I have a document-scheme like this:
{
"pair":"BTCUSDT",
"ask":{
"amount":33107101.800000004,
"total":507,
"high":72000,
"low":65132
},
"bid":{
"amount":32368164.399999995,
"total":498,
"high":65131.99,
"low":60200.2
},
"updateStamp":1636632371639
}
now my DB there are documents with different values in pair and also documents with the same value. Some of them have a updateStamp that is, lets say a few seconds old, and some have a updateStamp that is a few minutes old or older.
(I wrote simpler values in updateStamp for simplicity)
{
"pair":"BTCUSDT",
"ask": ...,
"updateStamp": 100
},
{
"pair":"BTCUSDT",
"ask": ...,
"updateStamp": 200
},
{
"pair":"ETHUDST",
"ask": ...,
"updateStamp": 500
},
{
"pair":"ETHUDST",
"ask": ...,
"updateStamp": 200
},
{
"pair":"DOGEUSDT",
"ask": ...,
"updateStamp": 600
},
Now I want to compare every latest document of a pair and find the 10 documents, for the pairs with the largest ask.total-value. Simple saif, like a Top-10 from the latest of every pair.
But I don't get it how do manage this? I have been fiddeling around with aggregation and multiple finds for a while now. Maybe someone knows how to solve this?
You can first $group by pair. Then use the result to perform sub-pipeline $lookup to fetch the "last 10" documents.
In the sub-pipeline:
$match with pair; let is used to assign the value in grouped pair into variable p; which is later refered to as $$p. The $match means the variable $$p is equals to pair in the $lookup, which is equal to we only getting record related to that specific pair
$sort by updateStamp
$limit by 10
db.collection.aggregate([
{
$group: {
_id: "$pair"
}
},
{
"$lookup": {
"from": "collection",
let: {
p: "$_id"
},
pipeline: [
{
$match: {
$expr: {
$eq: [
"$pair",
"$$p"
]
}
}
},
{
$sort: {
updateStamp: -1
}
},
{
$limit: 10
}
],
"as": "output array field"
}
}
])
Here is the Mongo playground for your reference.
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
Trying to wrap my head around aggregation features
and having trouble figuring out how can I filter out results
with no aggregated children?
lets say I have things:
{ _id: abc1, thingColor: "green" }
{ _id: abc2, thingColor: "red" }
{ _id: abc3, thingColor: "amazing" }
and I have birds:
{ _id: 1, thing_id: "abc1", type: "singing", isBiting: false }
{ _id: 2, thing_id: "abc1", type: "notFlying", isBiting: true }
{ _id: 3, thing_id: "abc3", type: "manEating", isBiting: false }
now I want to get a list of things, but only those that have at least one bird associated with them by id and only birds that bite.
So basically from this example I would like to get from things only:
{ _id: abc1, birds_id: "abc1" }
My query is like this - querying things:
{
$lookup:
{
from: 'birds',
let: { thingIdVar: '$_id'},
pipeline: [
{$match:
{$expr:
{$and: [
{$eq: ['$thing_id', '$$thingIdVar']},
{$eq: ['$isBiting', true]}
]}
}
}
],
as: 'birds'
}
},
this will return the things aggregated with the birds but it will get back all the things even if they don't have birds aggregated.
If I had 1 to 1 things to birds i could use {$unwind: '$birds'}
but each thing can have a lot of birds
At this point, Im doing the filtering programmatically but this messes up some other stuff (this example is a simplified version).
So I would prefer to get the results from mongo already filtered..
Is there a way to do so??
Thanks
Use a $match stage after the $lookup
{ "$match": { "birds": { "$ne": [] }}}
Since there is no barrier for the parent collection it returns all the documents. So to filter out the documents (things) which do not contain atleast one birds you have to use $match stage in the parent collection
I've two different collections that are connected by the id of the garden. I've a list of gardens and I've a list of allocations where it will be stored the start and the end date of the allocation. I can check if a garden is allocated by verifying if today is between both dates in the allocation table.
Garden
{
"_id": "5b98df3c9275f2291c0d7dc3",
"id": "h1",
"size": 43
}
Allocation
{
"_id": "5b9bcb8ecb9dee0015150549",
"user": "5b9a2cd21eb58700141a3449",
"garden": "5b98df5c9275f2291c0d7dc6",
"start_date":"2018-09-14T00:00:00.000Z",
"end_date": "2018-11-14T00:00:00.000Z"
}
How can I return all the existing gardens with an aditional field 'ocupied' with true or false depending on if they exist on the allocation document between start_date and end_date?
I'd like to get an array of gardens with the following data
{
"_id": "5b98df3c9275f2291c0d7dc3",
"id": "h1",
"size": 43,
"occupied": true
}
You can do it one of two ways.
var today = ISODate();
Using $lookup
db.garden.aggregate([
{"$lookup":{
"from":"allocation",
"localField":"_id",
"foreignField":"garden",
"as":"garden"
}},
{"$unwind":"$garden"},
{"$addFields":{
"occupied":{
"$and":[
{"$gte":["$garden.start_date",today]},
{"$lt":["$garden.end_date",today]}
]
}
}},
{"$project":{"garden":0}}
])
Using $lookup with pipeline
db.garden.aggregate([
{"$lookup":{
"from":"allocation",
"let":{"garden_id":"$_id"},
"pipeline":[
{"$match":{"$expr":{"$eq":["$$garden_id","$garden"]},"start_date":{"$gte":today},"end_date":{"$lt":today}}}
],
"as":"garden"
}},
{"$addFields":{
"occupied":{"$gt":[{"$size":"$garden"},0]}
}},
{"$project":{"garden":0}}
])
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!