This question already has answers here:
MongoDB $graphLookup get children all levels deep - nested result
(2 answers)
Closed 3 years ago.
I have an output from mongodb $graphLookup aggregation:
db.getCollection('projects').aggregate([
{
$lookup: {
from: "projects",
localField: "_id",
foreignField: "parent",
as: "childrens"
}
}
])
{
"_id" : "1",
"name" : "Project1",
"parent" : null,
"childrens" : [
{
"_id" : "3",
"name" : "ProjectForId1",
"parent" : "1"
}
]
},
{
"_id" : "3",
"name" : "ProjectForId1",
"parent" : "1",
"childrens" : [
{
"_id" : "6",
"name" : "ProjectForId3",
"parent" : "3"
},
{
"_id" : "7",
"name" : "ProjectForId3",
"parent" : "3"
}
]
}
I need to build hierarchy from this output in javascript or if is possible directly from query so the final output should look like:
{
"_id" : "1",
"name" : "Project1",
"parent" : null,
"childrens" : [
{
"_id" : "3",
"name" : "ProjectForId1",
"parent" : "1",
"childrens" : [
{
"_id" : "6",
"name" : "ProjectForId3",
"parent" : "3"
},
{
"_id" : "7",
"name" : "ProjectForId3",
"parent" : "3"
}
]
}
]
}
Also if someone have a brave heart to help in one more case where the hierarchy will be created by filtering _id:
ex: For _id = "1" the output will be same as above but if _id is 3 the final output should look like:
{
"_id" : "3",
"name" : "ProjectForId1",
"parent" : "1",
"childrens" : [
{
"_id" : "6",
"name" : "ProjectForId3",
"parent" : "3"
},
{
"_id" : "7",
"name" : "ProjectForId3",
"parent" : "3"
}
]
}
Below solution is more or less the same as one of my past answers so you can get thorough explanation here
db.projects.aggregate([
{
$graphLookup: {
from: "projects",
startWith: "$_id",
connectFromField: "_id",
connectToField: "parent",
as: "children",
maxDepth: 4,
depthField: "level"
}
},
{
$unwind: "$children"
},
{
$sort: { "children.level": -1 }
},
{
$group: {
_id: "$_id",
children: { $push: "$children" }
}
},
{
$addFields: {
children: {
$reduce: {
input: "$children",
initialValue: {
currentLevel: -1,
currentLevelProjects: [],
previousLevelProjects: []
},
in: {
$let: {
vars: {
prev: {
$cond: [
{ $eq: [ "$$value.currentLevel", "$$this.level" ] },
"$$value.previousLevelProjects",
"$$value.currentLevelProjects"
]
},
current: {
$cond: [
{ $eq: [ "$$value.currentLevel", "$$this.level" ] },
"$$value.currentLevelProjects",
[]
]
}
},
in: {
currentLevel: "$$this.level",
previousLevelProjects: "$$prev",
currentLevelProjects: {
$concatArrays: [
"$$current",
[
{ $mergeObjects: [
"$$this",
{ children: { $filter: { input: "$$prev", as: "e", cond: { $eq: [ "$$e.parent", "$$this._id" ] } } } }
] }
]
]
}
}
}
}
}
}
}
},
{
$addFields: { children: "$children.currentLevelProjects" }
},
{
$match: {
_id: "1"
}
}
])
Last stage is supposed to be the filtering so you can get the data for any level of depth here.
Related
Suppose that I have a collection with documents like below
{
"location" : "Tokyo",
"region" : "Asia",
"attraction": {
"transportation" : "Subway",
"food" : {
"food_0" : {
"name" : "Sushi",
"price" : 100,
"restaurant" : "Ookinza"
},
"food_1" : {
"name" : "Sashimi",
"price" : 200,
"restaurant" : "Hibiki"
},
"food_2" : {
"name" : "N/A",
"price" : "N/A",
"restaurant" : "N/A"
}
}
}
},
{
"location" : "Taipei",
"region" : "Asia",
"attraction": {
"transportation" : "Subway",
"food" : {
"food_0" : {
"name" : "Bubble tea",
"price" : 50,
"restaurant" : "The Alley"
},
"food_1" : {
"name" : "Oyster cake",
"price" : 100,
"restaurant" : "Night market"
},
"food_2" : {
"name" : "N/A",
"price" : "N/A",
"restaurant" : "N/A"
}
}
}
},
{
"location" : "Toronto",
"region" : "North America",
"attraction": {
"transportation" : "Uber",
"food" : {
"food_0" : {
"name" : "Raman",
"price" : 300,
"restaurant" : "Kinto"
},
"food_1" : {
"name" : "Bubble tea",
"price" : 200,
"restaurant" : "Fresh Fruit"
},
"food_2" : {
"name" : "N/A",
"price" : "N/A",
"restaurant" : "N/A"
}
}
}
},
How do I find documents that have matching field in the child object of Food?
