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Meteor Collection Find Documents Based on Latest Date Time in an Array

How to get the latest documents from a collection using date time?
I have searched in SO for this specific problem, but couldn't find an example that is similar to my data structure. I have this kind of data structure:
[
{
stationId: 'xxxxx',
stationName: 'xxxx',
state: 'xxxx',
lat: 'xxxxx',
long: 'xx.xxxxx',
waterLevel: [
{
wlDateTime: '11/04/2022 11:30',
wlSeverity: 'Danger',
wlLevel: 7.5
},
{
wlDateTime: '11/04/2022 09:00',
wlSeverity: 'Danger',
wlLevel: 7.3
},
{
wlDateTime: '11/04/2022 03:00',
wlSeverity: 'Normal',
wlLevel: 5.2
}
],
rainfallData: [
{
rfDateTime: '11/04/2022 11:30',
rfSeverity: 'Heavy',
rfLevel: 21
},
{
rfDateTime: '11/04/2022 10:30',
rfSeverity: 'Heavy',
rfLevel: 21
},
{
rfDateTime: '11/04/2022 9:30',
rfSeverity: 'Heavy',
rfLevel: 21
}
]
}
]
The question is, how can I get documents that have wlDateTime equal today, with wlSeverity equal to Danger, but I just want the latest record from the waterLevel array. The same case with the rainfallDataarray i.e. to return with the latest reading for today.
Sample expected return will be like this:
[
{
stationId: 'xxxxx',
stationName: 'xxxx',
state: 'xxxx',
lat: 'xxxxx',
long: 'xx.xxxxx',
waterLevelData: [
{
wlDateTime: '11/04/2022 11:30', //latest data compared to the array
wlSeverity: 'Danger',
wlLevel: 7.5
}
],
rainfallData: [
{
rfDateTime: '11/04/2022 11:30', //latest data compared to the array
rfSeverity: 'Heavy',
rfLevel: 21
}
]
}
]
I've tried querying it like this:
Meteor.publish('Alerts', function(){
return AlertLatest.find({
'waterLevelData.wlSeverity':'Danger',
}, {
fields : {
'stationName' : 1,
'state' : 1,
'lat' : 1,
'long' : 1,
'waterLevelData.wlDateTime' : 1,
'waterLevelData.wlSeverity' : 1,
'waterLevelData.wlLevel' : 1,
'rainfallData.rfSeverity' : 1,
}},{sort: { 'waterLevelData.wlDateTime' : -1}});
})
but the query returned data that isn't how I wanted. Any help will be much appreciated.
UPDATE
I've tried the solution provided by #YuTing, which is using aggregate to customise the publication query. I went ahead and read a bit about Mongodb Aggregation, and found a Meteorjs community package (tunguska:reactive-aggregate) which simplifies the process.
This is the sample of a working aggregation so far:
Meteor.publish('PIBDataAlerts', function(){
const start = dayjs().startOf('day'); // set to 12:00 am today
const end = dayjs().endOf('day'); // set to 23:59 pm today
ReactiveAggregate(this, PIBLatest, [
{
$match: {
'stationStatus' : 'ON',
'waterLevelData': { //trying to get only today's docs
"$elemMatch" : {
"wlDateTime" : {
$gte: start.format() , $lt: end.format()
}
}
}
}
},
{
$set: {
waterLevelHFZ: {
$filter: {
input: "$waterLevelData",
as: "w",
cond: {
$and: [
{ $or : [
{ $eq: [ "$$w.wlSeverity", "Alert" ] },
{ $eq: [ "$$w.wlSeverity", "Warning" ] },
{ $eq: [ "$$w.wlSeverity", "Danger" ] },
]},
{ $eq: [ "$$w.wlDateTime", { $max: "$waterLevelData.wlDateTime" } ] }
],
}
}
},
rainfallDataHFZ: {
$filter: {
input: "$rainfallData",
as: "r",
cond: { $eq: [ "$$r.rfDateTime", { $max: "$rainfallData.rfDateTime" } ] }
}
}
}
},
{
$project : {
"stationId": 1,
"stationName" :1,
"state": 1,
"waterLevelHFZ": 1,
"rainfallDataHFZ": 1
}
}
]);
})
I'm struggling to get documents that only have the wlDateTime that equals today. I've tried a query in the $match but it returned empty array. If the $match is set to {}, it'll return all 1548 records even though the wlDateTime is not equals to today.

