I need to match one of two fields that must not be equal to zero. How to implement it?
I try these solutions but no luck:
Solution 1:
Model.aggregate[
{
$project: {
accountID: "$_id.accountID",
locationID: "$_id.locationID",
time: "$_id.time",
value: "$value",
actualValue: "$actualValue",
total: { $add: ["$value", "$actualValue"] },
},
},
{
$match: {
total: { $ne: 0 },
},
},
]
With this solution, it will wrong when a negative plus with the opposite version. Example -1500 + 1500 will become zero.
Solution 2
Model.aggregate([
{
$group: {
_id: {
accountID: "$accountID",
locationID: "$locationID",
time: "$time",
},
value: { $sum: "$values.val" },
actualValue: { $sum: "$values.actualVal" },
},
},
{
$addFields: {
absVal: { $abs: "$value" },
absActualVal: { $abs: "$actualValue" },
},
},
{
$project: {
accountID: "$_id.accountID",
locationID: "$_id.locationID",
time: "$_id.time",
value: "$value",
actualValue: "$actualValue",
total: { $add: ["$absVal", "$absActualVal"] },
},
},
{
$match: {
total: { $ne: 0 },
},
},
])
It works, but I lost 1 second from 3.5s to 4.5s when searching in 1m document.
Any suggestion? Thank you first
Some basic boolean logic should suffice, use something like:
Model.aggregate([
{
$match: {
$or: [
{
value: {$ne: 0}
},
{
actualValue: {$ne: 0}
}
]
}
}
{
$project: {
accountID: "$_id.accountID",
locationID: "$_id.locationID",
time: "$_id.time",
value: "$value",
actualValue: "$actualValue",
total: {$add: ["$value", "$actualValue"]},
},
}
])
If you care about efficiency make sure you have a compound index that covers both value and actualValue.
Related
Let's say I have this data:
{"Plane":"5546","Time":"55.0", City:"LA"}
{"Plane":"5548","Time":"25.0", City:"CA"}
{"Plane":"5546","Time":"6.0", City:"LA"}
{"Plane":"5548","Time":"5.0", City:"CA"}
{"Plane":"5555","Time":"15.0", City:"XA"}
{"Plane":"5555","Time":"8.0", City:"XA"}
and more but I just visualize the data
I want to calculate and group all the time and plane, this is expected output:
{"_id:":["5546","LA"],"Sum":2,"LateRate":1,"Prob"0.5}
The sum is sum all the time, Late is sum all the time with time > "15" and Prob is Late/Sum
The code I have tried but it still is missing something:
db.Collection.aggregate([
{
$project: {
Sum: 1,
Late: {
$cond: [{ $gt: ["$Time", 15.0] }, 1, 0]
},
prob:1
}
},
{
$group:{
_id:{Plane:"$Plane", City:"$City"},
Sum: {$sum:1},
Late: {$sum: "$Late"}
}
},
{
$addFields: {
prob: {
"$divide": [
"$Late",
"$Sum"
]
}
}
},
])
db.collection.aggregate([
{
$project: {
Time: 1,
Late: {
$cond: [
{
$gt: [
{
$toDouble: "$Time"
},
15.0
]
},
"$Time",
0
]
},
prob: 1,
Plane: 1,
City: 1
}
},
{
$group: {
_id: {
Plane: "$Plane",
City: "$City"
},
Sum: {
$sum: {
"$toDouble": "$Time"
}
},
Late: {
$sum: {
$toDouble: "$Late"
}
}
}
},
{
$addFields: {
prob: {
"$divide": [
"$Late",
"$Sum"
]
}
}
}
])
Project limits the fields passed to the next stage
On string, you cannot perform all relational/arithmetic operations
Playground
This is my first experience using aggregate pipeline. I'm not able to get a "$match" expression to work inside the pipeline. If I remove the "_id" match, I get every document in the collection past the start date, but once I add the $eq expression, it returns empty.
I read a lot of other examples and tried many different ways, and this seems like it is correct. But the result is empty.
