_id: 'id2',
paid: true,
paymentSum: 10,
},
{
_id: 'id3',
paid: false,
paymentSum: 77,
}
]
}
I need to group this records by 'type' and get sum with conditions.
need to get sum of 'paid' records and sum of noPaid records.
for a better understanding, here is the result Ι need to get
Output is:
{
out { <-- type field
paid: 100, <-- sum of paid
noPaid: 50 <-- sum of noPaid
},
in: { <-- type field
paid: 20, <-- sum of paid
noPaid: 77 <-- sum of noPaid
}
}
Different solution would be this one. It may give better performance than solution of #YuTing:
db.collection.aggregate([
{
$project: {
type: 1,
paid: {
$filter: {
input: "$orderPayment",
cond: "$$this.paid"
}
},
noPaid: {
$filter: {
input: "$orderPayment",
cond: { $not: "$$this.paid" }
}
}
}
},
{
$set: {
paid: { $sum: "$paid.paymentSum" },
noPaid: { $sum: "$noPaid.paymentSum" }
}
},
{
$group: {
_id: "$type",
paid: { $sum: "$paid" },
noPaid: { $sum: "$noPaid" }
}
}
])
Mongo Playground
use $cond in $group
db.collection.aggregate([
{
"$unwind": "$orderPayment"
},
{
"$group": {
"_id": "$type",
"paid": {
"$sum": {
$cond: {
if: { $eq: [ "$orderPayment.paid", true ] },
then: "$orderPayment.paymentSum",
else: 0
}
}
},
"noPaid": {
"$sum": {
$cond: {
if: { $eq: [ "$orderPayment.paid", false ] },
then: "$orderPayment.paymentSum",
else: 0
}
}
}
}
}
])
mongoplayground
I am trying to filter a document by a sub-documents referred property. Assume that I have already created models for each schema. The simplified schemas are the following:
const store = new Schema({
name: { type: String }
})
const price = new Schema({
price: { type: Number },
store: {
type: mongoose.Schema.Types.ObjectId,
ref: 'Store'
},
})
const product = new Schema({
name: {type: String},
prices: [{
type: mongoose.Schema.Types.ObjectId,
ref: 'Price'
}]
})
/*
Notation:
lowercase for schemas: product
uppercase for models: Product
*/
As a first approach I tried:
Product.find({'prices.store':storeId}).populate('prices')
but this does not work as filtering by a sub-document property is not supported on mongoose.
My current approach is using the aggregation framework. This is how the aggregation looks:
{
$unwind: '$prices'
},
{
$lookup: {
from: 'prices',
localField: 'prices',
foreignField: '_id',
as: 'prices'
}
},
{
$unwind: '$prices'
},
{
$lookup: {
from: 'stores',
localField: 'prices.store',
foreignField: '_id',
as: 'prices.store'
}
}, // populate
{
$match: {
'prices.store._id': new mongoose.Types.ObjectId(storeId)
}
}, // filter by store id
{ $group: { _id: '$id', doc: { $first: '$$ROOT' } } },
{ $replaceRoot: { newRoot: '$doc' } }
// Error occurs in $group & $replaceRoot
For example, before the last two stages if the record being saved is:
{
name: 'Milk',
prices: [
{store: 1, price: 3.2},
{store: 2, price: 4.0}
]
}
then the aggregation returned: (notice the product is the same but displaying each price in different results)
[
{
id: 4,
name: 'Milk',
prices: {
id: 10,
store: { _id: 1, name : 'Walmart' },
price: 3.2
}
},
{
id: 4,
name: 'Milk',
prices: {
id: 11,
store: { _id: 2, name : 'CVS' },
price: 4.0
},
}
]
To solve this issue I added the last part:
{ $group: { _id: '$id', doc: { $first: '$$ROOT' } } },
{ $replaceRoot: { newRoot: '$doc' } }
But this last part only returns the following:
{
id: 4,
name: 'Milk',
prices: {
id: 10,
store: { _id: 1, name : 'Walmart' },
price: 3.2
}
}
Now prices is an object, it should be an array and it should contain all prices (2 in this case).
Question
How to return all prices (as an array) with the store field populated and filtered by storeId?
