this is my sample data in this I have a userId and a array "watchHistory", "watchHistory" array contains the list of videos that is watched by the user :
{
"_id": "62821344445c30b35b441f11",
"userId": 579,
"__v": 0,
"watchHistory": [
{
"seenTime": "2022-05-23T08:29:19.781Z",
"videoId": 789456,
"uploadTime": "2022-03-29T12:33:35.312Z",
"description": "Biography of Indira Gandhi",
"speaker": "andrews",
"title": "Indira Gandhi",
"_id": "628b45df775e3973f3a670ec"
},
{
"seenTime": "2022-05-23T08:29:39.867Z",
"videoId": 789455,
"uploadTime": "2022-03-31T07:37:39.712Z",
"description": "What are some healthy food habits to stay healthy",
"speaker": "morris",
"title": "Healthy Food Habits",
"_id": "628b45f3775e3973f3a670"
},
]
}
I need to match the userId and after that i need to sort it with "watchHistory.seenTime", seenTime field indicates when the user saw the video. so i need to sort like the last watched video should come first in the list.
I don't have permission to use unwind so can any one help me from this. Thank you.
If you are using MongoDB version 5.2 and above, you can use $sortArray operator in an aggregation pipeline. Your pipeline should look something like this:
db.collection.aggregate(
[
{"$match":
{ _id: '62821344445c30b35b441f11' }
},
{
"$project": {
_id: 1,
"userId": 1,
"__v": 1,
"watchHistory": {
"$sortArray": { input: "$watchHistory", sortBy: { seenTime: -1 }}
}
}
}
]
);
Please modify the filter for "$match" stage, according to the key and value you need to filter on. Here's the link to the documentation.
Without using unwind, it's not possible to do it via an aggregation pipeline, but you can use update method and $push operator, as a workaround like this:
db.collection.update({
_id: "62821344445c30b35b441f11"
},
{
$push: {
watchHistory: {
"$each": [],
"$sort": {
seenTime: -1
},
}
}
})
Please see the working example here
I have users collection in mongo db.
I want to sort the docs in the collection based on the delta between wins and losts
In the case of the document below: 2-11 = -9
How do I do that? (I am using mongo from node js )
{
"_id": {
"$oid": "615ebae9f5b277c71b886906"
},
"name": "jack",
"wins": {
"$numberInt": "2"
},
"losts": {
"$numberInt": "11"
},
"lastVisit": {
"$date": {
"$numberLong": "1633852348009"
}
}
}
$sort doens't allow us to use an expression and sort by it so the only solution i think is to add one more field, sort by it and then removed it.
Query
on sort you can set it 1/-1 depending on the order you want
aggregate(
[{"$set": {"dif": {"$subtract": ["$wins", "$losts"]}}},
{"$sort": {"dif": 1}},
{"$unset": ["dif"]}])
I have a collection with 2.7million documents. I need to fetch some data based on certain condition.
The problem is my query is scanning almost 1 million document to return only 5 documents.
Please help me to optimize this query and what index I should created to minimize the doc scan.
Here is my query
{
"aggregate": "posts",
"pipeline": [
{
"$match": {
"status": "A",
"hashtagIds": {
"$oid": "5d9c866d9f733d2359a3e0e0"
},
"mediaLocation.mediaType": 2,
"mediaLocation.thumbNailPath": {
"$exists": true,
"$ne": null
}
}
},
{
"$lookup": {
"from": "users",
"localField": "userId",
"foreignField": "_id",
"as": "ownerData"
}
},
{
"$unwind": {
"path": "$ownerData",
"preserveNullAndEmptyArrays": true
}
},
{
"$sort": {
"viewsCount": -1
}
},
{
"$limit": 5
}
]
}
A better index and a reordering of the stages should help a great deal.
Index
The current pipeline uses the index on
{
"mediaLocation.mediaType": 1,
status: 1,
genter: 1
}
While this index does support 2 out of the 4 queried fields, it does not support the sort operation, so the query executor must load all of the matching documents into memory and sort them to determine which 5 fields are first.