change your date string to date
filter the array to find the max one
db.collection.aggregate([
{
$match: {
$expr: {
$or: [
{
$ne: [
{
$filter: {
input: "$waterLevel",
as: "w",
cond: {
$eq: [
{
$dateTrunc: {
date: {
$dateFromString: {
dateString: "$$w.wlDateTime",
format: "%d/%m/%Y %H:%M"
}
},
unit: "day"
}
},
{
$dateTrunc: {
date: "$$NOW",
unit: "day"
}
}
]
}
}
},
[]
]
},
{
$ne: [
{
$filter: {
input: "$rainfallData",
as: "r",
cond: {
$eq: [
{
$dateTrunc: {
date: {
$dateFromString: {
dateString: "$$r.rfDateTime",
format: "%d/%m/%Y %H:%M"
}
},
unit: "day"
}
},
{
$dateTrunc: {
date: "$$NOW",
unit: "day"
}
}
]
}
}
},
[]
]
}
]
}
}
},
{
$set: {
waterLevel: {
$map: {
input: "$waterLevel",
as: "w",
in: {
$mergeObjects: [
"$$w",
{
wlDateTime: {
$dateFromString: {
dateString: "$$w.wlDateTime",
format: "%d/%m/%Y %H:%M"
}
}
}
]
}
}
},
rainfallData: {
$map: {
input: "$rainfallData",
as: "r",
in: {
$mergeObjects: [
"$$r",
{
rfDateTime: {
$dateFromString: {
dateString: "$$r.rfDateTime",
format: "%d/%m/%Y %H:%M"
}
}
}
]
}
}
}
}
},
{
$set: {
waterLevel: {
$filter: {
input: "$waterLevel",
as: "w",
cond: {
$and: [
{
$in: [
"$$w.wlSeverity",
[
"Alert",
"Warning",
"Danger"
]
]
},
{
$eq: [
"$$w.wlDateTime",
{
$max: "$waterLevel.wlDateTime"
}
]
},
{
$eq: [
{
$dateTrunc: {
date: "$$w.wlDateTime",
unit: "day"
}
},
{
$dateTrunc: {
date: "$$NOW",
unit: "day"
}
}
]
}
]
}
}
},
rainfallData: {
$filter: {
input: "$rainfallData",
as: "r",
cond: {
$and: [
{
$eq: [
"$$r.rfDateTime",
{
$max: "$rainfallData.rfDateTime"
}
]
},
{
$eq: [
{
$dateTrunc: {
date: "$$r.rfDateTime",
unit: "day"
}
},
{
$dateTrunc: {
date: "$$NOW",
unit: "day"
}
}
]
}
]
}
}
}
}
}
])
mongoplayground

I don't think you can sort by embedded document in an array field. It's not how mongodb works.

but I just want the latest
I you are only interested in the latest docs you can omit the sort and instead use a natural negative cursor:
Meteor.publish('Alerts', function(){
return AlertLatest.find({
'waterLevelData.wlSeverity':'Danger',
}, {
fields : {
'stationName' : 1,
'state' : 1,
'lat' : 1,
'long' : 1,
'waterLevelData.wlDateTime' : 1,
'waterLevelData.wlSeverity' : 1,
'waterLevelData.wlLevel' : 1,
'rainfallData.rfSeverity' : 1,
}},{ hint: { $natural: -1}});
})
It will start counting docs from the end, instead of the beginning.
https://docs.meteor.com/api/collections.html#Mongo-Collection-find

converting the datestring in a nested array of objects (MongoDB)

What I want to achieve is finding a specific document on that current month based on the provided date. The date is stored as a string, in order for me to compare the date I need to convert the date first. However I have trouble on converting the datestring in a nested array of objects.
My collections:
{
sections: [{
fields: [{
name: 'Date',
value: '2020-11-30T15:59:59.999Z' // this is string
},
{
name: 'Title',
value: 'My book'
},
{
name: 'Author',
value: 'Henry'
}
]
]
}
}
What I have tried:
1)
const existingReport = await Report.find({
$expr: {
$gte: [
{
$dateFromString: {
dateString: "$sections.field[0].value",
},
},
moment(payload.forPeriod).startOf("month").toDate(),
],
$lt: [
{
$dateFromString: {
dateString: "$sections.field[0].value",
},
},
moment(payload.forPeriod).endOf("month").toDate(),
],
},
});
const existingReport1 = await Report.aggregate([
{
$addFields: {
formattedData: {
$cond: {
if: {
$eq: ["$sections.field.value", "Date"],
},
then: {
$dateFromString: {
dateString: "$sections.field.value",
},
},
else: "$sections.field.value",
},
},
},
},
]);

