Just assume following data:
{_id:1,hotelcode:a,availdates:["2020-01-02","2020-02-03"]}
{_id:2,hotelcode:a,availdates:["2020-02-03"]}
{_id:3,hotelcode:b,availdates:[]}
{_id:4,hotelcode:b,availdates:["2020-01-02"]}
{_id:5,hotelcode:c,availdates:["2020-01-02","2020-02-03"]}
I wanna achieve:
select hotelcode,count(hotelcode) from table group by hotelcode where availdates.length>0
What should I do?
I tried:
db.getCollection('spl_rate_27').aggregate([
{$project:{
adlength:{$size:"$avail_dates"}}
},
{$match:{adlength:{$gt:1}}},
{$group:{_id:{hotelcode:"$hotel_code"},total:{$sum:1}}}
])
But I got :
{
"_id" : {
"hotelcode" : null
},
"total" : 99999,0
}
It seems something was wrong...But I can't find it out....
You can do something like following, first get the objects whose availdates is greater than 0
[
{
$match: {
$expr: {
$gt: [
{
$size: "$availdates"
},
0
]
}
}
},
{
$group: {
_id: "$hotelcode",
total: {
$sum: 1
}
}
},
{
$project: {
_id: 0,
hotelcode: "$_id",
total: 1
}
}
]
Working Mongo playground
There are two things you can change.
Instead of $project use $addFields - project restricts fields, addFields adds field to the document
Then use $gte in the query as you need >0.
play
db.collection.aggregate([
{
$addFields: {
adlength: {
$size: "$availdates" //misspelled
}
}
},
{
$match: {
adlength: {
$gte: 1
}
}
},
{
$group: {
_id: {
hotelcode: "$hotelcode" //misspelled
},
total: {
$sum: 1
}
}
}
])
Well , I got inspiration from #Gibbs' answer. And I changed a bit my script:
db.getCollection('table').aggregate([
{$project:{
hotelcode:1, ##I omit this!!!
adlength:{$size:"$availdates"}}
},
{$match:{"adlength":{$gt:0}}},
{$group:{_id:{hotelcode:"$hotelcode"},total:{$sum:1}}}
])
And it works perfectly!
I hope this is what you are expecting.
db.collection.aggregate({
$match: {
"availdates": {
"$gt": "1"
}
}
},
{
$group: {
_id: "$hotelcode",
"records": {
$push: "$$ROOT"
},
"dataCount": {
$sum: 1
}
}
})
Working demo url : Mongo Playground URL
Related
There are two collections (view and click) like following:
# View collection
_id publisher_id created_at
617f8ea98e0f54f05e10e796 1 2021-11-01T00:00:00.000Z
617f8eab8e0f54f05e10e798 1 2021-11-01T00:00:00.000Z
617f8eac8e0f54f05e10e79a 1 2021-11-01T00:00:00.000Z
617f90cea187d30ebbecdee9 2 2021-11-01T00:00:00.000Z
# Click collection
_id publisher_id created_at
617f8ea98e0f54f05e10e796 1 2021-11-01T00:00:00.000Z
617f8eab8e0f54f05e10e798 2 2021-11-01T00:00:00.000Z
How can I get the following expected results with one query?
(or)
What is the best way for the following expected results?
# Expected For Publisher ID(1)
_id view_count click_count
2021/11/1 3 1
# Expected For Publisher ID(2)
_id view_count click_count
2021/11/1 1 1
Currently, I am using 2 queries for both collections and combining results as one in code.
For View
db.view.aggregate([
/*FirstStage*/
{
$match:
{
"$and":
[
{
"publisher_id": 1
},
{
"created_at": {$gte: new ISODate("2021-11-01"), $lt: new ISODate("2021-11-28")}
}
]
}
},
/*SecondStage*/
{
$group:
{
_id: {$dateToString: {format: '%Y/%m/%d', date: "$created_at"}},
count: {
$sum: 1
}
}
}
])
For Click
db.click.aggregate([
/*FirstStage*/
{
$match:
{
"$and":
[
{
"publisher_id": 1
},
{
"created_at": {$gte: new ISODate("2021-11-01"), $lt: new ISODate("2021-11-28")}
}
]
}
},
/*SecondStage*/
{
$group:
{
_id: {$dateToString: {format: '%Y/%m/%d', date: "$created_at"}},
count: {
$sum: 1
}
}
}
])
Because you are querying two different collections there is no "good" way to merge this into one query, the only way I can think of is using $facet, where the first stage is the "normal" one, and the other stage starts with a $lookup from the other collection.