This query would be served much better by an index that includes all of the queried fields, and the sort field. Note that the equality-matched fields come before the sort field in the index spec:
{
"mediaLocation.mediaType": 1,
status: 1,
hashtagIds: 1,
viewsCount: -1,
"mediaLocation.thumbNailPath"
}
Stage order
In the existing pipeline:
$match: all 856k matching documents are retrieved
$lookup: 856k queries are executed against the users collection
$unwind: 856k array fields converted to object
$sort: in-memory sort of 856k documents
$limit: return the first 5 documents
A simple reordering of the fields, along with the above index, would significantly improve performance:
$match:
$sort:
$limit:
If the above index exists, placing these stages first allows the query planner to combine these 3 stages into one, identifying fields in pre-sorted order using the index, and stopping as soon as 5 matches are found. The combined stage will read 5 documents, plus the index keys
$lookup: executes 5 queries in the user collection
$unwind: convert 5 arrays to object
I'm fairly good with sql queries, but I can't seem to get my head around grouping and getting sum of mongo db documents,
With this in mind, I have a job model with schema like below :
{
name: {
type: String,
required: true
},
info: String,
active: {
type: Boolean,
default: true
},
all_service: [
price: {
type: Number,
min: 0,
required: true
},
all_sub_item: [{
name: String,
price:{ // << -- this is the price I want to calculate
type: Number,
min: 0
},
owner: {
user_id: { // <<-- here is the filter I want to put
type: Schema.Types.ObjectId,
required: true
},
name: String,
...
}
}]
],
date_create: {
type: Date,
default : Date.now
},
date_update: {
type: Date,
default : Date.now
}
}
I would like to have a sum of price column, where owner is present, I tried below but no luck
Job.aggregate(
[
{
$group: {
_id: {}, // not sure what to put here
amount: { $sum: '$all_service.all_sub_item.price' }
},
$match: {'not sure how to limit the user': given_user_id}
}
],
//{ $project: { _id: 1, expense: 1 }}, // you can only project fields from 'group'
function(err, summary) {
console.log(err);
console.log(summary);
}
);
Could someone guide me in the right direction. thank you in advance
Primer
As is correctly noted earlier, it does help to think of an aggregation "pipeline" just as the "pipe" | operator from Unix and other system shells. One "stage" feeds input to the "next" stage and so on.
The thing you need to be careful with here is that you have "nested" arrays, one array within another, and this can make drastic differences to your expected results if you are not careful.
Your documents consist of an "all_service" array at the top level. Presumably there are often "multiple" entries here, all containing your "price" property as well as "all_sub_item". Then of course "all_sub_item" is an array in itself, also containg many items of it's own.
You can think of these arrays as the "relations" between your tables in SQL, in each case a "one-to-many". But the data is in a "pre-joined" form, where you can fetch all data at once without performing joins. That much you should already be familiar with.
However, when you want to "aggregate" accross documents, you need to "de-normalize" this in much the same way as in SQL by "defining" the "joins". This is to "transform" the data into a de-normalized state that is suitable for aggregation.
So the same visualization applies. A master document's entries are replicated by the number of child documents, and a "join" to an "inner-child" will replicate both the master and initial "child" accordingly. In a "nutshell", this:
{
"a": 1,
"b": [
{
"c": 1,
"d": [
{ "e": 1 }, { "e": 2 }
]
},
{
"c": 2,
"d": [
{ "e": 1 }, { "e": 2 }
]
}
]
}
Becomes this:
{ "a" : 1, "b" : { "c" : 1, "d" : { "e" : 1 } } }
{ "a" : 1, "b" : { "c" : 1, "d" : { "e" : 2 } } }
{ "a" : 1, "b" : { "c" : 2, "d" : { "e" : 1 } } }
{ "a" : 1, "b" : { "c" : 2, "d" : { "e" : 2 } } }
And the operation to do this is $unwind, and since there are multiple arrays then you need to $unwind both of them before continuing any processing:
db.collection.aggregate([
{ "$unwind": "$b" },
{ "$unwind": "$b.d" }
])
So there the "pipe" first array from "$b" like so:
{ "a" : 1, "b" : { "c" : 1, "d" : [ { "e" : 1 }, { "e" : 2 } ] } }
{ "a" : 1, "b" : { "c" : 2, "d" : [ { "e" : 1 }, { "e" : 2 } ] } }
Which leaves a second array referenced by "$b.d" to further be de-normalized into the the final de-normalized result "without any arrays". This allows other operations to process.