You can simply do a $toDate with the help of 2 $reduce to iterate the sections and fields array.
db.collection.aggregate([
{
"$match": {
$expr: {
$eq: [
true,
{
"$reduce": {
"input": "$sections",
"initialValue": false,
"in": {
"$reduce": {
"input": "$$this.fields",
"initialValue": false,
"in": {
$or: [
"$$value",
{
$and: [
{
$gte: [
{
"$toDate": "$$this.value"
},
new Date("2020-11-01")
]
},
{
$lte: [
{
"$toDate": "$$this.value"
},
new Date("2020-11-30")
]
}
]
}
]
}
}
}
}
}
]
}
}
}
])
Here is the Mongo playground for your reference.

Build the total sum of aggregated result

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),
}

How to adjust Mongo Aggregation Query $group to place matching key in an object rather than having two items in a $group

I have an aggregation query that is producing results in a mostly desired way except I need to only group by branchId ( not branchId and name ) and place the "name" values in an object with their relevant results.
SCHEMA:
{
process: { type: String, required: true },
name: { type: String, required: true },
error: { type: String },
action: {
type: String,
required: true,
enum: ['started', 'stopped', 'processing', 'completed', 'errored']
},
location: {
branchId: { type: String }
},
},
{ timestamps: true }
This is the current aggregation query:
[
{
$match: {
createdAt: { $gte: ISODate("2020-06-24T00:00:00.000+0000"),
$lte: ISODate("2020-06-25T00:00:00.000+0000")
}
}
},
{
$group: {
_id: { branchId: '$location.branchId', name: '$name' },
started: { $sum: { $cond: [{ $eq: ['$action', 'started'] }, 1, 0] } },
processing: { $sum: { $cond: [{ $eq: ['$action', 'processing'] }, 1, 0] } },
errored: { $sum: { $cond: [{ $eq: ['$action', 'errored'] }, 1, 0] } },
completed: { $sum: { $cond: [{ $eq: ['$action', 'completed'] }, 1, 0] }
}
}
},
]
CURRENT RESPONSE:
{
"_id" : {
"branchId" : "1",
"name" : "Product 1"
},
"started" : 1.0,
"processing" : 1.0,
"errored" : 0.0,
"completed" : 0.0
},
{
"_id" : {
"branchId" : "1",
"name" : "Product 2"
},
"started" : 1.0,
"processing" : 1.0,
"errored" : 1.0,
"completed" : 1.0
}
How would I modify the query to produce something similar to the following DESIRED RESPONSE: ?
{
"_id" : "1",
"product_1": {
"started" : true, // although 1.0 and 0.0 is acceptable
"processing" : true,
"errored" : true,
"completed" : false
},
"product_2": {
"started" : true,
"processing" : true,
"errored": false,
"completed" : true
}
},
I created this playground to assist in testing: https://mongoplayground.net/p/zDaxC-SYtN4

We need to use the $objectToArray operator to create an object based on the value of the document.
{k:"hello", v:"world"} --> {"hello":"world"}
Try this one:
db.collection.aggregate([
{
$match: {}
},
{
$group: {
_id: "$location.branchId",
data: {
$push: "$$ROOT"
}
}
},
{
$replaceWith: {
$arrayToObject: [
{
$concatArrays: [
[
{ "k": "_id", "v": "$_id" }
],
{
$map: {
input: "$data",
in: {
k: "$$this.name",
v: {
started: { $eq: ["$$this.action","started"},
processing: { $eq: ["$$this.action","processing"]},
errored: { $eq: ["$$this.action","errored"]},
completed: {$eq: ["$$this.action","completed"]}
}
}
}
}
]
}
]
}
}
])
MongoPlayground |Alternative 3.4

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);
}
};