Can't reduce a deeply nested array on MongoDB - arrays

Question

I've a Mongo database with documents like these one inside a collection:
{
date:"2019-06-12T00:09:03.000Z",
actions:{
actionDate:"2019-06-12T00:15:25.000Z",
data:{
users:[
[{gender:"Male",age:24},
{gender:"Female",age:25}
],
[{gender:"Male",age:34},
{gender:"Male",age:26}
],
[{gender:"Female",age:19},
{gender:"Male",age:21}
]
]
}
}
}
I would like to summarize the users appearing inside the array users in a single document, like
{
"date":"2019-06-12T00:09:03.000Z",
"actionDate":"2019-06-12T00:15:25.000Z",
"summary":{
"countFemale":2,
"meanFemaleAge":22,
"countMale":4,
"meanMaleAge":26.25
}
}
Some considerations to be taken into account: there could be no cases for one gender and also, the users array might be limited to one or two arrays inside it.
I've tried to solve it using my, now I know, scarce knowledge of Mongo query language but it seems unsolvable to me. Thought this might be useful checking MongoDB: Reduce array of objects into a single object by computing the average of each field but can't catch up with the idea.
Any ideas, please?

Answer 1

Try below query :
db.collection.aggregate([
/** Merge all arrays inside 'users' & push to 'summary' field */
{
$project: {
date: 1,
actionDate: "$actions.actionDate",
summary: {
$reduce: {
input: "$actions.data.users",
initialValue: [],
in: { $concatArrays: ["$$value", "$$this"] },
},
},
},
},
{
$unwind: "$summary",
},
/** Group on 'date' to push data related to same date */
{
$group: {
_id: "$date",
actionDate: {$first: "$actionDate",},
countFemale: {$sum: {$cond: [{$eq: ["$summary.gender", "Female"]},1,0]}},
countMale: {$sum: {$cond: [{$eq: ["$summary.gender", "Male"]},1,0]}},
meanFemaleAge: {$sum: {$cond: [{$eq: ["$summary.gender", "Female"]},"$summary.age",0]}},
meanMaleAge: {$sum: {$cond: [{$eq: ["$summary.gender", "Male"]},"$summary.age",0]}}
}
},
/** Re-create 'meanFemaleAge' & 'meanMaleAge' fields to add mean */
{
$addFields: {
meanFemaleAge: {$cond: [{$ne: ["$meanFemaleAge", 0]},{$divide: ["$meanFemaleAge","$countFemale"]},0]},
meanMaleAge: {$cond: [{$ne: ["$meanMaleAge", 0]},{$divide: ["$meanMaleAge","$countMale"]},0]},
}
}
]);
Test : MongoDB-Playground
Note : No matter what how you do this, I would suggest you to do not implement this kind of operations on entire collection with huge datasets.

Answer 2

We need to perform $reduce operator.
In the first stage, we create separate arrays (Male|Female) and push users according to their gender.
In the second stage, we transform / calculate result.
Try this one:
db.collection.aggregate([
{
$addFields: {
"users": {
$reduce: {
input: "$actions.data.users",
initialValue: {
"Male": [],
"Female": []
},
in: {
Male: {
$concatArrays: [
"$$value.Male",
{
$filter: {
input: "$$this",
cond: {
$eq: [
"$$this.gender",
"Male"
]
}
}
}
]
},
Female: {
$concatArrays: [
"$$value.Female",
{
$filter: {
input: "$$this",
cond: {
$eq: [
"$$this.gender",
"Female"
]
}
}
}
]
}
}
}
}
}
},
{
$project: {
_id: 0,
date: 1,
actionDate: "$actions.actionDate",
summary: {
"countFemale": {
$size: "$users.Female"
},
"meanFemaleAge": {
$avg: "$users.Female.age"
},
"countMale": {
$size: "$users.Male"
},
"meanMaleAge": {
$avg: "$users.Male.age"
}
}
}
}
])
MongoPlayground