Solving
With just about "every" aggregation pipeline, the "first" thing you want to do is "filter" the documents to only those that contain your results. This is a good idea, as especially when doing operations such as $unwind, then you don't want to be doing that on documents that do not even match your target data.
So you need to match your "user_id" at the array depth. But this is only part of getting the result, since you should be aware of what happens when you query a document for a matching value in an array.
Of course, the "whole" document is still returned, because this is what you really asked for. The data is already "joined" and we haven't asked to "un-join" it in any way.You look at this just as a "first" document selection does, but then when "de-normalized", every array element now actualy represents a "document" in itself.
So not "only" do you $match at the beginning of the "pipeline", you also $match after you have processed "all" $unwind statements, down to the level of the element you wish to match.
Job.aggregate(
[
// Match to filter possible "documents"
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// De-normalize arrays
{ "$unwind": "$all_service" },
{ "$unwind": "$all_service.all_subitem" },
// Match again to filter the array elements
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// Group on the "_id" for the "key" you want, or "null" for all
{ "$group": {
"_id": null,
"total": { "$sum": "$all_service.all_sub_item.price" }
}}
],
function(err,results) {
}
)
Alternately, modern MongoDB releases since 2.6 also support the $redact operator. This could be used in this case to "pre-filter" the array content before processing with $unwind:
Job.aggregate(
[
// Match to filter possible "documents"
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// Filter arrays for matches in document
{ "$redact": {
"$cond": {
"if": {
"$eq": [
{ "$ifNull": [ "$owner", given_user_id ] },
given_user_id
]
},
"then": "$$DESCEND",
"else": "$$PRUNE"
}
}},
// De-normalize arrays
{ "$unwind": "$all_service" },
{ "$unwind": "$all_service.all_subitem" },
// Group on the "_id" for the "key" you want, or "null" for all
{ "$group": {
"_id": null,
"total": { "$sum": "$all_service.all_sub_item.price" }
}}
],
function(err,results) {
}
)
That can "recursively" traverse the document and test for the condition, effectively removing any "un-matched" array elements before you even $unwind. This can speed things up a bit since items that do not match would not need to be "un-wound". However there is a "catch" in that if for some reason the "owner" did not exist on an array element at all, then the logic required here would count that as another "match". You can always $match again to be sure, but there is still a more efficient way to do this:
Job.aggregate(
[
// Match to filter possible "documents"
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// Filter arrays for matches in document
{ "$project": {
"all_items": {
"$setDifference": [
{ "$map": {
"input": "$all_service",
"as": "A",
"in": {
"$setDifference": [
{ "$map": {
"input": "$$A.all_sub_item",
"as": "B",
"in": {
"$cond": {
"if": { "$eq": [ "$$B.owner", given_user_id ] },
"then": "$$B",
"else": false
}
}
}},
false
]
}
}},
[[]]
]
}
}},
// De-normalize the "two" level array. "Double" $unwind
{ "$unwind": "$all_items" },
{ "$unwind": "$all_items" },
// Group on the "_id" for the "key" you want, or "null" for all
{ "$group": {
"_id": null,
"total": { "$sum": "$all_items.price" }
}}
],
function(err,results) {
}
)
That process cuts down the size of the items in both arrays "drastically" compared to $redact. The $map operator processes each elment of an array to the given statement within "in". In this case, each "outer" array elment is sent to another $map to process the "inner" elements.
