Build the total sum of aggregated result - javascript

Is it somehow possible to add a field to the aggregated result? My goal is to have a total sum for all results. Currently, I just reduce the result, but I believe this is not as performant as solving it through a query.
aggregate([
{
$match: {
time: { $gte: start, $lte: end },
},
},
{
$group:
{
_id: { $dateToString: { format: '%Y-%m-%d', date: '$time' } },
totalAmount: { $sum: '$payment.amount' },
},
},
]).exec().then((result) => {
return {
total: result.reduce(((acc, curr) => acc + curr.totalAmount), 0),
dates: result,
};
});
result is:
{
"_id":"2020-06-06",
"totalAmount":12
},
{
"_id":"2020-07-06",
"totalAmount":12
}
Any idea how I can get the total amount for all, looking like this but without that reduce part?
{
"total": 24,
"dates": [
{
"_id": "2020-06-06",
"totalAmount": 12,
},
{
"_id": "2020-07-06",
"totalAmount": 12,
}
]
}


Either you can use two queries simultaneously
const [result, totalAmount] = await Promise.all([
Model.aggregate([
{ $match: { time: { $gte: start, $lte: end } } },
{
$group: {
_id: { $dateToString: { format: "%Y-%m-%d", date: "$time" } },
totalAmount: { $sum: "$payment.amount" },
}
},
]),
Model.aggregate([
{ $match: { time: { $gte: start, $lte: end } } },
{
$group: {
_id: null,
totalAmount: { $sum: "$payment.amount" },
}
},
])
])
return {
total: result,
dates: totalAmount,
}
Or can use $facet
const result = await Model.aggregate([
{ $match: { time: { $gte: start, $lte: end } } },
{
$facet: {
result: [
{
$group: {
_id: {
$dateToString: { format: "%Y-%m-%d", date: "$time" },
},
totalAmount: { $sum: "$payment.amount" },
},
},
],
totalAmount: [
{
$group: {
_id: null,
totalAmount: { $sum: "$payment.amount" },
},
},
],
},
},
]);
return {
total: _.get(result, "[0].result", []),
dates: _.get(result, "[0].totalAmount.totalAmount", 0),
}

Related

Find total unique counter based on unique id and date - MongoDB

I'm trying to count all and unique events on daily based based on the following data shape:
{
username: "jack",
events: [
{
eventType: "party",
createdAt: "2022-01-23T10:26:11.214Z",
visitorInfo: {
visitorId: "87654321-0ebb-4238-8bf7-87654321"
}
},
{
eventType: "party",
createdAt: "2022-01-23T10:26:11.214Z",
visitorInfo: {
visitorId: "87654321-0ebb-4238-8bf7-87654321"
}
},
{
eventType: "party",
createdAt: "2022-01-23T10:26:11.214Z",
visitorInfo: {
visitorId: "01234567-0ebb-4238-8bf7-01234567"
}
},
{
eventType: "party",
createdAt: "2022-01-30T10:26:11.214Z",
visitorInfo: {
visitorId: "12345678-0ebb-4238-8bf7-12345678"
}
},
{
eventType: "party",
createdAt: "2022-01-30T10:16:11.214Z",
visitorInfo: {
visitorId: "12345678-0ebb-4238-8bf7-12345678"
}
}
]
}
I'm trying to count events (all and unique ones based on visitorId) on date (daily).
This is what I have so far (thanks to #R2D2's guide on the approach):
Event.aggregate([
{ $match: { username: 'jack' } },
{ $unwind: "$events" },
{
$project: {
total: {
$cond: [
{
$eq: ["$events.eventType", "party"],
},
1,
0,
],
},
unique: { // where I'm stuck. I need to count unique events based on visitorId on current date.
$cond: [
{
$eq: ["$events.eventType", "party"],
},
1,
0,
],
},
date: "$events.createdAt",
},
},
{
$group: {
_id: {
$dateToString: { format: "%Y-%m-%d", date: "$date" },
},
total: {
$sum: "$total",
},
uniqueTotal: {
$sum: "$unique",
},
},
},
{
$project: {
date: "$_id",
total: 1,
uniqueTotal: 1,
},
},
{
$group: {
_id: "0",
dateAndEventFrequency: {
$push: "$$ROOT",
},
},
},
{
$project: {
_id: 0,
dateAndEventFrequency: 1,
},
},
]);
I tried using $addToSet but it's not used with $project (it works with $group).
Any new approach is welcome based on the data shape and the desired result I'm expecting. I used $project because I was already using it.
Basically what I'm hoping to get in the end:
dateAndEventFrequency: [
{
_id: "2022-01-23",
uniqueTotal: 2,
total: 3,
date: "2022-01-23",
},
{
_id: "2022-01-30",
uniqueTotal: 1,
total: 2,
date: "2022-01-30",
},
]
Any help or guidance is appreciated. Thanks!