i.e. If I want to find document that has restaurant:"Fresh Tea"?
Currently what I have:
app.get(route, (req, res) => {
var detail = {};
if(req.query.location){
detail['location'] = req.query.location.toUpperCase();
}
if(req.query.region){
detail['region'] = req.query.region.toUpperCase();
}
if(req.query.transportation){
detail['attraction.transportation'] = new RegExp(req.query.transportation.split(","), "i"),
}
if(req.query.restaurant){
detail['attraction.food.food_0'] = req.query.restaurant;
}
db.collection(config.dbCollections.foodDB)
.aggregate([
$match: detail,
},
{
$lookup: {
... // code continues
Right now detail['attraction.food.food_0'] = req.query.restaurant is only able to find document that has matching food_0.restaurant, but I still can't find a way to make it check all child objects within "food".
Updated with more info:
User has the option to enter multiple search categories, and I want to combine all the search requests into "detail" and find all matching results. I.e. If user looks for transportation="Subway" and food="Bubble tea", then both Taipei and Toronto should come up as result.
Using dynamic value as field name is generally considered as anti-pattern and should be avoided. Nevertheless, you can convert the object attraction.food to an array of k-v tuple and perform the search with your criteria. For your case, $anyElementTrue with $map will help with processing the array.
db.collection.aggregate([
{
"$addFields": {
"test": {
"$anyElementTrue": {
"$map": {
"input": {
"$objectToArray": "$attraction.food"
},
"as": "t",
"in": {
$or: [
{
$eq: [
"$$t.v.transportation",
"Subway"
]
},
{
$eq: [
"$$t.v.name",
"Bubble tea"
]
}
]
}
}
}
}
}
},
{
$match: {
test: true
}
},
{
"$unset": "test"
}
])
Here is the Mongo Playground for your reference.
A possible aggregation pipeline
Add a temporary field using $addFields and $objectToArray which does something similar to javascript Object.entries()
Do the matching
Remove the added temporary field using $project 0
playground
db.collection.aggregate([
{
"$addFields": {
"foodArray": {
"$objectToArray": "$attraction.food"
},
},
},
{
"$match": {
"foodArray.v.restaurant": "Fresh Fruit"
}
},
{
"$project": {
"foodArray": 0
},
},
])
Suppose I have data in bookOrder as:
{
"_id" : ObjectId("615fc295257d6d7cf57a39fe"),
"orderId" : "2001",
"itemId" : [
"615fc232257d6d7cf57a39d4",
"615fc251257d6d7cf57a39e0"
],
"Discount" : 10
}
Item data as:
{
"_id" : ObjectId("615fc232257d6d7cf57a39d4"),
"itemId" : "1001",
"Price" : 10.21
}
{
"_id" : ObjectId("615fc251257d6d7cf57a39e0"),
"itemId" : "1002",
"Price" : 100
}
I want to calculate the total price of order after discount,
i.e. total price as : 100+10.21-10 = 100.21
For this I tried as:
const data = await db.order.aggregate(
[
{
"$match": {
"orderId": orderId
}
},
{
"$lookup": {
"from": "item",
let: {
eid: "$itemId"
},
pipeline: [
{
"$match": {
$expr: {
$in: [
"$_id",
"$$eid"
]
}
}
},
],
"as": "items"
}
},
{
"$unwind": {
path: "$items"
}
},
]
)
So, I get the value as:
{
"orderId" : "2001",
"Discount":10,
"itemId":[{
"itemId" : "1001",
"Price" : 10.21
},
"itemId" : "1002",
"Price" : 100
]}
}
SO instead of having to loop over the itemId price and get total sun, and then subtracting from the discount price of order can we do all these calculations of db itself.
