I have a database which has the following structure:
{
"_id" : ObjectId("59b8d72ab515211f3c161c4b"),
"Transport_event_id" : 1,
"Carrier_id" : 23,
"Payload_id" : 0,
"StartTime" : 214392.0,
"EndTime" : 362707.0,
"Move_events" : [
{
"Timestamp" : 214398,
"x_pos" : 13,
"y_pos" : 202
},{
"Timestamp" : 214845,
"x_pos" : 12,
"y_pos" : 202
},{
"Timestamp" : 216399,
"x_pos" : 12,
"y_pos" : 216
},{
"Timestamp" : 216842,
"x_pos" : 11,
"y_pos" : 216
},{
"Timestamp" : 219586,
"x_pos" : 10,
"y_pos" : 216
}
]
}
I've made the following query which will return the next 2 Elements form a Array after a specific TimeStamp.
var cursor = db.Transport_eventBeta.aggregate([
{ "$match": { "StartTime": { "$lte": query_time } } },
{ "$match": { "EndTime": { "$gte": query_time } } },
{
"$project": {
"Move_events": {
"$let": {
"vars": {
"filtered": {
"$filter": {
"input": "$Move_events",
"as": "event",
"cond": { "$lte": ["$$event.Timestamp" , query_time] }
}
}
},
"in": {
"$slice": [
"$Move_events",
{"$size": "$$filtered"},
2
]
}
}
},
"Carrier_id": 1
}
}
])
while (cursor.hasNext()) {
print(cursor.next());
}
What I need are the documents befor and after this specific TimeStamp.
Some kind of this:
"$slice": [
"$Move_events",
{"$size": "$$filtered"} - 1,
2
]
But this doesn't work. How can I solve this problem? 2 separate queries are no option because of the duration.
You can try below aggregation query in 3.4.
The query will filter Move_events to keep events with timestamp less than input timestamp followed by $arrayElemAt to get the Move_events after and before event.
db.Transport_eventBeta.aggregatee([
{
"$match": {
"StartTime": {
"$lte": query_time
},
"EndTime": {
"$gte": query_time
}
}
},
{
"$project": {
"Move_events": {
"$let": {
"vars": {
"filtered": {
"$filter": {
"input": "$Move_events",
"as": "event",
"cond": {
"$lte": [
"$$event.Timestamp",
query_time
]
}
}
}
},
"in": [
{
"$arrayElemAt": [
"$Move_events",
{
"$subtract": [
{
"$size": "$$filtered"
},
1
]
}
]
},
{
"$arrayElemAt": [
"$Move_events",
{
"$size": "$$filtered"
}
]
}
]
}
}
}
}
])
Related
I have a collection of two-dimensional timeseries data as follows:
[
{
"value" : 9,
"timestamp" : "2020-12-30T02:06:33.000+0000",
"recipeId" : 15
},
{
"value" : 2,
"timestamp" : "2020-12-30T12:04:23.000+0000",
"recipeId" : 102
},
{
"value" : 5,
"timestamp" : "2020-12-30T15:09:23.000+0000",
"recipeId" : 102
},
...
]
The records have a recipeId which is the first level of grouping I'm looking for. All values for a day of a recipe should be summed up. I want an array of timeseries per recipeId. I need the missing days to be filled with a 0. I want this construct to be created for a provided start and end date range.
Some like this for date range of 2020-12-29 to 2020-12-31:
[
[
{
"sum" : 0,
"timestamp" : "2020-12-29",
"recipeId" : 15
},
{
"sum" : 9,
"timestamp" : "2020-12-30",
"recipeId" : 15
},
{
"sum" : 0,
"timestamp" : "2020-12-31",
"recipeId" : 15
},
...
],
[
{
"sum" : 0,
"timestamp" : "2020-12-29",
"recipeId" : 0
},
{
"sum" : 7,
"timestamp" : "2020-12-30",
"recipeId" : 102
},
{
"sum" : 0,
"timestamp" : "2020-12-31",
"recipeId" : 102
},
...
