I'm having a claim type:
type TClaim: {
insuredId: number,
treatmentInfo: { amount: number }[]
}
and a list of claims:
[
{
insuredId: 1,
treatmentInfo: [{amount: 1}, {amount: 2}]
},
{
insuredId: 1,
treatmentInfo: [{amount: 3}, {amount: 4}]
},
{
insuredId: 2,
treatmentInfo: [{amount: 1}, {amount: 2}]
}
]
I want to get the result like:
[{insuredId: 1, numberOfClaims: 2, amount: 10},{insuredId: 2, numberOfClaims: 1, amount: 3}]
I'm using the $facet operator in mongodb aggregation, one for counting numberOfClaims and one for calculating the amount of each insurer. But I can't combine it to get the result that I want.
$facet: {
totalClaims: [ { $group: { _id: '$insuredId', totalClaims: { $count: {} } } } ],
amount: [ { $unwind: { path: '$treatmentInfo'}},
{ $group:
{ _id: '$insuredId',
amount: { $sum: '$treatmentInfo.amount',
},
},
},
]
Is there a reason why you want to use $facet? - I am just curious
You just need to add a new fields that sums up all the amount in the array first and then do a group stage by insuredId. The query is pretty much self-explanatory.
db.collection.aggregate([
{
"$addFields": {
"totalAmount": {
"$sum": "$treatmentInfo.amount"
}
}
},
{
"$group": {
"_id": "$insuredId",
"numberOfClaims": {
"$sum": 1
},
"amount": {
"$sum": "$totalAmount"
}
}
}
])
Result:
[
{
"_id": 1,
"amount": 10,
"numberOfClaims": 2
},
{
"_id": 2,
"amount": 3,
"numberOfClaims": 1
}
]
MongoDB Playground
Related
I have following stat data stored daily for users.
{
"_id": {
"$oid": "638df4e42332386e0e06d322"
},
"appointment_count": 1,
"item_id": 2,
"item_type": "user",
"company_id": 5,
"created_date": "2022-12-05",
"customer_count": 1,
"lead_count": 1,
"door_knocks": 10
}
{
"_id": {
"$oid": "638f59a9bf33442a57c3aa99"
},
"lead_count": 2,
"item_id": 2,
"item_type": "user",
"company_id": 5,
"created_date": "2022-12-06",
"video_viewed": 2,
"door_knocks": 9
}
And I'm using the following query to get the items by rank
user_stats_2022_12.aggregate([{"$match":{"company_id":5,"created_date":{"$gte":"2022-12-04","$lte":"2022-12-06"}}},{"$setWindowFields":{"partitionBy":"$company_id","sortBy":{"door_knocks":-1},"output":{"item_rank":{"$denseRank":{}},"stat_sum":{"$sum":"$door_knocks"}}}},{"$facet":{"metadata":[{"$count":"total"}],"data":[{"$skip":0},{"$limit":100},{"$sort":{"item_rank":1}}]}}])
It's giving me the rank but with the above data, the record with item_id: 2 are having different rank for same item_id. So I wanted to group them by item_id and then applied rank.
It's a little messy, but here's a playground - https://mongoplayground.net/p/JrJOo4cl9X1.
If you're going to sort by knocks after grouping, I'm assuming that you'll want the sum of door_knocks for a given item_id for this sort.
