I have collection like below named as "FormData",
{
"_id": ObjectId("5e3c27bf1ef77236945ef07b"),
"eed12747-0923-4290-b09c-5a05107f5609": "20200206",
"bd637691-782d-4cfd-8624-feeedfe11b3e": "20200206_1#mail.com"
}
I have another collection named as "Form" which will have Title of Fields,
{
"_id": ObjectId("5e3c27bf1ef77236945ef07b"),
"Fields":[
{
"FieldID": "eed12747-0923-4290-b09c-5a05107f5609",
"Title": "Phone"
},
{
"FieldID": "bd637691-782d-4cfd-8624-feeedfe11b3e",
"Title": "Email"
}]
}
Now I have to map element name with Form field title and I need result like below,
{
"_id": ObjectId("5e3c27bf1ef77236945ef07b"),
"Phone": "20200206",
"Email": "20200206_1#mail.com"
}
Please help me to solve this.
Thanks in advance!
You can:
$objectToArray to convert the $$ROOT document into an array of k-v pairs for future lookups
use a sub-pipeline in $lookup to find the value by the uuid
use $mergeObject to combine the original values(i.e. "20200206"...) with the new field name looked up (i.e. "Phone"...)
wrangle the result back into original form using $arrayToObject and $replaceRoot
db.FormData.aggregate([
{
$match: {
"_id": ObjectId("5e3c27bf1ef77236945ef07b")
}
},
{
$project: {
kv: {
"$objectToArray": "$$ROOT"
}
}
},
{
$unwind: "$kv"
},
{
"$lookup": {
"from": "Form",
"let": {
uuid: "$kv.k"
},
"pipeline": [
{
$match: {
"_id": ObjectId("5e3c27bf1ef77236945ef07b")
}
},
{
"$unwind": "$Fields"
},
{
$match: {
$expr: {
$eq: [
"$$uuid",
"$Fields.FieldID"
]
}
}
},
{
$project: {
_id: false,
k: "$Fields.Title"
}
}
],
"as": "formLookup"
}
},
{
$unwind: "$formLookup"
},
{
$project: {
kv: {
"$mergeObjects": [
"$kv",
"$formLookup"
]
}
}
},
{
$group: {
_id: "$_id",
kv: {
$push: "$kv"
}
}
},
{
"$project": {
newDoc: {
"$arrayToObject": "$kv"
}
}
},
{
"$replaceRoot": {
"newRoot": {
"$mergeObjects": [
{
"_id": "$_id"
},
"$newDoc"
]
}
}
}
])
Mongo Playground
Another option is to start from Form collection and avoid $unwind:
$match and $lookup to get all needed data into one document
$objectToArray to get known keys for FormData
Match the items using $indexOfArray and $arrayElemAt and merge them using $mergeObjects. Then use arrayToObject to format the response
db.Form.aggregate([
{$match: {_id: ObjectId("5e3c27bf1ef77236945ef07b")}},
{$lookup: {
from: "FormData",
localField: "_id",
foreignField: "_id",
as: "formLookup",
pipeline: [{$project: {_id: 0}}]
}},
{$set: {formLookup: {$objectToArray: {$first: "$formLookup"}}}},
{$replaceRoot: {
newRoot: {
$mergeObjects: [
{$arrayToObject: {
$map: {
input: "$formLookup",
in: {$mergeObjects: [
{v: "$$this.v"},
{k: {$getField: {
input: {$arrayElemAt: [
"$Fields",
{$indexOfArray: ["$Fields.FieldID", "$$this.k"]}
]},
field: "Title"
}}}
]}
}
}},
{_id: "$_id"}
]
}
}}
])
See how it works on the playground example
Related
MongoDB Collection A contains documents with an array with some document ids of collection B:
Collection A:
{
some_ids_of_b: ["id1", ...]
}
Collection B:
{
_id: "id1"
},
{
_id: "id2"
},
...
How do I query all documents from B whose _ids are NOT in contained in the some_ids_of_b arrays of documents of A?
Simple lookup from collection B to A and filter to keep only those documents where you don't find any matches.
db.collb.aggregate([
{
"$lookup": {
"from": "colla",
"localField": "_id",
"foreignField": "someIdsOfB",
"as": "a"
}
},
{
$match: {
$expr: {
$eq: [{$size: "$a"}, 0]
}
}
}
])
Demo
One option is:
db.collectionB.aggregate([
{$lookup: {
from: "collectionA",
let: {my_id: "$_id"},
pipeline: [
{$match: {$and: [
{_id: collADocId},
{$expr: {$in: ["$$my_id", "$some_ids_of_b"]}}
]}},
{$project: {_id: 1}}
],
as: "some_ids_of_b"
}},
{$match: {"some_ids_of_b.0": {$exists: false}}},
{$unset: "some_ids_of_b"}
])
See how it works on the playground example
You can do it with Aggregation Framework:
$group and $addToSet - To get all $some_ids_of_b from all the documents in A collection.