first group by date and visitorId together and then do another group just by date
you can test it here mongo playground
db.collection.aggregate([
{
$match: {
username: "jack"
}
},
{
"$unwind": "$events"
},
{
"$group": {
"_id": {
date: {
"$dateToString": {
format: "%Y-%m-%d",
date: "$events.createdAt"
}
},
"visitorId": "$events.visitorInfo.visitorId",
},
"count": {
"$count": {}
}
}
},
{
"$group": {
"_id": "$_id.date",
"uniqueTotal": {
"$count": {}
},
total: {
"$sum": "$count"
}
}
}
])

Reshape the mongodb aggregation result with dynamic keys

Sample Collection document:
{
"_id" : ObjectId("5fec3b978b34e8b047b7ae14"),
"duration" : 20.0,
"createdOn" : ISODate("2020-12-16T22:28:44.000Z"),
"ClockInTime" : ISODate("2020-12-31T14:57:13.041Z"),
"states" : "PROCESSED"
}
Currently i'm using the following query.
db.collection.aggregate([{
$match: {
states: 'PROCESSED'
}
},
{
$group: {
_id: {
month: {
$month: "$createdOn"
},
year: {
$year: "$createdOn"
}
},
count: {
$sum: 1
},
date: {
$first: "$createdOn"
}
}
},
{
$project: {
_id: 0,
year: {
$year: "$date"
},
month: {
$month: "$date"
},
count: 1
}
}
]);
Which gives me the result in the following format.
[{
"count" : 2.0,
"year" : 2020,
"month" : 11
}, {
"count" : 5.0,
"year" : 2020,
"month" : 12
}, ...]
But i want the following format.
{
"2020": {
"11": 2,
"12": 5
}
}
Right now i'm able to get the above output by application level coding but i'm trying to get the same output from the mongodb query itself.

Based on the result you already have, add this one:
db.collection.aggregate([
{ $set: { result: [{ k: { $toString: "$month" }, v: "$count" }] } },
{ $set: { result: { $arrayToObject: "$result" } } },
{ $set: { result: [{ k: { $toString: "$year" }, v: "$result" }] } },
{ $replaceRoot: { newRoot: { $arrayToObject: "$result" } } }
])
Note, date: { $first: "$createdOn" } is not determined. Either use date: { $min: "$createdOn" } or insert {$sort: {...}} stage before you run $group. Well, if you get always just one document, then it does not matter of course. (but then you would not need count: { $sum: 1 } either)
Update based on additional input
db.collection.aggregate([
{ $match: { states: "PROCESSED" } },
{
$group: {
_id: {
month: { $month: "$createdOn" },
year: { $year: "$createdOn" }
},
count: { $sum: 1 },
date: { $first: "$createdOn" }
}
},
{ $group: { _id: "$_id.year", data: { $push: "$$ROOT" } } },
{
$set: {
data: {
$map: {
input: "$data",
in: {
k: { $toString: "$$this._id.month" },
v: "$$this.count"
}
}
}
}
},
{ $set: { data: { $arrayToObject: "$data" } } },
{ $set: { data: [ { k: { $toString: "$_id" }, v: "$data" } ] } },
{ $replaceRoot: { newRoot: { $arrayToObject: "$data" } } }
])
See Mongo Playground
In older MonogDB version use $addFields which is an alias for $set
following code works for older mongodb versions:
db.collection.aggregate([{
$match: {
states: "PROCESSED"
}
}, {
$group: {
_id: {
month: {
$dateToString: { format: "%m", date: "$createdOn" }
},
year: {
$dateToString: { format: "%Y", date: "$createdOn" }
}
},
count: {
$sum: 1
},
date: {
$first: "$createdOn"
}
}
}, {
$group: {
_id: "$_id.year",
data: {
$push: "$$ROOT"
}
}
}, {
$addFields: {
data: {
$map: {
input: "$data",
in: {
k: "$$this._id.month",
v: "$$this.count"
}
}
}
}
}, {
$addFields: {
data: {
$arrayToObject: "$data"
}
}
}, {
$addFields: {
data: [{
k: "$_id",
v: "$data"
}]
}
}, {
$replaceRoot: {
newRoot: {
$arrayToObject: "$data"
}
}
}])

