Payload in excel sheets that consist of 4 columns i.e Date, status, amount, orderId.You need to structure the data / categorize the columns according to months and in each month orders are categorized as per status.
Umbrella Status:
INTRANSIT - ‘intransit’, ‘at hub’, ‘out for delivery’
RTO - ‘RTO Intransit’, ‘RTO Delivered’
PROCESSING - ‘processing’
For example:
Response should look like: -
May :
1.INTRANSIT
2. RTO
3.PROCESSING
June:
1.INTRANSIT
2. RTO
3.PROCESSING
You can use different aggregation operators provided in MongoDB.For example: -group, facet, Match, unwind, bucket, project, lookup, etc.
I tried it with this:
const pipeline = [{
$facet:
{
"INTRANSIT": [{ $match: { Status: { $in: ['INTRANSIT', 'AT HUB', 'OUT FOR
DELIVERY'] } } }, { $group: { _id: "$Date", numberofbookings: { $sum: 1 } }
}],
"RTO": [{ $match: { Status: { $in: ['RTO INTRANSIT', 'RTO DELIVERED'] } } },
{ $group: { _id: "$Date", numberofbookings: { $sum: 1 } } }],
"PROCESSING": [{ $match: { Status: { $in: ['PROCESSING'] } } }, {
$group: {
_id: date.getMonth("$Date"),
numberofbookings: { $sum: 1 }
}
}]
}
}];
const aggCursor = coll.aggregate(pipeline);
Related
I have this collection(some irrelevant fields were omitted for brevity):
clients: {
userId: ObjectId,
clientSalesValue: Number,
currentDebt: Number,
}
Then I have this query that matches all the clients for a specific user, then calculates the sum of all debts and sales and put those results in a separate field each of them:
await clientsCollection.aggregate([
{
$match: { userId: new ObjectId(userId) }
},
{
$group: {
_id: null,
totalSalesValue: { $sum: '$clientSalesValue' },
totalDebts: { $sum: '$currentDebt' },
}
},
{
$unset: ['_id']
}
]).exec();
This works as expected, it returns an array with only one item which is an object, but now I need to also include in that resultant object a field for the amount of debtors, that is for the amount of clients that have currentDebt > 0, how can I do that is the same query? is it possible?
PD: I cannot modify the $match condition, it need to always return all the clients for the corresponding users.
To include a count of how many matching documents have a positive currentDebt, you can use the $sum and $cond operators like so:
await clientsCollection.aggregate([
{
$match: { userId: new ObjectId(userId) }
},
{
$group: {
_id: null,
totalSalesValue: { $sum: '$clientSalesValue' },
totalDebts: { $sum: '$currentDebt' },
numDebtors: {
$sum: {
$cond: [{ $gt: ['$currentDebt', 0] }, 1, 0]
}
},
}
},
{
$unset: ['_id']
}
]).exec();
I'm trying to query specific fields in my document and sort them by one of the fields, however, the engine seems to completely ignore the sort.
I use the query:
db.symbols.find({_id:'AAPL'}, {'income_statement.annual.totalRevenue':1,'income_statement.annual.fiscalDateEnding':1}).sort({'income_statement.annual.totalRevenue': 1})
This is the output:
[
{
_id: 'AAPL',
income_statement: {
annual: [
{
fiscalDateEnding: '2021-09-30',
totalRevenue: '363172000000'
},
{
fiscalDateEnding: '2020-09-30',
totalRevenue: '271642000000'
},
{
fiscalDateEnding: '2019-09-30',
totalRevenue: '256598000000'
},
{
fiscalDateEnding: '2018-09-30',
totalRevenue: '265595000000'
},
{
fiscalDateEnding: '2017-09-30',
totalRevenue: '229234000000'
}
]
}
}
]
I would expect to have the entries sorted by fiscalDateEnding, starting with 2017-09-30 ascending.
However, the order is fixed, even if I use -1 for sorting.
Any ideas?
The sort you are using is for the ordering of documents in the result set. This is different from the ordering of array elements inside the document.
For your case, if you are using a newer version of MongoDB (5.2+), you can use the $sortArray.
db.symbols.aggregate([
{
$project: {
_id: 1,
annual: {
$sortArray: {
input: "$income_statement.annual",
sortBy: {
fiscalDateEnding: 1
}
}
}
}
}
])
If you are using older version of MongoDB, you can do the followings to perform the sorting.
db.collection.aggregate([
{
"$unwind": "$income_statement.annual"
},
{
$sort: {
"income_statement.annual.fiscalDateEnding": 1
}
},
{
$group: {
_id: "$_id",
annual: {
$push: "$income_statement.annual"
}
}
},
{
"$project": {
_id: 1,
income_statement: {
annual: "$annual"
}
}
}
])
Here is the Mongo Playground for your reference.
