MongoDB/Mongoose - Querying an array of objects by date - node.js

Question

I have the following metrics collection:
{
name: "Hello",
values: [
{
value: 2629,
date: "2016-10-28T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee8"
},
{
value: 1568,
date: "2016-10-29T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee7"
},
{
value: 1547,
date: "2016-10-30T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee6"
},
{
value: 1497,
date: "2016-10-31T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee5"
},
{
value: 3031,
date: "2016-11-01T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee4"
},
{
value: 2559,
date: "2016-11-02T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee3"
},
{
value: 2341,
date: "2016-11-03T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee2"
},
{
value: 2188,
date: "2016-11-04T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee1"
},
{
value: 3280,
date: "2016-11-05T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdee0"
},
{
value: 4638,
date: "2016-11-06T07:00:00.000Z",
_id: "58453abfef7aaa15ac1fdedf"
}
]
},
.... more of the same
What I would like to get is all the values between a custom date range.
I've tried the following query but I still get the entire values array returned:
{
name: "Hello",
values: {
$elemMatch: {
date: {
$lt: "2016-11-03T07:00:00.000Z",
$gt: "2016-10-28T07:00:00.000Z"
}
}
}
}
Maybe I saved my dates in a wrong format ? Any help would be greatly appreciated.

Answer 1

You can run an aggregation pipeline that uses the $filter operator on the values array. The following mongo shell query demonstrates this:
var start = new Date("2016-10-28T07:00:00.000Z"),
end = new Date("2016-11-03T07:00:00.000Z");
db.metrics.aggregate([
{
"$match": {
"name": "Hello",
"values.date": { "$gt": start, "$lt": end }
}
},
{
"$project": {
"name": 1,
"values": {
"$filter": {
"input": "$values",
"as": "value",
"cond": {
"$and": [
{ "$gt": [ "$$value.date", start ] },
{ "$lt": [ "$$value.date", end ] }
]
}
}
}
}
}
])
Sample Output
/* 1 */
{
"_id" : ObjectId("5845453145fda1298fa50db9"),
"name" : "Hello",
"values" : [
{
"value" : 1568,
"date" : ISODate("2016-10-29T07:00:00.000Z"),
"_id" : ObjectId("58453abfef7aaa15ac1fdee7")
},
{
"value" : 1547,
"date" : ISODate("2016-10-30T07:00:00.000Z"),
"_id" : ObjectId("58453abfef7aaa15ac1fdee6")
},
{
"value" : 1497,
"date" : ISODate("2016-10-31T07:00:00.000Z"),
"_id" : ObjectId("58453abfef7aaa15ac1fdee5")
},
{
"value" : 3031,
"date" : ISODate("2016-11-01T07:00:00.000Z"),
"_id" : ObjectId("58453abfef7aaa15ac1fdee4")
},
{
"value" : 2559,
"date" : ISODate("2016-11-02T07:00:00.000Z"),
"_id" : ObjectId("58453abfef7aaa15ac1fdee3")
}
]
}
For MongoDB 3.0, the following workaround applies:
var start = new Date("2016-10-28T07:00:00.000Z"),
end = new Date("2016-11-03T07:00:00.000Z");
db.metrics.aggregate([
{
"$match": {
"name": "Hello",
"values.date": { "$gt": start, "$lt": end }
}
},
{
"$project": {
"name": 1,
"values": {
"$setDifference": [
{
"$map": {
"input": "$values",
"as": "value",
"in": {
"$cond": [
{
"$and": [
{ "$gt": [ "$$value.date", start ] },
{ "$lt": [ "$$value.date", end ] }
]
},
"$$value",
false
]
}
}
},
[false]
]
}
}
}
])

Answer 2

The Aggregation Framework in MongoDB 2.2+ provides an alternative to Map/Reduce. The $unwind operator can be used to separate your values array into a stream of documents that can be matched:
db.tmp.aggregate(
// Start with a $match pipeline which can take advantage of an index and limit documents processed
{ $match : {
name: "Hello",
"values.date": {
$lt: "2016-11-03T07:00:00.000Z",
$gt: "2016-10-28T07:00:00.000Z" }
}},
{ $unwind : "$values" },
{ $match : {
name: "Hello",
"values.date": {
$lt: "2016-11-03T07:00:00.000Z",
$gt: "2016-10-28T07:00:00.000Z" }
}}
)
Sample output:
{
"_id":ObjectId("5845432720ce37bdc7e9ca1c"),
"name":"Hello",
"values":{
"value":1568,
"date":"2016-10-29T07:00:00.000Z",
"_id":"58453abfef7aaa15ac1fdee7"
}
},{
"_id":ObjectId("5845432720ce37bdc7e9ca1c"),
"name":"Hello",
"values":{
"value":1547,
"date":"2016-10-30T07:00:00.000Z",
"_id":"58453abfef7aaa15ac1fdee6"
}
},{
"_id":ObjectId("5845432720ce37bdc7e9ca1c"),
"name":"Hello",
"values":{
"value":1497,
"date":"2016-10-31T07:00:00.000Z",
"_id":"58453abfef7aaa15ac1fdee5"
}
},{
"_id":ObjectId("5845432720ce37bdc7e9ca1c"),
"name":"Hello",
"values":{
"value":3031,
"date":"2016-11-01T07:00:00.000Z",
"_id":"58453abfef7aaa15ac1fdee4"
}
},{
"_id":ObjectId("5845432720ce37bdc7e9ca1c"),
"name":"Hello",
"values":{
"value":2559,
"date":"2016-11-02T07:00:00.000Z",
"_id":"58453abfef7aaa15ac1fdee3"
}
}