We have the following set of documents stored in mongodb:
Conversation record:
{_id: "x", lang: "en", timestamp: "", ... }
Each conversation has many processes, each process has set of messages as child-document list.
Process record:
{_id: "y", conversationId: "x", name: "", timestamp: "", messages: [
{
"direction" : "out",
"text" : "How can I help you?",
"timestamp" : ISODate("2019-05-23T11:08:18.423Z"),
"_id" : 3
},
{
"direction" : "out",
"text" : "Hello",
"timestamp" : ISODate("2019-05-23T11:08:17.423Z"),
"_id" : 1
},
{
"direction" : "in",
"text" : "Hi",
"timestamp" : ISODate("2019-05-23T11:08:18.423Z"),
"_id" : 2
}
], completed: "true"}
I need to make aggregate query, and get list of conversations, while each conversation should have list of processes ordered by timestamp, and each process should have only the last message (based on id field) from both "in" and "out" directions.
We need to get something like this:
[
{
conversationId: "x",
timestamp: "",
processes: [
{
_id: "y",
name: "",
timestamp: "",
lastInMessage: {
"direction": "in",
"text": "Hi",
"timestamp": ISODate("2019-05-23T11:08:18.423Z"),
"_id": 2
},
lastOutMessage: {
"direction": "out",
"text": "How can I help you?",
"timestamp": ISODate("2019-05-23T11:08:18.423Z"),
"_id": 3
}
},
{
_id: "y",
name: "",
....
}
]
}
]
The query I tried is:
conversation.aggregate([
{
$match: {
timestamp: query.timestamp
}
},
{
$project: {
_id: "$conversationId",
timestamp: "$timestamp",
conversationId: "$conversationId"
}
},
{
"$lookup":
{
"from": "processes",
"localField": "conversationId",
"foreignField": "conversationId",
"as": "process"
}
},
{
$project: {
_id: "$_id",
timestamp: "$timestamp",
// messages: "$process.messages",
processes: "$process"
}
},
// here I don't know what to do.
You can use $reduce to calculate min and max value of an array and use $map to generate aggregates for all process values:
{
$project: {
_id: "$_id",
timestamp: "$timestamp",
processes: {
$map: {
input: "$processes",
as: "process",
in: {
_id: "$$process._id",
name: "$$process.name",
timestamp: "$$process.timestamp",
lastInMessage: {
$reduce: {
input: "$$process.messages",
initialValue: null,
in: {
$cond: [
{ $and: [ { $eq: [ "$$this.direction", "in" ] }, { $gt: [ "$$this.timestamp", "$$value.timestamp" ] } ] },
"$$this",
"$$value"
]
}
}
},
lastOutMessage: {
$reduce: {
input: "$$process.messages",
initialValue: null,
in: {
$cond: [
{ $and: [ { $eq: [ "$$this.direction", "out" ] }, { $gt: [ "$$this.timestamp", "$$value.timestamp" ] } ] },
"$$this",
"$$value"
]
}
}
}
}
}
}
}
}
MongoDB Playground example
Related
In the below collection, column "qty" holds the integer values but the datatype is string.
I want to compare the "qty" field with an integer in the aggregate and "warehouse" field with a string "A". ("qty" > 2 and "warehouse" = "A")
[Can't change the datatype in the collection to integer as huge dependency is present]
Edit : Need to retrieve all the columns and all the documents matching the criteria.
Query : getting improper results
db.runCommand(
{
aggregate: "products", pipeline: [
{
$match: {
instock: {
$elemMatch: {
warehouse: "A",
qty: { $gt: "2" }
}
}
}
},
{ $project: { _id: 0 } }],
cursor: { batchSize: 200 }
});
Result : not getting documents where item = journal though it satisfies the conditions
/* 1 */
{
"item" : "paper",
"instock" : [
{
"warehouse" : "A",
"qty" : "60"
},
{
"warehouse" : "B",
"qty" : "15"
}
]
},
/* 2 */
{
"item" : "planner",
"instock" : [
{
"warehouse" : "A",
"qty" : "22"
},
{
"warehouse" : "B",
"qty" : "5"
}
]
}
Products Collection
[
{
"item": "journal",
"instock": [
{
"warehouse": "A",
"qty": "11"
},
{
"warehouse": "C",
"qty": "15"
}
]
},
{
"item": "paper",
"instock": [
{
"warehouse": "A",
"qty": "60"
},
{
"warehouse": "B",
"qty": "15"
}
]
},
{
"item": "planner",
"instock": [
{
"warehouse": "A",
"qty": "22"
},
{
"warehouse": "B",
"qty": "5"
}
]
}
]
Getting improper results as greater than operator in this case is working lexicographically but it should work like integers. Though I tried converting that to double but I am getting no results.
