I have a collection from which I need specific obj e.g. notes.blok2 and notes.curse5 as an object, not as an array
{
"year":2020,
"grade":4,
"seccion":"A",
"id": 100,
"name": "pedro",
"notes":[{"curse":5,
"block":1,
"score":{ "a1": 5,"a2": 10, "a3": 15}
},{"curse":5,
"block":2,
"score":{ "b1": 10,"b2": 20, "b3": 30}
}
]
}
My query
notas.find({
"$and":[{"grade":1},{"seccion":"A"},{"year":2020}]},
{"projection":{ "grade":1, "seccion":1,"name":1,"id":1,
"notes":{"$elemMatch":{"block":2,"curse":5}},"notes.score":1} })
It works but returns notes like array
{
"_id": "55",
"id": 100,
"grade": 5,
"name": "pedro",
"seccion": "A",
"notes": [
{"score": { "b1": 10,"b2": 20, "b3": 30} }
]
}
But I NEED LIKE THIS: score at the same level as others and if doesn't exist show empty "score":{}
{
"year":2020,
"grade":5,
"seccion":"A",
"id": 100,
"name": "pedro",
"score":{ "b1": 10,"b2": 20, "b3": 30}
}
Demo - https://mongoplayground.net/p/XlJqR2DYW1X
You can use aggregation query
db.collection.aggregate([
{
$match: { // filter
"grade": 1,
"seccion": "A",
"year": 2020,
"notes": {
"$elemMatch": {
"block": 2,
"curse": 5
}
}
}
},
{ $unwind: "$notes" }, //break into individual documents
{
$match: { // match query on individual note
"notes.block": 2,
"notes.curse": 5
}
},
{
$project: { // projection
"grade": 1,
"seccion": 1,
"name": 1,
"id": 1,
"score": "$notes.score"
}
}
])
Update
Demo - https://mongoplayground.net/p/mq5Kue3UG42
Use $filter
db.collection.aggregate([
{
$match: {
"grade": 1,
"seccion": "A",
"year": 2020
}
},
{
$set: {
"score": {
"$filter": {
"input": "$notes",
"as": "note",
"cond": {
$and: [
{
$eq: [ "$$note.block",3]
},
{
$eq: [ "$$note.curse", 5 ]
}
]
}
}
}
}
},
{
$project: {
// projection
"grade": 1,
"seccion": 1,
"name": 1,
"id": 1,
"score": {
"$first": "$score.score"
}
}
}
])
If you want empty object for score when match not found you can do -
Demo - https://mongoplayground.net/p/dumax58kgrc
{
$set: {
score: {
$cond: [
{ $size: "$score" }, // check array length
{ $first: "$score" }, // true - take 1st
{ score: {} } // false - set empty object
]
}
}
},
I have three fields, id, date and qty in my DB. I want to group them by the id and find the qty which is the latest quantity of every month. So for every month, the date with the latest day of every month, the qty will be returned for it.
If the input is
[
{
"id": "ABC",
"date": "2020-10-02 15:03:00.00",
"qty": 500,
},
{
"id": "ABC",
"date": "2020-10-31 20:22:00.00",
"qty": 100,
},
{
"id": "ABC",
"date": "2020-11-03 04:22:00.00",
"qty": 200,
},
{
"id": "ABC",
"date": "2020-11-18 04:22:00.00",
"qty": 50,
},
{
"id": "ABC1",
"date": "2020-11-05 04:22:00.00",
"qty": 5000,
},
{
"id": "ABC1",
"date": "2020-11-15 04:22:00.00",
"qty": 4580,
},
]
then the output should be
[
{
"id": "ABC",
"qtys": [
{
"date": "2020-10-31 20:22:00.00",
"qty": 100
},
{
"date": "2020-11-18 04:22:00.00",
"qty": 50
}
]
},
{
"id": "ABC1",
"qtys": [
{
"date": "2020-11-15 04:22:00.00",
"qty": 4580
}
]
},
]