MongoDB $size is not allowed in this atlas tier, alternatives?

I would like to return this only if there are 2 by in the data array. The number of _id can be unlimited.
However, the code { $size: { data: 2 }, } does not work because I get $size is not allowed in this atlas tier error.
Expected return:
[
{
"_id": "Something1?",
"data": [
{
"by": "user1",
},
{
"by": "user2",
}
]
},
]
I want to include something like $size in the code, otherwise it will return the data even if there is only 1 by, or 3 by, or 0 by. I only want to return the data if there are 2 by.
What should I do? Full code without $size:
let x = await Answer.aggregate([
{
$match: {
$and: [
{
by: {
$in: [user.email, user2[0].email],
},
},
],
},
},
{
$group: {
_id: "$question",
data: {
$push: "$$ROOT",
},
},
},
{
$project: {
"data._id": 0,
"data.question": 0,
"data.__v": 0,
},
},
{ $sort: { "data.date": -1 } },
]);

Looks like your atlas tier doesn't support $size.
But you can have a field like count that increments by 1 when grouping:
db.collection.aggregate([
{
$group: {
_id: "$question",
data: {
$push: "$$ROOT",
},
count: {
$sum: 1
}
}
},
{
$match: {
count: 2
}
}
])
Try this in playground
Update
Finally, your aggregation should look like this:
[
{
$match: {
$and: [
{
by: {
$in: [user.email, user2[0].email],
},
},
],
},
},
{
$group: {
_id: "$question",
data: {
$push: "$$ROOT",
},
count: {
$sum: 1
}
},
},
{
$match: {
count: 2
}
},
{
$project: {
"data._id": 0,
"data.question": 0,
"data.__v": 0,
"count": 0
},
},
{ $sort: { "data.date": -1 } },
]
You can learn more about $sum here.

Presuming your model is called Employee:
Employee.find({ { "social_account.2": { "$exists": false }} },function(err,docs) {
})
As $exists asks for the 2 index of an array which means it has something in it.
The same applies to a maximum number:
Employee.find({ { "social_account.9": { "$exists": true}} },function(err,docs) {
})
For your perspective I think this should be your answer:
Employee.find({ { "data.2": { "$exists": false }} },function(err,docs) {
})