Query with $convert to double : no result
db.runCommand(
{
aggregate: "products", pipeline: [
//{ $match: { "item": { $in: ["planner", "paper","journal"] } } },
{
$match: {
instock: {
$elemMatch: {
warehouse: "A",
qty: {
$gt: [
{$convert:{ input: "$qty", to: "double" }}, 5]
}
}
}
}
},
{ $project: { _id: 0 } }],
cursor: { batchSize: 200 }
});
Try this:
db.products.aggregate([
{
$unwind: "$instock"
},
{
$match: {
$expr: {
$and: [
{
$eq: [
"$instock.warehouse",
"A"
]
},
{
$gt: [
{
$toInt: "$instock.qty"
},
2
]
}
]
}
}
},
{
$group: {
_id: "$_id",
item: {
$first: "$item"
},
instock: {
$push: "$instock"
}
}
},
{
$project: {
_id: 0
}
}
])
MongoPlayground
Try this, it uses $filter to retain objects has criteria :
db.runCommand(
{
aggregate: "products", pipeline: [
{ $match: { 'instock.warehouse': 'A' } },
{
$addFields: {
instockCheck: {
$filter: {
input: '$instock', as: 'each', cond: {
$and: [{ $gt: [{ $toInt: '$$each.qty' }, 2] },
{ $eq: ['$$each.warehouse', 'A'] }]
}
}
}
}
}, { $match: { instockCheck: { $gt: [] } } }, { $project: { instockCheck: 0, _id: 0 } }],
cursor: { batchSize: 200 }
});
Test : MongoDB-Playground
Given the following Mongo collection called "members"
{
{name: "Joe", hobby: "Food"}, {name: "Lyn", hobby: "Food"},
{name: "Rex", hobby: "Play"}, {name: "Rex", hobby: "Shop"},...
}
I have an aggregation query that returns a paged set of records along with metadata for the total records found:
db.members.aggregate([
{
$facet: {
pipe1: [{ $count: 'count' }],
pipe2: [{ $skip: 0 }, { $limit: 4 }],
},
},
{
$unwind: '$pipe1',
},
{
$project: {
count: '$pipe1.count',
results: '$pipe2',
},
},
])
This gives me:
{count: 454, results: [<First 4 records here>]}
I am now trying to add to each record, an array of all member names that have the same hobby. So for the collection above, something like:
{
count: 454,
results: [
{name: "Joe", hobby: "Food", fanClub: ["Joe", "Lyn", "Alfred"]},
{name: "Lyn", hobby: "Food", fanClub: ["Joe", "Lyn", "Alfred"]},
{name: "Rex", hobby: "Play", fanClub: ["Rex"]},
{name: "Rex", hobby: "Shop", fanClub: ["Rex", "Rita"]}
]
}
I can't figure out how to run the follow up query within the aggregate. I've tried:
db.members.aggregate([
{
$facet: {
pipe1: [{ $count: 'count' }],
pipe2: [
{ $skip: 0 },
{ $limit: 2 },
{
$lookup: {
from: 'members',
pipeline: [{ $match: { hobby: '$hobby' } }],
as: 'fanClub',
},
},
],
},
},
{
$unwind: '$pipe1',
},
{
$project: {
count: '$pipe1.count',
results: '$pipe2',
},
},
])
Alas, the fanClub array is always empty.
