I have a collection with documents in below format: (shown below 2 sample document)
1st doc:
{
"date": 20221101,
"time":1500,
"productCode": "toycar",
"purchaseHistory": [
{
"clientid": 123,
"status": "SUCCESS"
},
{
"clientid": 456,
"status": "FAILURE"
}
]
}
2nd doc:
{
"date": 20221101,
"time": 1500,
"productCode": "toycar",
"purchaseHistory": [
{
"clientid": 890,
"status": "SUCCESS"
},
{
"clientid": 678,
"status": "SUCCESS"
}
]
}
I want to query above and print output in below format where purchaseHistory.status = 'SUCCESS' and date = 20221101:
{productCode:"toycar", "time": 1500, "docCount": 2, "purchaseHistCount":3}
How can I achieve this?
I tried below:
db.products.aggregate({
$match : {date:20221101, 'purchaseHistory.status':'SUCCESS'},
"$group": {
"_id": {
"pc": "$productCode",
"time": "$time"
},
"docCount": {$sum :1}
}
})
Something like this maybe:
db.collection.aggregate([
{
$match: {
date: 20221101,
"purchaseHistory.status": "SUCCESS"
}
},
{
"$addFields": {
"purchaseHistory": {
"$filter": {
"input": "$purchaseHistory",
"as": "ph",
"cond": {
$eq: [
"$$ph.status",
"SUCCESS"
]
}
}
}
}
},
{
$group: {
_id: {
t: "$time",
pc: "$productCode"
},
docCount: {
$sum: 1
},
purchaseHistCount: {
$sum: {
$size: "$purchaseHistory"
}
}
}
}
])
Explained:
Filter the matched documents.
Filter the purchaseHistory SUCCESS only.
Group the result to see count of matching documents & matching purchaseHistory.
Playground
Related
I need to fetch distinct nested documents.
Please find the sample document:
{
"propertyId": 1001820437,
"date": ISODate("2020-07-17T00:00:00.000Z"),
"HList":[
{
"productId": 123,
"name": "Dubai",
"tsh": true
}
],
"PList":[
{
"productId": 123,
"name": "Dubai",
"tsh": false
},
{
"productId": 234,
"name": "India",
"tsh": true
}
],
"CList":[
{
"productId": 234,
"name": "India",
"tsh": false
}
]
}
Expected result is:
{
"produts":[
{
"productId": 123,
"name": "Dubai"
},
{
"productId": 234,
"name": "India"
}
]
}
I tried with this query:
db.property.aggregate([
{
$match: {
"propertyId": 1001820437,
"date": ISODate("2020-07-17T00:00:00.000Z")
}
},
{
"$project": {
"_id": 0,
"unique": {
"$filter": {
"input": {
"$setDifference": [
{
"$concatArrays": [
"$HList.productId",
"$PList.productId",
"$CList.productId"
]
},
[]
]
},
"cond": {
"$ne": [ "$$this", "" ]
}
}
}
}
}
]);
Is $setDifference aggregation is correct choice here?
My query returns only unique product ids but i need a productId with name.
Could someone help me to solve this?
Thanks in advance
You can use $projectfirst to get rid of tsh field and then run $setUnion which ignores duplicated entries:
db.collection.aggregate([
{
$project: {
"HList.tsh": 0,
"PList.tsh": 0,
"CList.tsh": 0,
}
},
{
$project: {
products: {
$setUnion: [ "$HList", "$PList", "$CList" ]
}
}
}
])
Mongo Playground
The following two aggregations return the expected and same result (you can use any of the two):
db.collection.aggregate( [
{
$project: {
_id: 0,
products: {
$reduce: {
input: { $setUnion: [ "$HList", "$PList", "$CList" ] },
initialValue: [],
in: {
$setUnion: [ "$$value", [ { productId: "$$this.productId", name: "$$this.name" } ] ]
}
}
}
}
}
] )
This one is little verbose:
db.collection.aggregate( [
{
$project: { list: { $setUnion: [ "$HList", "$PList", "$CList" ] } }
},
{
$unwind: "$list"
},
{
$group: {
_id: null,
products: { $addToSet: { "productId": "$list.productId", "name": "$list.name" } }
}
},
{
$project: { _id: 0 }
}
] )
db.collection.aggregate([
{
$match: {
"propertyId": 1001820437,
"date": ISODate("2020-07-17T00:00:00.000Z")
}
},
{
$project: {
products: {
$filter: {
input: { "$setUnion" : ["$CList", "$HList", "$PList"] },
as: 'product',
cond: {}
}
}
}
},
{
$project: {
"_id":0,
"products.tsh": 1,
"products.name": 1,
}
},
])
I am struggling to find some examples of using the mongo aggregation framework to process documents which has an array of items where each item also has an array of other obejects (array containing an array)
