I have collection in mongodb (3.0):
{
_id: 1,
m: [{_id:11, _t: 'type1'},
{_id:12, _t: 'type2'},
{_id:13, _t: 'type3'}]
},
{
_id: 2,
m: [{_id:21, _t: 'type1'},
{_id:22, _t: 'type21'},
{_id:23, _t: 'type3'}]
}
I want to find documents with m attributes where m._t containing ['type1', 'type2'].
Like this:
{
_id: 1,
m: [{_id:11, _t: 'type1'},
{_id:12, _t: 'type2'}]
},
{
_id: 2,
m: [{_id:21, _t: 'type1'}]
}
I tried to use $ and $elemMatch, but couldn't get required result.
How to do it, using find()?
Help me, please! Thanks!
Because the $elemMatch operator limits the contents of the m array field from the query results to contain only the first element matching the $elemMatch condition, the following will only return the an array with the first matching elements
{
"_id" : 11,
"_t" : "type1"
}
and
{
"_id" : 21,
"_t" : "type1"
}
Query using $elemMatch projection:
db.collection.find(
{
"m._t": {
"$in": ["type1", "type2"]
}
},
{
"m": {
"$elemMatch": {
"_t": {
"$in": ["type1", "type2"]
}
}
}
}
)
Result:
/* 0 */
{
"_id" : 1,
"m" : [
{
"_id" : 11,
"_t" : "type1"
}
]
}
/* 1 */
{
"_id" : 2,
"m" : [
{
"_id" : 21,
"_t" : "type1"
}
]
}
One approach you can take is the aggregation framework, where your pipeline would consist of a $match operator, similar to the find query above to filter the initial stream of documents. The next pipeline step would be the crucial $unwind operator that "splits" the array elements to be further streamlined with another $match operator and then the final $group pipeline to restore the original data structure by using the accumulator operator $push.
The following illustrates this path:
db.collection.aggregate([
{
"$match": {
"m._t": {
"$in": ["type1", "type2"]
}
}
},
{
"$unwind": "$m"
},
{
"$match": {
"m._t": {
"$in": ["type1", "type2"]
}
}
},
{
"$group": {
"_id": "$_id",
"m": {
"$push": "$m"
}
}
}
])
Sample Output:
/* 0 */
{
"result" : [
{
"_id" : 2,
"m" : [
{
"_id" : 21,
"_t" : "type1"
}
]
},
{
"_id" : 1,
"m" : [
{
"_id" : 11,
"_t" : "type1"
},
{
"_id" : 12,
"_t" : "type2"
}
]
}
],
"ok" : 1
}
To get your "filtered" result, the $redact with the aggregation pipeline is the fastest way:
db.junk.aggregate([
{ "$match": { "m._t": { "$in": ["type1", "type2"] } } },
{ "$redact": {
"$cond": {
"if": {
"$or": [
{ "$eq": [ { "$ifNull": ["$_t", "type1"] }, "type1" ] },
{ "$eq": [ { "$ifNull": ["$_t", "type2"] }, "type2" ] }
],
},
"then": "$$DESCEND",
"else": "$$PRUNE"
}
}}
])
The $redact operator sets up a logical filter for the document that can also traverse into the array levels. Note that this is matching on _t at all levels of the document, so make sure there are no other elements sharing this name.
The query uses $in for selection just as the logical filter uses $or. Anything that does not match, gets "pruned".
{
"_id" : 1,
"m" : [
{
"_id" : 11,
"_t" : "type1"
},
{
"_id" : 12,
"_t" : "type2"
}
]
}
{
"_id" : 2,
"m" : [ { "_id" : 21, "_t" : "type1" } ]
}
Short and sweet and simple.
A bit more cumbersome, but a reasonably safer is to use this construct with $map and $setDifference to filter results:
db.junk.aggregate([
{ "$match": { "m._t": { "$in": ["type1", "type2"] } } },
{ "$project": {
"m": {
"$setDifference": [
{ "$map": {
"input": "$m",
"as": "el",
"in": {
"$cond": {
"if": {
"$or": [
{ "$eq": [ "$$el._t", "type1" ] },
{ "$eq": [ "$$el._t", "type2" ] }
]
},
"then": "$$el",
"else": false
}
}
}},
[false]
]
}
}}
])
The $map evaluates the conditions against each element and the $setDifference removes any of those condtions that returned false rather than the array content. Very similar to the $cond in redact above, but it is just working specifically with the one array and not the whole document.