db.collection.aggregate([
{
$match: {
company_id: 5,
created_date: {
"$gte": "2022-12-04",
"$lte": "2022-12-06"
}
}
},
{
$group: {
_id: {
item_id: "$item_id",
company_id: "$company_id"
},
docs: {
$push: "$$ROOT"
},
total_door_knocks: {
$sum: "$door_knocks"
}
}
},
{
$setWindowFields: {
partitionBy: "$company_id",
sortBy: {
total_door_knocks: -1
},
output: {
item_rank: {
"$denseRank": {}
},
stat_sum: {
"$sum": "$total_door_knocks"
}
}
}
},
{
$unwind: "$docs"
},
{
$project: {
_id: "$docs._id",
appointment_count: "$docs.appointment_count",
company_id: "$docs.company_id",
created_date: "$docs.created_date",
customer_count: "$docs.customer_count",
door_knocks: "$docs.door_knocks",
item_id: "$docs.item_id",
item_type: "$docs.item_type",
lead_count: "$docs.lead_count",
item_rank: 1,
stat_sum: 1,
total_door_knocks: 1
}
},
{
$facet: {
metadata: [
{
"$count": "total"
}
],
data: [
{
"$skip": 0
},
{
"$limit": 100
},
{
"$sort": {
"item_rank": 1
}
}
]
}
}
])
Will try to keep this concise with the input, result and desired/expected result. Need to find the minimum, maximum number of rows/records between the same "winCode" and the last time it occurred in the ordered data. So it makes me want to first group them by "winCode" which works perfectly, but I am not able to come up with something that would display how many records it took for the same "winCode" to appear last time, the minimum and maximum. Check desired output for more details. Below is the paste from: https://mongoplayground.net/p/bCzTO8ZLxNi
Input/collection
[
{
code: "1",
results: {
winCode: 3
}
},
{
code: "10",
results: {
winCode: 3
}
},
{
code: "8",
results: {
winCode: 2
}
},
{
code: "5",
results: {
winCode: 5
}
},
{
code: "5",
results: {
winCode: 4
}
},
{
code: "6",
results: {
winCode: 4
}
},
{
code: "7",
results: {
winCode: 5
}
},
{
code: "3",
results: {
winCode: 3
}
},
{
code: "9",
results: {
winCode: 2
}
},
{
code: "2",
results: {
winCode: 2
}
}
]
Current query
db.collection.aggregate([
{
$sort: {
code: -1
}
},
{
$group: {
_id: "$results.winCode",
count: {
$sum: 1
},
lastTimeOccurredCode: {
$first: "$code" // Any way to get it to display a count from the start to this point on how many records it went through to get the $first result?
},
}
},
{
$sort: {
_id: -1
}
},
])
Current output
[
{
"_id": 5,
"count": 2,
"lastTimeOccurredCode": "5"
},
{
"_id": 4,
"count": 2,
"lastTimeOccurredCode": "5"
},
{
"_id": 3,
"count": 3,
"lastTimeOccurredCode": "1"
},
{
"_id": 2,
"count": 3,
"lastTimeOccurredCode": "2"
}
]
Desired output
[
{
"_id": 5,
"count": 2,
"lastTimeOccurredRecordsCount": 4,
"minRecordsBetween": 3,
"maxRecordsBetween": 3
},
{
"_id": 4,
"count": 2,
"lastTimeOccurredRecordsCount": 5,
"minRecordsBetween": 1,
"maxRecordsBetween": 1
},
{
"_id": 3,
"count": 3,
"lastTimeOccurredRecordsCount": 1,
"minRecordsBetween": 1,
"maxRecordsBetween": 6
},
{
"_id": 2,
"count": 3,
"lastTimeOccurredRecordsCount": 3,
"minRecordsBetween": 1,
"maxRecordsBetween": 6
}
]
I have tried to add an $accumulator function, but I would need the $first functions result in it, but it's not available at the same $group stage. Feel like I am missing something here.
You can use $setWindowFields to define index and reduce to find the diff between them. If you want the index to be according to {$sort: {code: -1}}, then keep the $setWindowFields sortBy according to this example and remove the redundant {$sort: {code: -1}} step. If you want the index to be according to another sorting logic that only update the $setWindowFields sortBy.
Use $setWindowFields to define index
$sort according to your what you need (if it is different than the prev sort)
$group according to the $results.winCode and keep all index data.