This approach does add overhead, which is why I recommend to just keep doing the merge in code, however for the sake of answering here is a sample:
db.view.aggregate([
{
$facet: {
views: [
{
$match: {
"$and": [
{
"publisher_id": 1
},
{
"created_at": {
$gte: ISODate("2021-11-01"),
$lt: ISODate("2021-11-28")
}
}
]
}
},
],
clicks: [
{
$limit: 1
},
{
$lookup: {
from: "click",
let: {},
pipeline: [
{
$match: {
"$and": [
{
"publisher_id": 1
},
{
"created_at": {
$gte: ISODate("2021-11-01"),
$lt: ISODate("2021-11-28")
}
}
]
}
},
],
as: "clicks"
}
},
{
$unwind: "$clicks"
},
{
$replaceRoot: {
newRoot: "$clicks"
}
}
]
}
},
{
$project: {
merged: {
"$concatArrays": [
"$views",
"$clicks"
]
}
}
},
{
$unwind: "$merged"
},
{
$group: {
_id: {
$dateToString: {
format: "%Y/%m/%d",
date: "$merged.created_at"
}
},
count: {
$sum: 1
}
}
}
])
Mongo Playground
I have a aggregate query , which returns result like
{
count:1,
status: 'FAILED',
article_id: 1
},
{
count:1,
status: 'DELIVERED',
article_id: 1
}
I want to group by on the article_id and get the count based on the status , something like this:
{
article_id:1,
FAILED:1,
DELIVERED:2
}
How can i archive this?
Thanks in advance.
The other answers may work in principle, however they are limited hard-coded to status FAILED and DELIVERED.
In case you like to have a generic solution for arbitrary status, you can use this one:
db.collection.aggregate([
{ $set: { data: [{ k: "$status", v: "$count" }] } },
{
$replaceRoot: {
newRoot: {
$mergeObjects: [
{ $arrayToObject: "$data" }, { article_id: "$article_id" }
]
}
}
},
{
$group: {
_id: "$article_id",
status: { $push: "$$ROOT" }
}
},
{ $set: { status: { $mergeObjects: ["$status"] } } },
{ $replaceRoot: { newRoot: "$status" } },
])
Mongo playground
Try this code
db.getCollection('artwork').aggregate([
{
$group: {
_id: '$article_id',
FAILED: {
'$sum': {
"$cond": [{ "$eq": ["$status", "FAILED"] }, 1, 0]
}
},
DELIVERED: {
'$sum': {
"$cond": [{ "$eq": ["$status", "DELIVERED"] }, 1, 0]
}
}
}
}
])
mongoplayground
{
$group:{
_id:"$_id",
articleId:{$addToset:"$article_id"},
failed:{$addToset:"$failed"},
delivered:{$addToset:"$delivered"}
}
},
{ $addFields:{
article_id:{$size:articleId},
fail:{$size:"$faild"},
deliver:{$size:"$faild"},
}
In group $addToSet will return array and in addField will return total length of array. you cans earch $size in mongo
}
I have the data like below:
{
"order_id" : 1234567,
"order_pay_time" : 1437373297,
"pay_info" : [
{
"pay_type" : 0,
"pay_time" : 1437369046
},
{
"pay_type" : 0,
"pay_time" : 1437369123
},
{
"pay_type" : 0,
"pay_time" : 1437369348
}
]}
what I want to get is the last payment is of type 1, but $elemMatch just match the list where pay_type:1 exists, how can I match the orders which last payment is of "pay_type" : 1
You can use aggregation to get expected output. The query will be like following:
db.collection.aggregate({
$unwind: "$pay_info"
}, {
$match: {
"pay_info.pay_type": 1
}
}, {
$group: {
_id: "$_id",
"pay_info": {
$push: "$pay_info"
},
"order_id": {
$first: "$order_id"
},
"order_pay_time": {
$first: "$order_pay_time"
}
}
})
Moreover if you want latest pay_info.pay_time then you can sort it by descending order with limit 1, some what like following:
db.collection.aggregate({
$unwind: "$pay_info"
}, {
$match: {
"pay_info.pay_type": 1
}
}, {
$sort: {
"pay_info.pay_time": -1
}
}, {
$limit: 1
}, {
$group: {
_id: "$_id",
"pay_info": {
$push: "$pay_info"
},
"order_id": {
$first: "$order_id"
},
"order_pay_time": {
$first: "$order_pay_time"
}
}
})
Edit
Also you can use $redact to avoid $unwind like following:
db.collection.aggregate({
$match: {
"pay_info": {
$elemMatch: {
"pay_type": 1
}
}
}
}, {
$sort: {
"pay_info.pay_time": -1
}
}, {
$limit: 1
}, {
$redact: {
$cond: {
if: {
$eq: [{
"$ifNull": ["$pay_type", 1]
}, 1]
},
then: "$$DESCEND",
else: "$$PRUNE"
}
}
}).pretty()
Just found this thread for a similar problem I've had.