In future MongoDB releases ( currently available in development releases ) there will be the $filter operator, which is very simple to follow:
db.junk.aggregate([
{ "$match": { "m._t": { "$in": ["type1", "type2"] } } },
{ "$project": {
"m": {
"$filter": {
"input": "$m",
"as": "el",
"cond": {
"$or": [
{ "$eq": [ "$$el._t", "type1" ] },
{ "$eq": [ "$$el._t", "type2" ] }
]
}
}
}
}}
])
And that will simply remove any array element that does not match the specified conditions.
If you want to filter array content on the server, the aggregation framework is the way to do it.
Related
In a mongodb aggregation pipeline, I want to $project the indices of an embedded array (a sub-document) that matches a previous $match stage.
Say, I have the following docs.
{_id: '1', tags: ['aaa', 'bbb', 'ccc']},
{_id: '2', tags: ['baa', 'aaa', 'aaa']},
{_id: '3', tags: ['aac', 'cbb', 'aca']},
Now, if I query with {tags: 'aaa'}, I want to output something similar to
{_id: '1', tags: [0]},
{_id: '2', tags: [1, 2]}
db.inventory.aggregate([
{ $match : {tags : 'aaa' }},
{ $unwind : { path: "$tags", includeArrayIndex: "arrayIndex"}},
{ $match : {tags : 'aaa' }},
{ $group : {
_id : '$_id',
tags : { $push : '$arrayIndex'}
}
}
])
Output :
{ "_id" : "2", "tags" : [ NumberLong(1), NumberLong(2) ] }
{ "_id" : "1", "tags" : [ NumberLong(0) ] }
Another way :
db.inventory.aggregate([
{ $match : {tags : 'aaa' }},
{ $project : {
tags: {
$filter: {
input: {
$zip: {
inputs: [ "$tags", { $range: [0, { $size: "$tags" }] } ]
}
},
as: "tagWithIndex",
cond: {
$let: {
vars: {
tag : { $arrayElemAt: [ "$$tagWithIndex", 0 ] }
},
in: { $eq: [ "$$tag", 'aaa' ] }
}
}
}
}
}},
{ $unwind : '$tags'},
{ $group : {
_id : '$_id',
tags : {
$push : { $arrayElemAt: [ "$tags", 1]}
}
}
}
])
Output :
{ "_id" : "2", "tags" : [ 1, 2 ] }
{ "_id" : "1", "tags" : [ 0 ] }
hope this helps.
You need to $map over the $size of the $tags array to include index of the each element inside the tags array and then you can easily use $filter aggregation to exclude the elements which do contain letter aaa
db.collection.aggregate([
{ "$match": { "tags": "aaa" }},
{ "$project": {
"tags": {
"$filter": {
"input": {
"$map": {
"input": { "$range": [0, { "$size": "$tags" }] },
"in": {
"string": { "$arrayElemAt": ["$tags", "$$this"] },
"index": "$$this"
}
}
},
"cond": { "$eq": ["$$this.string", "aaa"] }
}
}
}},
{ "$project": { "tags": "$tags.index" }}
])
Output
[
{
"_id": "1",
"tags": [0]
},
{
"_id": "2",
"tags": [1, 2]
}
]
If you're searching for an array, you should use $in.
db.inventory.find( { tags: { $in: [ 'aaa' ] } } )
You can also write the same in the match. spelling is the same.
Will help for detail. That's what you're looking for.
Source : https://docs.mongodb.com/manual/reference/operator/query/in/
db.inventory.find( { "tags": { $in: 'aaa' } },
{ "tags.$": 1 } )
This is probably what you want.
Essentially I'm trying to filter OUT subdocuments and sub-subdocuments that have been "trashed". Here's a stripped-down version of my schema:
permitSchema = {
_id,
name,
...
feeClassifications: [
new Schema({
_id,
_trashed,
name,
fees: [
new Schema({
_id,
_trashed,
name,
amount
})
]
})
],
...
}
So I'm able to get the effect I want with feeClassifications. But I'm struggling to find a way to have the same effect for feeClassifications.fees as well.