Calculate the diff
Format
db.collection.aggregate([
{$setWindowFields: {
sortBy: {code: -1},
output: {index: {$sum: 1, window: {documents: ["unbounded", "current"]}}}
}},
{$sort: {code: -1}},
{$group: {
_id: "$results.winCode",
count: {$sum: 1},
lastTimeOccurredCode: {$first: "$code"},
index: {$push: "$index"}
}},
{$project: {
count: 1,
lastTimeOccurredCode: 1,
diff: {
$reduce: {
input: {$range: [1, {$size: "$index"}]},
initialValue: [],
in: {$concatArrays: [
"$$value",
[{$subtract: [
{$arrayElemAt: ["$index", "$$this"]},
{$arrayElemAt: ["$index", {$subtract: ["$$this", 1]}]}
]}]
]
}
}
}
}},
{$set: {
minRecordsBetween: {$min: "$diff"},
maxRecordsBetween: {$max: "$diff"},
diff: "$$REMOVE"
}},
{$sort: {_id: -1}}
])
See how it works on the playground example
db.setting.aggregate([
{
$match: {
status: true,
deleted_at: 0,
_id: {
$in: [
ObjectId("5c4ee7eea4affa32face874b"),
ObjectId("5ebf891245aa27c290672325")
]
}
}
},
{
$lookup: {
from: "site",
localField: "_id",
foreignField: "admin_id",
as: "data"
}
},
{
$project: {
name: 1,
status: 1,
price: 1,
currency: 1,
numberOfRecord: {
$size: "$data"
}
}
},
{
$sort: {
numberOfRecord: 1
}
}
])
how to push the currency into price object using project please guide thanks a lot, also eager to know what is difference between $addtoSet and $push, what is good option to opt it from project or fix it from $addField
https://mongoplayground.net/p/RiWnnRtksb4
Output should be like this:
[
{
"_id": ObjectId("5ebf891245aa27c290672325"),
"currency": "USD",
"name": "Menz",
"numberOfRecord": 0,
"price": {
"numberDecimal": "20",
"currency": "USD",
},
"status": true
},
{
"_id": ObjectId("5c4ee7eea4affa32face874b"),
"currency": "USD",
"name": "Dave",
"numberOfRecord": 2,
"price": {
"numberDecimal": "10",
"currency": "USD"
},
"status": true
}
]
You can insert a field into an object with project directly, like this (field price):
$project: {
name: 1,
status: 1,
price: {
numberDecimal: "$price.numberDecimal",
currency: "$currency"
},
numberOfRecord: {
$size: "$data"
}
}
By doing it with project, there is no need to use $addField.
For the difference between $addToSet and $push, read this great answer.
You can just set the object structure while projecting, so in this case there's no need for either $push or $addToSet.
{
$project: {
name: "1",
status: 1,
price: {
currency: "$currency",
numberDecimal: "$price.numberDecimal"
},
currency: 1,
numberOfRecord: {
$size: "$data",
}
}
}
Now the difference between $push and $addToSet is pretty trivial and derived from the name, $push saves all items while $addToSet will just create a set of them, for example:
input:
[
//doc1
{
item: 1
},
//doc2
{
item: 2
},
//doc3
{
item: 1
}
]
Now this:
{
$group: {
_id: null,
items: {$push: "$item"}
}
}
Will result in:
{_id: null, items: [1, 2, 1]}
While:
{
$group: {
_id: null,
items: {$addToSet: "$item"}
}
}
Will result in:
{_id: null, items: [1, 2]}
The best way I can describe what I want to achieve is using an example. Basically I would have a list of cars say:
[
{
_id: 1,
make: 'Toyota',
model: 'Prius'
},
{
_id: 2,
make: 'Toyota',
model: 'Prius'
},
{
_id: 3,
make: 'Toyota',
model: 'Yaris'
},
{
_id: 4,
make: 'Lexus',
model: 'RX400'
}
]
And now I want to group/distinct them by make and model (and possibly more fields) and count the totals. The final result should look something like:
{
makes: [
{
name: 'Toyota',
total: 3
}, {
name: 'Lexus',
total: 1
}
],
models: [
{
name: 'Prius',
total: 2
},
{
name: 'Yaris',
total: 1
},
{
name: 'RX400',
total: 1
}
]
}
I'm completely stuck with this one. So far, the only way I can achieve this is by calling several async aggregation calls for each field. However, I would prefer to do it in a single aggregation call, if that is possible at all (unless it's not a good idea performance wise).
Use $facet for this:
db.collection.aggregate([
{ "$facet": {
"makes": [
{ "$group": {
"_id": "$make",
"total": { "$sum": 1 }
} },
{ "$project": {
"_id": 0,
"name": "$_id",
"total": 1
} }
],
"models": [
{ "$group": {
"_id": "$model",
"total": { "$sum": 1 }
} },
{ "$project": {
"_id": 0,
"name": "$_id",
"total": 1
} }
]
} }
])
I am struggling with MongoDb in order to achieve a desirable result.
