This is my database collection:
{"productId" : 1,
"isVariant": 1,
"isComplete" : 1,
"variantId" : 1,
"attributeSet" : [
{
"name" : "Capacity",
"value" : "500 GB",
"id" : 3
},
{
"name" : "Form Factor",
"value" : "5 inch",
"id" : 4
},
{
"id" : 5,
"name" : "Memory Components",
"value" : "3D NAND",
"isVariation" : 0
}
]
},
{"productId" : 2,
"isVariant": 1,
"isComplete" : 1,
"variantId" : 1,
"attributeSet" : [
{
"name" : "Capacity",
"value" : "1 TB",
"id" : 3
},
{
"name" : "Form Factor",
"value" : "5 inch",
"id" : 4
},
{
"id" : 5,
"name" : "Memory Components",
"value" : "3D NAND",
"isVariation" : 0
}
]
},
{"productId" : 3,
"isVariant": 1,
"isComplete" : 0,
"variantId" : 1,
"attributeSet" : [
{
"name" : "Capacity",
"value" : "500 GB",
"id" : 3
},
{
"name" : "Form Factor",
"value" : "2.5 inch",
"id" : 4
},
{
"id" : 5,
"name" : "Memory Components",
"value" : "3D NAND",
"isVariation" : 0
}
]
},
{"productId" : 4,
"isVariant": 1,
"isComplete" : 0,
"variantId" : 1,
"attributeSet" : [
{
"name" : "Capacity",
"value" : "1 TB",
"id" : 3
},
{
"name" : "Form Factor",
"value" : "2.5 inch",
"id" : 4
},
{
"id" : 5,
"name" : "Memory Components",
"value" : "3D NAND",
"isVariation" : 0
}
]
}
Now I want to send the data of only the attribute where isVariation is not 0. Also I want to send the variant values of each attribute where isComplete =1. Hence the result should look like this
result : [{
"id": 3,
"name": "Capacity",
"value": [
"500 GB",
"1 TB"
]
}, {
"id": 4,
"name": "Form Factor",
"value": [
"5 inch"
]
}]
The above result does not have value of 2.5 inch as the isComplete is 0 for this document. Can anyone help me with the query
$match isComplete is 1
$project to show required fields
$unwind deconstruct attributeSet array
$match attributeSet.isVariation is not 0
$group by attributeSet.id and get first name and get unique value using $addToSet
db.collection.aggregate([
{ $match: { isComplete: 1 } },
{
$project: {
_id: 0,
attributeSet: 1
}
},
{ $unwind: "$attributeSet" },
{ $match: { "attributeSet.isVariation": { $ne: 0 } } },
{
$group: {
_id: "$attributeSet.id",
name: { $first: "$attributeSet.name" },
value: { $addToSet: "$attributeSet.value" }
}
}
])
Playground
The $project stage is not required in your query, i have added because this will optimize your query performance.
I have an array of objects.
I need to get an array with a unique website name with the newest date.
Sample data
"data" : [
{
"position" : 2,
"website" : "abc.com",
"owned" : false,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 3,
"website" : "qwe.com",
"owned" : false,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 1,
"website" : "qwe.com",
"owned" : false,
"date" : "2020-04-06",
"dateTime" : ISODate("2020-04-06T00:00:00.000Z")
},
{
"position" : 6,
"website" : "xyz.agency",
"owned" : false,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 4,
"website" : "opq.com",
"owned" : true,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 2,
"website" : "opq.com",
"owned" : true,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 4,
"website" : "opq.com",
"owned" : true,
"date" : "2020-04-01",
"dateTime" : ISODate("2020-04-01T00:00:00.000Z")
}
]
Based on DateTime, position, and website. Need a MongoDB query or Javascript code.