$addFields to convert date field from string type to date type, if its already date type then ignore this stage
$sort by date in descending order
$group by id, month and year after extracting from date field
using $year and $month to get first document
$group by only id and construct array of quantities in qtys
db.collection.aggregate([
{ $addFields: { date: { $toDate: "$date" } } },
{ $sort: { date: -1 } },
{
$group: {
_id: {
id: "$id",
month: { $month: "$date" },
year: { $year: "$date" }
},
qtys: { $first: { date: "$date", qty: "$qty" } }
}
},
{
$group: {
_id: "$_id.id",
qtys: { $push: "$qtys" }
}
}
])
Playground
How do I get counts data grouped by every hour in 24 hours even if data is not present i.e. IF 0 will select 0
MonogDB 3.6
Input
[
{
"_id": ObjectId("5ccbb96706d1d47a4b2ced4b"),
"date": "2019-05-03T10:39:53.108Z",
"id": 166,
"update_at": "2019-05-03T02:45:36.208Z",
"type": "image"
},
{
"_id": ObjectId("5ccbb96706d1d47a4b2ced4c"),
"date": "2019-05-03T10:39:53.133Z",
"id": 166,
"update_at": "2019-05-03T02:45:36.208Z",
"type": "image"
},
{
"_id": ObjectId("5ccbb96706d1d47a4b2ced4d"),
"date": "2019-05-03T10:39:53.180Z",
"id": 166,
"update_at": "2019-05-03T20:45:36.208Z",
"type": "image"
},
{
"_id": ObjectId("5ccbb96706d1d47a4b2ced7a"),
"date": "2019-05-10T10:39:53.218Z",
"id": 166,
"update_at": "2019-12-04T10:45:36.208Z",
"type": "image"
},
{
"_id": ObjectId("5ccbb96706d1d47a4b2ced7b"),
"date": "2019-05-03T10:39:53.108Z",
"id": 166,
"update_at": "2019-05-05T10:45:36.208Z",
"type": "image"
},
{
"_id": ObjectId("5ccbb96706d1d47a4b2cedae"),
"date": "2019-05-03T10:39:53.133Z",
"id": 166,
"update_at": "2019-05-05T10:45:36.208Z",
"type": "image"
},
{
"_id": ObjectId("5ccbb96706d1d47a4b2cedad"),
"date": "2019-05-03T10:39:53.180Z",
"id": 166,
"update_at": "2019-05-06T10:45:36.208Z",
"type": "image"
},
{
"_id": ObjectId("5ccbb96706d1d47a4b2cedab"),
"date": "2019-05-10T10:39:53.218Z",
"id": 166,
"update_at": "2019-12-06T10:45:36.208Z",
"type": "image"
}
]
Implementation
db.collection.aggregate({
$match: {
update_at: {
"$gte": "2019-05-03T00:00:00.0Z",
"$lt": "2019-05-05T00:00:00.0Z"
},
id: {
"$in": [
166
]
}
}
},
{
$group: {
_id: {
$substr: [
"$update_at",
11,
2
]
},
count: {
"$sum": 1
}
},
},
{
$project: {
_id: 0,
hour: "$_id",
count: "$count"
}
},
{
$sort: {
hour: 1
}
})
Actual Output:
{
"count": 2,
"hour": "02"
},
{
"count": 1,
"hour": "20"
}
My expectation code show 24 hours event data is 0 or null and convert from example "02" as "02 AM" , "13" as "01 PM":
Expected Output
{
"count": 0,
"hour": "01" // 01 AM
},
{
"count": 2,
"hour": "02"
},
{
"count": 0,
"hour": "03"
},
{
"count": 0,
"hour": "04"
},
{
"count": 0,
"hour": "05"
},
{
"count": 1,
"hour": "20" // to 08 pm
}
Try this solution:
Explanation
We group by hour to count how many images are uploaded.
Then, we add extra field hour to create time interval (if you had v4.x, there is a better solution).
We flattern hour field (will create new documents) and split first 2 digits to match count and split last 2 digits to put AM / PM periods.