]
]
This is what I currently have and it's only partially solving my requirements. I can't manage to get the last few stages right:
[
{
"$match": {
"timestamp": {
"$gte": "2020-12-29T00:00:00.000Z",
"$lte": "2020-12-31T00:00:00.000Z"
}
}
},
{
"$addFields": {
"timestamp": {
"$dateFromParts": {
"year": { "$year": "$timestamp" },
"month": { "$month": "$timestamp" },
"day": { "$dayOfMonth": "$timestamp" }
}
},
"dateRange": {
"$map": {
"input": {
"$range": [
0,
{
"$trunc": {
"$divide": [
{
"$subtract": [
"2020-12-31T00:00:00.000Z",
"2020-12-29T00:00:00.000Z"
]
},
1000
]
}
},
86400
]
},
"in": {
"$add": [
"2020-12-29T00:00:00.000Z",
{ "$multiply": ["$$this", 1000] }
]
}
}
}
}
},
{ "$unwind": "$dateRange" },
{
"$group": {
"_id": { "date": "$dateRange", "recipeId": "$recipeId" },
"count": {
"$sum": { "$cond": [{ "$eq": ["$dateRange", "$timestamp"] }, 1, 0] }
}
}
},
{
"$group": {
"_id": "$_id.date",
"total": { "$sum": "$count" },
"byRecipeId": {
"$push": {
"k": { "$toString": "$_id.recipeId" },
"v": { "$sum": "$count" }
}
}
}
},
{ "$sort": { "_id": 1 } },
{
"$project": {
"_id": 0,
"timestamp": "$_id",
"total": "$total",
"byRecipeId": {
"$arrayToObject": {
"$filter": { "input": "$byRecipeId", "cond": "$$this.v" }
}
}
}
}
]
which results in:
[
{
"timestamp": "2020-12-29T00:00:00.000Z",
"total": 21,
"byRecipeId": {}
},
{
"timestamp": "2020-12-30T00:00:00.000Z",
"total": 0,
"byRecipeId": {
"15": 9,
"102": 7
}
},
{
"timestamp": "2020-12-31T00:00:00.000Z",
"total": 0,
"byRecipeId": {}
}
]
I'm open to alternative solution of course. For examples I came across this post: https://medium.com/#alexandro.ramr777/fill-missing-values-using-mongodb-aggregation-framework-f011114e83e0 but it doesn't deal with multi-dimensions.
You could use the $redcue function. This code fills the gabs of Minutes for current day. Should be easy to adapt it to give missing Days.
{
$addFields: {
data: {
$reduce: {
input: { $range: [0, 24 * 60] },
initialValue: [],
in: {
$let: {
vars: {
ts: {
$add: [
moment().startOf('day').toDate(),
{ $multiply: ["$$this", 1000 * 60] }
]
}
},
in: {
$concatArrays: [
"$$value",
[{
$cond: {
if: { $in: ["$$ts", "$data.timestamp"] },
then: {
$first: {
$filter: {
input: "$data",
cond: { $eq: ["$$this.timestamp", "$$ts"] }
}
}
},
else: { timestamp: "$$ts", total: 0 }
}
}]
]
}
}
}
}
}
}
}
In my opinion, $reduce is more elegant than $map, however based on my experience the performance is much worse with $reduce.
I have some documents in a collection which looks like this
{
"_id" : "5a2e50b32d43ba00010041e5",
account_id:"23232323"
status:"accepted",
keyname:"java"
},
{
"_id" : "5a2e54332d43ba00010041e5",
account_id:"2323233"
status:"pending",
keyname:"java"
},
{
"_id" : "5a2e54332d43ba00010041e5",
account_id:"23232sdsd3"
status:"pending",
keyname:"Nodejs"
}
I need to get the counts of the pending and accepted status for each keyname for a particular account_id
eg: should give a result like this.
{
keyname:"java",
pending:10,
accepted:10
}
This is the code that I have tried out
db.getCollection("programs").aggregate([
{ "$match": { "account_id": "1" } },
{ "$group": { "_id": "$keyname", "count": { "$sum": 1 } } },
{ "$match": { "_id": { "$ne": null } } }
])
which gives a result like this
{
"_id" : "java",
"count" : 3.0
},
{
"_id" : "nodejs",
"count" : 3.0
},
{
"_id" : "C#",
"count" : 3.0
}
You can use below aggregation
db.collection.aggregate([
{ "$match": { "account_id": "1" } },
{ "$group": {
"_id": "$keyname",
"accepted": {
"$sum": {
"$cond": [
{ "$eq": ["$status", "accepted"] },
0,
1
]
}
},
"pending": {
"$sum": {
"$cond": [
{ "$eq": ["$status", "pending"] },
0,
1
]
}
}
}}
])
I have a mongo db collection like below,
{
"id": ObjectId("132456"),
reading :[
{
"weight" : {
"measurement" : 82.0,
"unit" : "kg"
}
}
],
"date" : ISODate("2018-09-12T11:45:08.174Z")
},
{
"id": ObjectId("132457"),
reading :[
{
"weight" : {
"measurement" : 80.0,
"unit" : "kg"
}
}
],
"date" : ISODate("2018-09-12T10:45:08.174Z")
},
{
"id": ObjectId("132458"),
reading :[
{
"weight" : {
"measurement" : 85.0,
"unit" : "kg"
}
}
],
"date" : ISODate("2018-09-11T09:45:08.174Z")
}
I need a mongo db query that will give me the current weight and the weight difference between the current and next record.