So, this works as desired:
Permit.aggregate([
{ $match: { _id: mongoose.Types.ObjectId(req.params.id) }},
{ $project: {
_id: 1,
_name: 1,
feeClassifications: {
$filter: {
input: '$feeClassifications',
as: 'item',
cond: { $not: {$gt: ['$$item._trashed', null] } }
}
}
}}
])
But I also want to filter the nested array fees. I've tried a few things including:
Permit.aggregate([
{ $match: { _id: mongoose.Types.ObjectId(req.params.id) }},
{ $project: {
_id: 1,
_name: 1,
feeClassifications: {
$filter: {
input: '$feeClassifications',
as: 'item',
cond: { $not: {$gt: ['$$item._trashed', null] } }
},
fees: {
$filter: {
input: '$fees',
as: 'fee',
cond: { $not: {$gt: ['$$fee._trashed', null] } }
}
}
}
}}
])
Which seems to follow the mongodb docs the closest. But I get the error:
this object is already an operator expression, and can't be used as a document expression (at 'fees')
Update: -----------
As requested, here's a sample document:
{
"_id" : ObjectId("57803fcd982971e403e3e879"),
"_updated" : ISODate("2016-07-11T19:24:27.204Z"),
"_created" : ISODate("2016-07-09T00:05:33.274Z"),
"name" : "Single Event",
"feeClassifications" : [
{
"_updated" : ISODate("2016-07-11T19:05:52.418Z"),
"_created" : ISODate("2016-07-11T17:49:12.247Z"),
"name" : "Event Type 1",
"_id" : ObjectId("5783dc18e09be99840fad29f"),
"fees" : [
{
"_updated" : ISODate("2016-07-11T18:51:10.259Z"),
"_created" : ISODate("2016-07-11T18:41:16.110Z"),
"name" : "Basic Fee",
"amount" : 156.5,
"_id" : ObjectId("5783e84cc46a883349bb2339")
},
{
"_updated" : ISODate("2016-07-11T19:05:52.419Z"),
"_created" : ISODate("2016-07-11T19:05:47.340Z"),
"name" : "Secondary Fee",
"amount" : 50,
"_id" : ObjectId("5783ee0bad7bf8774f6f9b5f"),
"_trashed" : ISODate("2016-07-11T19:05:52.410Z")
}
]
},
{
"_updated" : ISODate("2016-07-11T18:22:21.567Z"),
"_created" : ISODate("2016-07-11T18:22:21.567Z"),
"name" : "Event Type 2",
"_id" : ObjectId("5783e3dd540078de45bbbfaf"),
"_trashed" : ISODate("2016-07-11T19:24:27.203Z")
}
]
}
And here's the desired output ("trashed" subdocuments are excluded from BOTH feeClassifications AND fees):
{
"_id" : ObjectId("57803fcd982971e403e3e879"),
"_updated" : ISODate("2016-07-11T19:24:27.204Z"),
"_created" : ISODate("2016-07-09T00:05:33.274Z"),
"name" : "Single Event",
"feeClassifications" : [
{
"_updated" : ISODate("2016-07-11T19:05:52.418Z"),
"_created" : ISODate("2016-07-11T17:49:12.247Z"),
"name" : "Event Type 1",
"_id" : ObjectId("5783dc18e09be99840fad29f"),
"fees" : [
{
"_updated" : ISODate("2016-07-11T18:51:10.259Z"),
"_created" : ISODate("2016-07-11T18:41:16.110Z"),
"name" : "Basic Fee",
"amount" : 156.5,
"_id" : ObjectId("5783e84cc46a883349bb2339")
}
]
}
]
}
Since we want to filter both the outer and inner array fields, we can use the $map variable operator which return an array with the "values" we want.
In the $map expression, we provide a logical $conditional $filter to remove the non matching documents from both the document and subdocument array field.
The conditions are $lt which return true when the field "_trashed" is absent in the sub-document and or in the sub-document array field.
Note that in the $cond expression we also return false for the <false case>. Of course we need to apply filter to the $map result to remove all false.
