db.setting.aggregate([
{
$match: {
status: true,
deleted_at: 0,
_id: {
$in: [
ObjectId("5c4ee7eea4affa32face874b"),
ObjectId("5ebf891245aa27c290672325")
]
}
}
},
{
$lookup: {
from: "site",
localField: "_id",
foreignField: "admin_id",
as: "data"
}
},
{
$project: {
name: 1,
status: 1,
price: 1,
currency: 1,
numberOfRecord: {
$size: "$data"
}
}
},
{
$sort: {
numberOfRecord: 1
}
}
])
how to push the currency into price object using project please guide thanks a lot, also eager to know what is difference between $addtoSet and $push, what is good option to opt it from project or fix it from $addField
https://mongoplayground.net/p/RiWnnRtksb4
Output should be like this:
[
{
"_id": ObjectId("5ebf891245aa27c290672325"),
"currency": "USD",
"name": "Menz",
"numberOfRecord": 0,
"price": {
"numberDecimal": "20",
"currency": "USD",
},
"status": true
},
{
"_id": ObjectId("5c4ee7eea4affa32face874b"),
"currency": "USD",
"name": "Dave",
"numberOfRecord": 2,
"price": {
"numberDecimal": "10",
"currency": "USD"
},
"status": true
}
]
You can insert a field into an object with project directly, like this (field price):
$project: {
name: 1,
status: 1,
price: {
numberDecimal: "$price.numberDecimal",
currency: "$currency"
},
numberOfRecord: {
$size: "$data"
}
}
By doing it with project, there is no need to use $addField.
For the difference between $addToSet and $push, read this great answer.
You can just set the object structure while projecting, so in this case there's no need for either $push or $addToSet.
{
$project: {
name: "1",
status: 1,
price: {
currency: "$currency",
numberDecimal: "$price.numberDecimal"
},
currency: 1,
numberOfRecord: {
$size: "$data",
}
}
}
Now the difference between $push and $addToSet is pretty trivial and derived from the name, $push saves all items while $addToSet will just create a set of them, for example:
input:
[
//doc1
{
item: 1
},
//doc2
{
item: 2
},
//doc3
{
item: 1
}
]
Now this:
{
$group: {
_id: null,
items: {$push: "$item"}
}
}
Will result in:
{_id: null, items: [1, 2, 1]}
While:
{
$group: {
_id: null,
items: {$addToSet: "$item"}
}
}
Will result in:
{_id: null, items: [1, 2]}
Related
I'm having a claim type:
type TClaim: {
insuredId: number,
treatmentInfo: { amount: number }[]
}
and a list of claims:
[
{
insuredId: 1,
treatmentInfo: [{amount: 1}, {amount: 2}]
},
{
insuredId: 1,
treatmentInfo: [{amount: 3}, {amount: 4}]
},
{
insuredId: 2,
treatmentInfo: [{amount: 1}, {amount: 2}]
}
]
I want to get the result like:
[{insuredId: 1, numberOfClaims: 2, amount: 10},{insuredId: 2, numberOfClaims: 1, amount: 3}]
I'm using the $facet operator in mongodb aggregation, one for counting numberOfClaims and one for calculating the amount of each insurer. But I can't combine it to get the result that I want.
$facet: {
totalClaims: [ { $group: { _id: '$insuredId', totalClaims: { $count: {} } } } ],
amount: [ { $unwind: { path: '$treatmentInfo'}},
{ $group:
{ _id: '$insuredId',
amount: { $sum: '$treatmentInfo.amount',
},
},
},
]
Is there a reason why you want to use $facet? - I am just curious
You just need to add a new fields that sums up all the amount in the array first and then do a group stage by insuredId. The query is pretty much self-explanatory.
db.collection.aggregate([
{
"$addFields": {
"totalAmount": {
"$sum": "$treatmentInfo.amount"
}
}
},
{
"$group": {
"_id": "$insuredId",
"numberOfClaims": {
"$sum": 1
},
"amount": {
"$sum": "$totalAmount"
}
}
}
])
Result:
[
{
"_id": 1,
"amount": 10,
"numberOfClaims": 2
},
{
"_id": 2,
"amount": 3,
"numberOfClaims": 1
}
]
MongoDB Playground
I have a Customer collection with the following document:
{
"_id": 1,
firstname: "John",
lastname: "Doe",
credits: [
{
cDate: "2020-01-16",
cAmount: 350
},
{
cDate: "2021-02-07",
cAmount: 180
},
{
cDate: "2021-06-25",
cAmount: 650
},
]
}
{
"_id": 2,
firstname: "Bob",
lastname: "Smith",
credits: [
{
cDate: "2020-03-19",
cAmount: 200
},
{
cDate: "2020-08-20",
cAmount: 90
},
{
cDate: "2021-11-11",
cAmount: 300
},
]
}
Now I would like to return the total spent for a specific year i.e. 2021.