Optimize combinational MongoDB query in Node.js

I have ten stations stored in the stations collection: Station A, Station B, Station C, Station D, Station E, Station F, Station G, Station H, Station I, Station J.
Right now, to create a count list of all inter-station rides between all possible pairs of stations, I do the following in my Node.js code (using Mongoose):
const stationCombinations = []
// get all stations from the stations collection
const stationIds = await Station.find({}, '_id name').lean().exec()
// list of all possible from & to combinations with their names
stationIds.forEach(fromStation => {
stationIds.forEach(toStation => {
stationCombinations.push({ fromStation, toStation })
})
})
const results = []
// loop through all station combinations
for (const stationCombination of stationCombinations) {
// create aggregation query promise
const data = Ride.aggregate([
{
$match: {
test: false,
state: 'completed',
duration: { $gt: 2 },
fromStation: mongoose.Types.ObjectId(stationCombination.fromStation._id),
toStation: mongoose.Types.ObjectId(stationCombination.toStation._id)
}
},
{
$group: {
_id: null,
count: { $sum: 1 }
}
},
{
$addFields: {
fromStation: stationCombination.fromStation.name,
toStation: stationCombination.toStation.name
}
}
])
// push promise to array
results.push(data)
}
// run all aggregation queries
const stationData = await Promise.all(results)
// flatten nested/empty arrays and return
return stationData.flat()
Executing this function give me the result in this format:
[
{
"fromStation": "Station A",
"toStation": "Station A",
"count": 1196
},
{
"fromStation": "Station A",
"toStation": "Station B",
"count": 1
},
{
"fromStation": "Station A",
"toStation": "Station C",
"count": 173
},
]
And so on for all other combinations...
The query currently takes a lot of time to execute and I keep getting alerts from MongoDB Atlas about excessive load on the database server because of these queries. Surely there must be an optimized way to do something like this?

You need to use MongoDB native operations. You need to $group by fromStation and toStation and with $lookup join two collections.
Note: I assume you have MongoDB >=v3.6 and Station._id is ObjectId
db.ride.aggregate([
{
$match: {
test: false,
state: "completed",
duration: {
$gt: 2
}
}
},
{
$group: {
_id: {
fromStation: "$fromStation",
toStation: "$toStation"
},
count: {
$sum: 1
}
}
},
{
$lookup: {
from: "station",
let: {
fromStation: "$_id.fromStation",
toStation: "$_id.toStation"
},
pipeline: [
{
$match: {
$expr: {
$in: [
"$_id",
[
"$$fromStation",
"$$toStation"
]
]
}
}
}
],
as: "tmp"
}
},
{
$project: {
_id: 0,
fromStation: {
$reduce: {
input: "$tmp",
initialValue: "",
in: {
$cond: [
{
$eq: [
"$_id.fromStation",
"$$this._id"
]
},
"$$this.name",
"$$value"
]
}
}
},
toStation: {
$reduce: {
input: "$tmp",
initialValue: "",
in: {
$cond: [
{
$eq: [
"$_id.toStation",
"$$this._id"
]
},
"$$this.name",
"$$value"
]
}
}
},
count: 1
}
},
{
$sort: {
fromStation: 1,
toStation: 1
}
}
])
MongoPlayground
Not tested:
const data = Ride.aggregate([
{
$match: {
test: false,
state: 'completed',
duration: { $gt: 2 }
}
},
{
$group: {
_id: {
fromStation: "$fromStation",
toStation: "$toStation"
},
count: { $sum: 1 }
}
},
{
$lookup: {
from: "station",
let: {
fromStation: "$_id.fromStation",
toStation: "$_id.toStation"
},
pipeline: [
{
$match: {
$expr: {
$in: [
"$_id",
[
"$$fromStation",
"$$toStation"
]
]
}
}
}
],
as: "tmp"
}
},
{
$project: {
_id: 0,
fromStation: {
$reduce: {
input: "$tmp",
initialValue: "",
in: {
$cond: [
{
$eq: [
"$_id.fromStation",
"$$this._id"
]
},
"$$this.name",
"$$value"
]
}
}
},
toStation: {
$reduce: {
input: "$tmp",
initialValue: "",
in: {
$cond: [
{
$eq: [
"$_id.toStation",
"$$this._id"
]
},
"$$this.name",
"$$value"
]
}
}
},
count: 1
}
},
{
$sort: {
fromStation: 1,
toStation: 1
}
}
])

Mongo/mongoose $facet filters, return all product's brands/tags in response if customer applied filters