Permit.aggregate(
[
{ "$match": { "_id": mongoose.Types.ObjectId(req.params.id) } },
{ "$project": {
"_updated": 1,
"_created": 1,
"name": 1,
"feeClassifications": {
"$filter": {
"input": {
"$map": {
"input": "$feeClassifications",
"as": "fclass",
"in": {
"$cond": [
{ "$lt": [ "$$fclass._trashed", 0 ] },
{
"_updated": "$$fclass._updated",
"_created": "$$fclass._created",
"name": "$$fclass.name",
"_id": "$$fclass._id",
"fees": {
"$filter": {
"input": "$$fclass.fees",
"as": "fees",
"cond": { "$lt": [ "$$fees._trashed", 0 ] }
}
}
},
false
]
}
}
},
"as": "cls",
"cond": "$$cls"
}
}
}}
]
)
In the upcoming MongoDB release (as of this writing and since MongoDB 3.3.5), You can replace the $cond expression in the the $map expression with a $switch expression:
Permit.aggregate(
[
{ "$match": { "_id": mongoose.Types.ObjectId(req.params.id) } },
{ "$project": {
"_updated": 1,
"_created": 1,
"name": 1,
"feeClassifications": {
"$filter": {
"input": {
"$map": {
"input": "$feeClassifications",
"as": "fclass",
"in": {
"$switch": {
"branches": [
{
"case": { "$lt": [ "$$fclass._trashed", 0 ] },
"then": {
"_updated": "$$fclass._updated",
"_created": "$$fclass._created",
"name": "$$fclass.name",
"_id": "$$fclass._id",
"fees": {
"$filter": {
"input": "$$fclass.fees",
"as": "fees",
"cond": { "$lt": [ "$$fees._trashed", 0 ] }
}
}
}
}
],
"default": false
}
}
}
},
"as": "cls",
"cond": "$$cls"
}
}
}}
]
)
For more complicated bigdats, it would be unnecessarily difficult.
Just edit it in $filter input by adding a dotted annotation field.You can search the document to any depth of JSON by dotted annotation without further complicated $filter mapping.
"$filter":{
"input": "$feeClassifications._trashed",
"as": "trashed",
"cond": { "$lt": [ "$$trashed._trashed", 0 ] }
}
I have collection of products. Each product contains array of items.
> db.products.find().pretty()
{
"_id" : ObjectId("54023e8bcef998273f36041d"),
"shop" : "shop1",
"name" : "product1",
"items" : [
{
"date" : "01.02.2100",
"purchasePrice" : 1,
"sellingPrice" : 10,
"count" : 15
},
{
"date" : "31.08.2014",
"purchasePrice" : 10,
"sellingPrice" : 1,
"count" : 5
}
]
}
So, can you please give me an advice, how I can query MongoDB to retrieve all products with only single item which date is equals to the date I pass to query as parameter.
The result for "31.08.2014" must be:
{
"_id" : ObjectId("54023e8bcef998273f36041d"),
"shop" : "shop1",
"name" : "product1",
"items" : [
{
"date" : "31.08.2014",
"purchasePrice" : 10,
"sellingPrice" : 1,
"count" : 5
}
]
}
What you are looking for is the positional $ operator and "projection". For a single field you need to match the required array element using "dot notation", for more than one field use $elemMatch:
db.products.find(
{ "items.date": "31.08.2014" },
{ "shop": 1, "name":1, "items.$": 1 }
)
Or the $elemMatch for more than one matching field:
db.products.find(
{ "items": {
"$elemMatch": { "date": "31.08.2014", "purchasePrice": 1 }
}},
{ "shop": 1, "name":1, "items.$": 1 }
)
These work for a single array element only though and only one will be returned. If you want more than one array element to be returned from your conditions then you need more advanced handling with the aggregation framework.
db.products.aggregate([
{ "$match": { "items.date": "31.08.2014" } },
{ "$unwind": "$items" },
{ "$match": { "items.date": "31.08.2014" } },
{ "$group": {
"_id": "$_id",
"shop": { "$first": "$shop" },
"name": { "$first": "$name" },
"items": { "$push": "$items" }
}}
])
Or possibly in shorter/faster form since MongoDB 2.6 where your array of items contains unique entries:
db.products.aggregate([
{ "$match": { "items.date": "31.08.2014" } },
{ "$project": {
"shop": 1,
"name": 1,
"items": {
"$setDifference": [
{ "$map": {
"input": "$items",
"as": "el",
"in": {
"$cond": [
{ "$eq": [ "$$el.date", "31.08.2014" ] },
"$$el",
false
]
}
}},
[false]
]
}
}}
])
Or possibly with $redact, but a little contrived:
db.products.aggregate([
{ "$match": { "items.date": "31.08.2014" } },
{ "$redact": {
"$cond": [
{ "$eq": [ { "$ifNull": [ "$date", "31.08.2014" ] }, "31.08.2014" ] },
"$$DESCEND",
"$$PRUNE"
]
}}
])
More modern, you would use $filter:
db.products.aggregate([
{ "$match": { "items.date": "31.08.2014" } },
{ "$addFields": {
"items": {
"input": "$items",
"cond": { "$eq": [ "$$this.date", "31.08.2014" ] }
}
}}
])
And with multiple conditions, the $elemMatch and $and within the $filter:
db.products.aggregate([
{ "$match": {
"$elemMatch": { "date": "31.08.2014", "purchasePrice": 1 }
}},
{ "$addFields": {
"items": {
"input": "$items",
"cond": {
"$and": [
{ "$eq": [ "$$this.date", "31.08.2014" ] },
{ "$eq": [ "$$this.purchasePrice", 1 ] }
]
}
}
}}
])
So it just depends on whether you always expect a single element to match or multiple elements, and then which approach is better. But where possible the .find() method will generally be faster since it lacks the overhead of the other operations, which in those last to forms does not lag that far behind at all.