Update 1
If I hardcode the hobby, for instance replace
{ $match: { hobby: '$hobby' }
with
{ $match: { hobby: 'Food' }
Then I do get results and all the fanClub arrays contain the results for Joe, Lyn and Alfred. So I must not be referring to the value within the pipeline correctly
Please try this :
db.membersHobby.aggregate([
{
$facet: {
pipe1: [{ $count: 'count' }],
pipe2: [{
$lookup:
{
from: "membersHobby",
let: { hobby: "$hobby" },
pipeline: [
{
$match:
{ $expr: { $eq: ["$hobby", "$$hobby"] } }
},
{ $project: { name: 1, _id: 0 } }
],
as: "fanClub"
}
}, { $skip: 0 }, { $limit: 4 }]
}
},
{
$unwind: '$pipe1'
},
{
$project: {
count: '$pipe1.count',
results: '$pipe2'
}
}
])
Result :
/* 1 */
{
"count" : 4,
"results" : [
{
"_id" : ObjectId("5e20a63ed3c98f2a7100fd4a"),
"name" : "Joe",
"hobby" : "Food",
"fanClub" : [
{
"name" : "Joe"
},
{
"name" : "Lyn"
}
]
},
{
"_id" : ObjectId("5e20a63ed3c98f2a7100fd4b"),
"name" : "Lyn",
"hobby" : "Food",
"fanClub" : [
{
"name" : "Joe"
},
{
"name" : "Lyn"
}
]
},
{
"_id" : ObjectId("5e20a63ed3c98f2a7100fd4c"),
"name" : "Rex",
"hobby" : "Play",
"fanClub" : [
{
"name" : "Rex"
}
]
},
{
"_id" : ObjectId("5e20a63ed3c98f2a7100fd4d"),
"name" : "Rex",
"hobby" : "Shop",
"fanClub" : [
{
"name" : "Rex"
}
]
}
]
}
If #srinivasy's answer meets your requierements, please grant my points him :)
If you want to get such structure:
{
count: 454,
results: [
{name: "Joe", hobby: "Food", fanClub: ["Joe", "Lyn", "Alfred"]},
{name: "Lyn", hobby: "Food", fanClub: ["Joe", "Lyn", "Alfred"]},
{name: "Rex", hobby: "Play", fanClub: ["Rex"]},
{name: "Rex", hobby: "Shop", fanClub: ["Rex", "Rita"]}
]
}
Use this query ($reduce is used to return single value, in you case fanClub as array):
db.members.aggregate([
{
$facet: {
pipe1: [
{
$count: "count"
}
],
pipe2: [
{
$skip: 0
},
{
$limit: 4
},
{
$lookup: {
from: "members",
let: {
hobby: "$hobby"
},
pipeline: [
{
$match: {
$expr: {
$eq: [
"$hobby",
"$$hobby"
]
}
}
}
],
as: "fanClub"
}
}
]
}
},
{
$unwind: "$pipe1"
},
{
$project: {
count: "$pipe1.count",
results: {
$map: {
input: "$pipe2",
as: "pipe2",
in: {
_id: "$$pipe2._id",
hobby: "$$pipe2.hobby",
name: "$$pipe2.name",
fanClub: {
$reduce: {
input: "$$pipe2.fanClub",
initialValue: [],
in: {
$concatArrays: [
"$$value",
[
"$$this.name"
]
]
}
}
}
}
}
}
}
}
])
MongoPlayground
I have the following structure as an input from which data needs to be aggregated:
I need to aggregate the data such that I end up with the following structure:
start: A {
tripdetails: [{
destination: B [{
duration: 10,
type: male
},
duration: 12,
type: female
},
duration: 9,
type: female
}]
]}
}
Basically I need to group "type" and "duration" together under the same destination.
I came up with the following query, but this results in a a single field for "type" for each "destination", but not for every "duration".
db.test.aggregate(
{
$group: {
_id: {"StationID": "$start", "EndStationID": "$destination"},
durations: {$addToSet: "$duration" },
usertypes: {$addToSet: "$type" }
}
},
{
$group: {
_id: "$_id.StationID",
Tripcount_out: {$sum: "durations"},
Trips_out: { $addToSet: { EndStationID: "$_id.EndStationID", Tripduration: "$durations", Usertype: "$usertypes"} }
}
}
)
My question is how I can achieve the structure described above.
You could try running the following aggregate pipeline:
db.test.aggregate([
{
"$group": {
"_id": { "StationID": "$start", "EndStationID": "$destination" },
"details": {
"$push": {
"duration": "$duration",
"type": "$type"
}
}
}
},
{
"$group": {
"_id": "$_id.StationID",
"tripdetails": {
"$push": {
"destination": "$_id.EndStationID",
"trips": "$details"
}
}
}
}
])
which yields:
{
"_id" : "A",
"tripdetails" : [
{
"destination" : "B",
"trips" : [
{
"duration" : 10,
"type" : "male"
},
{
"duration" : 9,
"type" : "female"
},
{
"duration" : 12,
"type" : "female"
}
]
}
]
}
I need to export customer records from database of mongoDB. Exported customer records should not have duplicated values. "firstName+lastName+code" is the key to DE-duped the record and If there are two records present in database with same key then I need to give preference to source field with value other than email.
customer (id,firstName,lastName,code,source) collection is this.