I have this endpoint, it's the initial endpoint when a customer is visiting the eshop:
export const getAllProductsByCategory = async (req, res, next) => {
const pageSize = parseInt(req.query.pageSize);
const sort = parseInt(req.query.sort);
const skip = parseInt(req.query.skip);
const { order, filters } = req.query;
const { brands, tags, pricesRange } = JSON.parse(filters);
try {
const aggregate = Product.aggregate();
aggregate.lookup({
from: 'categories',
localField: 'categories',
foreignField: '_id',
as: 'categories'
});
aggregate.match({
productType: 'product',
available: true,
categories: {
$elemMatch: {
url: req.params
}
}
});
aggregate.lookup({
from: 'tags',
let: { tags: '$tags' },
pipeline: [
{
$match: {
$expr: { $in: ['$_id', '$$tags'] }
}
},
{
$project: {
_id: 1,
name: 1,
slug: 1
}
}
],
as: 'tags'
});
aggregate.lookup({
from: 'brands',
let: { brand: '$brand' },
pipeline: [
{
$match: {
$expr: { $eq: ['$_id', '$$brand'] }
}
},
{
$project: {
_id: 1,
name: 1,
slug: 1
}
}
],
as: 'brand'
});
if (brands.length > 0) {
const filterBrands = brands.map((_id) => utils.toObjectId(_id));
aggregate.match({
$and: [{ brand: { $elemMatch: { _id: { $in: filterBrands } } } }]
});
}
if (tags.length > 0) {
const filterTags = tags.map((_id) => utils.toObjectId(_id));
aggregate.match({ tags: { $elemMatch: { _id: { $in: filterTags } } } });
}
if (pricesRange.length > 0 && pricesRange !== 'all') {
const filterPriceRange = pricesRange.map((_id) => utils.toObjectId(_id));
aggregate.match({
_id: { $in: filterPriceRange }
});
}
aggregate.facet({
tags: [
{ $unwind: { path: '$tags' } },
{ $group: { _id: '$tags', tag: { $first: '$tags' }, total: { $sum: 1 } } },
{
$group: {
_id: '$tag._id',
name: { $addToSet: '$tag.name' },
total: { $addToSet: '$total' }
}
},
{
$project: {
name: { $arrayElemAt: ['$name', 0] },
total: { $arrayElemAt: ['$total', 0] },
_id: 1
}
},
{ $sort: { total: -1 } }
],
brands: [
{ $unwind: { path: '$brand' } },
{
$group: {
_id: '$brand._id',
name: { $first: '$brand.name' },
slug: { $first: '$brand.slug' },
total: {
$sum: 1
}
}
},
{ $sort: { name: 1 } }
],
pricesRange: [
{
$bucket: {
groupBy: {
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
boundaries: [0, 20.01, 50.01],
default: 'other',
output: {
count: { $sum: 1 },
products: { $push: '$_id' }
}
}
}
],
products: [
{ $skip: (skip - 1) * pageSize },
{ $limit: pageSize },
{
$project: {
_id: 1,
images: 1,
onSale: 1,
price: 1,
quantity: 1,
slug: 1,
sale: 1,
sku: 1,
status: 1,
title: 1,
brand: 1,
tags: 1,
description: 1
}
},
{ $sort: { [order]: sort } }
],
total: [
{
$group: {
_id: null,
count: { $sum: 1 }
}
},
{
$project: {
count: 1,
_id: 0
}
}
]
});
aggregate.addFields({
total: {
$arrayElemAt: ['$total', 0]
}
});
const [response] = await aggregate.exec();