Example output below,
{
"id": ObjectId("132456"),
"currentWeight": 75.0,
"weightDifference": 2.0,
"date" : ISODate("2018-09-12T11:45:08.174Z")
},
{
"id": ObjectId("132457"),
"currentWeight": 80.0,
"weightDifference": -5.0,
"date" : ISODate("2018-09-12T10:45:08.174Z")
}
I was not able to get the weight from next document to subtract the weight from current document.
Thanks in advance for your help
My try for the above problem,
db.measurementCollection.aggregate([
{
$match : { "date" : { $gte : new ISODate("2018-09-01T00:00:00.000Z") , $lte : new ISODate("2018-09-12T23:59:59.000Z") } }
},
{
$project : { "date" : 1 ,
"currentWeight" : {$arrayElemAt: [ "$reading.weight.measurement", 0 ]}
},
{ $sort: {"date":-1} },
{
$addFields : {
"weigtDifference" :
{
{
$limit: 2
},
{
$group: {
_id: null,
'count1': {$first: '$currentWeight'},
'count2': {$last: '$currentWeight'}
}
},
{
$subtract: ['$count1', '$count2']
}
}
}
}
])
You can try below aggregation but I will not recommend you to use this with the large data set.
db.collection.aggregate([
{ "$match": {
"date" : {
"$gte": new ISODate("2018-09-01T00:00:00.000Z"),
"$lte": new ISODate("2018-09-12T23:59:59.000Z")
}
}},
{ "$unwind": "$reading" },
{ "$sort": { "date": -1 }},
{ "$group": { "_id": null, "data": { "$push": "$$ROOT" }}},
{ "$project": {
"data": {
"$filter": {
"input": {
"$map": {
"input": { "$range": [0, { "$size": "$data" }] },
"as": "tt",
"in": {
"$let": {
"vars": {
"first": { "$arrayElemAt": ["$data", "$$tt"] },
"second": { "$arrayElemAt": ["$data", { "$add": ["$$tt", 1] }] }
},
"in": {
"currentWeight": "$$first.reading.weight.measurement",
"weightDifference": { "$subtract": ["$$second.reading.weight.measurement", "$$first.reading.weight.measurement"] },
"_id": "$$first._id",
"date": "$$first.date"
}
}
}
}
},
"cond": { "$ne": ["$$this.weightDifference", null] }
}
}
}
},
{ "$unwind": "$data" },
{ "$replaceRoot": { "newRoot": "$data" }}
])
I am new to MongoDb and would appreciate some help with this query. I wrote the following aggregation pipeline. I wrote the query from collection1 I got the output ("Conventional Energy" : 0.0036) and I wrote the query collection2 I got the output (LastMonthConsumption" : 2.08) but how to add two collection with single aggregation with(LastMonthConsumption" : 2.08 * Conventional Energy" : 0.0036/Conventional Energy" : 0.0036) this is my required output
I have this data in mongodb:
COLLECTION 1:DATA
{
"slcId" : "51",
"clientId" : "1",
"dcuId" : "1",
"type" : "L",
"officeId" : "200-24",
"lampStatus" : "OFF",
"cummulativeKWH" : 133.7,
"powerFactor" : 1.0,
"createDate" : ISODate("2018-09-06T00:01:34.816Z")
},
{
"slcId" : "52",
"clientId" : "1",
"dcuId" : "1",
"type" : "L",
"officeId" : "200-24",
"lampStatus" : "OFF",
"cummulativeKWH" : 133.7,
"powerFactor" : 1.0,
"createDate" : ISODate("2018-09-07T21:01:34.816Z")
}
COLLECTION2:DATA
{
"_class" : "MongoStreetLightMonthlyVo",
"timeId" : ISODate("2018-08-04T16:40:08.817Z"),
"vendor" : "CIMCON",
"slcId" : "123450",
"mongoStreetLightChildVo" : {
"totalConsumptionMtd" : 2.08,
"prevConsumptionMtd" : 3.45,
"perChargeKWH" : 9.85,
}
},
{
"_class" : "MongoStreetLightMonthlyVo",
"timeId" : ISODate("2018-09-04T16:40:08.817Z"),
"vendor" : "CIMCON",
"slcId" : "123450",
"mongoStreetLightChildVo" : {
"totalConsumptionMtd" : 2.08,
"prevConsumptionMtd" : 3.45,
"perChargeKWH" : 9.85,
}