In the example document below what I would really like is an example that sums the itemValue in the results array of all cases in the document and accross the collection where the result.decision was 'accepted'and group by the document locationCode
However, even an example that found all documents where the result.decision was 'accepted' to show or that summmed the itemValue for the same would help
Many thanks
{
"_id": "333212",
"data": {
"locationCode": "UK-555-5566",
"mode": "retail",
"caseHandler": "A N Other",
"cases": [{
"caseId": "CSE525666",
"items": [{
"id": "333212-CSE525666-1",
"type": "hardware",
"subType": "print cartridge",
"targetDate": "2020-06-15",
"itemDetail": {
"description": "acme print cartridge",
"quantity": 2,
"weight": "1.5"
},
"result": {
"decision": "rejected",
"decisionDate": "2019-02-02"
},
"isPriority": true
},
{
"id": "333212-CSE525666-2",
"type": "Stationery",
"subType": "other",
"targetDate": "2020-06-15",
"itemDetail": {
"description": "staples box",
"quantity": 3,
"weight": "1.66"
},
"result": {
"decision": "accepted",
"decisionDate": "2020-03-03",
"itemValue": "23.01"
},
"isPriority": true
}
]
},
{
"caseId": "CSE885655",
"items": [{
"id": "333212-CSE885655-1",
"type": "marine goods",
"subType": "fish food",
"targetDate": "2020-06-04",
"itemDetail": {
"description": "fish bait",
"quantity": 5,
"weight": "0.65"
},
"result": {
"decision": "accepted",
"decisionDate": "2020-03-02"
},
"isPriority": false
},
{
"id": "333212-CSE885655-4",
"type": "tobacco products",
"subType": "cigarettes",
"deadlineDate": "2020-06-15",
"itemDetail": {
"description": "rolling tobbaco",
"quantity": 42,
"weight": "2.25"
},
"result": {
"decision": "accepted",
"decisionDate": "2020-02-02",
"itemValue": "48.15"
},
"isPriority": true
}
]
}
]
},
"state": "open"
}
You're probably looking for $unwind. It takes an array within a document and creates a separate document for each array member.
{ foos: [1, 2] } -> { foos: 1 }, { foos: 2}
With that you can create a flat document structure and match & group as normal.
db.collection.aggregate([
{
$unwind: "$data.cases"
},
{
$unwind: "$data.cases.items"
},
{
$match: {
"data.cases.items.result.decision": "accepted"
}
},
{
$group: {
_id: "$data.locationCode",
value: {
$sum: {
$toDecimal: "$data.cases.items.result.itemValue"
}
}
}
},
{
$project: {
_id: 0,
locationCode: "$_id",
value: "$value"
}
}
])
https://mongoplayground.net/p/Xr2WfFyPZS3
Alternative solution...
We group by data.locationCode and sum all items with this condition:
cases[*].items[*].result.decision" == "accepted"
db.collection.aggregate([
{
$group: {
_id: "$data.locationCode",
itemValue: {
$sum: {
$reduce: {
input: "$data.cases",
initialValue: 0,
in: {
$sum: {
$concatArrays: [
[ "$$value" ],
{
$map: {
input: {
$filter: {
input: "$$this.items",
as: "f",
cond: {
$eq: [ "$$f.result.decision", "accepted" ]
}
}
},
as: "item",
in: {
$toDouble: {
$ifNull: [ "$$item.result.itemValue", 0 ]
}
}
}
}
]
}
}
}
}
}
}
}
])
MongoPlayground
I'm struggling to understand how to query my data using MQL. My dataset looks a bit like this:
{
"_id": {
"$oid": "5dcadda84d59f2e0b0d56974"
},
"object_kind": "pipeline",
"object_attributes": {
"status": "success",
"created_at": "2019-11-12 16:28:22 UTC",
"variables": []
}
},
{
"_id": {
"$oid": "5dcadda84d59f2e0b0d56998"
},
"object_kind": "pipeline",
"object_attributes": {
"status": "failed",
"created_at": "2019-11-13 12:22:22 UTC",
"variables": []
}
}
I'm adding $eventDate using this in my aggregation, which works:
{
eventDate: { $dateFromString: {
dateString: {
$substr: [ "$object_attributes.created_at",0, 10 ]
}
}},
}
And I'm trying to turn it into this:
{
"eventDate": "2019-11-12",
"counts": {
"success": 1,
"failure": 0
}
},
{
"eventDate": "2019-11-13",
"counts": {
"success": 0,
"failure": 1
}
},
So far I can't seem to understand how to group the data twice, as if I group by "$eventDate" then I can't then group by status. Why can't I just group all docs from the same $eventDate into an array, without losing all the other fields?