If there are record 3 records with same unique key and 3 different sources then i need to choose only one record between 2 sources(TV,internet){or if there are n number of sources i need the one record only}not with the 'email'(as email will be choosen when only one record is present with the unique key and source is email)
query using:
db.customer.aggregate([
{
"$match": {
"active": true,
"dealerCode": { "$in": ["111391"] },
"source": { "$in": ["email", "TV", "internet"] }
}
},
{
$group: {
"_id": {
"firstName": "$personalInfo.firstName",
"lastName": "$personalInfo.lastName",
"code": "$vehicle.code"
},
"source": {
$addToSet: { "source": "$source" }
}
}
},
{
$redact:
{
$cond: [
{ $eq: [{ $ifNull: ["$source", "other"] }, "email"] },
"$$PRUNE",
"$$DESCEND"
]
}
},
{
$project:
{
"source":
{
$map:
{
"input": {
$cond: [
{ $eq: [{ $size: "$source" }, 0] },
[{ "source": "email" }],
"$source"
]
},
"as": "inp",
"in": "$$inp.source"
}
},
"record": { "_id": 1 }
}
}
])
sample output:
{ "_id" : { "firstName" : "sGI6YaJ36WRfI4xuJQzI7A==", "lastName" : "99eQ7i+uTOqO8X+IPW+NOA==", "code" : "1GTHK23688F113955" }, "source" : ["internet"] }
{ "_id" : { "firstName" : "WYDROTF/9vs9O7XhdIKd5Q==", "lastName" : "BM18Uq/ltcbdx0UJOXh7Sw==", "code" : "1G4GE5GV5AF180133" }, "source" : ["internet"] }
{ "_id" : { "firstName" : "id+U2gYNHQaNQRWXpe34MA==", "lastName" : "AIs1G33QnH9RB0nupJEvjw==", "code" : "1G4GE5EV0AF177966" }, "source" : ["internet"] }
{ "_id" : { "firstName" : "qhreJVuUA5l8lnBPVhMAdw==", "lastName" : "petb0Qx3YPfebSioY0wL9w==", "code" : "1G1AL55F277253143" }, "source" : ["TV"] }
{ "_id" : { "firstName" : "qhreJVuUA5l8lnBPVhMAdw==", "lastName" : "6LB/NmhbfqTagbOnHFGoog==", "code" : "1GCVKREC0EZ168134" }, "source" : ["TV", "internet"] }
This is a problem with this query please suggest :(
Your code doesn't work, because $cond is not an accumulator operator. Only these accumulator operators, can be used in a $group stage.
Assuming your records contain not more than two possible values of source as you mention in your question, you could add a conditional $project stage and modify the $group stage as,
Code:
db.customer.aggregate([
{
$group: {
"_id": {
"id": "$id",
"firstName": "$firstName",
"lastName": "$lastName",
"code": "$code"
},
"sourceA": { $first: "$source" },
"sourceB": { $last: "$source" }
}
},
{
$project: {
"source": {
$cond: [
{ $eq: ["$sourceA", "email"] },
"$sourceB",
"$sourceA"
]
}
}
}
])
In case there can be more that two possible values for source, then you could do the following:
Group by the id, firstName, lastName and code. Accumulate
the unique values of source, using the $addToSet operator.
Use $redact to keep only the values other than email.
Project the required fields, if the source array is empty(all the elements have been removed), add a
value email to it.
Unwind the source field to list it as a field and not an array.
(optional)
Code:
db.customer.aggregate([
{
$group: {
"_id": {
"id": "$id",
"firstName": "$firstName",
"lastName": "$lastName",
"code": "$code"
},
"sourceArr": { $addToSet: { "source": "$source" } }
}
},
{
$redact: {
$cond: [
{ $eq: [{ $ifNull: ["$source", "other"] }, "email"] },
"$$PRUNE",
"$$DESCEND"
]
}
},
{
$project: {
"source": {
$map: {
"input":
{
$cond: [
{ $eq: [{ $size: "$sourceArr" }, 0] },
[{ "source": "item" }],
"$sourceArr"]
},
"as": "inp",
"in": "$$inp.source"
}
}
}
}
])
Here's the structure part of my collection:
_id: ObjectId("W"),
names: [
{
number: 1,
subnames: [ { id: "X", day: 1 }, { id: "Y", day: 10 }, { id: "Z", day: 2 } ],
list: ["A","B","C"],
day: 1
},
{
number: 2,
day: 5
},
{
number: 3,
subnames: [ { id: "X", day: 8 }, { id: "Z", day: 5 } ],
list: ["A","C"],
day: 2
},
...
],
...