Is there any way that I can query the total price from DB only instead of having to fetch data and applying any loop and then calculating the total price?
Please let me know if anyone needs any further explanation from my side.
use sum
aggregate
db.orders.aggregate([
{
"$match": {
"orderId": "2001"
}
},
{
"$lookup": {
"from": "items",
"localField": "itemId",
"foreignField": "_id",
"as": "items"
}
},
{
"$project": {
"orderId": 1,
"total": {
$subtract: [
{
"$sum": "$items.Price"
},
"$Discount"
]
}
}
}
])
mongoplayground
You can do this in a couple of ways, here is the most straight forward one using $map and some math operators.
db.order.aggregate([
{
"$match": {
"orderId": "2001"
}
},
{
"$lookup": {
"from": "item",
let: {
eid: "$itemId"
},
pipeline: [
{
"$match": {
$expr: {
$in: [
"$_id",
"$$eid"
]
}
}
},
],
"as": "items"
}
},
{
$project: {
orderId: 1,
finalSum: {
$subtract: [
{
$sum: {
$map: {
input: "$items",
in: "$$this.Price"
}
}
},
"$Discount"
]
}
}
}
])
Mongo Playground
Here I am trying to get entire data but if date less then current then do not fetch that date from the database.
{
"_id" : ObjectId("5d6fad0f9e0dc027fc6b5ab5"),
"highlights" : [
"highlights-1",
],
"notes" : [
"Listen"
],
"soldout" : false,
"active" : false,
"operator" : ObjectId(""),
"title" : "2D1N Awesome trip to Knowhere 99",
"destinations" : [
{
"coordinatesType" : "Point",
"_id" : ObjectId("5d6fad0f9e0dc027fc6b5ab6"),
}
],
"difficulty" : "Easy",
"duration" : {
"_id" : ObjectId("5d6fad0f9e0dc027fc6b5ab7"),
"days" : NumberInt(2),
"nights" : NumberInt(1)
},
"media" : {
"_id" : ObjectId("5d6fad0f9e0dc027fc6b5ab8"),
"images" : [
],
"videos" : [
]
},
"description" : "Surrounded ",
"inclusions" : [
{
"_id" : ObjectId(""),
"text" : "Included"
}
],
"itinerary" : "Surrounded .",
"thingsToCarry" : [
{
"_id" : ObjectId(""),
"text" : "Yourself"
}
],
"exclusions" : [
{
"_id" : ObjectId(""),
"text" : "A Lot"
}
],
"policy" : "Fully refundable 7777 Days before the date of Experience",
"departures" : [
{
"dates" : [
ISODate("2019-11-19T02:44:58.989+0000"),
ISODate("2019-11-23T17:19:47.878+0000")
],
"_id" : ObjectId(""),
"bookingCloses" : "2 Hours Before",
"maximumSeats" : NumberInt(20),
"source" : {
"coordinatesType" : "Point",
"_id" : ObjectId("5d6fad0f9e0dc027fc6b5ac2"),
"code" : "code",
"name" : "Manali",
"state" : "Himachal Pradesh",
"region" : "North",
"country" : "India",
"coordinates" : [
23.33,
NumberInt(43),
NumberInt(33)
]
},
"pickupPoints" : [
{
"coordinatesType" : "Point",
"_id" : ObjectId("5d6fad0f9e0dc027fc6b5ac3"),
"name" : "name-3",
"address" : "address-3",
"time" : "time-3",
"coordinates" : [
23.33,
NumberInt(43),
NumberInt(33)
]
}
],
"prices" : {
"3" : NumberInt(5)
},
"mrps" : {
"3" : NumberInt(5)
},
"markup" : NumberInt(25),
"discount" : NumberInt(0),
"b2m" : {
"3" : NumberInt(5)
},
"m2c" : {
"3" : 6.25
},
"minimumOccupancy" : NumberInt(3),
"maximumOccupancy" : NumberInt(3)
}
],
"bulkDiscounts" : [
{
"_id" : ObjectId("5d6fad0f9e0dc027fc6b5ac4")
}
],
}
In this I am trying to get all the data except the date section should be different. Means I should get my output as below
{
"_id": "5d6fad0f9e0dc027fc6b5ab5",
"highlights": [
"highlights-1",
"highlights-2",
"highlights-3",
"highlights-4",