Any suggestions?
let now = new Date()
let doc = await Team.aggregate([
{ $match: { created_by: mongoose.Types.ObjectId(req.params.user_oid)} },
{ $sort: { create_date: 1 } },
{ $lookup: {
from: 'events',
let: { "team_oid": "$team_oid" },
pipeline: [
{ $addFields: { "team_oid" : { "$toObjectId": "$team_oid" }}},
{ $match: {
$expr: {
$and: [
{ $gt: [ "$start", now ] },
{ $eq: [ "$_id", "$$team_oid" ] }
]
},
}
},
{
$sort: { start: 1 }
},
{
$limit: 1
}
],
as: 'events',
}},
{
$group: {
_id: "$_id",
team_name: { $first: "$team_name" },
status: { $first: "$status" },
invited: { $first: "$invited" },
uninvited: { $first: "$uninvited" },
events: { $first: "$events.action" },
dates: { $first: "$events.start" } ,
team_oid: { $first: "$events.team_oid" }
}
}])
Example Docs (added by request)
Events:
_id:ObjectId("60350837c57b3a15a414d265")
invitees:null
accepted:null
sequence:7
team_oid:ObjectId("60350837c57b3a15a414d263")
type:"Calendar Invite"
action:"Huddle"
status:"Questions Issued"
title:"Huddle"
body:"This is a Huddle; you should receive new questions 5 days befor..."
creator_oid:ObjectId("5ff9e50a206b1924dccd691e")
start:2021-02-26T07:00:59.999+00:00
end:2021-02-26T07:30:59.999+00:00
__v:0
Team:
_id:ObjectId("60350837c57b3a15a414d263")
weekly_schedule:1
status:"Live"
huddle_number:2
reminders:2
active:true
created_by:ObjectId("5ff9e50a206b1924dccd691e")
team_name:"tESTI"
create_date:2021-02-23T13:50:47.172+00:00
__v:0
This is just a guess since you don't have schema in your question. But it looks like your have some of your _ids mixed up. Where you are currently trying to $match events whose _id is equal to a team_oid. Rather than the event's team_oid field being equal to the current 'team' _id.
I'm pretty confident this will produce the correct output. If you post any schema or sample docs I will edit it.
https://mongoplayground.net/p/5i1w2Ii7KCR
let now = new Date()
let doc = await Team.aggregate([
{ $match: { created_by: mongoose.Types.ObjectId(req.params.user_oid)} },
{ $sort: { create_date: 1 } },
{ $lookup: {
from: 'events',
// Set tea_oid as the current team _id
let: { "team_oid": "$_id" },
pipeline: [
{ $match: {
$expr: {
$and: [
{ $gt: [ "$start", now ] },
// Match events whose 'team_oid' field matches the 'team' _id set above
{ $eq: [ "$team_oid", "$$team_oid" ] }
]
},
}
},
{
$sort: { start: 1 }
},
{
$limit: 1
}
],
as: 'events',
}},
{
$group: {
_id: "$_id",
team_name: { $first: "$team_name" },
status: { $first: "$status" },
invited: { $first: "$invited" },
uninvited: { $first: "$uninvited" },
events: { $first: "$events.action" },
dates: { $first: "$events.start" } ,
team_oid: { $first: "$events.team_oid" }
}
}])
My objective is to write an efficient query, that with the given input, gives me the expected output. I have some working solution, but all "types" are "manually" written, so I guess I'm looking for help to get the same output but in a different way.
input
reportId
type
weight
A
"fish"
4
A
"fish"
2
A
"cow"
0
B
"fish"
2
B
"tuna"
1
B
"bird"
Expected output
[
{
reportId: "A",
totalCount: 3,
totalWeight: 6,
fishCount: 2,
tunaCount: 0,
cowCount: 1,
birdCount: 0
},
{
reportId: "A",
totalCount: 3,
totalWeight: 2,
fishCount: 1,
tunaCount: 1,
cowCount: 0,
birdCount: 1
},
]
Partial "hard-coded" solution
What I have been doing so far is to create 2 group-by steps: It kind of get's the job done, but in my real use-case there are a lot of types, and therefore the group-stages are very long.
[
{
$group: {
_id: { reportId: "$reportId", type: $type },
count: { $sum: 1 },
totalWeight: { $sum: "$weight" }
}
},
{
$group: {
_id: "$_id.reportId",
totalCount: { $sum: "$totalCount" },
totalWeight: { $sum: "$totalWeight" },
fishCount: {
$sum: {
$cond: {
"if": { $eq: ["$_id.type", "fish"] },
then: "$count",
else: 0
}
}
},
tunaCount: {
$sum: {
$cond: {
"if": { $eq: ["$_id.type", "tuna"] },
then: "$count",
else: 0
}
}
},
// <== And here I have a count blog for each type. Can I get the same result in a better way?
