I want to count specific objects if present in collections like:
{
id: 1,
obj1: {...},
obj2: {...}
},{
id: 2,
obj2: {...}
},{
id: 3,
obj1: {...},
obj3: {...}
}
In above example i need the sum of count of objects(i.e. obj1, obj2, obj3). And query should return 5 in above scenario.
You can try the below $group aggregation.
$cond with $ifNull expression to check for existence of field, output 1 when present 0 when absent.
inner $sum to count values in each document with outer $sum to sum the values across collection.
db.col.aggregate([
{
"$group":{
"_id":null,
"count":{
"$sum":{
"$sum":[
{"$cond":[{"$ifNull":["$obj1",false]},1,0]},
{"$cond":[{"$ifNull":["$obj2",false]},1,0]},
{"$cond":[{"$ifNull":["$obj3",false]},1,0]}
]
}
}
}
}
])
Related
I have a group collection that has the array order that contains ids.
I would like to use updateOne to set multiple items in that order array.
I tried this which updates one value in the array:
db.groups.updateOne({
_id: '831e0572-0f04-4d84-b1cf-64ffa9a12199'
},
{$set: {'order.0': 'b6386841-2ff7-4d90-af5d-7499dd49ca4b'}}
)
That correctly updates (or sets) the array value with index 0.
However, I want to set more array values and updateOne also supports a pipeline so I tried this:
db.slides.updateOne({
_id: '831e0572-0f04-4d84-b1cf-64ffa9a12199'
},
[
{$set: {'order.0': 'b6386841-2ff7-4d90-af5d-7499dd49ca4b1'}}
]
)
This does NOTHING if the order array is empty. But if it's not, it replaces every element in the order array with an object { 0: 'b6386841-2ff7-4d90-af5d-7499dd49ca4b1' }.
I don't understand that behavior.
In the optimal case I would just do
db.slides.updateOne({
_id: '831e0572-0f04-4d84-b1cf-64ffa9a12199'
},
[
{$set: {'order.0': 'b6386841-2ff7-4d90-af5d-7499dd49ca4b1'}},
{$set: {'order.1': 'otherid'}},
{$set: {'order.2': 'anotherone'}},
]
)
And that would just update the order array with the values.
What is happening here and how can I achieve my desired behavior?
The update by index position in the array is only supported in regular update queries, but not in aggregation queries,
They have explained this feature in regular update query $set operator documentation, but not it aggregation $set.
The correct implementation in regular update query:
db.slides.updateOne({
_id: '831e0572-0f04-4d84-b1cf-64ffa9a12199'
},
{
$set: {
'order.0': 'b6386841-2ff7-4d90-af5d-7499dd49ca4b1',
'order.1': 'otherid',
'order.2': 'anotherone'
}
}
)
If you are looking for only an aggregation query, it is totally long process than the above regular update query, i don't recommend that way instead, you can format your input in your client-side language and use regular query.
If you have to use aggregation framework, try this (you will have to pass array of indexes and array of updated values separately):
$map and $range to iterate over the order array by indexes
$cond and $arrayElemAt to check if the current index is in the array of indexes that has to be updates. If it is, update it with the same index from the array of new values. If it is not, keep the current value.
NOTE: This will work only if the array of indexes that you want to update starts from 0 and goes up (as in your example).
db.collection.update({
_id: '831e0572-0f04-4d84-b1cf-64ffa9a12199'
},
[
{
"$set": {
"order": {
"$map": {
input: {
$range: [
0,
{
$size: "$order"
}
]
},
in: {
$cond: [
{
$in: [
"$$this",
[
0,
1,
2
]
]
},
{
$arrayElemAt: [
[
"b6386841-2ff7-4d90-af5d-7499dd49ca4b1",
"otherid",
"anotherone"
],
"$$this"
]
},
{
$arrayElemAt: [
"$order",
"$$this"
]
}
]
}
}
}
}
}
])
Here is the working example: https://mongoplayground.net/p/P4irM9Ouyza
I am new to using mongodb and mongoose for my backend stack and Im having a hard time getting from SQL to NoSQL when it comes to query building.
I have an array of object that looks like this:
{
timestamp: "12313113",
symbol: "XY",
amount: 121212
value: 24324234
}
I want to query the collection to get the following output grouped by symbol:
{
symbol: xy,
occurences: 1231
summedAmount: 2131231
summedValue: 23131313
}
Could anyone tell me how to do it using aggregate on the Model? My timestamp filtering works already, but the grouping throws errors
let result = await TransactionEvent.aggregate([
{
$match : {
timestamp : { $gte: new Date(Date.now() - INTERVALS[timeframe]) }
}
},
{
$group : {
what to do in here
}
]);
Lets say I have another field in my object with a key of "direction" that can either be "IN" our "OUT". How could I also group the occurences of these values?
Expected output
{
symbol: xy,
occurences: 1231
summedAmount: 2131231
summedValue: 23131313
in: occurrences where direction property is "IN"
out: occurences where direction property is "OUT"
}
In MongoDB's $group stage, the _id key is mandatory and
it should be the keys which you want to be merged (It's symbol in your case).
Make sure that you pre-fix it with a `$ sign since you are referencing a key in your document.
Following the _id key, you can add all the additional operations to be performed for the required keys. In your specific use case, use $sum to add values to the user-defined key.
Note: Use "$sum": 1 to add 1 for each occurences ans "$sum": "$<Key-Name>" to add existing key's value.
Below code should be your $group stage
{
"$group": {
"_id": "$symbol", // Group by key (Use Sub-Object to group by multiple keys
"occurences": {"$sum": 1}, // Add `1` for each occurences
"summedAmount": {"$sum": "$amount"}, // Add `amount` values of grouped data
"summedValue": {"$sum": "$value"}, // Add `value` values of grouped data
}
}
Comment if you have any additional doubts.