(It's fine with DateTime and website. But getting stuck with position)
(Extracting the object from which have the highest date and unique website name with top position)
Expected response
"data" : [
{
"position" : 2,
"website" : "abc.com",
"owned" : false,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 3,
"website" : "qwe.com",
"owned" : false,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 6,
"website" : "xyz.agency",
"owned" : false,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
},
{
"position" : 2,
"website" : "opq.com",
"owned" : true,
"date" : "2020-05-06",
"dateTime" : ISODate("2020-05-06T00:00:00.000Z")
}
],
Using the below utility you can achieve the required output in javascript. Hope this will help
const getUniqueWebsites = data => {
return data.reduce((result, d) => {
if(result[d.website]) {
if(new Date(result[d.website].dateTime).getTime() < new Date(d.dateTime).getTime()){
result[d.website] = d;
}
else if(new Date(result[d.website].dateTime).getTime() === new Date(d.dateTime).getTime() &&
(result[d.website].position) > d.position){
result[d.website] = d;
}
} else {
result[d.website] = d;
}
return result;
},{})
}
let data = [
{
"position": 2,
"website": "abc.com",
"owned": false,
"date": "2020-05-06",
"dateTime": "2020-05-06T00:00:00.000Z"
},
{
"position": 3,
"website": "qwe.com",
"owned": false,
"date": "2020-05-06",
"dateTime": "2020-05-06T00:00:00.000Z"
},
{
"position": 1,
"website": "qwe.com",
"owned": false,
"date": "2020-04-06",
"dateTime": "2020-04-06T00:00:00.000Z"
},
{
"position": 6,
"website": "xyz.agency",
"owned": false,
"date": "2020-05-06",
"dateTime": "2020-05-06T00:00:00.000Z"
},
{
"position": 4,
"website": "opq.com",
"owned": true,
"date": "2020-05-06",
"dateTime": "2020-05-06T00:00:00.000Z"
},
{
"position": 2,
"website": "opq.com",
"owned": true,
"date": "2020-05-06",
"dateTime": "2020-05-06T00:00:00.000Z"
},
{
"position": 4,
"website": "opq.com",
"owned": true,
"date": "2020-04-01",
"dateTime": "2020-04-01T00:00:00.000Z"
}
]
let result = getUniqueWebsites(data)
console.log(Object.values(result))
If you want to do from JS level (warning! low performance):
var maxDate = {}; // temporary variable to store latest date of a website
var websites = data
.map(entry => {
if ((maxDate[entry.website] || 0) < entry.dateTime.getTime()) {
maxDate[entry.website] = entry.dateTime.getTime();
}
return entry;
})
.filter(entry => {
return maxDate[entry.website] === entry.dateTime.getTime();
});
If you want to do that with mongodb query: Select Max() with "group by" in mongodb
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 some market feed data that i'm trying to use mongodb 3.4.5 on Ubuntu 16.04 to produce a nice summary given a date range.
Here is one example document.. each document might contain multiple batched data in Buys, Sells and Fills.
{
"MarketName" : "BIX",
"Nounce" : 12040,
"Buys" : [
{
"Type" : 2,
"Rate" : 0.08068147,
"Quantity" : 55.57280163
},
{
"Type" : 1,
"Rate" : 0.07980372,
"Quantity" : 0
},
{
"Type" : 0,
"Rate" : 0.07962334,
"Quantity" : 34.96018931
},
{
"Type" : 1,
"Rate" : 0.07960592,
"Quantity" : 0
}
],
"Sells" : [
{
"Type" : 0,
"Rate" : 0.08070098,
"Quantity" : 4.08189692
},
{
"Type" : 0,
"Rate" : 0.08112318,
"Quantity" : 10
},
{
"Type" : 1,
"Rate" : 0.08112319,
"Quantity" : 0
},
{
"Type" : 1,
"Rate" : 0.08149567,
"Quantity" : 0
}
],
"Fills" : [
{
"OrderType" : "SELL",
"Rate" : 0.08068147,
"Quantity" : 0.51627134,
"TimeStamp" : "2017-07-25T15:20:34.357"
},
{
"OrderType" : "BUY",
"Rate" : 0.08048147,
"Quantity" : 0.51007134,
"TimeStamp" : "2017-07-25T15:20:34.357"
}
],
"TimeStamp" : ISODate("2017-07-26T22:32:20.741+08:00")
}
What I've been trying with various unwinds and groups, projects and push etc.. but i'm not getting anywhere close to the output i want. Because the type is the object key name i am struggling to get something to group on.
The output i'm looking for is something like this.