Expected result:
{
id: 4,
name: 'Milk',
prices: [
{
id: 10,
store: { _id: 1, name : 'Walmart' },
price: 3.2
},
{
id: 11,
store: { _id: 2, name : 'CVS' },
price: 4.0
}]
}
EDIT
I want to filter products that contain prices in a given store. It should return the product with its prices, all of them.
I'm not totally convinced your existing pipeline is the most optimal, but without sample data to work from it's hard to really tell otherwise. So just working onward from what you have:
Using $unwind
var pipeline = [
// { $unwind: '$prices' }, // note: should not need this past MongoDB 3.0
{ $lookup: {
from: 'prices',
localField: 'prices',
foreignField: '_id',
as: 'prices'
}},
{ $unwind: '$prices' },
{ $lookup: {
from: 'stores',
localField: 'prices.store',
foreignField: '_id',
as: 'prices.store'
}},
// Changes from here
{ $unwind: '$prices.store' },
{ $match: {'prices.store._id': mongoose.Types.ObjectId(storeId) } },
{ $group: {
_id: '$_id',
name: { $first: '$name' },
prices: { $push: '$prices' }
}}
];
The points there start with:
Initial $unwind - Should not be required. Only in very early MongoDB 3.0 releases was this ever a requirement to $unwind an array of values before using $lookup on those values.
$unwind after $lookup - Is always required if you expect a "singular" object as matching, since $lookup always returns an array.
$match after $unwind - Is actually an "optimization" for pipeline processing and in fact a requirement in order to "filter". Without $unwind it's just a verification that "something is there" but items that did not match would not be removed.
$push in $group - This is the actual part the re-builds the "prices"array.
The key point you were basically missing was using $first for the "whole document" content. You really don't ever want that, and even if you want more than just "name" you always want to $push the "prices".
In fact you probably do want more fields than just name from the original document, but really you should therefore be using the following form instead.
Expressive $lookup
An alternate is available with most modern MongoDB releases since MongoDB 3.6, which frankly you should be using at minimum:
var pipeline = [
{ $lookup: {
from: 'prices',
let: { prices: '$prices' },
pipeline: [
{ $match: {
store: mongoose.Types.ObjectId(storeId),
$expr: { $in: [ '$_id', '$$prices' ] }
}},
{ $lookup: {
from: 'stores',
let: { store: '$store' },
pipeline: [
{ $match: { $expr: { $eq: [ '$_id', '$$store' ] } }
],
as: 'store'
}},
{ $unwind: '$store' }
],
as: 'prices'
}},
// remove results with no matching prices
{ $match: { 'prices.0': { $exists: true } } }
];
So the first thing to notice there is the "outer" pipeline is actually just a single $lookup stage, since all it really needs to do is "join" to the prices collection. From the perspective of joining to your original collection this is also true since the additional $lookup in the above example is actually related from prices to another collection.
This is then exactly what this new form does, so instead of using $unwind on the resulting array and then following on the join, only the matching items for "prices" are then "joined" to the "stores" collection, and before those are returned into the array. Of course since there is a "one to one" relationship with the "store", this will actually $unwind.
In short, the output of this simply has the original document with a "prices" array inside it. So there is no need to re-construct via $group and no confusion of what you use $first on and what you $push.
NOTE: I'm more than a little suspect of your "filter stores" statement and attempting to match the store field as presented in the "prices" collection. The question shows expected output from two different stores even though you specify an equality match.
If anything I suspect you might mean a "list of stores", which would instead be more like:
store: { $in: storeList.map(store => mongoose.Types.ObjectId(store)) }
Which is how you would work with a "list of strings" in both cases, using $in for matching against a "list" and the Array.map() to work with a supplied list and return each as ObjectId() values.
TIP: With mongoose you use a "model" rather than working with collection names, and the actual MongoDB collection names is typically the plural of the model name you registered.
So you don't have to "hardcode" the actual collection names for $lookup, simply use:
Model.collection.name
The .collection.name is an accessible property on all models, and can save you the trouble of remembering to actually name the collection for $lookup. It also protects you should you ever change your mongoose.model() instance registration in a way which alters the stored collection name with MongoDB.