}
Collection1:
db.collection1.aggregate([
{ $match:{"type" : "L"}},
{
$count: "TOTAL_Lights"
},
{ "$project": {
"Conventional Energy": {
"$divide": [
{ "$multiply": [
{ "$multiply": [ "$TOTAL_Lights" ,0.12 ] },
]},
1000
]
}
}},
])
output: {"Conventional Energy" : 0.0036}
Collection2:
db.collection2.aggregate(
[
// Stage 1
{
$group: {
_id:{year:{$year:"$timeId"},month:{$month:"$timeId"} },
LastMonthConsumption : {$sum:"$mongoStreetLightChildVo.totalConsumptionMtd"},
}
},
{
$redact: {
$cond: { if: { $and:[
{$eq: [ "$_id.year", {$year:new Date()} ]},
{$eq: [-1, {$subtract:[ "$_id.month", {$month:new Date()} ]}]}
]},
then: "$$KEEP",
else: "$$PRUNE"
}
}
},
{$project:{
_id:0,
LastMonthConsumption :1
}
}
]
);
output:{
"LastMonthConsumption" : 2.08
}
Expected output:
LastMonthConsumption - Conventional Energy/Conventional Energy*100
You can try below aggregation
db.collection2.aggregate([
{ "$group": {
"_id": { "year": { "$year": "$timeId" }, "month": { "$month": "$timeId" }},
"LastMonthConsumption": { "$sum": "$mongoStreetLightChildVo.totalConsumptionMtd" }
}},
{ "$redact": {
"$cond": {
"if": {
"$and": [
{ "$eq": ["$_id.year", { "$year": new Date() }] },
{ "$eq": [-1, { "$subtract": ["$_id.month", { "$month": new Date() }] }]
}
]
},
"then": "$$KEEP",
"else": "$$PRUNE"
}
}},
{ "$lookup": {
"from": "collection1",
"pipeline": [
{ "$match": { "type": "L" } },
{ "$count": "TOTAL_Lights" },
{ "$project": {
"ConventionalEnergy": {
"$divide": [{ "$multiply": [{ "$multiply": ["$TOTAL_Lights", 0.12] }] }, 1000]
}
}}
],
"as": "ConventionalEnergy"
}},
{ "$project": {
"_id": 0,
"totalConsumption": {
"$multiply": [
{
"$divide": [
{
"$subtract": [
"$LastMonthConsumption",
{ "$arrayElemAt": ["$ConventionalEnergy.ConventionalEnergy", 0] }
]
},
{ "$arrayElemAt": ["$ConventionalEnergy.ConventionalEnergy", 0] }
]
},
100
]
}
}}
])
My collection will look like this,
{
"_id" : ObjectId("591c5971240033283736860a"),
"status" : "Done",
"createdDate" : ISODate("2017-05-17T14:09:20.653Z")
"communications" : [
{
"communicationUUID" : "df07948e-4a14-468e-beb1-db55ff72b215",
"communicationType" : "CALL",
"recipientId" : 12345,
"createdDate" : ISODate("2017-05-18T14:09:20.653Z")
"callResponse" : {
"Status" : "completed",
"id" : "dsd45554545ds92a9bd2c12e0e6436d",
}
}
]}
{
"_id" : ObjectId("45sdsd59124003345121450a"),
"status" : "ToDo",
"createdDate" : ISODate("2017-05-17T14:09:20.653Z")
"communications" : [
{
"communicationUUID" : "45sds55-4a14-468e-beb1-db55ff72b215",
"communicationType" : "CALL",
"recipientId" : 1234,
"createdDate" : ISODate("2017-05-18T14:09:20.653Z")
"callResponse" : {
"Status" : "completed",
"id" : "84fe862f1924455dsds5556436d",
}
}
]}
Currently I am writing two aggregate query to achieve my requirement and my query will be below
db.collection.aggregate(
{ $project: {
dayMonthYear: { $dateToString: { format: "%d/%m/%Y", date: "$createdDate" } },
status: 1,
}},
{ $group: {
_id: "$dayMonthYear",
Pending: { $sum: { $cond : [{ $eq : ["$status", "ToDo"]}, 1, 0]} },
InProgress: { $sum: { $cond : [{ $eq : ["$status", "InProgress"]}, 1, 0]} },
Done: { $sum: { $cond : [{ $eq : ["$status", "Done"]}, 1, 0]} },
Total: { $sum: 1 }
}}
My output will be,
{"_id" : "17/05/2017", "Pending" : 1.0, "InProgress" : 0.0, "Done" : 1.0, "Total" : 2.0 }
Using above query I can able to get count but I need to find the count based on communication Status too so I am writing one more query to achieve,