{
"MarketName" : "RRG",
"Buys" : {
totalCount: 99, //size/count of all items in Buys array
avgRate: 0.07980372, //avg rate of all buy object items
totalQuantity: 3.09239812, //sum of all buy array items, quantity values
totalValue: 306.14741388, //avgRate * totalQuantity
type0: {
totalCount: 19, //count of items in Buy array oftype 0
avgRate: 0.07980372, //avg rate of all buy object items of type 0
totalQuantity: 3.09239812, //sum of all buy object quantity values oftype 0
totalValue: 30.14741388, //avgRate * totalQuantity
},
type1: {
totalCount: 9,
avgRate: 0.07980372,
totalQuantity: 3.09239812,
totalValue: 30.14741388,
},
type2: {
totalCount: 12,
avgRate: 0.07980372,
totalQuantity: 3.09239812,
totalValue: 30.14741388,
}
},
"Sells" : {
..same as buys format
},
"Fills" : {
..same as buys format
}
}
If someone could assist with this in any way i'd be very grateful.
Here is query i managed to get working, but it's a long way from what i want and i'm no mongo expert so struggling to know how to proceed.
db.getCollection('tinfo').aggregate(
[
{
$match: {
'$and': [
{'Type': {$eq: 'market'}},
{'TimeStamp': {$lte: new Date()}},
{'TimeStamp': {$gte: new Date(Date.now() - 24 * 60 * 60 * 1000)}},
{'MarketName': {$eq: 'BIX'}}
]
}
},
},
{ $unwind: "$Buys" },
{ $unwind: "$Sells" },
{ $unwind: "$Fills" },
{
$group: {
_id: {_id: "$_id", type: "$Buys.Type"},
count: {$sum: 1},
avgRate: {$avg: "$Buys.Rate"},
quantity: {$sum: "$Buys.Quantity"}
}
},{
$project: {
type: "$_id.type",
count: 1,
avgRate: 1,
quantity: 1,
total: {$multiply: ["$quantity", "$avgRate"]}
}
},{
$group: {
"_id": {
"_id" : "$_id._id"
},
"results" : {
$push: {
"k": "$type",
"v": {
"count": "$count",
"avgRate": "$avgRate",
"quantity": "$quantity"
}
}
}
}
}
])
It's certainly possible, and note that I'm actually quite deliberately using the actual MongoDB 3.4 features "in the last stage only", which generally highlights that you do not really need them since the main output can be achieved without the "named keys" in output.
The general listing goes like this:
var endDate = new Date(),
startDate = new Date(endDate.valueOf() - 24 * 60 * 60 * 60 * 1000 );
db.getCollection('tinfo').aggregate([
/*
{ "$match": {
"TimeStamp": {
"$gte": startDate, "$lt": endDate
}
}},
*/
{ "$project": {
"MarketName": 1,
"combined": {
"$concatArrays": [
{ "$map": {
"input": "$Buys",
"as": "b",
"in": {
"ttype": "Buys",
"Type": "$$b.Type",
"Rate": "$$b.Rate",
"Quantity": "$$b.Quantity"
}
}},
{ "$map": {
"input": "$Sells",
"as": "s",
"in": {
"ttype": "Sells",
"Type": "$$s.Type",
"Rate": "$$s.Rate",
"Quantity": "$$s.Quantity"
}
}},
{ "$map": {
"input": "$Fills",
"as": "f",
"in": {
"ttype": "Fills",
"Type": "$$f.OrderType",
"Rate": "$$f.Rate",
"Quantity": "$$f.Quantity"
}
}}
]
}
}},
{ "$unwind": "$combined" },
{ "$group": {
"_id": {
"MarketName": "$MarketName",
"ttype": "$combined.ttype",
"Type": "$combined.Type"
},
"totalCount": { "$sum": 1 },
"avgRate": { "$avg": "$combined.Rate" },
"totalQuantity": { "$sum": "$combined.Quantity" },
}},
{ "$group": {
"_id": {
"MarketName": "$_id.MarketName",
"ttype": "$_id.ttype",
},
"Types": {
"$push": {
"k": {
"$concat": [
"type",
{ "$cond": {
"if": { "$eq": [ "$_id.ttype", "Fills" ] },
"then": "$_id.Type",
"else": { "$substr": ["$_id.Type",0,1] }
}}
]
},
"v": {
"totalCount": "$totalCount",
"avgRate": "$avgRate",
"totalQuantity": "$totalQuantity",
"totalValue": { "$multiply": [ "$totalQuantity", "$avgRate" ] }
