How to aggregate into a map [duplicate] - mongodb

Question

I have a data in profile collection
[
{
name: "Harish",
gender: "Male",
caste: "Vokkaliga",
education: "B.E"
},
{
name: "Reshma",
gender: "Female",
caste: "Vokkaliga",
education: "B.E"
},
{
name: "Rangnath",
gender: "Male",
caste: "Lingayath",
education: "M.C.A"
},
{
name: "Lakshman",
gender: "Male",
caste: "Lingayath",
education: "B.Com"
},
{
name: "Reshma",
gender: "Female",
caste: "Lingayath",
education: "B.E"
}
]
here I need to calculate total Number of different gender, total number of different caste and total number of different education.
Expected o/p
{
gender: [{
name: "Male",
total: "3"
},
{
name: "Female",
total: "2"
}],
caste: [{
name: "Vokkaliga",
total: "2"
},
{
name: "Lingayath",
total: "3"
}],
education: [{
name: "B.E",
total: "3"
},
{
name: "M.C.A",
total: "1"
},
{
name: "B.Com",
total: "1"
}]
}
using mongodb aggregation how can I get the expected result.

Answer 1

There are different approaches depending on the version available, but they all essentially break down to transforming your document fields into separate documents in an "array", then "unwinding" that array with $unwind and doing successive $group stages in order to accumulate the output totals and arrays.
MongoDB 3.4.4 and above
Latest releases have special operators like $arrayToObject and $objectToArray which can make transfer to the initial "array" from the source document more dynamic than in earlier releases:
db.profile.aggregate([
{ "$project": {
"_id": 0,
"data": {
"$filter": {
"input": { "$objectToArray": "$$ROOT" },
"cond": { "$in": [ "$$this.k", ["gender","caste","education"] ] }
}
}
}},
{ "$unwind": "$data" },
{ "$group": {
"_id": "$data",
"total": { "$sum": 1 }
}},
{ "$group": {
"_id": "$_id.k",
"v": {
"$push": { "name": "$_id.v", "total": "$total" }
}
}},
{ "$group": {
"_id": null,
"data": { "$push": { "k": "$_id", "v": "$v" } }
}},
{ "$replaceRoot": {
"newRoot": {
"$arrayToObject": "$data"
}
}}
])
So using $objectToArray you make the initial document into an array of it's keys and values as "k" and "v" keys in the resulting array of objects. We apply $filter here in order to select by "key". Here using $in with a list of keys we want, but this could be more dynamically used as a list of keys to "exclude" where that was shorter. It's just using logical operators to evaluate the condition.
The end stage here uses $replaceRoot and since all our manipulation and "grouping" in between still keeps that "k" and "v" form, we then use $arrayToObject here to promote our "array of objects" in result to the "keys" of the top level document in output.
MongoDB 3.6 $mergeObjects
As an extra wrinkle here, MongoDB 3.6 includes $mergeObjects which can be used as an "accumulator" in a $group pipeline stage as well, thus replacing the $push and making the final $replaceRoot simply shifting the "data" key to the "root" of the returned document instead:
db.profile.aggregate([
{ "$project": {
"_id": 0,
"data": {
"$filter": {
"input": { "$objectToArray": "$$ROOT" },
"cond": { "$in": [ "$$this.k", ["gender","caste","education"] ] }
}
}
}},
{ "$unwind": "$data" },
{ "$group": { "_id": "$data", "total": { "$sum": 1 } }},
{ "$group": {
"_id": "$_id.k",
"v": {
"$push": { "name": "$_id.v", "total": "$total" }
}
}},
{ "$group": {
"_id": null,
"data": {
"$mergeObjects": {
"$arrayToObject": [
[{ "k": "$_id", "v": "$v" }]
]
}
}
}},
{ "$replaceRoot": { "newRoot": "$data" } }
])
This is not really that different to what is being demonstrated overall, but simply demonstrates how $mergeObjects can be used in this way and may be useful in cases where the grouping key was something different and we did not want that final "merge" to the root space of the object.
Note that the $arrayToObject is still needed to transform the "value" back into the name of the "key", but we just do it during the accumulation rather than after the grouping, since the new accumulation allows the "merge" of keys.
MongoDB 3.2
Taking it back a version or even if you have a MongoDB 3.4.x that is less than the 3.4.4 release, we can still use much of this but instead we deal with the creation of the array in a more static fashion, as well as handling the final "transform" on output differently due to the aggregation operators we don't have:
db.profile.aggregate([
{ "$project": {
"data": [
{ "k": "gender", "v": "$gender" },