$set with $reduce - To create an array with all unique values of the IDs from the B collection.
$lookup - To fetch the documents from the B collection, where the _id of the document is not present in the $b_ids array.
$project - To project data as expected output.
db.A.aggregate([
{
"$group": {
"_id": null,
"b_ids": {
"$addToSet": "$some_ids_of_b"
}
}
},
{
"$set": {
b_ids: {
$reduce: {
input: "$b_ids",
initialValue: [],
in: {
$setUnion: [
"$$value",
"$$this"
]
}
}
}
}
},
{
"$lookup": {
from: "B",
let: {
b_ids: "$b_ids"
},
pipeline: [
{
"$match": {
"$expr": {
$ne: [
{
"$in": [
"$_id",
"$$b_ids"
]
},
true
]
}
}
}
],
as: "data"
}
},
{
"$project": {
data: 1,
_id: 0
}
}
])
Working Example
I have a problem with MongoDB syntax.
I have two documents:
alley(the "tree" field is the ID of the tree):
{
"_id": {"$oid": "62572d82cc40164fef7f1a56"},
"name": "good alley",
"tree": [
{"$oid": "626976eb4b93122bc617d701"},
{"$oid": "626976eb4b93122bc617d702"}
]
},
.......
tree:
{
"_id": {"$oid": "626976eb4b93122bc617d701"},
"dateInstall": {"$date": "2021-02-27T00:00:00.000Z"},
"species": [
{"$oid": "62585a63edfc726a4ff24fb8"}
]
},
.......
I need to write a query "an alley where trees were not planted last year"
My Code
db.alley.aggregate([
{
$lookup: {
from: "tree",
localField: "tree",
foreignField: "_id",
as: "tree"
}
},
{
$match: {{$not:{$and:[
{"tree.dateInstall": {$gt: new ISODate("2020-12-31")}},
{"tree.dateInstall": {$lt: new ISODate("2022-01-01")}}
]
}}}
}
]);
You should first $unwind trees in alleys so you can properly $lookup the trees in tree collection. Use pipeline inside lookup to query only trees planted last year. Finally $group trees into alleys again and use $match to filter out those alleys without trees.
db.getCollection("alley").aggregate([
{
$unwind: "$tree",
},
{
$lookup: {
from: "tree",
let: { tree: "$tree" },
pipeline: [
{
$match: {
$expr: {
$and: [
{ $eq: [ "$$tree", "$_id" ] },
{ $gt: [ "$dateInstall", new ISODate("2020-12-31") ] },
{ $lt: [ "$dateInstall", new ISODate("2022-01-01") ] },
]
}
}
}
],
localField: "tree",
foreignField: "_id",
as: "tree"
}
},
{
$group: {
_id: { id: "$_id", name: "$name" },
trees: { $addToSet: { $first: "$tree" } }
}
},
{
$match: {
trees: { $size: 0 }
}
}
])
I'm trying to do this for days, but can't find any success
I'm using MongoDB, and I tried to do it with many pipeline steps but I couldn't find a way.
I have a players collection, each player contains an items array
{
"_id": ObjectId("5fba17c1c4566e57fafdcd7e"),
"username": "moshe",
"items": [
{
"_id": ObjectId("5fbb5ac178045a985690b5fd"),
"equipped": false,
"itemId": "5fbb5ab778045a985690b5fc"
}
]
}
I have an items collection where there is more information about each item
in the player items array.