Full Demonstration
The following is a self contained listing demonstrating both approaches as work and how they produce the same results:
const { Schema, Types: { ObjectId } } = mongoose = require('mongoose');
const uri = 'mongodb://localhost:27017/shopping';
const opts = { useNewUrlParser: true };
mongoose.set('useFindAndModify', false);
mongoose.set('useCreateIndex', true);
mongoose.set('debug', true);
const storeSchema = new Schema({
name: { type: String }
});
const priceSchema = new Schema({
price: { type: Number },
store: { type: Schema.Types.ObjectId, ref: 'Store' }
});
const productSchema = new Schema({
name: { type: String },
prices: [{ type: Schema.Types.ObjectId, ref: 'Price' }]
});
const Store = mongoose.model('Store', storeSchema);
const Price = mongoose.model('Price', priceSchema);
const Product = mongoose.model('Product', productSchema);
const log = data => console.log(JSON.stringify(data, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri, opts);
// Clean data
await Promise.all(
Object.entries(conn.models).map(([k, m]) => m.deleteMany())
);
// Insert working data
let [StoreA, StoreB, StoreC] = await Store.insertMany(
["StoreA", "StoreB", "StoreC"].map(name => ({ name }))
);
let [PriceA, PriceB, PriceC, PriceD, PriceE, PriceF]
= await Price.insertMany(
[[StoreA,1],[StoreB,2],[StoreA,3],[StoreC,4],[StoreB,5],[StoreC,6]]
.map(([store, price]) => ({ price, store }))
);
let [Milk, Cheese, Bread] = await Product.insertMany(
[
{ name: 'Milk', prices: [PriceA, PriceB] },
{ name: 'Cheese', prices: [PriceC, PriceD] },
{ name: 'Bread', prices: [PriceE, PriceF] }
]
);
// Test 1
{
log("Single Store - expressive")
const pipeline = [
{ '$lookup': {
'from': Price.collection.name,
'let': { prices: '$prices' },
'pipeline': [
{ '$match': {
'store': ObjectId(StoreA._id), // demo - it's already an ObjectId
'$expr': { '$in': [ '$_id', '$$prices' ] }
}},
{ '$lookup': {
'from': Store.collection.name,
'let': { store: '$store' },
'pipeline': [
{ '$match': { '$expr': { '$eq': [ '$_id', '$$store' ] } } }
],
'as': 'store'
}},
{ '$unwind': '$store' }
],
as: 'prices'
}},
{ '$match': { 'prices.0': { '$exists': true } } }
];
let result = await Product.aggregate(pipeline);
log(result);
}
// Test 2
{
log("Dual Store - expressive");
const pipeline = [
{ '$lookup': {
'from': Price.collection.name,
'let': { prices: '$prices' },
'pipeline': [
{ '$match': {
'store': { '$in': [StoreA._id, StoreB._id] },
'$expr': { '$in': [ '$_id', '$$prices' ] }
}},
{ '$lookup': {
'from': Store.collection.name,
'let': { store: '$store' },
'pipeline': [
{ '$match': { '$expr': { '$eq': [ '$_id', '$$store' ] } } }
],
'as': 'store'
}},
{ '$unwind': '$store' }
],
as: 'prices'
}},
{ '$match': { 'prices.0': { '$exists': true } } }
];
let result = await Product.aggregate(pipeline);
log(result);
}
// Test 3
{
log("Single Store - legacy");
const pipeline = [
{ '$lookup': {
'from': Price.collection.name,
'localField': 'prices',
'foreignField': '_id',
'as': 'prices'
}},
{ '$unwind': '$prices' },
// Alternately $match can be done here
// { '$match': { 'prices.store': StoreA._id } },
{ '$lookup': {
'from': Store.collection.name,
'localField': 'prices.store',
'foreignField': '_id',
'as': 'prices.store'
}},
{ '$unwind': '$prices.store' },