"highlights-5"
],
"notes": [
"Listen"
],
"soldout": false,
"active": false,
"operator": "5d5d84e8c89fbf00063095f6",
"title": "2D1N Awesome trip to Knowhere 99",
"destinations": [
{
"code": "code",
"name": "Manali",
"coordinates": [
23.33,
43,
33
]
}
],
"difficulty": "Easy",
"duration": {
"_id": "5d6fad0f9e0dc027fc6b5ab7",
"days": 2,
"nights": 1
},
"media": {
"_id": "5d6fad0f9e0dc027fc6b5ab8",
"images": [
],
"videos": []
},
"description": "Surrounded.",
"inclusions": [
{
"_id": "5d6fad0f9e0dc027fc6b5abe",
"text": "Included"
}
],
"itinerary": "Surrounded",
"thingsToCarry": [
{
"_id": "5d6fad0f9e0dc027fc6b5abf",
"text": "Yourself"
}
],
"exclusions": [
{
"_id": "5d6fad0f9e0dc027fc6b5ac0",
"text": "A Lot"
}
],
"policy": "Fully refundable 7777 Days before the date of Experience",
"departures": [
{
"dates": [
"2019-11-23T17:19:47.878Z"
],
"_id": "5d6fad0f9e0dc027fc6b5ac1",
"bookingCloses": "2 Hours Before",
"maximumSeats": 20,
"source": {
"code": "code",
"name": "Manali",
"coordinates": [
23.33,
43,
33
]
},
"pickupPoints": [
{
"coordinatesType": "Point",
"_id": "5d6fad0f9e0dc027fc6b5ac3",
"name": "name-3",
"address": "address-3",
"time": "time-3",
"coordinates": [
23.33,
43,
33
]
}
],
"mrps": {
"3": 5
},
"markup": 25,
"discount": 0,
"b2m": {
"3": 5
},
"m2c": {
"3": 6.25
},
"minimumOccupancy": 3,
"maximumOccupancy": 3
}
],
"bulkDiscounts": [
{
"_id": "5d6fad0f9e0dc027fc6b5ac4"
}
],
"url": "",
}
]
I mean to say that no difference in output except dates array. If dates are less than current date then no need to fetch else fetch from DB with filtered dates array.
If you use mongo 3.4> then you can try with $addFields and $filter:
myCollection.aggregate([
{$match: {
'departures.dates': {
$elemMatch: {$gt: new Date()}}
}
},
{$addFields: {
'departures.dates': {
$filter: {
input: '$departures.dates',
as: 'date',
cond: {
$gt: ['$$date', new Date()]
}
}
}
}}
])
I was missing one terms here that my documnet structure is like below
{
_id: ObjecId(),
departure: [{
dates: [Array]
}]
}
So, here is my solution in the below code
pipeline = [
{ $unwind: '$departures' },
{
$addFields: {
'departures.dates': {
$filter: {
input: '$departures.dates',
as: 'date',
cond: {
$gt: ['$$date', new Date()]
}
}
}
}
}
];
I have a basic social media app that allows users to follow each other. When need to find some specific persons "followers", i look for users who have the id of this specific person in their "following"s;
{
$and: [
{
$and: [
{
"following.userId": mongoose.Types.ObjectId(targetId)
},
{
"following.following": true
}
]
},
{
$or: [{ firstName: firstNameRegex }, { lastName: lastNameRegex }]
},
{ blockedUsers: { $nin: mongoose.Types.ObjectId(req.userId) } }
]
};
If a user stops following someone, "following.following" property becomes false.
When run this query, I get every person who has followed that specific person in some time without looking "following.following": true property at all.
"following.following" doesn't evaluate the times when "following.userId" matches, rather it looks for whole array and matches if some of them has "following.following" true.
Here is the file structure
You are querying embedded array documents, simple $and query will not be helpful here the way you are using it.