I use this request:
db.publication.aggregate( [ { $match: { _id: ObjectId("W") } }, { $group: { _id: "$_id", SizeName: { $first: { $size: { $ifNull: [ "$names", [] ] } } }, names: { $first: "$names" } } }, { $unwind: "$names" }, { $sort: { "names.day": 1 } }, { $group: { _id: "$_id", SzNames: { $sum: 1 }, names: { $push: { number: "$names.number", subnames: "$names.subnames", list: "$names.list", SizeList: { $size: { $ifNull: [ "$names.list", [] ] } } } } } } ] );
but I would now use $sort for my names array AND my subnames array to obtain this result (subnames may not exist) :
_id: ObjectId("W"),
names: [
{
number: 2,
SizeList: 0,
day: 5
},
{
number: 3,
subnames: [ { id: "Z", day: 5 }, { id: "X", day: 8 } ],
list: ["A","C"],
SizeList: 2,
day: 2
},
{
number: 1,
subnames: [ { id: "X", day: 1 }, { id: "Z", day: 2 }, { id: "Y", day: 10 } ],
list: ["A","B","C"],
SizeList: 3,
day: 1
}
...
],
...
Can you help me ?
You can do this, but with great difficulty. I for one would gladly vote for an inline version of $sort along the lines of the $map operator. That would makes things so much easier.
For now though you need to de-construct and re-build the arrays after sorting. And you have to be very careful about this. Hence make false arrays with a single entry before processing $unwind:
db.publication.aggregate([
{ "$project": {
"SizeNames": {
"$size": {
"$ifNull": [ "$names", [] ]
}
},
"names": { "$ifNull": [{ "$map": {
"input": "$names",
"as": "el",
"in": {
"SizeList": {
"$size": {
"$ifNull": [ "$$el.list", [] ]
}
},
"SizeSubnames": {
"$size": {
"$ifNull": [ "$$el.subnames", [] ]
}
},
"number": "$$el.number",
"day": "$$el.day",
"subnames": { "$ifNull": [ "$$el.subnames", [0] ] },
"list": "$$el.list"
}
}}, [0] ] }
}},
{ "$unwind": "$names" },
{ "$unwind": "$names.subnames" },
{ "$sort": { "_id": 1, "names.subnames.day": 1 } },
{ "$group": {
"_id": {
"_id": "$_id",
"SizeNames": "$SizeNames",
"names": {
"SizeList": "$names.SizeList",
"SizeSubnames": "$names.SizeSubnames",
"number": "$names.number",
"list": "$names.list",
"day": "$names.day"
}
},
"subnames": { "$push": "$names.subnames" }
}},
{ "$sort": { "_id._id": 1, "_id.names.day": 1 } },
{ "$group": {
"_id": "$_id._id",
"SizeNames": { "$first": "$_id.SizeNames" },
"names": {
"$push": { "$cond": [
{ "$ne": [ "$_id.names.SizeSubnames", 0 ] },
{
"number": "$_id.names.number",
"subnames": "$subnames",
"list": "$_id.names.list",
"SizeList": "$_id.names.SizeList",
"day": "$_id.names.day"
},
{
"number": "$_id.names.number",
"list": "$_id.names.list",
"SizeList": "$_id.names.SizeList",
"day": "$_id.names.day"
}
]}
}
}},
{ "$project": {
"SizeNames": 1,
"names": {
"$cond": [
{ "$ne": [ "$SizeNames", 0 ] },
"$names",
[]
]
}
}}
])
You can kind of "hide away" the original empty array from the inner document as shown, but it's really difficult to remove all presence of the outer "names" array without pulling a similar conditional array "push" technique, and that really isn't a practical approach.
If all of this is just about sorting array elements in individual documents though, the aggregation framework should not be the tool to do this. It can be done as shown, but per document this is much easier to do in client side code.
Output:
{
"_id" : ObjectId("54b5cff8102f292553ce9bb5"),
"SizeNames" : 3,
"names" : [
{
"number" : 1,
"subnames" : [
{
"id" : "X",
"day" : 1
},
{
"id" : "Z",
"day" : 2
},
{
"id" : "Y",
"day" : 10
}
],
"list" : [
"A",
"B",
"C"
],
"SizeList" : 3,
"day" : 1
},
{
"number" : 3,
"subnames" : [
{
"id" : "Z",
"day" : 5
},
{
"id" : "X",
"day" : 8
}
],
"list" : [
"A",
"C"
],
"SizeList" : 2,
"day" : 2
},
{
"number" : 2,
"SizeList" : 0,
"day" : 5
}
]
}