You use $group and $sum
db.collection.aggregate([
{
"$group": {
"_id": "$symbol",
"summbedAmount": {
"$sum": "$amount"
},
"summbedValue": {
"$sum": "$value"
},
"occurences": {
$sum: 1
}
}
}
])
Working Mongo playground
Update 1
you can use $cond to check condition.
First parameter what is the condition
Second parameter - what we need to do if the condition is true (We need to increase by 1 if condition true)
Third parameter - what we need to do if the condition is false (No need to increase anything)
Here is the code
db.collection.aggregate([
{
"$group": {
"_id": "$symbol",
"summbedAmount": { "$sum": "$amount" },
"summbedValue": { "$sum": "$value" },
"occurences": { $sum: 1 },
in: {
$sum: {
$cond: [ { $eq: [ "$direction", "in" ] }, 1, 0 ]
}
},
out: {
$sum: {
$cond: [ { $eq: [ "$direction", "out" ] }, 1, 0 ] }
}
}
}
])
Working Mongo playground
I'm trying to update a field (totalPrice) in my document(s) based on a value in a nested, nested array (addOns > get matching array from x number of arrays > get int in matching array[always at pos 2]).
Here's an example of a document with 2 arrays nested in addOns:
{
"_id": ObjectID('.....'),
"addOns": [
["appleId", "Apples", 2],
["bananaID", "Bananas", 1]
],
"totalPrice": 5.7
}
Let's say the price of bananas increased by 20cents, so I need to look for all documents that have bananas in its addOns, and then increase the totalPrice of these documents depending on the number of bananas bought. I plan on using a updateMany() query using an aggregation pipeline that should roughly look like the example below. The ??? should be the number of bananas bought, but I'm not sure how to go about retrieving that value. So far I've thought of using $unwind, $filter, and $arrayElemAt, but not sure how to use them together. Would appreciate any help!
db.collection('orders').updateMany(
{ $elemMatch: { $elemMatch: { $in: ['bananaID'] } } },
[
{ $set: {'totalPrice': { $add: ['$totalPrice', { $multiply: [{$toDecimal: '0.2'}, ???] } ] } } }
]
I'm not exactly sure whats my mongo version is, but I do know that I can use the aggregation pipeline because I have other updateMany() calls that also use the pipeline without any issues, so it should be (version >= 4.2).
**Edit: Thought of this for the ???, the filter step seems to work somewhat as the documents are getting updated, but the value is null instead of the updated price. Not sure why
Filter the '$addOns' array to return the matching nested array.
For the condition, use $eq to check if the element at [0] ('$$this.0') matches the string 'bananaID', and if it does return this nested array.
Get the array returned from step 1 using $arrayElemAt and position [0].
Use $arrayElemAt again on the array from step 3 with positon [2] as it is the index of the quantity element
{ $arrayElemAt: [{ $arrayElemAt: [ { $filter: { input: "$addOns", as:'this', cond: { $eq: ['$$this.0', 'bananaID'] } } } , 0 ] }, 2 ] }
Managed to solve it myself - though only use this if you don't mind the risk of updateMany() as stated by Joe in the question's comments. It's very similar to what I originally shared in **Edit, except you cant use $$this.0 to access elements in the array.
Insert this into where the ??? is and it'll work, below this code block is the explanation:
{ $arrayElemAt: [{ $arrayElemAt: [ { $filter: { input: "$addOns", as:'this', cond: { $eq: [{$arrayElemAt:['$$this', 0]}, 'bananaID'] } } } , 0 ] }, 2 ] }
Our array looks like this: [["appleId", "Apples", 2], ["bananaID", "Bananas", 1]]
Use $filter to return a subset of our array that only contains the elements that matches our condition - in this case we want the array for bananas. $$this represents each element in input, and we check whether the first element of $$this matches 'bananaID'.
{ $filter: { input: "$addOns", as:'this', cond: { $eq: [{$arrayElemAt:['$$this', 0]}, 'bananaID'] } } }
// how the result from $filter should look like
// [["bananaID", "Bananas", 1]]
Because the nested banana array will always be the first element, we use $arrayElemAt on the result from step 2 to retrieve the element at position 0.
// How it looks like after step 3: ["bananaID", "Bananas", 1]
The last step is to use $arrayElemAt again, except this time we want to retrieve the element at position 2 (quantity of bananas bought)
This is how the final updateMany() query looks like after steps 1-4 are evaluated.
db.collection('orders').updateMany(
{ $elemMatch: { $elemMatch: { $in: ['bananaID'] } } },
[
{ $set: {'totalPrice': { $add: ['$totalPrice', { $multiply: [{$toDecimal: '0.2'}, 1] } ] } } }
], *callback function code*)
// notice how the ??? is now replaced by the quantity of bananas which is 1
I want to be able to query a field that does not contain any of the elements in an array.
for example, I have an array of objects (venue) :
db.collection.aggregate([{ $match: { roomNo: {$ne:venue}}},
])
how shall I access the array in the object and query using $ne?
Is there a way to do this?
I was not able to achieve what I wanted using the above method.
Use $nin in the query
For Eg: db.collection.find( { roomNo: { $nin: [ 5, 15 ] } } )
This example works:
db.collection.save({ roomNo : 1, floor : 1})
db.collection.save({ roomNo : 2, floor : 1})
db.collection.save({ roomNo : 3, floor : 1})
db.collection.aggregate([
{
$addFields: {venue: [2,3]}
},
{
$match: { roomNo: {$nin : venue}}
}
])
try it
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!