Basically we want to match multiple fields from single embedded documents in an array.
so let's consider this example:
For simplicity, I have added a few fields from my understanding, and
considering you are facing an issue with $and query, I will and
accordingly assuming rest query does not change and works.
db.followers.find().pretty()
{
"_id" : ObjectId("5d984403d933b7b079038ca9"),
"userId" : "1",
"followers" : [
{
"fId" : "4",
"following" : true
},
{
"fId" : "2",
"following" : true
},
{
"fId" : "3",
"following" : false
}
]
}
{
"_id" : ObjectId("5d984422d933b7b079038caa"),
"userId" : "2",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "3",
"following" : false
},
{
"fId" : "4",
"following" : false
}
]
}
{
"_id" : ObjectId("5d984432d933b7b079038cab"),
"userId" : "3",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "2",
"following" : true
},
{
"fId" : "4",
"following" : true
}
]
}
{
"_id" : ObjectId("5d984446d933b7b079038cac"),
"userId" : "4",
"followers" : [
{
"fId" : "1",
"following" : false
},
{
"fId" : "2",
"following" : true
},
{
"fId" : "3",
"following" : true
}
]
}
ANS 1:
db.followers.find({followers:{ "fId": "1", "following": true }}).pretty()
{
"_id" : ObjectId("5d984422d933b7b079038caa"),
"userId" : "2",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "3",
"following" : false
},
{
"fId" : "4",
"following" : false
}
]
}
{
"_id" : ObjectId("5d984432d933b7b079038cab"),
"userId" : "3",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "2",
"following" : true
},
{
"fId" : "4",
"following" : true
}
]
}
Notice how the followers array is used in the query. ref enter link description here
In your case, we can modify your query like this:
{
$and: [ // by default it's $and only, you don't have to mention explicitly
{
$and: [ // you can even remove this $and
"following":
{
"userId": mongoose.Types.ObjectId(targetId),
"following": true
}
]
},
{
$or: [{ firstName: firstNameRegex }, { lastName: lastNameRegex }]
},
{ blockedUsers: { $nin: mongoose.Types.ObjectId(req.userId) } }
]
}
ANS 2:
You can use $elemMatch
$elemMatch is used to query multiple fields from a single document in an array.
db.followers.find({followers: {$elemMatch: { "fId": "1", "following": true }}}).pretty()
{
"_id" : ObjectId("5d984422d933b7b079038caa"),
"userId" : "2",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "3",
"following" : false
},
{
"fId" : "4",
"following" : false
}
]
}
{
"_id" : ObjectId("5d984432d933b7b079038cab"),
"userId" : "3",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "2",
"following" : true
},
{
"fId" : "4",
"following" : true
}
]
}
Query for you will be:
{
$and: [
{
"following":
{$elemMatch: {
"userId": mongoose.Types.ObjectId(targetId),
"following": true
}
}
},
{
$or: [{ firstName: firstNameRegex }, { lastName: lastNameRegex }]
},
{ blockedUsers: { $nin: mongoose.Types.ObjectId(req.userId) } }
]
}
BUT THIS WILL BE WRONG (See Query Below):
db.followers.find({"followers.fId": "1", "followers.following": true }).pretty()
{
"_id" : ObjectId("5d984422d933b7b079038caa"),
"userId" : "2",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "3",
"following" : false
},
{
"fId" : "4",
"following" : false
}
]
}
{
"_id" : ObjectId("5d984432d933b7b079038cab"),
"userId" : "3",
"followers" : [
{
"fId" : "1",
"following" : true
},
{
"fId" : "2",
"following" : true
},
{
"fId" : "4",
"following" : true
}
]
}
{
"_id" : ObjectId("5d984446d933b7b079038cac"),
"userId" : "4",
"followers" : [
{
"fId" : "1",
"following" : false
},
{
"fId" : "2",
"following" : true
},
{
"fId" : "3",
"following" : true
}
]
}
Note
To see only match documents, you can use
db.followers.find({followers: {$elemMatch: { "fId": "1", "following": true }}},{"followers.$": 1}).pretty()
db.followers.find({followers: {$elemMatch: { "fId": "1", "following": true }}},{"followers.$": 1}).pretty()
I've managed to solve this problem using $elemMatch operator like this:
{
$and: [
{
following: {
$elemMatch: {
userId: mongoose.Types.ObjectId(targetId),
following: true
}
}
},
{
$or: [{ firstName: firstNameRegex }, { lastName: lastNameRegex }]
},
{ blockedUsers: { $nin: mongoose.Types.ObjectId(req.userId) } }
]
};
How can I add a filter after an $lookup or is there any other method to do this?