The data should look something like this:
{"firstname": "John", "lastname": "Doe", "total": 830},
{"firstname": "Bob", "lastname": "Smith", "total": 300}
First I tried to match the records that contain cDates within the expected year (2021) to reduce the number of records (the actual dataset has hundreds of customers) and then projected the wanted fields:
Customer.aggregate([
{
$match: {
credits: {
$elemMatch: {
cDate: {
$gte: ISODate("2021-01-01"),
$lte: ISODate("2021-12-31"),
},
},
},
},
},
{
$project: {
_id: 0,
firstname: 1,
lastname: 1,
total: {
$sum: "$credits.cAmount",
},
},
}
])
the result is:
{"firstname": "John", "lastname": "Doe", "total": 1180},
{"firstname": "Bob", "lastname": "Smith", "total": 590}
Almost there, now I'd like to skip the credit records that do not contain the expected year (2021), so that only the values with a cDate equal to 2021 are calculated.
The $match I kept the same and I tried to add a $cond in the $project bit.
Customer.aggregate([
{
$match: {
credits: {
$elemMatch: {
cDate: {
$gte: ISODate("2021-01-01"),
$lte: ISODate("2021-12-31"),
},
},
},
},
},
{
$project: {
_id: 0,
firstname: 1,
lastname: 1,
total: {
$cond: {
if: { credits: { cDate: { regex: "2021-" } } }, // if cDate contains 2021-
then: { $sum: "$credits.cAmount" }, // add the cAmount
else: { $sum: 0 } // else add 0
},
},
},
}
])
This results is still the same, all totals get calulated from all years.
{"firstname": "John", "lastname": "Doe", "total": 1180},
{"firstname": "Bob", "lastname": "Smith", "total": 590}
What am I missing?
Thanks for your help.
Property cDate has string value, you can not match by date type,
$match cDate by $regex and match "2021" year
$reduce to iterate loop of credits array, set initial value to 0
$substr to get substring of the cDate from 0 index and 4 character that is year
$cond to check is substring is "2021" then $sum the initial value with cAmount otherwise return initial value
Customer.aggregate([
{
$match: {
"credits.cDate": {
$regex: "2021"
}
}
},
{
$project: {
_id: 0,
firstname: 1,
lastname: 1,
total: {
$reduce: {
input: "$credits",
initialValue: 0,
in: {
$cond: [
{
$eq: [
{ $substr: ["$$this.cDate", 0, 4] },
"2021"
]
},
{ $sum: ["$$value", "$$this.cAmount"] },
"$$value"
]
}
}
}
}
}
])
Playground
I have following stat data stored daily for users.
{
"_id": {
"$oid": "638df4e42332386e0e06d322"
},
"appointment_count": 1,
"item_id": 2,
"item_type": "user",
"company_id": 5,
"created_date": "2022-12-05",
"customer_count": 1,
"lead_count": 1,
"door_knocks": 10
}
{
"_id": {
"$oid": "638f59a9bf33442a57c3aa99"
},
"lead_count": 2,
"item_id": 2,
"item_type": "user",
"company_id": 5,
"created_date": "2022-12-06",
"video_viewed": 2,
"door_knocks": 9
}
And I'm using the following query to get the items by rank
user_stats_2022_12.aggregate([{"$match":{"company_id":5,"created_date":{"$gte":"2022-12-04","$lte":"2022-12-06"}}},{"$setWindowFields":{"partitionBy":"$company_id","sortBy":{"door_knocks":-1},"output":{"item_rank":{"$denseRank":{}},"stat_sum":{"$sum":"$door_knocks"}}}},{"$facet":{"metadata":[{"$count":"total"}],"data":[{"$skip":0},{"$limit":100},{"$sort":{"item_rank":1}}]}}])
It's giving me the rank but with the above data, the record with item_id: 2 are having different rank for same item_id. So I wanted to group them by item_id and then applied rank.
It's a little messy, but here's a playground - https://mongoplayground.net/p/JrJOo4cl9X1.
If you're going to sort by knocks after grouping, I'm assuming that you'll want the sum of door_knocks for a given item_id for this sort.