db.collection.aggregate(
{"$unwind":"$communications"},
{ $project: {
dayMonthYear: { $dateToString: { format: "%d/%m/%Y", date: "$createdDate" } },
communications: 1
}},
{ "$group": {
_id: "$dayMonthYear",
"total_call": { $sum: { $cond : [{ $or : [ { $eq: [ "$communications.callResponse.Status", "failed"] },
{ $eq: [ "$communications.callResponse.Status", "busy"] },
{ $eq: [ "$communications.callResponse.Status", "completed"] },
{ $eq: [ "$communications.callResponse.Status", "no-answer"] }
]}, 1, 0 ] }},
"engaged": { $addToSet: { $cond : [{ $eq : ["$communications.callResponse.Status", "completed"]},
"$communications.recipientId", "null" ]} },
"not_engaged": { $addToSet: { $cond: [{ $or : [ { $eq: [ "$communications.callResponse.Status", "failed"] },
{ $eq: [ "$communications.callResponse.Status", "busy"] },
{ $eq: [ "$communications.callResponse.Status", "no-answer"] } ]},
"$communications.recipientId", "null" ] }}
}},
{ "$project": {
"_id": 1,
"total_call": 1,
"engaged": { "$setDifference": [ "$ngaged", ["null"] ] },
"not_engaged": { "$setDifference": [ "$not_engaged", ["null"] ] },
}},
{ "$project": {
"total_call": 1,
"engaged": { "$size": "$engaged" },
"not_engaged": { "$size": { "$setDifference": [ "$not_engaged", "$engaged" ] }},
}})
My output will be,
{"_id" : "18/05/2017", "total_call" : 2.0, "engaged" : 2, "not_engaged" : 0}
Using above query I can able to get count but I want to achieve it in single query
I am looking for output like
{"_id":"17/05/2017", "Pending" : 1.0, "InProgress" : 0.0, "Done" : 1.0, "total_call" : 0, "engaged" : 0, "not_engaged" : 0}
{"_id":"18/05/2017", "Pending" : 0.0, "InProgress" : 0.0, "Done" : 0.0, "total_call" : 2, "engaged" : 2, "not_engaged" : 0}
Can anyone suggest or provide me good way to get above result.
You can use $concatArrays to merge the status& createdDate documents followed by $group to count the occurrences.
db.collection.aggregate([
{
"$project": {
"statusandcreateddate": {
"$concatArrays": [
[
{
"status": "$status",
"createdDate": "$createdDate"
}
],
{
"$map": {
"input": "$communications",
"as": "l",
"in": {
"status": "$$l.callResponse.Status",
"createdDate": "$$l.createdDate"
}
}
}
]
}
}
},
{
"$unwind": "$statusandcreateddate"
},
{
"$group": {
"_id": {
"$dateToString": {
"format": "%d/%m/%Y",
"date": "$statusandcreateddate.createdDate"
}
},
"total_call": {
"$sum": {
"$cond": [
{
"$or": [
{
"$eq": [
"$statusandcreateddate.status",
"failed"
]
},
{
"$eq": [
"$statusandcreateddate.status",
"busy"
]
},
{
"$eq": [
"$statusandcreateddate.status",
"completed"
]
},
{
"$eq": [
"$statusandcreateddate.status",
"no-answer"
]
}
]
},
1,
0
]
}
},
"engaged": {
"$sum": {
"$cond": [
{
"$eq": [
"$statusandcreateddate.status",
"completed"
]
},
1,
0
]
}
},
"not_engaged": {
"$sum": {
"$cond": [
{
"$or": [
{
"$eq": [
"$statusandcreateddate.status",
"failed"
]
},
{
"$eq": [
"$statusandcreateddate.status",
"busy"
]
},
{
"$eq": [
"$statusandcreateddate.status",
"no-answer"
]
}
]
},
1,
0
]
}
},
"Pending": {
"$sum": {
"$cond": [
{
"$eq": [
"$statusandcreateddate.status",
"ToDo"
]
},
1,
0
]
}
},
"InProgress": {
"$sum": {
"$cond": [
{
"$eq": [
"$statusandcreateddate.status",
"InProgress"
]
},
1,
0
]
}
},
"Done": {
"$sum": {
"$cond": [
{
"$eq": [
"$statusandcreateddate.status",
"Done"
]
},
1,
0
]
}
}
}
}
])