}
}
},
"totalCount": { "$sum": "$totalCount" },
"avgRate": { "$avg": "$avgRate" },
"totalQuantity": { "$sum": "$totalQuantity" }
}},
{ "$group": {
"_id": "$_id.MarketName",
"data": {
"$push": {
"k": "$_id.ttype",
"v": {
"totalCount": "$totalCount",
"avgRate": "$avgRate",
"totalQuantity": "$totalQuantity",
"totalValue": { "$multiply": [ "$totalQuantity", "$avgRate" ] },
"Types": "$Types"
}
}
}
}},
{ "$replaceRoot": {
"newRoot": {
"$arrayToObject": {
"$concatArrays": [
[{ "k": "MarketName", "v": "$_id" }],
{ "$map": {
"input": "$data",
"as": "d",
"in": {
"k": "$$d.k",
"v": {
"$arrayToObject": {
"$concatArrays": [
[
{ "k": "totalCount", "v": "$$d.v.totalCount" },
{ "k": "avgRate", "v": "$$d.v.avgRate" },
{ "k": "totalQuantity", "v": "$$d.v.totalQuantity" },
{ "k": "totalValue", "v": "$$d.v.totalValue" }
],
"$$d.v.Types"
]
}
}
}
}}
]
}
}
}}
])
The broad strokes of which are.
Project a combined array which is needed to avoid a "cartesian product" which would otherwise occur if you used $unwind on each array separately. So you want to combine all the arrays into a single one with a consistent format for later processing. This is what we do using $concatArrays and $map to do a little "reshaping" and identify which "ttype" so we know the "source" array.
Gradually Group Since the "totals" occur at different levels we first want to $group on the "inner" "type" property and then gradually work outwards until you get back to the top level. At each stage you do the other calculations such as the $multiply on the $sum and $avg results.
Finally Reshape Which is all essentially about $arrayToObject when used in the aggregation framework. Here we just supply everything as "key" and "value" pairs in array format ( also explaining the "k" and "v" as used in earlier stages ) so that this operator can "transform" into an object format with "named keys".
Of course that final stage can also be done in client code, but since the version is supported I include the actual pipeline stage to do it.
The output is then:
{
"MarketName" : "BIX",
"Buys" : {
"totalCount" : 4.0,
"avgRate" : 0.08000321,
"totalQuantity" : 90.53299094,
"totalValue" : 7.24292988610092,
"type2" : {
"totalCount" : 1.0,
"avgRate" : 0.08068147,
"totalQuantity" : 55.57280163,
"totalValue" : 4.4836953275268
},
"type1" : {
"totalCount" : 2.0,
"avgRate" : 0.07970482,
"totalQuantity" : 0.0,
"totalValue" : 0.0
},
"type0" : {
"totalCount" : 1.0,
"avgRate" : 0.07962334,
"totalQuantity" : 34.96018931,
"totalValue" : 2.7836470398945
}
},
"Sells" : {
"totalCount" : 4.0,
"avgRate" : 0.081110755,
"totalQuantity" : 14.08189692,
"totalValue" : 1.14219329101337,
"type1" : {
"totalCount" : 2.0,
"avgRate" : 0.08130943,
"totalQuantity" : 0.0,
"totalValue" : 0.0
},
"type0" : {
"totalCount" : 2.0,
"avgRate" : 0.08091208,
"totalQuantity" : 14.08189692,
"totalValue" : 1.13939557014279
}
},
"Fills" : {
"totalCount" : 2.0,
"avgRate" : 0.08058147,
"totalQuantity" : 1.02634268,
"totalValue" : 0.0827042018781396,
"typeBUY" : {
"totalCount" : 1.0,
"avgRate" : 0.08048147,
"totalQuantity" : 0.51007134,
"totalValue" : 0.0410512912480698
},
"typeSELL" : {
"totalCount" : 1.0,
"avgRate" : 0.08068147,
"totalQuantity" : 0.51627134,
"totalValue" : 0.0416535306300698
}
}
}
Based on the data provided of course. The actual "calcs" may vary in implementation ( I'm just following your own notes ), but this is the general structure to follow.