I ended up doing this, maybe that will be of interest to someone:
db.collection.find({
$where: function(){
return this.pay_info[this.pay_info.length-1].pay_type === 1
}
})
I want to return an aggregate of blog post tags and their total count. My blog posts are stored like so:
{
"_id" : ObjectId("532c323bb07ab5aace243c8e"),
"title" : "Fitframe.js - Responsive iframes made easy",
"tags" : [
"JavaScript",
"jQuery",
"RWD"
]
}
I'm then executing the following pipeline:
printjson(db.posts.aggregate(
{
$project: {
tags: 1,
count: { $add: 1 }
}
},
{
$unwind: '$tags'
},
{
$group: {
_id: '$tags',
count: {
$sum: '$count'
},
tags_lower: { $toLower: '$tags' }
}
},
{
$sort: {
_id: 1
}
}
));
So that the results are sorted correctly I need to sort on a lowercase version of each tag. However, when executing the above code I get the following error:
aggregate failed: {
"errmsg" : "exception: unknown group operator '$toLower'",
"code" : 15952,
"ok" : 0
}
Do I need to do another projection to add the lowercase tag?
Yes, you must add it to the projection. It will not work in the group, only specific operators like $sum ( http://docs.mongodb.org/manual/reference/operator/aggregation-group/ ) are counted as $group operators and capable of being used on that level of the group
You don't need to add another projection ... you could fix it when you do the $group:
db.posts.aggregate(
{
$project: {
tags: 1,
count: { $add: 1 }
}
},
{
$unwind: '$tags'
},
{
$group: {
_id: { tag: '$tags', lower: { $toLower : '$tags' } },
count: {
$sum: '$count'
}
}
},
{
$sort: {
"_id.lower": 1
}
}
)
In the above example, I've preserved the original name and added the lower case version to the _id.
Add another projection step between $unwind and $grop:
...
{$project: {
tags: {$toLower: '$tags'},
count: 1
}}
...
And remove tags_lower from $group
I'm trying to sum some values in an array of documents, with no luck.
This is the Document
db.Cuentas.find().pretty()
{
"Agno": "2013",
"Egresos": [
{
"Fecha": "28-01-2013",
"Monto": 150000,
"Detalle": "Pago Nokia Lumia a #josellop"
},
{
"Fecha": "29-01-2013",
"Monto": 4000,
"Detalle": "Cine, Pelicula fome"
}
],
"Ingresos": [],
"Mes": "Enero",
"Monto": 450000,
"Usuario": "MarioCares"
"_id": ObjectId(....)
}
So, i need the sum of all the "Monto" in "Egresos" for the "Usuario": "MarioCares". In this example 154000
Using aggregation i use this:
db.Cuentas.aggregate(
[
{ $match: {"Usuario": "MarioCares"} },
{ $group:
{
_id: null,
"suma": { $sum: "$Egresos.Monto" }
}
}
]
)
But i always get
{ "result" : [{ "_id" : null, "suma" : 0 }], "ok" : 1 }
What am i doing wrong ?
P.D. already see this and this
As Sammaye indicated, you need to $unwind the Egresos array to duplicate the matched doc per array element so you can $sum over each element:
db.Cuentas.aggregate([
{$match: {"Usuario": "MarioCares"} },
{$unwind: '$Egresos'},
{$group: {
_id: null,
"suma": {$sum: "$Egresos.Monto" }
}}
])
You can do also by this way. don't need to group just project your fields.
db.Cuentas.aggregate([
{ $match: { "Usuario": "MarioCares" } },
{
$project: {
'MontoSum': { $sum: "$Egresos.Monto" }
}
}
])
Since mongoDB version 3.4 you can use $reduce to sum array items:
db.collection.aggregate([
{
$match: {Usuario: "MarioCares"}
},
{
$project: {
suma: {
$reduce: {
input: "$Egresos",
initialValue: 0,
in: {$add: ["$$value", "$$this.Monto"]}
}
}
}
}
])
Playground example