db.collection.aggregate([
{
$match: {
update_at: {
"$gte": "2019-05-03T00:00:00.0Z",
"$lt": "2019-05-05T00:00:00.0Z"
},
id: {
"$in": [
166
]
}
}
},
{
$group: {
_id: {
$substr: [
"$update_at",
11,
2
]
},
count: {
"$sum": 1
}
}
},
{
$addFields: {
hour: [
"0000",
"0101",
"0202",
"0303",
"0404",
"0505",
"0606",
"0707",
"0808",
"0909",
"1010",
"1111",
"1212",
"1301",
"1402",
"1503",
"1604",
"1705",
"1806",
"1907",
"2008",
"2109",
"2210",
"2311"
]
}
},
{
$unwind: "$hour"
},
{
$project: {
_id: 0,
hour: 1,
count: {
$cond: [
{
$eq: [
{
$substr: [
"$hour",
0,
2
]
},
"$_id"
]
},
"$count",
0
]
}
}
},
{
$group: {
_id: "$hour",
count: {
"$sum": "$count"
}
}
},
{
$sort: {
_id: 1
}
},
{
$project: {
_id: 0,
hour: {
$concat: [
{
$substr: [
"$_id",
2,
2
]
},
{
$cond: [
{
$gt: [
{
$substr: [
"$_id",
0,
2
]
},
"12"
]
},
" PM",
" AM"
]
}
]
},
count: "$count"
}
}
])
MongoPlayground
There's no "magic" solution, you'll have to hardcode it into your aggregation:
Heres an example using Mongo v3.2+ syntax with some $map and $filter magic:
db.collection.aggregate([
{
$match: {
update_at: {
"$gte": "2019-05-03T00:00:00.0Z",
"$lt": "2019-05-05T00:00:00.0Z"
},
id: {"$in": [166]}
}
},
{
$group: {
_id: {$substr: ["$update_at", 11, 2]},
count: {"$sum": 1}
}
},
{
$group: {
_id: null,
hours: {$push: {hour: "$_id", count: "$count"}}
}
},
{
$addFields: {
hours: {
$map: {
input: {
$concatArrays: [
"$hours",
{
$map: {
input: {
$filter: {
input: ["00", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23"],
as: "missingHour",
cond: {
$not: {
$in: [
"$$missingHour",
{
$map: {
input: "$hours",
as: "hourObj",
in: "$$hourObj.hour"
}
}
]
}
}
}
},
as: "missingHour",
in: {hour: "$$missingHour", count: 0}
}
}
]
},
as: "hourObject",
in: {
count: "$$hourObject.count",
hour: {
$cond: [
{$eq: [{$substr: ["$$hourObject.hour", 0, 1]}, "0"]},
{$concat: ["$$hourObject.hour", " AM"]},
{
$concat: [{
$switch: {
branches: [
{case: {$eq: ["$$hourObject.hour", "13"]}, then: "1"},
{case: {$eq: ["$$hourObject.hour", "14"]}, then: "2"},
{case: {$eq: ["$$hourObject.hour", "15"]}, then: "3"},
{case: {$eq: ["$$hourObject.hour", "16"]}, then: "4"},
{case: {$eq: ["$$hourObject.hour", "17"]}, then: "5"},
{case: {$eq: ["$$hourObject.hour", "18"]}, then: "6"},
{case: {$eq: ["$$hourObject.hour", "19"]}, then: "7"},
{case: {$eq: ["$$hourObject.hour", "20"]}, then: "8"},
{case: {$eq: ["$$hourObject.hour", "21"]}, then: "9"},
{case: {$eq: ["$$hourObject.hour", "22"]}, then: "10"},
{case: {$eq: ["$$hourObject.hour", "23"]}, then: "11"},
],
default: "None"
}
}, " PM"]
}
]
}
}
}
}
}
},
{
$unwind: "$hours"
},
{
$project: {
_id: 0,
hour: "$hours.hour",
count: "$hours.count"
}
},
{
$sort: {
hour: 1
}
}
]);
A short explanation of the $addFields stage: we first add hours that we're missing, we then merge the two arrays (of the original found hours and the "new" missing hours), finally we convert to the required output ("01" to "01 AM").
If you're using Mongo v4+ I recommend you change the $group _id stage to use $dateFromString as its more consistent.
_id: {$hour: {$dateFromString: {dateString: "$update_at"}}}
If you do do that, you'll have to update the $filter and $map section to use numbers and not strings and eventually using $toString to cast into the format you want, hence the v4+ requirement.
You should store date values as Date objects instead of strings. I would do the formatting like this:
db.collection.aggregate(
[
{ $match: { ... } },
{
$group: {
_id: { h: { $hour: "$update_at" } },
count: { $sum: 1 }
}
},
{
$project: {
_id: 0,
hour: {
$switch: {
branches: [
{ case: { $lt: ["$_id.h", 10] }, then: { $concat: ["0", { $toString: "$_id.h" }, " AM"] } },
{ case: { $lt: ["$_id.h", 13] }, then: { $concat: [{ $toString: "$_id.h" }, " AM"] } },
{ case: { $lt: ["$_id.h", 22] }, then: { $concat: ["0", { $toString: { $subtract: ["$_id.h", 12] } }, " PM"] } },
{ case: { $lt: ["$_id.h", 24] }, then: { $concat: [{ $toString: { $subtract: ["$_id.h", 12] } }, " PM"] } }
]
}
},
hour24: "$_id.h",
count: 1
}
},
{ $sort: { hour24: 1 } }
])
As non-American I am not familiar with AM/PM rules, esp. for midnight and midday but I guess you get the principle.