My Collection looks like:
{
_id: ...
place: 1
city: 6
user: 306
createDate: 2014-08-10 12:20:21,
lastUpdate: 2014-08-14 10:11:01,
data: [
{
customId4: 4,
entryDate: 2014-07-12 12:01:11,
exitDate: 2014-07-12 13:12:12
},
{
customId4: 4,
entryDate: 2014-07-14 00:00:01,
},
{
customId4: 5,
entryDate: 2014-07-15 11:01:11,
exitDate: 2014-07-15 11:05:15
},
{
customId4: 5,
entryDate: 2014-07-22 21:01:11,
exitDate: 2014-07-22 21:23:22
},
{
customId4: 4,
entryDate: 2014-07-23 14:00:11,
},
{
customId4: 4,
entryDate: 2014-07-29 22:00:11,
exitDate: 2014-07-29 23:00:12
},
{
customId4: 5,
entryDate: 2014-08-12 12:01:11,
exitDate: 2014-08-12 13:12:12
},
]
}
So what I would like to achieve is the array data that meets the requirements of a certain interval and that has both, entryDate and exitDate values set.
For example, if I filter by the interval "2014-07-23 00:00:00 to 2014-08-31 00:00:00" I would like the result like:
{
result: [
{
_id: {
place: 1,
user: 306
},
city: 6,
place: 1,
user: 306,
data: [
{
customMap: 4,
entryDate: 2014-07-22 21:01:11,
exitDate: 2014-07-22 21:23:22
},
{
customId4: ,
entryDate: 2014-07-29 22:00:11,
exitDate: 2014-07-29 23:00:12
},
]
}
],
ok: 1
}
My custom mongodb query looks like (from, to and placeIds are variables properly configured)
db.myColl.aggregate(
{ $match: {
'user': 1,
'data.entryDate': { $gte: from, $lte: to },
'place': { $in: placeIds },
}},
{ $unwind : "$data" },
{ $project: {
'city': 1,
'place': 1,
'user': 1,
'lastUpdate': 1,
'data.entryDate': 1,
'data.exitDate': 1,
'data.custom': 1,
fromValid: { $gte: ["$'data.entryDate'", from]},
toValid: { $lte: ["$'data.entryDate'", to]}}
},
{ $group: {
'_id': {'place': '$place', 'user': '$user'},
'city': {'$first': '$city'},
'place': {'$first': '$place'},
'user': {'$first': '$user'},
'data': { '$push': '$data'}
}}
)
But this doesn't filter the way I want because it outputs every document that meets the $match operand conditions, inside the $project operand I am unable to define the condition (I don't know if this is how it has to be done in mongoDB)
Thanks in advance!
You were on the right track, but what you might be missing with the aggregation "pipeline" is that just like the "|" pipe operator in the unix shell you "chain" the pipeline stages together just as you would chain commands.
So in fact to can have a second $match pipeline stage that does the filtering for you:
db.myColl.aggregate([
{ "$match": {
"user": 1,
"data.entryDate": { "$gte": from, "$lte": to },
"place": { "$in": "placeIds" },
}},
{ "$unwind": "$data" },
{ "$match": {
"data.entryDate": { "$gte": from, "$lte": to },
}},
{ "$group": {
"_id": "$_id",
"place": { "$first": "$place" },
"city": { "$first": "$city" },
"user": { "$first": "$user" },
"data": { "$push": "$data" }
}}
])
Using the actual _id of the document as a grouping key presuming that you want the document back but just with a filtered array.
From MongoDB 2.6, as long as your matching array elements are unique, you could just do the same thing within $project using the $map and $setDifference** operators:
db.myColl.aggregate([
{ "$match": {
"user": 1,
"data.entryDate": { "$gte": from, "$lte": to },
"place": { "$in": "placeIds" },
}},
{ "$project": {
"place": 1,
"city": 1,
"user": 1,
"data": {
"$setDifference": [
{ "$map": {
"input": "$data",
"as": "el",
"in": {"$cond": [
{ "$and": [
{ "$gte": [ "$$el.entryDate", from ] },
{ "$lte": [ "$$el.entryDate", to ] }
]},
"$$el",
false
]}
}},
[false]
]
}
}}
])
That does the same logical thing by processing each array element and evaluating whether it meets the conditions. If so then the element content is returned, if not the false is returned. The $setDifference filters out all the false values so that only those that match remain.