db.collection.aggregate([
{
$match: {
company_id: 5,
created_date: {
"$gte": "2022-12-04",
"$lte": "2022-12-06"
}
}
},
{
$group: {
_id: {
item_id: "$item_id",
company_id: "$company_id"
},
docs: {
$push: "$$ROOT"
},
total_door_knocks: {
$sum: "$door_knocks"
}
}
},
{
$setWindowFields: {
partitionBy: "$company_id",
sortBy: {
total_door_knocks: -1
},
output: {
item_rank: {
"$denseRank": {}
},
stat_sum: {
"$sum": "$total_door_knocks"
}
}
}
},
{
$unwind: "$docs"
},
{
$project: {
_id: "$docs._id",
appointment_count: "$docs.appointment_count",
company_id: "$docs.company_id",
created_date: "$docs.created_date",
customer_count: "$docs.customer_count",
door_knocks: "$docs.door_knocks",
item_id: "$docs.item_id",
item_type: "$docs.item_type",
lead_count: "$docs.lead_count",
item_rank: 1,
stat_sum: 1,
total_door_knocks: 1
}
},
{
$facet: {
metadata: [
{
"$count": "total"
}
],
data: [
{
"$skip": 0
},
{
"$limit": 100
},
{
"$sort": {
"item_rank": 1
}
}
]
}
}
])
I'm trying to get a list of current holders at specific times from a collection. My collection looks like this:
[
{
"time": 1,
"holdings": [
{ "owner": "A", "tokens": 2 },
{ "owner": "B", "tokens": 1 }
]
},
{
"time": 2,
"holdings": [
{ "owner": "B", "tokens": 2 }
]
},
{
"time": 3,
"holdings": [
{ "owner": "A", "tokens": 3 },
{ "owner": "B", "tokens": 1 },
{ "owner": "C", "tokens": 1 }
]
},
{
"time": 4,
"holdings": [
{ "owner": "C", "tokens": 0 }
]
}
]
tokens show the current holdings of an owner if the holdings have changed to the last document. I would like to change the collection so that holdings always includes the full current holdings for any point in time.
At time: 1, the holdings are: A: 2, B: 1.
At time: 2, the holdings are: A: 2, B: 2. The collections does not include A's holdings however, because they haven't changed. So what I'd like to get is:
[
{
"time": 1,
"holdings": [
{ "owner": "A", "tokens": 2 },
{ "owner": "B", "tokens": 1 }
]
},
{
"time": 2,
"holdings": [
{ "owner": "A", "tokens": 2 }, // merged from prev doc.
{ "owner": "B", "tokens": 2 }
]
},
{
"time": 3,
"holdings": [
{ "owner": "A", "tokens": 3 },
{ "owner": "B", "tokens": 1 },
{ "owner": "C", "tokens": 1 }
]
},
{
"time": 4,
"holdings": [
{ "owner": "A", "tokens": 3 }, // merged from prev
{ "owner": "B", "tokens": 1 }, // merged from prev
{ "owner": "C", "tokens": 0 }
]
}
]
From what I understand $mergeObjects does that, but I don't understand how I can merge all previous docs in order up to the current doc for each doc. So I'm looking for a way to combine setWindowFields with mergeObjects I think.
This is a nice challenge.
So far, I got this complicated solution:
Get all of our timestamps in all of our documents. This is the purpose of the first 4 steps. $setWindowFields is used to accumulate this data.
$group by owner and calculate the empty timestamps as wantedTimes- next 5 steps.
$set empty timestamps with tokens: null to be filled with actual data and $unwind to separate - next 3 steps
Use $setWindowFields to find the last known token for each owner at each timestamp.
Fill this last known state for documents with unknown token - 2 steps
$group and format answer:
db.collection.aggregate([
{
$setWindowFields: {
sortBy: {time: 1},
output: {
allTimes: {$addToSet: "$time", window: {documents: ["unbounded", "current"]}
}
}
}
},
{
$setWindowFields: {
sortBy: {time: -1},
output: {
allTimes: {$addToSet: "$allTimes", window: {documents: ["unbounded", "current"]}
}
}
}
},
{
$set: {
allTimes: {
$reduce: {
input: "$allTimes",
initialValue: [],
in: {"$concatArrays": ["$$value", "$$this"]}
}
}
}
},
{$set: {allTimes: {$setIntersection: "$allTimes"}}},
{$unwind: "$holdings"},
{$sort: {time: 1}},
{$group: { _id: "$holdings.owner",
tokens: {$push: {tokens: "$holdings.tokens", time: "$time"}},
times: {$push: "$time"}, firstTime: {$first: "$time"},
allTimes: {$first: "$allTimes"}}
},
{
$addFields: {
wantedTimes: {
$filter: {
input: "$allTimes",
as: "item",
cond: {$gte: ["$$item", "$firstTime"]}
}
}
}
},
{
$project: {
tokens: 1,
wantedTimes: {$setDifference: ["$wantedTimes", "$times"]}
}
},
{
$set: {
data: {
$map: {
input: "$wantedTimes",
as: "item",
in: {time: "$$item", tokens: null}
}
}
}
},
{$project: {tokens: {"$concatArrays": ["$tokens", "$data"]}}},
{$unwind: "$tokens"},
{
$setWindowFields: {
partitionBy: "$_id",
sortBy: {"tokens.time": 1},
output: {
lastTokens: {
$push: "$tokens.tokens",
window: {documents: ["unbounded", "current"]}
}
}
}
},
{
$set: {
lastTokens: {
$filter: {
input: "$lastTokens",
as: "item",
cond: {$ne: ["$$item", null]}
}
}
}
},
{
$set: {
"tokens.tokens": {$ifNull: ["$tokens.tokens", {$last: "$lastTokens"}]}
}
},
{
$group: {
_id: "$tokens.time",
holdings: {$push: {owner: "$_id", tokens: "$tokens.tokens" }}
}
},
{$project: {time: "$_id", holdings: 1, _id: 0}},
{$sort: {time: 1}}
])
Playground example
From a performance perspective I recommend you split it into 2 calls, the first will be a quick findOne just to get the maximum time value in the collection.