{ '$match': { 'prices.store._id': StoreA._id } },
{ '$group': {
'_id': '$_id',
'name': { '$first': '$name' },
'prices': { '$push': '$prices' }
}}
];
let result = await Product.aggregate(pipeline);
log(result);
}
// Test 4
{
log("Dual Store - legacy");
const pipeline = [
{ '$lookup': {
'from': Price.collection.name,
'localField': 'prices',
'foreignField': '_id',
'as': 'prices'
}},
{ '$unwind': '$prices' },
// Alternately $match can be done here
{ '$match': { 'prices.store': { '$in': [StoreA._id, StoreB._id] } } },
{ '$lookup': {
'from': Store.collection.name,
'localField': 'prices.store',
'foreignField': '_id',
'as': 'prices.store'
}},
{ '$unwind': '$prices.store' },
//{ '$match': { 'prices.store._id': { '$in': [StoreA._id, StoreB._id] } } },
{ '$group': {
'_id': '$_id',
'name': { '$first': '$name' },
'prices': { '$push': '$prices' }
}}
];
let result = await Product.aggregate(pipeline);
log(result);
}
} catch(e) {
console.error(e);
} finally {
mongoose.disconnect();
}
})()
Which produces the output:
Mongoose: stores.deleteMany({}, {})
Mongoose: prices.deleteMany({}, {})
Mongoose: products.deleteMany({}, {})
Mongoose: stores.insertMany([ { _id: 5c7c79bcc78675135c09f54b, name: 'StoreA', __v: 0 }, { _id: 5c7c79bcc78675135c09f54c, name: 'StoreB', __v: 0 }, { _id: 5c7c79bcc78675135c09f54d, name: 'StoreC', __v: 0 } ], {})
Mongoose: prices.insertMany([ { _id: 5c7c79bcc78675135c09f54e, price: 1, store: 5c7c79bcc78675135c09f54b, __v: 0 }, { _id: 5c7c79bcc78675135c09f54f, price: 2, store: 5c7c79bcc78675135c09f54c, __v: 0 }, { _id: 5c7c79bcc78675135c09f550, price: 3, store: 5c7c79bcc78675135c09f54b, __v: 0 }, { _id: 5c7c79bcc78675135c09f551, price: 4, store: 5c7c79bcc78675135c09f54d, __v: 0 }, { _id: 5c7c79bcc78675135c09f552, price: 5, store: 5c7c79bcc78675135c09f54c, __v: 0 }, { _id: 5c7c79bcc78675135c09f553, price: 6, store: 5c7c79bcc78675135c09f54d, __v: 0 } ], {})
Mongoose: products.insertMany([ { prices: [ 5c7c79bcc78675135c09f54e, 5c7c79bcc78675135c09f54f ], _id: 5c7c79bcc78675135c09f554, name: 'Milk', __v: 0 }, { prices: [ 5c7c79bcc78675135c09f550, 5c7c79bcc78675135c09f551 ], _id: 5c7c79bcc78675135c09f555, name: 'Cheese', __v: 0 }, { prices: [ 5c7c79bcc78675135c09f552, 5c7c79bcc78675135c09f553 ], _id: 5c7c79bcc78675135c09f556, name: 'Bread', __v: 0 } ], {})
"Single Store - expressive"
Mongoose: products.aggregate([ { '$lookup': { from: 'prices', let: { prices: '$prices' }, pipeline: [ { '$match': { store: 5c7c79bcc78675135c09f54b, '$expr': { '$in': [ '$_id', '$$prices' ] } } }, { '$lookup': { from: 'stores', let: { store: '$store' }, pipeline: [ { '$match': { '$expr': { '$eq': [ '$_id', '$$store' ] } } } ], as: 'store' } }, { '$unwind': '$store' } ], as: 'prices' } }, { '$match': { 'prices.0': { '$exists': true } } } ], {})
[
{
"_id": "5c7c79bcc78675135c09f554",
"prices": [
{
"_id": "5c7c79bcc78675135c09f54e",
"price": 1,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
}
],
"name": "Milk",
"__v": 0
},
{
"_id": "5c7c79bcc78675135c09f555",
"prices": [
{
"_id": "5c7c79bcc78675135c09f550",
"price": 3,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
}
],
"name": "Cheese",
"__v": 0
}
]
"Dual Store - expressive"