{
"_id": ObjectId("5fbb5ab778045a985690b5fc"),
"name": "Axe",
"damage": 4,
"defense": 6
}
My goal is to have a player document with all the information about the item inside his items array, so it will look like that:
{
"_id": ObjectId("5fba17c1c4566e57fafdcd7e"),
"username": "moshe",
"items": [
{
"_id": ObjectId("5fbb5ac178045a985690b5fd"),
"equipped": false,
"itemId": "5fbb5ab778045a985690b5fc",
"name": "Axe",
"damage": 4,
"defense": 6
}
]
}
$unwind deconstruct items array
$lookup to join items collection, pass itemsId into let after converting it to object id using $toObjectId and pass items object,
$match itemId condition
$mergeObject merge items object and $$ROOT object and replace to root using $replaceRoot
$group reconstruct items array again, group by _id and get first username and construct items array
db.players.aggregate([
{ $unwind: "$items" },
{
$lookup: {
from: "items",
let: {
itemId: { $toObjectId: "$items.itemId" },
items: "$items"
},
pipeline: [
{ $match: { $expr: { $eq: ["$_id", "$$itemId" ] } } },
{ $replaceRoot: { newRoot: { $mergeObjects: ["$$items", "$$ROOT"] } } }
],
as: "items"
}
},
{
$group: {
_id: "$_id",
username: { $first: "$username" },
items: { $push: { $first: "$items" } }
}
}
])
Playground
Second option using $map, and without $unwind,
$addFields for items convert itemId string to object type id using $toObjectId and $map
$lookup to join items collection
$project to show required fields, and merge items array and itemsCollection using $map to iterate loop of items array $filter to get matching itemId and $first to get first object from return result, $mergeObject to merge current object and returned object from $first
db.players.aggregate([
{
$addFields: {
items: {
$map: {
input: "$items",
in: {
$mergeObjects: ["$$this", { itemId: { $toObjectId: "$$this.itemId" } }]
}
}
}
}
},
{
$lookup: {
from: "items",
localField: "items.itemId",
foreignField: "_id",
as: "itemsCollection"
}
},
{
$project: {
username: 1,
items: {
$map: {
input: "$items",
as: "i",
in: {
$mergeObjects: [
"$$i",
{
$first: {
$filter: {
input: "$itemsCollection",
cond: { $eq: ["$$this._id", "$$i.itemId"] }
}
}
}
]
}
}
}
}
}
])
Playground
First I'd strongly suggest that you should store the items.itemId as ObjectId, not strings.
Then another simple solution can be:
db.players.aggregate([
{
$lookup: {
from: "items",
localField: "items.itemId",
foreignField: "_id",
as: "itemsDocuments",
},
},
{
$addFields: {
items: {
$map: {
input: { $zip: { inputs: ["$items", "$itemsDocuments"] } },
in: { $mergeObjects: "$$this" },
},
},
},
},
{ $unset: "itemsDocuments" },
])
I have a collection of Orders. each order has a list of Items, and each Item has catalog_id, which is an ObjectId pointing to the Catalogs collection.
I need an aggregate query that will retrieve certain orders - each order with its Items in extended fashion including the Catalog name and SKU. i.e:
Original data structure:
Orders: [{
_id : ObjectId('ord1'),
items : [{
catalog_id: ObjectId('xyz1'),
qty: 5
},
{
catalog_id: ObjectId('xyz2'),
qty: 3
}]
Catalogs: [{
_id : ObjectId('xyz1')
name: 'my catalog name',
SKU: 'XxYxZx1'
},{
_id : ObjectId('xyz2')
name: 'my other catalog name',
SKU: 'XxYxZx2'
}
]
ideal outcome would be:
Orders: [{
_id : ObjectId('ord1'),
items : [{
catalog_id: ObjectId('xyz1'),
catalog_name: 'my catalog name',
catalog_SKU: 'XxYxZx1' ,
qty: 5
},
{
catalog_id: ObjectId('xyz2'),
catalog_name: 'my other catalog name',
catalog_SKU: 'XxYxZx2' ,
qty: 3
}
]
What I did so far was:
db.orders.aggregate(
[
{
$match: {merchant_order_id: 'NIM333'}
},
{
$lookup: {
from: "catalogs",
//localField: 'items.catalog_id',
//foreignField: '_id',
let: { 'catalogId' : 'items.catalog_id' },
pipeline: [
{
$match : {$expr:{$eq:["$catalogs._id", "$$catalogId"]}}
},
{
$project: {"name": 1, "merchant_SKU": 1 }
}
],
as: "items_ex"
},
},
])
but items_ex comes out empty for some reason i cannot understand.