Here is the query you can test it out, for MongoDB 4.0+
i will be improving query and update
const query = [{
$match: {
update_at: {
"$gte": ISODate("2019-05-03T00:00:00.0Z"),
"$lt": ISODate("2019-05-05T00:00:00.0Z")
},
id: {
"$in": [
166
]
}
}
},
{
$group: {
_id: { $hour: "$update_at" },
count: {
"$sum": 1
}
},
},
{
$addFields: {
hourStr: { $toString: { $cond: { if: { $gte: ["$_id", 12] }, then: { $subtract: [12, { $mod: [24, '$_id'] }] }, else: "$_id" } } },
}
},
{
$project: {
formated: { $concat: ["$hourStr", { $cond: { if: { $gt: ["$_id", 12] }, then: " PM", else: " AM" } }] },
count: "$count",
hour: 1,
}
}]
If you want to output in Indian Time formate. then below code work!
const query = [
{
$match: {
update_at: {
"$gte": ISODate("2019-05-03T00:00:00.0Z"),
"$lt": ISODate("2019-05-05T00:00:00.0Z")
},
id: {
"$in": [
166
]
}
}
},
{
$project: {
"h": { "$hour": { date: "$update_at", timezone: "+0530" } },
}
},
{
$group:
{
_id: { $hour: "$h" },
count: { $sum: 1 }
}
}
];
Data in mongo
[{
"_id": "5d71d1432f7c8151c58c4481",
"payment": {
"transactions": [
{
"_id": "5d71d1ff2f7c8151c58c44cf",
"method": "paytm",
"amount": 100,
"paymentOn": "2019-09-06T03:26:44.959Z"
},
{
"_id": "5d71d1ff2f7c8151c58c44ce",
"method": "cash",
"amount": 650,
"paymentOn": "2019-09-06T03:26:55.531Z"
}
],
"status": "partial"
},
"customer": "5d66c434c24f2b1fb6772014",
"order": {
"orderNumber": "WP-ORD-06092019-001",
"total": 770,
"balance": 20
}
},
{
"_id": "5d71d1432f7c8151c58c4481",
"payment": {
"transactions": [
{
"_id": "5d71d1ff2f7c8151c58c44cf",
"method": "paytm",
"amount": 100,
"paymentOn": "2019-09-06T03:26:44.959Z"
}
],
"status": "partial"
},
"customer": "5d66c434c24f2b1fb6772014",
"order": {
"orderNumber": "WP-ORD-06092019-001",
"total": 200,
"balance": 100
}
}]
I want to aggregate payments by method.
So the result would look like below:
Output:
Paytm: 200
Cash : 650
Unpaid(Balance): 120
I have tried:
[
{
'$unwind': {
'path': '$payment.transactions',
'preserveNullAndEmptyArrays': true
}
}, {
'$project': {
'amount': '$payment.transactions.amount',
'method': '$payment.transactions.method'
}
}, {
'$group': {
'_id': '$method',
'amount': {
'$sum': '$amount'
}
}
}
]
But how to include balance calculation as well
Using the above dataset, use the aggregate pipeline for calculation using aggregate as:
db.collection.aggregate([
{
$facet: {
paidAmounts: [
{ '$unwind': { 'path': '$payment.transactions', 'preserveNullAndEmptyArrays': true } },
{
$group: {
_id: "$payment.transactions.method",
amount: {
$sum: "$payment.transactions.amount"
}
}
}
],
leftAmounts: [
{
$group: {
_id: null,
balance: {
$sum: "$order.balance"
}
}
}
]
}
}
])
giving output:
here leftAmounts has left balance and paidAmounts having grouped paid data on basis of payment type
[
{
"leftAmounts": [
{
"_id": null,
"balance": 120
}
],
"paidAmounts": [
{
"_id": "cash",
"amount": 650
},
{
"_id": "paytm",
"amount": 200
}
]
}
]
Working solution : https://mongoplayground.net/p/7IWELKKMsWe
db.collection.aggregate([
{
"$unwind": "$payment.transactions"
},
{
"$group": {
"_id": "$_id",
"balance": {
"$first": "$order.balance"
},
"paytm": {
"$sum": {
"$cond": [
{
"$eq": [
"$payment.transactions.method",
"paytm"
]
},
"$payment.transactions.amount",
0
]
}
},
"cash": {
"$sum": {
"$cond": [
{
"$eq": [
"$payment.transactions.method",
"cash"
]
},
"$payment.transactions.amount",
0
]
}
}
}
},
{
"$group": {
"_id": null,
"balance": {
"$sum": "$balance"
},
"cash": {
"$sum": "$cash"
},
"paytm": {
"$sum": "$paytm"
}
}
}
])
I have a sample data like below:
(Every floor has multiple sensor data)
{"Floor_Id": "Galileo_001",
"name": "Forklifts",
"Sensor_data": [{
"Floor_Id": "Galileo_001",
"Floor_name": "Forklifts",
"Name": "forkLift_002",
"Asset_Id": 123,
"Load": 1.7096133,
"Timestamp": 1537878750996
},
{
"Floor_Id": "Galileo_001",
"Floor_name": "Forklifts",
"Name": "forkLift_003",
"Asset_Id": 456,
"Load": 1.7096133,
"Timestamp": 1537878750996,
},
{
"Floor_Id": "Galileo_001",
"Floor_name": "Forklifts",
"Name": "forkLift_005 ",
"Asset_Id": 127,
"Load": 1.7096133,
"Timestamp": 1537878750996
},
{
"Floor_Id": "Galileo_001",
"Floor_name": "Forklifts",
"Name": "forkLift_001",
"Asset_Id": 157,
"Load": 1.7096133,
"Timestamp": 1537878750996,
}
]}
For the response, I need total load calculated for every floor and individual load for every day. The desired response is like below:
{
"TotalLoad": 3214,
"Floor_Id": "Galileo_001",
"LoadUnit": "Kgs",
"AssetStatus": [{
"TotalLoad": 200,
"LoadUnit": "Kgs",
"Date": "1539588994"
}, {
"TotalLoad": 400,
"LoadUnit": "Kgs",
"Date": "1539475200"
}, {
"TotalLoad": 100,
"LoadUnit": "Kgs",
"Date": "1539388800"
}]
}
I am writing the below Mongo aggregation:
db.sensordata.aggregate([{"$unwind" : "$Sensor_data" },
{"$group": {
"_id": {"Floor_Id": "$Floor_Id",
"DailyDate":{"$dateFromParts":{
"year":{"$year":{"$add": [new Date("1970-01-01"), "$Sensor_data.Timestamp"]}},
"month":{"$month":{"$add": [new Date("1970-01-01"), "$Sensor_data.Timestamp"]}},
"day":{"$dayOfMonth":{"$add": [new Date("1970-01-01"), "$Sensor_data.Timestamp"]}}
}
}
},
"AssetLoad": {"$sum": "$Sensor_data.Load" }
}
},
{
"$group" : {
"_id": {"Floor_Id": "$_id.Floor_Id"},
"TotalLoad": { "$sum": "$Sensor_data.Load" },
"AssetStatus":{
"$push":{
"TotalLoad": "$AssetLoad",
"LoadUnit": "Kgs",
"Date": "$_id.DailyDate"
}
}
}
}
])
Problem:
For the total load calculated for every floor, I am getting 0.
{
"_id" : {
"Floor_Id" : "Galileo_001"
},
"TotalLoad" : 0,
"AssetStatus" : [
{
"TotalLoad" : 8.5480665,
"LoadUnit" : "Kgs",
"Date" : ISODate("2018-09-25T00:00:00.000Z")
}
]
}
What am I doing wrong here?
How can I get the desired output?
You're getting zero because the second $group pipeline does not recognise the field Sensor_data.Load so it defaults to 0.
Replace the expression
"TotalLoad": { "$sum": "$Sensor_data.Load" },
with the pipeline-changed field AssetLoad
"TotalLoad": { "$sum": "$AssetLoad" },
To calculate the number of sensors each floor has in total, your first pipeline needs to calculate the size of the
sensor data using $size, store that in a new field using $addFields then retain the field in the preceding pipeline stages pipelines by using the $first operators.audit
Amend your pipeline to the following:
db.sensordata.aggregate([
{ "$addFields": {
"TotalSensors": { "$size": "Sensor_data" }
} },
{"$unwind" : "$Sensor_data" },
{ "$group": {
"_id": {
"Floor_Id": "$Floor_Id",
"DailyDate": {
"$dateFromParts": {
"year": { "$year": {
"$add": [new Date("1970-01-01"), "$Sensor_data.Timestamp"]
} },
"month": { "$month": {
"$add": [new Date("1970-01-01"), "$Sensor_data.Timestamp"]
} },
"day": { "$dayOfMonth": {
"$add": [new Date("1970-01-01"), "$Sensor_data.Timestamp"]
} }
}
}
},
"AssetLoad": {"$sum": "$Sensor_data.Load" },
"TotalSensors": { "$first": "$TotalSensors" }
} },
{ "$group" : {
"_id": {"Floor_Id": "$_id.Floor_Id"},
"TotalLoad": { "$sum": "$Sensor_data.Load" },
"AssetStatus":{
"$push": {
"TotalLoad": "$AssetLoad",
"LoadUnit": "Kgs",
"Date": "$_id.DailyDate"
}
},
"TotalSensors": { "$first": "$TotalSensors" }
} }
])