Once you have that value the pipeline can be much leaner:
const maxItem = await db.collection.findOne({}).sort({ time: -1 });
db.collection.aggregate([
{
$unwind: "$holdings"
},
{
$group: {
_id: "$holdings.owner",
times: {
$push: {
time: "$time",
tokens: "$holdings.tokens"
}
},
minTime: {
$min: "$time"
}
}
},
{
$addFields: {
times: {
$reduce: {
input: {
$range: [
"$minTime",
maxItem.time + 1 // this is max time
]
},
initialValue: {
values: [],
lastIndex: 0
},
in: {
values: {
"$concatArrays": [
"$$value.values",
[
{
$cond: [
{
$in: [
"$$this",
"$times.time"
]
},
{
"$arrayElemAt": [
"$times",
"$$value.lastIndex"
]
},
{
"$mergeObjects": [
{
tokens: 0
},
{
"$arrayElemAt": [
"$times",
{
$subtract: [
"$$value.lastIndex",
1
]
}
]
},
{
time: "$$this"
}
]
}
]
}
]
]
},
lastIndex: {
$cond: [
{
$in: [
"$$this",
"$times.time"
]
},
{
$sum: [
"$$value.lastIndex",
1
]
},
"$$value.lastIndex"
]
}
}
}
}
}
},
{
$unwind: "$times.values"
},
{
$group: {
_id: "$times.values.time",
holdings: {
$push: {
owner: "$_id",
tokens: "$times.values.tokens"
}
}
}
},
{
$project: {
_id: 0,
time: "$_id",
holdings: 1
}
},
{
$sort: {
time: 1
}
}
])
This is still quite a heavy query as it requires to $unwind and $group the entire collection, however there is no workaround this due to the requirements. if the collection is too big for this approach I recommend iteration owner by owner, or time by time and doing separate updates accordingly.
Mongo Playground
If you don't care about performance at all and want it in a single query you can still use the same pipeline, you will have to first extract the max time in the collection, this will require you to add an initial $group stage, like so:
db.collection.aggregate([
{
$group: {
_id: null,
maxTime: {
$max: "$time"
},
roots: {
$push: "$$ROOT"
}
}
},
{
$unwind: "$roots"
},
{
$replaceRoot: {
newRoot: {
"$mergeObjects": [
"$roots",
{
maxTime: "$maxTime"
}
]
}
}
},
... same pipeline ...
])
I have following 2 collection schema
images:{
imageId:"string", avgRating:{ rating1:decimal,rating2:decimal}, ratingCount:int}
}
ratings:{
imageId:"string", rating1:decimal, rating2:decimal
}
//here rating1 nd rating2 are ratings for different features(just according to my requirements)
so I am calculating avg as follows
db.images.aggregate([
{
$match: {
imageId: "someid",
},
},
{
$lookup:
{
from: "ratings",
let: {id: '$imageId'},
pipeline: [
{
$match: {
{
$eq: ['$imageId','$$id']
},
},
},{
$group:
{
_id: 0,
aggRating1: {$avg: "$rating1"},
aggRating2: {$avg: '$rating2'},
count: {$sum: 1}
}
},
{$project: {_id: 0,count:1,aggRating1:1,aggRating2:1}},
],
as: "rating"
}
},
{
$set: {
ratingCount: '$count',
'avgRating.rating1':'$review.aggRating1'
'avgRating.rating2':'$review.aggRating2'
}
},
]);
I am getting results like this
"data":[
{
"_id": "somedocId",
"imageId":"someid",
"ratingCount": 10,
"avgRating": {
"aggRating1": [
"rating1":{
"$numberDecimal": "3.25"
}],
"aggRating2": [
"rating2":{
"$numberDecimal": "3.25"
}]
},
"rating":[
{
"aggRating1": {
"$numberDecimal": "3.25"
},
"aggRating2": {
"$numberDecimal": "3.25"
},
"count": 10
}
],
}
]
So if u see when I set the aggRating1 and aggRating2 from rating lookup I got, it converts to array. But in rating it is an object. Idk why is that happening.
So how do i get just the decimal value of the avg results? and not like above? :/