You need to first $unwind the items and reconstruct the array back using $group to match the exact position of qty with the catalogs_id inside the items array
db.orders.aggregate([
{ "$match": { "merchant_order_id": "NIM333" }},
{ "$unwind": "$items" },
{ "$lookup": {
"from": "catalogs",
"let": { "catalogId": "$items.catalog_id", "qty": "$items.qty" },
"pipeline": [
{ "$match": { "$expr": { "$eq": ["$_id", "$$catalogId"] } }},
{ "$project": { "name": 1, "merchant_SKU": 1, "qty": "$$qty" }}
],
"as": "items"
}},
{ "$unwind": "$items" },
{ "$group": {
"_id": "$_id",
"items": { "$push": "$items" },
"data": { "$first": "$$ROOT" }
}},
{ "$replaceRoot": {
"newRoot": {
"$mergeObjects": ["$data", { "items": "$items" }]
}
}}
])
MongoPlayground
You're missing a dollar sign when you define your pipeline variable. There should be:
let: { 'catalogId' : '$items.catalog_id' },
and also this expression returns an array to you need $in instead of $eq:
{
$lookup: {
from: "catalogs",
let: { 'catalogId' : 'items.catalog_id' },
pipeline: [
{
$match : {$expr:{$in:["$_id", "$$catalogId"]}}
},
{
$project: {"name": 1, "merchant_SKU": 1 }
}
],
as: "items_ex"
}
}
Mongo Playground
This is locations collection data.
{
_id: "1",
location: "loc1",
sublocations: [
{
_id: 2,
sublocation: "subloc1",
},
{
_id: 3,
sublocation: "subloc2",
}
]
},
{
_id: "4",
location: "loc2",
sublocations: [
{
_id: 5,
sublocation: "subloc1",
},
{
_id: 6,
sublocation: "subloc2",
}
]
}
This is products collection data
{
_id: "1",
product: "product1",
prices: [
{
_id: 2,
sublocationid: 2, //ObjectId of object in sublocations array
price: 500
},
{
_id: 3,
sublocationid: 5, //ObjectId of object in sublocations array
price: 200
}
]
}
Now I need to get the sublocation in product schema in the prices array. Expected result is as below.
{
_id: "1",
product: "product1",
prices: [
{
_id: 2,
sublocationid: 3,
sublocation: "subloc2",
price: 500
},
{
_id: 3,
sublocationid: 5,
sublocation: "subloc1"
price: 200
}
]
}
To achieve it, I did it like in the following way.
First, performing aggregation on locations collection - $unwind the sublocations array and store the $out in the new collection.
Second, perform aggregation on 'products' collection - $unwind the prices, $lookup the sublocationid from the new collection and $group them.
Third, after getting data delete the data of new collection.
Is there any other simplified way? Please let me know if there is any.
If you want to stick with 3.4 version, you can try this query:
db.products.aggregate([
{
$unwind: {
"path": "$prices"
}
},
{
$lookup: {
"from": "locations",
"localField": "prices.sublocationid",
"foreignField": "sublocations._id",
"as": "locations"
}
},
{
$unwind: {
"path": "$locations"
}
},
{
$unwind: {
"path": "$locations.sublocations"
}
},
{
$addFields: {
"keep": {
"$eq": [
"$prices.sublocationid",
"$locations.sublocations._id"
]
}
}
},
{
$match: {
"keep": true
}
},
{
$addFields: {
"price": {
"_id": "$prices._id",
"sublocationid": "$prices.sublocationid",
"sublocation": "$locations.sublocations.sublocation",
"price": "$prices.price"
}
}
},
{
$group: {
"_id": "$_id",
"product": { "$first": "$product" },
"prices": { "$addToSet": "$price" }
}
}
]);
It's not as nice as 3.6 version though, because of a higher memory consumption.
You can try below aggregation query in 3.6 version.
Since both local field and foreign field are array you have to $unwind both to do equality comparison.
For this you will have to use new $lookup syntax.
$match with $expr provides comparsion between document fields to look up the location's sublocation document for each product's sublocation id.
$project to project the matching sublocation doc.
$addFields with $arrayElemAt to convert the looked up sublocation array into a document.
$group to push all prices with matching sublocation's document for each product.
db.products.aggregate[
{
"$unwind": "$prices"
},
{
"$lookup": {
"from": "locations",
"let": {
"prices": "$prices"
},
"pipeline": [
{
"$unwind": "$sublocations"
},
{
"$match": {
"$expr": [
"$$prices.sublocationid",
"$sublocations._id"
]
}
},
{
"$project": {
"sublocations": 1,
"_id": 0
}
}
],
"as": "prices.sublocations"
}
},
{
"$addFields": {
"prices.sublocations": {
"$arrayElemAt": [
"$prices.sublocations",
0
]
}
}
},
{
"$group": {
"_id": "$_id",
"product": {
"$first": "$product"
},
"prices": {
"$push": "$prices"
}
}
}
])