My data collection test is:
{ "_id" : ObjectId("570557d4094a4514fc1291d6"), "id" : 100, "value" : "0", "contain" : [ ] }
{ "_id" : ObjectId("570557d4094a4514fc1291d7"), "id" : 110, "value" : "1", "contain" : [ 100 ] }
{ "_id" : ObjectId("570557d4094a4514fc1291d8"), "id" : 120, "value" : "1", "contain" : [ 100 ] }
{ "_id" : ObjectId("570557d4094a4514fc1291d9"), "id" : 121, "value" : "2", "contain" : [ 100, 120 ] }
I select id 100 and aggregate the childs:
db.test.aggregate([ {
$match : {
id: 100
}
}, {
$lookup : {
from : "test",
localField : "id",
foreignField : "contain",
as : "childs"
}
}]);
I get back:
{
"_id":ObjectId("570557d4094a4514fc1291d6"),
"id":100,
"value":"0",
"contain":[ ],
"childs":[ {
"_id":ObjectId("570557d4094a4514fc1291d7"),
"id":110,
"value":"1",
"contain":[ 100 ]
},
{
"_id":ObjectId("570557d4094a4514fc1291d8"),
"id":120,
"value":"1",
"contain":[ 100 ]
},
{
"_id":ObjectId("570557d4094a4514fc1291d9"),
"id":121,
"value":"2",
"contain":[ 100, 120 ]
}
]
}
But I want only childs that match with "value: 1"
At the end I expect this result:
{
"_id":ObjectId("570557d4094a4514fc1291d6"),
"id":100,
"value":"0",
"contain":[ ],
"childs":[ {
"_id":ObjectId("570557d4094a4514fc1291d7"),
"id":110,
"value":"1",
"contain":[ 100 ]
},
{
"_id":ObjectId("570557d4094a4514fc1291d8"),
"id":120,
"value":"1",
"contain":[ 100 ]
}
]
}
The question here is actually about something different and does not need $lookup at all. But for anyone arriving here purely from the title of "filtering after $lookup" then these are the techniques for you:
MongoDB 3.6 - Sub-pipeline
db.test.aggregate([
{ "$match": { "id": 100 } },
{ "$lookup": {
"from": "test",
"let": { "id": "$id" },
"pipeline": [
{ "$match": {
"value": "1",
"$expr": { "$in": [ "$$id", "$contain" ] }
}}
],
"as": "childs"
}}
])
Earlier - $lookup + $unwind + $match coalescence
db.test.aggregate([
{ "$match": { "id": 100 } },
{ "$lookup": {
"from": "test",
"localField": "id",
"foreignField": "contain",
"as": "childs"
}},
{ "$unwind": "$childs" },
{ "$match": { "childs.value": "1" } },
{ "$group": {
"_id": "$_id",
"id": { "$first": "$id" },
"value": { "$first": "$value" },
"contain": { "$first": "$contain" },
"childs": { "$push": "$childs" }
}}
])
If you question why would you $unwind as opposed to using $filter on the array, then read Aggregate $lookup Total size of documents in matching pipeline exceeds maximum document size for all the detail on why this is generally necessary and far more optimal.
For releases of MongoDB 3.6 and onwards, then the more expressive "sub-pipeline" is generally what you want to "filter" the results of the foreign collection before anything gets returned into the array at all.
Back to the answer though which actually describes why the question asked needs "no join" at all....
Original
Using $lookup like this is not the most "efficient" way to do what you want here. But more on this later.