}
}
]
I will focus to the second part, which is the difficult one. I don't know whether there is a shorter and better solution, but this one should work:
db.collection.aggregate([
{
$unset: "_id"
},
{
$set: {
data: {
"$objectToArray": "$$ROOT"
}
}
},
{
$group: {
_id: "$reportId",
data: {
$push: "$data"
}
}
},
{
$set: {
data: {
$reduce: {
input: "$data",
initialValue: [],
in: {
$concatArrays: [
"$$value",
"$$this"
]
}
}
}
}
},
{
$set: {
data: {
$filter: {
input: "$data",
cond: {
$not: {
$in: [
"$$this.k",
[
"totalCount",
"totalWeight"
]
]
}
}
}
}
}
},
{
$unwind: "$data"
},
{
$group: {
_id: "$_id",
data: {
$push: "$data"
}
}
},
{
$replaceRoot: {
newRoot: {
$arrayToObject: "$data"
}
}
}
])
See Mongo playground
I have the following stage in my MongoDB aggregation pipeline that returns the qty and sum of sales, which works fine:
{
$lookup: {
from: 'sales',
let: { part: '$_id' },
pipeline: [
{ $match: { $and: [{ $expr: { $eq: ['$partner', '$$part'] } }] } },
{ $group: { _id: null, qty: { $sum: 1 }, soldFor: { $sum: '$soldFor' } } },
{ $project: { _id: 0, qty: 1, soldFor: 1 } }],
as: 'sales'}},
{ $unwind: { path: '$sales', preserveNullAndEmptyArrays: true } },
{ $project: { _id: 1, sales: 1 }
}
However, if there are no sales, then the $project projection returns an empty sales object, but what I'd really like is it to return a completed object, but with 0 - like this:
{
sales: {
qty: 0,
soldFor: 0
}
}
You can use $cond operator here
{
"$project": {
"_id": 1,
"sales": {
"$cond": [
{ "$eq": [{ "$size": "$sales" }, 0] },
{
"sales": {
"qty": 0,
"soldFor": 0
}
},
"$sales"
]
}
}
}
I have documents in mongodb like this
{
_id: "5cfed55974c7c52ecc33ada8",
name: "Garona",
realm: "Blackrock",
faction: "Horde",
race: "Orc",
class: "Rogue",
guild: "",
level: 33,
lastSeen: "2019-06-10T00:00:00.000Z",
__v: 0
},
{
_id: "5cfed55974c7c52ecc33ade8",
name: "Muradin",
realm: "Alleria",
faction: "Alliance",
race: "Dwarf",
class: "Warrior",
guild: "Stormstout Brewing Co",
level: 42,
lastSeen: "2019-06-11T00:00:00.000Z",
__v: 0
}
What I'm trying to do, is to group by a fields and get a sum of it. So far I figured it out to do it for one field at once like so
{
$group: {
_id: {
classes: '1',
class: '$class'
},
total: { $sum: 1 }
}
},
{
$group: {
_id: '$_id.classes',
total: { $sum: '$total' },
classes: {
$push: {
class: '$_id.class',
total: '$total'
}
}
}
}
Which produces something like this
{
_id: "1",
total: 40,
classes: [
{
class: "Warrior",
total: 17
},
{
class: "Rogue",
total: 23
}
}
But I want to do it for more than one field at once, so that I can get an output like this.
{
_id: "1",
total: 40,
classes: [
{
class: "Warrior",
total: 17
},
{
class: "Rogue",
total: 23
},
factions: [
{
faction: "Alliance",
total: 27
},
{
faction: "Horde",
total: 13
}
}
No I'm wondering if it is even possible to do it in one query in an easy way or if I would be better to do a seperate query for each field.
You can do this by using the $facet aggregation stage
Processes multiple aggregation pipelines within a single stage on the same set of input documents. Each sub-pipeline has its own field in the output document where its results are stored as an array of documents.
I only slightly modified your original pipeline, and then just copied it for the 'factions' field.
The last 3 stages in my solution aren't really necessary, they just clean up the output a little bit.
You can probably take it from here, good luck.
db.collection.aggregate([
{
"$facet": {
"classes": [
{
$group: {
_id: "$class",
total: {
$sum: 1
}
}
},
{
$group: {
_id: null,
total: {
$sum: "$total"
},
"classes": {
$push: {
class: "$_id",
total: "$total"
}
}
}
}
],
"factions": [
{
$group: {
_id: "$faction",
total: {
$sum: 1
}
}
},
{
$group: {
_id: null,
total: {
$sum: "$total"
},
"factions": {
$push: {
faction: "$_id",
total: "$total"
}
}
}
}
]
}
},
{
$unwind: "$classes"
},
{
$unwind: "$factions"
},
{
$project: {
"classes._id": 0,
"factions._id": 0
}
}
])
Output
[
{
"classes": {
"classes": [
{
"class": "Warrior",
"total": 1
},
{
"class": "Rogue",
"total": 1
}
],
"total": 2
},
"factions": {
"factions": [
{
"faction": "Alliance",
"total": 1
},
{
"faction": "Horde",
"total": 1
}
],
"total": 2
}
}
]