if (!response.total) {
response.total = 0;
}
res.status(httpStatus.OK);
return res.json(response);
} catch (error) {
console.log(error);
return next(error);
}
};
If no filters are applied all products matches the category requested with no problem.
My issue is when a customer selects a brand or tag, then the facet returns the products, but returns only one brand/tag (as it should be since the products filtered have only this brand).
What I must do in order to retain all brands/tags and let the user select more than one brand/tag? If customer selects a brand, then the tags should match the returned products tags and vice versa.
Is there a better way to implement tags stage in $facet since tags is an array and the desired output is: [{_id: 123, name: {label: 'test', value: 123]}]
The request is like:(1,2,3,4 represents _id)
http://locahost:3000/get-products/?filters={brands: [1, 2], tags: [3,4], pricesRange:[]}
Update
This is the products schema with tags and brands:
brand: {
ref: 'Brand',
type: Schema.Types.ObjectId
},
tags: [
{
ref: 'Tags',
type: Schema.Types.ObjectId
}
]
tags schema:
{
metaDescription: {
type: String
},
metaTitle: {
type: String
},
name: {
label: {
type: String,
index: true
},
value: {
type: Schema.Types.ObjectId
},
},
slug: {
type: String,
index: true
},
status: {
label: {
type: String
},
value: {
default: true,
type: Boolean
}
}
}
brands schema:
description: {
default: '',
type: String
},
name: {
required: true,
type: String,
unique: true
},
slug: {
type: String,
index: true
},
status: {
label: {
default: 'Active',
type: String
},
value: {
default: true,
type: Boolean
}
}
Scenario:
User visits store, selects a category and all matching products should return with matched brands, tags, priceRange & pagination.
Case 1:
User clicks a brand from checkbox, then the request returns matching products,tags & priceRanges and all brands of the selected category, not of matched products
Case 2:
User selects a brand like Case 1, but then decides to check a tag too, then the request should return all brands and tags again, but products matched against them.
Case 3:
User do not select brand but selects a tag only, the request should return all matching products that have that tag/tags and return the brands that matched the products returned.
Case 4:
Same as case 3, but user selects a brand after selecting a tag/tags, the request should return matching products, brands & tags.
In all cases pagination should return proper total, also priceRanges should match the returned results.
I hope it's clear now, I think I've not missed any other case. I could probably grey out/disable the tags/brands that do not match the response in the front end but I don't know if this is user friendly.