A logical test is performed here with $cond whereby if the "condiition" is met then the "inner" array elment is returned, otherwise the false value is returned.
The $setDifference is used to filter down any false values that are returned. Or as in the "outer" case, any "blank" arrays resulting from all false values being filtered from the "inner" where there is no match there. This leaves just the matching items, encased in a "double" array, e.g:
[[{ "_id": 1, "price": 1, "owner": "b" },{..}],[{..},{..}]]
As "all" array elements have an _id by default with mongoose (and this is a good reason why you keep that) then every item is "distinct" and not affected by the "set" operator, apart from removing the un-matched values.
Process $unwind "twice" to convert these into plain objects in their own documents, suitable for aggregation.
So those are the things you need to know. As I stated earlier, be "aware" of how the data "de-normalizes" and what that implies towards your end totals.
It sounds like you want to, in SQL equivalent, do "sum (prices) WHERE owner IS NOT NULL".
On that assumption, you'll want to do your $match first, to reduce the input set to your sum. So your first stage should be something like
$match: { all_service.all_sub_items.owner : { $exists: true } }
Think of this as then passing all matching documents to your second stage.
Now, because you are summing an array, you have to do another step. Aggregation operators work on documents - there isn't really a way to sum an array. So we want to expand your array so that each element in the array gets pulled out to represent the array field as a value, in its own document. Think of this as a cross join. This will be $unwind.
$unwind: { "$all_service.all_sub_items" }
Now you've just made a much larger number of documents, but in a form where we can sum them. Now we can perform the $group. In your $group, you specify a transformation. The line:
_id: {}, // not sure what to put here
is creating a field in the output document, which is not the same documents as the input documents. So you can make the _id here anything you'd like, but think of this as the equivalent to your "GROUP BY" in sql. The $sum operator will essentially be creating a sum for each group of documents you create here that match that _id - so essentially we'll be "re-collapsing" what you just did with $unwind, by using the $group. But this will allow $sum to work.
I think you're looking for grouping on just your main document id, so I think your $sum statement in your question is correct.
$group : { _id : $_id, totalAmount : { $sum : '$all_service.all_sub_item.price' } }
This will output documents with an _id field equivalent to your original document ID, and your sum.
I'll let you put it together, I'm not super familiar with node. You were close but I think moving your $match to the front and using an $unwind stage will get you where you need to be. Good luck!
Here is my sample :
Two simple Mongoose models:
a Note model, with among other fields an id field that is a ref for the Notebook model.
a Notebook model, with the id I mentioned above.
My goal is to output something like that:
[
{
"notes_count": 7,
"title": "first notebook",
"id": "5585a9ffc9506e64192858c1"
},
{
"notes_count": 3,
"title": "second notebook",
"id": "558ab637cab9a2b01dae9a97"
}
]
Using aggregation and population on the Note model like this :
Note.aggregate(
[{
"$group": {
"_id": "$notebook",
"notes_count": {
"$sum": 1
}
}
}, {
"$project": {
"notebook": "$_id",
"notes_count": "$notes_count",
}
}]
gives me this kind of result :
{
"_id": "5585a9ffc9506e64192858c1",
"notes_count": 7,
"notebook": {
"_id": "5585a9ffc9506e64192858c1",
"title": "un carnet court",
"__v": 0
}
}
Forget about __v and _id fields, would be easy to handle with a modified toJSON function.
But in this function neither doc nor ret params gives me access to the computed notes_count value.
Obviously, I could manage this in the route handler (parse result and recreate the datas that will be returned) but, is there a proper way to do that with mongoose ?
You can't use the aggregate method to update. As you have noted, you'll need to use output from the aggregate constructor to update the relevant documents.
As the Mongoose aggregate method will return a collection of plain objects, you can iterate through this and utilise the _id field (or similar) to update the documents.