As a basic concept, just use $filter on the resulting array:
db.test.aggregate([
{ "$match": { "id": 100 } },
{ "$lookup": {
"from": "test",
"localField": "id",
"foreignField": "contain",
"as": "childs"
}},
{ "$project": {
"id": 1,
"value": 1,
"contain": 1,
"childs": {
"$filter": {
"input": "$childs",
"as": "child",
"cond": { "$eq": [ "$$child.value", "1" ] }
}
}
}}
]);
Or use $redact instead:
db.test.aggregate([
{ "$match": { "id": 100 } },
{ "$lookup": {
"from": "test",
"localField": "id",
"foreignField": "contain",
"as": "childs"
}},
{ "$redact": {
"$cond": {
"if": {
"$or": [
{ "$eq": [ "$value", "0" ] },
{ "$eq": [ "$value", "1" ] }
]
},
"then": "$$DESCEND",
"else": "$$PRUNE"
}
}}
]);
Both get the same result:
{
"_id":ObjectId("570557d4094a4514fc1291d6"),
"id":100,
"value":"0",
"contain":[ ],
"childs":[ {
"_id":ObjectId("570557d4094a4514fc1291d7"),
"id":110,
"value":"1",
"contain":[ 100 ]
},
{
"_id":ObjectId("570557d4094a4514fc1291d8"),
"id":120,
"value":"1",
"contain":[ 100 ]
}
]
}
Bottom line is that $lookup itself cannot "yet" query to only select certain data. So all "filtering" needs to happen after the $lookup
But really for this type of "self join" you are better off not using $lookup at all and avoiding the overhead of an additional read and "hash-merge" entirely. Just fetch the related items and $group instead:
db.test.aggregate([
{ "$match": {
"$or": [
{ "id": 100 },
{ "contain.0": 100, "value": "1" }
]
}},
{ "$group": {
"_id": {
"$cond": {
"if": { "$eq": [ "$value", "0" ] },
"then": "$id",
"else": { "$arrayElemAt": [ "$contain", 0 ] }
}
},
"value": { "$first": { "$literal": "0"} },
"childs": {
"$push": {
"$cond": {
"if": { "$ne": [ "$value", "0" ] },
"then": "$$ROOT",
"else": null
}
}
}
}},
{ "$project": {
"value": 1,
"childs": {
"$filter": {
"input": "$childs",
"as": "child",
"cond": { "$ne": [ "$$child", null ] }
}
}
}}
])
Which only comes out a little different because I deliberately removed the extraneous fields. Add them in yourself if you really want to:
{
"_id" : 100,
"value" : "0",
"childs" : [
{
"_id" : ObjectId("570557d4094a4514fc1291d7"),
"id" : 110,
"value" : "1",
"contain" : [ 100 ]
},
{
"_id" : ObjectId("570557d4094a4514fc1291d8"),
"id" : 120,
"value" : "1",
"contain" : [ 100 ]
}
]
}
So the only real issue here is "filtering" any null result from the array, created when the current document was the parent in processing items to $push.
What you also seem to be missing here is that the result you are looking for does not need aggregation or "sub-queries" at all. The structure that you have concluded or possibly found elsewhere is "designed" so that you can get a "node" and all of it's "children" in a single query request.
That means just the "query" is all that is really needed, and the data collection ( which is all that is happening since no content is really being "reduced" ) is just a function of iterating the cursor result:
var result = {};
db.test.find({
"$or": [
{ "id": 100 },
{ "contain.0": 100, "value": "1" }
]
}).sort({ "contain.0": 1 }).forEach(function(doc) {
if ( doc.id == 100 ) {
result = doc;
result.childs = []
} else {
result.childs.push(doc)
}
})
printjson(result);
This does exactly the same thing:
{
"_id" : ObjectId("570557d4094a4514fc1291d6"),
"id" : 100,
"value" : "0",
"contain" : [ ],
"childs" : [
{
"_id" : ObjectId("570557d4094a4514fc1291d7"),
"id" : 110,
"value" : "1",
"contain" : [
100
]
},
{
"_id" : ObjectId("570557d4094a4514fc1291d8"),
"id" : 120,
"value" : "1",
"contain" : [
100
]
}
]
}
And serves as proof that all you really need to do here is issue the "single" query to select both the parent and children. The returned data is just the same, and all you are doing on either server or client is "massaging" into another collected format.
This is one of those cases where you can get "caught up" in thinking of how you did things in a "relational" database, and not realize that since the way the data is stored has "changed", you no longer need to use the same approach.
That is exactly what the point of the documentation example "Model Tree Structures with Child References" in it's structure, where it makes it easy to select parents and children within one query.