This is what I ended up with:
export const getAllProductsByCategory = async (req, res, next) => {
const pageSize = parseInt(req.query.pageSize);
const sort = parseInt(req.query.sort);
const skip = parseInt(req.query.skip);
const { order, filters } = req.query;
const { brands, tags, pricesRange } = JSON.parse(filters);
try {
const aggregate = Product.aggregate();
aggregate.lookup({
from: 'categories',
localField: 'categories',
foreignField: '_id',
as: 'categories'
});
aggregate.match({
productType: 'product',
available: true,
categories: {
$elemMatch: {
url: `/${JSON.stringify(req.params['0']).replace(/"/g, '')}`
}
}
});
aggregate.lookup({
from: 'tags',
let: { tags: '$tags' },
pipeline: [
{
$match: {
$expr: { $in: ['$_id', '$$tags'] }
}
},
{
$project: {
_id: 1,
name: 1,
slug: 1
}
}
],
as: 'tags'
});
aggregate.lookup({
from: 'brands',
let: { brand: '$brand' },
pipeline: [
{
$match: {
$expr: { $eq: ['$_id', '$$brand'] }
}
},
{
$project: {
_id: 1,
name: 1,
slug: 1
}
}
],
as: 'brand'
});
const filterBrands = brands.map((_id) => utils.toObjectId(_id));
const filterTags = tags.map((_id) => utils.toObjectId(_id));
const priceRanges = pricesRange ? pricesRange.match(/\d+/g).map(Number) : '';
aggregate.facet({
tags: [
{ $unwind: { path: '$brand' } },
{ $unwind: { path: '$tags' } },
{
$match: {
$expr: {
$and: [
filterBrands.length ? { $in: ['$brand._id', filterBrands] } : true
]
}
}
},
{ $group: { _id: '$tags', tag: { $first: '$tags' }, total: { $sum: 1 } } },
{
$group: {
_id: '$tag._id',
name: { $addToSet: '$tag.name' },
total: { $addToSet: '$total' }
}
},
{
$project: {
name: { $arrayElemAt: ['$name', 0] },
total: { $arrayElemAt: ['$total', 0] },
_id: 1
}
},
{ $sort: { name: 1 } }
],
brands: [
{ $unwind: { path: '$brand' } },
{ $unwind: { path: '$tags' } },
{
$match: {
$expr: {
$and: [
filterTags.length ? { $in: ['$tags._id', filterTags] } : true
]
}
}
},
{
$group: {
_id: '$brand._id',
name: { $first: '$brand.name' },
slug: { $first: '$brand.slug' },
total: {
$sum: 1
}
}
},
{ $sort: { name: 1 } }
],
products: [
{ $unwind: { path: '$brand', preserveNullAndEmptyArrays: true } },
{ $unwind: { path: '$tags', preserveNullAndEmptyArrays: true } },
{
$match: {
$expr: {
$and: [
filterBrands.length ? { $in: ['$brand._id', filterBrands] } : true,
filterTags.length ? { $in: ['$tags._id', filterTags] } : true,
pricesRange.length
? {
$and: [
{
$gte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
priceRanges[0]
]
},
{
$lte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
priceRanges[1]
]
}
]
}
: true
]
}
}
},
{ $skip: (skip - 1) * pageSize },
{ $limit: pageSize },
{
$project: {
_id: 1,
brand: 1,
description: 1,
images: 1,
onSale: 1,
price: 1,
quantity: 1,
sale: 1,
shipping: 1,
sku: 1,
skuThreshold: 1,
slug: 1,
status: 1,
stock: 1,
tags: 1,
title: 1
}
},
{ $sort: { [order]: sort } }
],
pricesRange: [
{ $unwind: { path: '$brand', preserveNullAndEmptyArrays: true } },
{ $unwind: { path: '$tags', preserveNullAndEmptyArrays: true } },
{
$match: {
$expr: {
$and: [
filterBrands.length ? { $in: ['$brand._id', filterBrands] } : true,
filterTags.length ? { $in: ['$tags._id', filterTags] } : true
]
}
}
},
{
$project: {
price: 1,
onSale: 1,
sale: 1,
range: {
$cond: [
{
$and: [
{
$gte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
0
]
},
{
$lte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
20
]
}
]
},
'0-20',
{
$cond: [
{
$and: [
{
$gte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
20
]
},
{
$lte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
50
]
}
]
},
'20-50',
'50+'
]
}
]
}
}
},
{
$group: {
_id: '$range',
count: { $sum: 1 }
}
},
{
$project: {
_id: 0,
range: '$_id',
count: 1
}
},
{ $unwind: { path: '$range', preserveNullAndEmptyArrays: true } },
{
$sort: {
range: 1
}
}
],
total: [
{ $unwind: { path: '$brand', preserveNullAndEmptyArrays: true } },
{ $unwind: { path: '$tags', preserveNullAndEmptyArrays: true } },
{
$match: {
$expr: {
$and: [
filterBrands.length ? { $in: ['$brand._id', filterBrands] } : true,
filterTags.length ? { $in: ['$tags._id', filterTags] } : true,
pricesRange.length
? {
$and: [
{
$gte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
priceRanges[0]
]
},
{
$lte: [
{
$cond: {
if: { $ne: ['$onSale.value', true] },
then: '$price',
else: '$sale.salePrice'
}
},
priceRanges[1]
]
}
]
}
: true
]
}
}
},
{
$group: {
_id: null,
count: { $sum: 1 }
}
},
{
$project: {
count: 1,
_id: 0
}
}
]
});
aggregate.addFields({
total: {
$arrayElemAt: ['$total', 0]
}
});
const [response] = await aggregate.exec();
if (!response.total) {
response.total = 0;
}
res.status(httpStatus.OK);
return res.json(response);
} catch (error) {
console.log(error);
return next(error);
}
};

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