{ "k": "caste", "v": "$caste" },
{ "k": "education", "v": "$education" }
]
}},
{ "$unwind": "$data" },
{ "$group": {
"_id": "$data",
"total": { "$sum": 1 }
}},
{ "$group": {
"_id": "$_id.k",
"v": {
"$push": { "name": "$_id.v", "total": "$total" }
}
}},
{ "$group": {
"_id": null,
"data": { "$push": { "k": "$_id", "v": "$v" } }
}},
/*
{ "$replaceRoot": {
"newRoot": {
"$arrayToObject": "$data"
}
}}
*/
]).map( d =>
d.data.map( e => ({ [e.k]: e.v }) )
.reduce((acc,curr) => Object.assign(acc,curr),{})
)
This is exactly the same thing, except instead of having a dynamic transform of the document into the array, we actually "explicitly" assign each array member with the same "k" and "v" notation. Really just keeping those key names for convention at this point since none of the aggregation operators here depend on that at all.
Also instead of using $replaceRoot, we just do exactly the same thing as what the previous pipeline stage implementation was doing there but in client code instead. All MongoDB drivers have some implementation of cursor.map() to enable "cursor transforms". Here with the shell we use the basic JavaScript functions of Array.map() and Array.reduce() to take that output and again promote the array content to being the keys of the top level document returned.
MongoDB 2.6
And falling back to MongoDB 2.6 to cover the versions in between, the only thing that changes here is the usage of $map and a $literal for input with the array declaration:
db.profile.aggregate([
{ "$project": {
"data": {
"$map": {
"input": { "$literal": ["gender","caste", "education"] },
"as": "k",
"in": {
"k": "$$k",
"v": {
"$cond": {
"if": { "$eq": [ "$$k", "gender" ] },
"then": "$gender",
"else": {
"$cond": {
"if": { "$eq": [ "$$k", "caste" ] },
"then": "$caste",
"else": "$education"
}
}
}
}
}
}
}
}},
{ "$unwind": "$data" },
{ "$group": {
"_id": "$data",
"total": { "$sum": 1 }
}},
{ "$group": {
"_id": "$_id.k",
"v": {
"$push": { "name": "$_id.v", "total": "$total" }
}
}},
{ "$group": {
"_id": null,
"data": { "$push": { "k": "$_id", "v": "$v" } }
}},
/*
{ "$replaceRoot": {
"newRoot": {
"$arrayToObject": "$data"
}
}}
*/
])
.map( d =>
d.data.map( e => ({ [e.k]: e.v }) )
.reduce((acc,curr) => Object.assign(acc,curr),{})
)
Since the basic idea here is to "iterate" a provided array of the field names, the actual assignment of values comes by "nesting" the $cond statements. For three possible outcomes this means only a single nesting in order to "branch" for each outcome.
Modern MongoDB from 3.4 have $switch which makes this branching simpler, yet this demonstrates the logic was always possible and the $cond operator has been around since the aggregation framework was introduced in MongoDB 2.2.
Again, the same transformation on the cursor result applies as there is nothing new there and most programming languages have the ability to do this for years, if not from inception.
Of course the basic process can even be done way back to MongoDB 2.2, but just applying the array creation and $unwind in a different way. But no-one should be running any MongoDB under 2.8 at this point in time, and official support even from 3.0 is even fast running out.
Output
For visualization, the output of all demonstrated pipelines here has the following form before the last "transform" is done:
/* 1 */
{
"_id" : null,
"data" : [
{
"k" : "gender",
"v" : [
{
"name" : "Male",
"total" : 3.0
},
{
"name" : "Female",
"total" : 2.0
}
]
},
{
"k" : "education",
"v" : [
{
"name" : "M.C.A",
"total" : 1.0
},
{
"name" : "B.E",
"total" : 3.0
},
{
"name" : "B.Com",
"total" : 1.0
}
]
},
{
"k" : "caste",
"v" : [
{
"name" : "Lingayath",
"total" : 3.0
},
{
"name" : "Vokkaliga",
"total" : 2.0
}
]
}
]
}
And then either by the $replaceRoot or the cursor transform as demonstrated the result becomes:
/* 1 */
{
"gender" : [
{
"name" : "Male",
"total" : 3.0
},
{
"name" : "Female",
"total" : 2.0
}
],
"education" : [
{
"name" : "M.C.A",
"total" : 1.0
},
{
"name" : "B.E",
"total" : 3.0
},
{
"name" : "B.Com",
"total" : 1.0
}
],
"caste" : [
{
"name" : "Lingayath",
"total" : 3.0
},
{
"name" : "Vokkaliga",
"total" : 2.0
}
]
}
So whilst we can put some new and fancy operators into the aggregation pipeline where we have those available, the most common use case is in these "end of pipeline transforms" in which case we may as well simply do the same transformation on each document in the cursor results returned instead.