It would be ideal if the success and failure fields which could be inferred from different statuses that appear in object_attributes.status
This can be done in several different ways, heres a quick example using a conditional sum:
db.collection.aggregate([
{
"$addFields": {
"eventDate": {
"$dateFromString": {
"dateString": {
"$substr": [
"$object_attributes.created_at",
0.0,
10.0
]
}
}
}
}
},
{
"$group": {
"_id": "$eventDate",
"success": {
"$sum": {
"$cond": [
{
"$eq": [
"$object_attributes.status",
"success"
]
},
1.0,
0.0
]
}
},
"failure": {
"$sum": {
"$cond": [
{
"$eq": [
"$object_attributes.status",
"failed"
]
},
1.0,
0.0
]
}
}
}
},
{
"$project": {
"eventDate": "$_id",
"counts": {
"success": "$success",
"failure": "$failure"
},
"_id": 0
}
}
]);
Here's how one document looks like:
{
"login_Id": "c",
"name": "Abhishek Soni",
"location": "BLAHBLAH",
"work": [
{
"date":ISODate("2014-01-01"),
"total_time": 100,
},
{
"date":ISODate("2014-09-02"),
"total_time": 100,
},
{
"date":ISODate("2014-01-01"),
"total_time": 10,
},
]
}
What I want to do is to run a query that'll give an output like this:
{login_Id: 'c', work:{'01' : 110, '02': 100, ... and so on}}
Basically, I just want to group the work part month wise.
This is what I have tried:
db.employees.aggregate([
{
"$project": {
"_id": 0,
"login_Id": 1,
"time": {
"$sum": "$work.total_time"
}
}
},
{
"$group": {
"_id": {
"$dayOfYear": "$work.date"
},
"time": {
"$sum": "$work.total_time"
}
}
}
]);
But it outputs null. If I remove the group clause, I get the total sum (i.e., 210) What's wrong?
You can try below aggregation
db.collection.aggregate([
{ "$unwind": "$work" },
{ "$match": { "work.date": { "$type": "date" }}},
{ "$group": {
"_id": { "date": { "$dayOfMonth": "$work.date" }},
"time": { "$sum": "$work.total_time" },
"login_Id": { "$first": "$login_Id" }
}},
{ "$group": {
"_id": "$login_Id",
"data": {
"$push": {
"k": { "$toString": "$_id.date" },
"v": "$time"
}
}
}},
{ "$project": {
"work": { "$arrayToObject": "$data" },
"_id": 0,
"login_id": "$_id"
}}
])
Output
[
{
"login_id": "c",
"work": {
"1": 110,
"2": 100
}
}
]
I have many tweets object like this:
{
"_id" : ObjectId("5a2f4a381cb29b482553e2c9"),
"user_id" : 21898942,
"created_at" : ISODate("2009-03-09T19:48:50Z"),
"id" : 1301923516,
"place" : "",
"retweet_count" : 0,
"tweet" : "Save the Date! March 28th Vietnamese Cooking Class! Call to Reserve 312.255.0088",
"favorite_count" : 0
"type": A
}
I'm using this code to qroup the tweets by date and by type:
pipeline = [
{
"$group": {
"_id": {
"date": {
"$dateToString": {
"format": "%Y-%m-%d",
"date": "$created_at"
}
},
"type": "$type"
},
"count": {
"$sum": 1
}
}
}
]
results = mongo.db.tweets.aggregate(pipeline)
Here is the result I get:
{
"_id": {
"date": "2009-03-17",
"type": A
},
"count": 4
,
{
"_id": {
"date": "2009-03-17",
"type": B
},
"count": 6
}
But now I want to have the result in this format:
{date: "2009-03-17", A: 4, B: 6, C: 9}
Is there anyway I can achieve this through aggregate directly?
Note: I'm using MongoDB and PyMongo
You can try the below aggregation query in 3.6 version.
Added the second group to create array of type and count value pairs followed by $mergeObjects to merge date key value with $arrayToObject, which produces create a type value key and count value pairs, to generate the expected response.
$replaceRoot to promote the document to the top level.
pipeline = [
{
"$group": {
"_id": {
"date": {
"$dateToString": {
"format": "%Y-%m-%d",
"date": "$created_at"
}
},
"type": "$type"
},
"count": {
"$sum": 1
}
}
},
{
"$group": {
"_id": "$_id.date",
"typeandcount": {
"$push": {
"k": "$_id.type",
"v": "$count"
}
}
}
},
{
"$replaceRoot": {
"newRoot": {
"$mergeObjects": [
{
"date": "$_id"
},
{
"$arrayToObject": "$typeandcount"
}
]
}
}
}
]
Mongo 3.4 version:
Replace the last stage with below
{
"$replaceRoot": {
"newRoot": {
"$arrayToObject": {
"$concatArrays": [
[
{
"k": "date",
"v": "$_id"
}
],
"$typeandcount"
]
}
}
}
}