Earlier Versions
As noted the output format here really does not need the new features of $replaceRoot and $arrayToObject in order to get to the end result. All that is being transformed is the final document response on the cursor.
So if you look at the output before the $replaceRoot stage is invoked you see:
{
"_id" : "BIX",
"data" : [
{
"k" : "Buys",
"v" : {
"totalCount" : 4.0,
"avgRate" : 0.08000321,
"totalQuantity" : 90.53299094,
"totalValue" : 7.24292988610092,
"Types" : [
{
"k" : "type2",
"v" : {
"totalCount" : 1.0,
"avgRate" : 0.08068147,
"totalQuantity" : 55.57280163,
"totalValue" : 4.4836953275268
}
},
{
"k" : "type1",
"v" : {
"totalCount" : 2.0,
"avgRate" : 0.07970482,
"totalQuantity" : 0.0,
"totalValue" : 0.0
}
},
{
"k" : "type0",
"v" : {
"totalCount" : 1.0,
"avgRate" : 0.07962334,
"totalQuantity" : 34.96018931,
"totalValue" : 2.7836470398945
}
}
]
}
},
{
"k" : "Sells",
"v" : {
"totalCount" : 4.0,
"avgRate" : 0.081110755,
"totalQuantity" : 14.08189692,
"totalValue" : 1.14219329101337,
"Types" : [
{
"k" : "type1",
"v" : {
"totalCount" : 2.0,
"avgRate" : 0.08130943,
"totalQuantity" : 0.0,
"totalValue" : 0.0
}
},
{
"k" : "type0",
"v" : {
"totalCount" : 2.0,
"avgRate" : 0.08091208,
"totalQuantity" : 14.08189692,
"totalValue" : 1.13939557014279
}
}
]
}
},
{
"k" : "Fills",
"v" : {
"totalCount" : 2.0,
"avgRate" : 0.08058147,
"totalQuantity" : 1.02634268,
"totalValue" : 0.0827042018781396,
"Types" : [
{
"k" : "typeBUY",
"v" : {
"totalCount" : 1.0,
"avgRate" : 0.08048147,
"totalQuantity" : 0.51007134,
"totalValue" : 0.0410512912480698
}
},
{
"k" : "typeSELL",
"v" : {
"totalCount" : 1.0,
"avgRate" : 0.08068147,
"totalQuantity" : 0.51627134,
"totalValue" : 0.0416535306300698
}
}
]
}
}
]
}
And we can easily do that same transformation in client code when processing the cursor using .map() and .reduce() JavaScript functions as a shell example:
var endDate = new Date(),
startDate = new Date(endDate.valueOf() - 24 * 60 * 60 * 60 * 1000 );
db.getCollection('tinfo').aggregate([
/*
{ "$match": {
"TimeStamp": {
"$gte": startDate, "$lt": endDate
}
}},
*/
{ "$project": {
"MarketName": 1,
"combined": {
"$concatArrays": [
{ "$map": {
"input": "$Buys",
"as": "b",
"in": {
"ttype": "Buys",
"Type": "$$b.Type",
"Rate": "$$b.Rate",
"Quantity": "$$b.Quantity"
}
}},
{ "$map": {
"input": "$Sells",
"as": "s",
"in": {
"ttype": "Sells",
"Type": "$$s.Type",
"Rate": "$$s.Rate",
"Quantity": "$$s.Quantity"
}
}},
{ "$map": {
"input": "$Fills",
"as": "f",
"in": {
"ttype": "Fills",
"Type": "$$f.OrderType",
"Rate": "$$f.Rate",
"Quantity": "$$f.Quantity"
}
}}
]
}
}},
{ "$unwind": "$combined" },
{ "$group": {
"_id": {
"MarketName": "$MarketName",
"ttype": "$combined.ttype",
"Type": "$combined.Type"
},
"totalCount": { "$sum": 1 },
"avgRate": { "$avg": "$combined.Rate" },
"totalQuantity": { "$sum": "$combined.Quantity" },
}},
{ "$group": {
"_id": {