Assume I have a collection with millions of documents. Below is a sample of how the documents look like
[
{ _id:"1a1", points:[2,3,5,6] },
{ _id:"1a2", points:[2,6] },
{ _id:"1a3", points:[3,5,6] },
{ _id:"1b1", points:[1,5,6] },
{ _id:"1c1", points:[5,6] },
// ... more documents
]
I want to query a document by _id and return a document that looks like below:
{
_id:"1a1",
totalPoints: 16,
rank: 29
}
I know I can query the whole document, sort by descending order then get the index of the document I want by _id and add one to get its rank. But I have worries about this method.
If the documents are in millions won't this be 'overdoing' it. Querying a whole collection just to get one document? Is there a way to achieve what I want to achieve without querying the whole collection? Or the whole collection has to be involved because of the ranking?
I cannot save them ranked because the points keep on changing. The actual code is more complex but the take away is that I cannot save them ranked.
Total points is the sum of the points in the points array. The rank is calculated by sorting all documents in descending order. The first document becomes rank 1 and so on.
an aggregation pipeline like the following can get the result you want. but how it operates on a collection of millions of documents remains to be seen.
db.collection.aggregate(
[
{
$group: {
_id: null,
docs: {
$push: { _id: '$_id', totalPoints: { $sum: '$points' } }
}
}
},
{
$unwind: '$docs'
},
{
$replaceWith: '$docs'
},
{
$sort: { totalPoints: -1 }
},
{
$group: {
_id: null,
docs: { $push: '$$ROOT' }
}
},
{
$set: {
docs: {
$map: {
input: {
$filter: {
input: '$docs',
as: 'x',
cond: { $eq: ['$$x._id', '1a3'] }
}
},
as: 'xx',
in: {
_id: '$$xx._id',
totalPoints: '$$xx.totalPoints',
rank: {
$add: [{ $indexOfArray: ['$docs._id', '1a3'] }, 1]
}
}
}
}
}
},
{
$unwind: '$docs'
},
{
$replaceWith: '$docs'
}
])
I am working on data analysis of CV data of a large mongoDB collection. I try to count the absolute frequencey of words in the job title (jobs.jobTitle field in below schema).
The documents are structured like this:
{
firstName: String,
lastName: String,
jobs: [{jobTitle: 'software architect', company: String, ...}, {jobTitle: 'full stack software engineer', company: String, ...}, {jobTitle: 'javascript developer', company: String, ...}],
...
}
I would like to iterate over the entire collection and get an outcome like this:
[{word: 'manager', count: 3245},{word: 'engineer', count: 3102}, {word: 'software', count: 3021}, ..]
I tried the following aggregation:
db.cvs.aggregate([
{
$project: {
words: { $split: ["$jobs.jobTitle", " "] }
}
},
{
$unwind: {
path: "$words"
}
},
{
$group: {
_id: "$words",
count: { $sum: 1 }
}
},
{ $sort: { "count": -1 } }
])
Which results to the following error message:
$split requires an expression that evaluates to a string as a first argument, found: array
Can I concat the string values of jobs.jobTitle first to a string by using an aggregation? Or is there any other way to achive the expected result?
Thanks for the quick comment #NeilLunn
I would like to share the corrected query with everyone:
db.cvs.aggregate([
{ "$unwind": "$jobs" },
{
$project: {
words: { $split: ["$jobs.jobTitle", " "] }
}
},
{
$unwind: {
path: "$words"
}
},
{
$group: {
_id: "$words",
count: { $sum: 1 }
}
},
{ $sort: { "count": -1 } }
])
I have this model for purchases:
{
purchase_date: 2018-03-11 00:00:00.000,
total_cost: 400,
items: [
{
title: 'Pringles',
price: 200,
quantity: 2,
category: 'Snacks'
}
]
}
What I'm trying to do is to, first of all, to group the purchases by date, by doing so:
{$group: {
_id: {
date: $purchase_date,
items: '$items'
}
}}
However, now what I want to do is group the purchases of each day by items[].category and calculate how much was spent for each category in that day. I was able to do that with one day, but when I grouped each purchase by date I no longer able to $unwind the items.
I tried passing the path $items and it doesn't find it at all. If I try to use $_id.$items or _id.$items in both cases I get an error stating that it is not a valid path for $unwind.
You can use purchase_data and items.category as a grouping _id but you need to use $unwind on items before and then you can add another $group to get all groups per day
db.col.aggregate([
{ $unwind: "$items" },
{
$group: {
_id: {
purchase_date: "$purchase_date",
category: "$items.category",
},
total: { $sum: { $multiply: [ "$items.price", "$items.quantity" ] } }
}
},
{
$group: {
_id: "$_id.purchase_date",
categories: { $push: { name: "$_id.category", total: "$total" } }
}
}
])