Mongoose: products.aggregate([ { '$lookup': { from: 'prices', let: { prices: '$prices' }, pipeline: [ { '$match': { store: { '$in': [ 5c7c79bcc78675135c09f54b, 5c7c79bcc78675135c09f54c ] }, '$expr': { '$in': [ '$_id', '$$prices' ] } } }, { '$lookup': { from: 'stores', let: { store: '$store' }, pipeline: [ { '$match': { '$expr': { '$eq': [ '$_id', '$$store' ] } } } ], as: 'store' } }, { '$unwind': '$store' } ], as: 'prices' } }, { '$match': { 'prices.0': { '$exists': true } } } ], {})
[
{
"_id": "5c7c79bcc78675135c09f554",
"prices": [
{
"_id": "5c7c79bcc78675135c09f54e",
"price": 1,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
},
{
"_id": "5c7c79bcc78675135c09f54f",
"price": 2,
"store": {
"_id": "5c7c79bcc78675135c09f54c",
"name": "StoreB",
"__v": 0
},
"__v": 0
}
],
"name": "Milk",
"__v": 0
},
{
"_id": "5c7c79bcc78675135c09f555",
"prices": [
{
"_id": "5c7c79bcc78675135c09f550",
"price": 3,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
}
],
"name": "Cheese",
"__v": 0
},
{
"_id": "5c7c79bcc78675135c09f556",
"prices": [
{
"_id": "5c7c79bcc78675135c09f552",
"price": 5,
"store": {
"_id": "5c7c79bcc78675135c09f54c",
"name": "StoreB",
"__v": 0
},
"__v": 0
}
],
"name": "Bread",
"__v": 0
}
]
"Single Store - legacy"
Mongoose: products.aggregate([ { '$lookup': { from: 'prices', localField: 'prices', foreignField: '_id', as: 'prices' } }, { '$unwind': '$prices' }, { '$lookup': { from: 'stores', localField: 'prices.store', foreignField: '_id', as: 'prices.store' } }, { '$unwind': '$prices.store' }, { '$match': { 'prices.store._id': 5c7c79bcc78675135c09f54b } }, { '$group': { _id: '$_id', name: { '$first': '$name' }, prices: { '$push': '$prices' } } } ], {})
[
{
"_id": "5c7c79bcc78675135c09f555",
"name": "Cheese",
"prices": [
{
"_id": "5c7c79bcc78675135c09f550",
"price": 3,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
}
]
},
{
"_id": "5c7c79bcc78675135c09f554",
"name": "Milk",
"prices": [
{
"_id": "5c7c79bcc78675135c09f54e",
"price": 1,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
}
]
}
]
"Dual Store - legacy"
Mongoose: products.aggregate([ { '$lookup': { from: 'prices', localField: 'prices', foreignField: '_id', as: 'prices' } }, { '$unwind': '$prices' }, { '$match': { 'prices.store': { '$in': [ 5c7c79bcc78675135c09f54b, 5c7c79bcc78675135c09f54c ] } } }, { '$lookup': { from: 'stores', localField: 'prices.store', foreignField: '_id', as: 'prices.store' } }, { '$unwind': '$prices.store' }, { '$group': { _id: '$_id', name: { '$first': '$name' }, prices: { '$push': '$prices' } } } ], {})
[
{
"_id": "5c7c79bcc78675135c09f555",
"name": "Cheese",
"prices": [
{
"_id": "5c7c79bcc78675135c09f550",
"price": 3,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
}
]
},
{
"_id": "5c7c79bcc78675135c09f556",
"name": "Bread",
"prices": [
{
"_id": "5c7c79bcc78675135c09f552",
"price": 5,
"store": {
"_id": "5c7c79bcc78675135c09f54c",
"name": "StoreB",
"__v": 0
},
"__v": 0
}
]
},
{
"_id": "5c7c79bcc78675135c09f554",
"name": "Milk",
"prices": [
{
"_id": "5c7c79bcc78675135c09f54e",
"price": 1,
"store": {
"_id": "5c7c79bcc78675135c09f54b",
"name": "StoreA",
"__v": 0
},
"__v": 0
},
{
"_id": "5c7c79bcc78675135c09f54f",
"price": 2,
"store": {
"_id": "5c7c79bcc78675135c09f54c",
"name": "StoreB",
"__v": 0
},
"__v": 0
}
]
}
]