"MarketName": "$_id.MarketName",
"ttype": "$_id.ttype",
},
"Types": {
"$push": {
"k": {
"$concat": [
"type",
{ "$cond": {
"if": { "$eq": [ "$_id.ttype", "Fills" ] },
"then": "$_id.Type",
"else": { "$substr": ["$_id.Type",0,1] }
}}
]
},
"v": {
"totalCount": "$totalCount",
"avgRate": "$avgRate",
"totalQuantity": "$totalQuantity",
"totalValue": { "$multiply": [ "$totalQuantity", "$avgRate" ] }
}
}
},
"totalCount": { "$sum": "$totalCount" },
"avgRate": { "$avg": "$avgRate" },
"totalQuantity": { "$sum": "$totalQuantity" }
}},
{ "$group": {
"_id": "$_id.MarketName",
"data": {
"$push": {
"k": "$_id.ttype",
"v": {
"totalCount": "$totalCount",
"avgRate": "$avgRate",
"totalQuantity": "$totalQuantity",
"totalValue": { "$multiply": [ "$totalQuantity", "$avgRate" ] },
"Types": "$Types"
}
}
}
}},
/*
{ "$replaceRoot": {
"newRoot": {
"$arrayToObject": {
"$concatArrays": [
[{ "k": "MarketName", "v": "$_id" }],
{ "$map": {
"input": "$data",
"as": "d",
"in": {
"k": "$$d.k",
"v": {
"$arrayToObject": {
"$concatArrays": [
[
{ "k": "totalCount", "v": "$$d.v.totalCount" },
{ "k": "avgRate", "v": "$$d.v.avgRate" },
{ "k": "totalQuantity", "v": "$$d.v.totalQuantity" },
{ "k": "totalValue", "v": "$$d.v.totalValue" }
],
"$$d.v.Types"
]
}
}
}
}}
]
}
}
}}
*/
])
.map( doc => Object.assign(
{ "MarketName": doc._id },
doc.data.map( d => ({
"k": d.k,
"v": Object.assign(
Object.keys(d.v)
.filter(k => k !== 'Types')
.map( k => ({ [k]: d.v[k] }))
.reduce((acc,curr) => Object.assign(acc,curr),{}),
d.v.Types.reduce((acc,curr) => Object.assign(acc,{ [curr.k]: curr.v }),{})
)
}))
.reduce((acc,curr) => Object.assign(acc,{ [curr.k]: curr.v }),{})
))
Which of course produces the exact same output:
{
"MarketName" : "BIX",
"Buys" : {
"totalCount" : 4.0,
"avgRate" : 0.08000321,
"totalQuantity" : 90.53299094,
"totalValue" : 7.24292988610092,
"type2" : {
"totalCount" : 1.0,
"avgRate" : 0.08068147,
"totalQuantity" : 55.57280163,
"totalValue" : 4.4836953275268
},
"type1" : {
"totalCount" : 2.0,
"avgRate" : 0.07970482,
"totalQuantity" : 0.0,
"totalValue" : 0.0
},
"type0" : {
"totalCount" : 1.0,
"avgRate" : 0.07962334,
"totalQuantity" : 34.96018931,
"totalValue" : 2.7836470398945
}
},
"Sells" : {
"totalCount" : 4.0,
"avgRate" : 0.081110755,
"totalQuantity" : 14.08189692,
"totalValue" : 1.14219329101337,
"type1" : {
"totalCount" : 2.0,
"avgRate" : 0.08130943,
"totalQuantity" : 0.0,
"totalValue" : 0.0
},
"type0" : {
"totalCount" : 2.0,
"avgRate" : 0.08091208,
"totalQuantity" : 14.08189692,
"totalValue" : 1.13939557014279
}
},
"Fills" : {
"totalCount" : 2.0,
"avgRate" : 0.08058147,
"totalQuantity" : 1.02634268,
"totalValue" : 0.0827042018781396,
"typeBUY" : {
"totalCount" : 1.0,
"avgRate" : 0.08048147,
"totalQuantity" : 0.51007134,
"totalValue" : 0.0410512912480698
},
"typeSELL" : {
"totalCount" : 1.0,
"avgRate" : 0.08068147,
"totalQuantity" : 0.51627134,
"totalValue" : 0.0416535306300698
}
}
}