As a side note, your "dates" are represented as strings which is not a very good idea going forward. Consider changing these to proper Date object types, which will greatly help you in the future.
Based on Neil Lunn's code I work with this solution, it includes automatically all first level keys (but you could also exclude keys if you want):
db.products.find(
{ "items.date": "31.08.2014" },
{ "shop": 1, "name":1, "items.$": 1 }
{ items: { $elemMatch: { date: "31.08.2014" } } },
)
With multiple requirements:
db.products.find(
{ "items": {
"$elemMatch": { "date": "31.08.2014", "purchasePrice": 1 }
}},
{ items: { $elemMatch: { "date": "31.08.2014", "purchasePrice": 1 } } },
)
Mongo supports dot notation for sub-queries.
See: http://docs.mongodb.org/manual/reference/glossary/#term-dot-notation
Depending on your driver, you want something like:
db.products.find({"items.date":"31.08.2014"});
Note that the attribute is in quotes for dot notation, even if usually your driver doesn't require this.
Using the example zipcodes collection, I have a query like this:
db.zipcodes.aggregate([
{ "$match": {"state": {"$in": ["PA","NY"]}}},
{ "$group": { "_id": { "city": "$city" }, "ZipsPerCity": {"$addToSet": "$_id"}}},
{ "$match": { "ZipsPerCity" : { "$size": 2 }}},
]).pretty()
This is just an example that looks for cities (in the state of NY and PA) that have 2 zipcodes:
{
"_id" : {
"city" : "BETHLEHEM"
},
"ZipsPerCity" : [
"18018",
"18015"
]
}
{
"_id" : {
"city" : "BEAVER SPRINGS"
},
"ZipsPerCity" : [
"17843",
"17812"
]
}
Now suppose that I want to compare "BEAVER SPRINGS" zip codes to "BETHLEHEM" zip codes, using the "$setDifference" set operator? I tried using the "$setDifference" operator in a $project operator, like this:
db.zipcodes.aggregate([
{ "$match": { "state": {"$in": ["PA","NY"]}}},
{ "$group": { "_id: {city : "$city"},"ZipsPerCity": {$addToSet: "$_id"}}},
{ "$match": { "ZipsPerCity" : { $size: 2 }}},
{ "$project": {
"int": { "$setDifference":[
"$_id.city.BETHLEHEM.ZipsPerCity",
"$_id.city.BEAVER SPRINGS.ZipsPerCity"
]}
}}
]).pretty()
That doesn't even look right, let alone produce results. No errors though.
How would you refer to a couple of arrays built using $addToSet like this, using $setDifference (or any of the set operators)?
The first thing about what you are trying to do here is that the arrays you want to compare are actually in two different documents. All of the aggregation framework operators in fact work on only one document at a time, with the exception of $group which is meant to "aggregate" documents and possibly $unwind which essentially turns one document into many.
In order to compare you would need the data to occur in one document, or at least be "paired" in some way. So there is a technique to do that:
db.zipcodes.aggregate([
{ "$match": {"state": { "$in": [ "PA","NY" ] } }},
{ "$group": {
"_id": "$city",
"ZipsPerCity": { "$addToSet": "$_id"}
}},
{ "$match": { "ZipsPerCity" : { "$size": 2 } }},
{ "$group": {
"_id": null,
"A": { "$min": {
"$cond": [
{ "$eq": [ "$_id", "BETHLEHEM" ] },
{ "city": "$_id", "ZipsPerCity": "$ZipsPerCity" },
false
]
}},
"B": { "$min": {
"$cond": [
{ "$eq": [ "$_id", "BEAVER SPRINGS" ] },
{ "city": "$_id", "ZipsPerCity": "$ZipsPerCity" },
false
]
}}
}},
{ "$project": {
"A": 1,
"B": 1,
"C": { "$setDifference": [ "$A.ZipsPerCity", "$B.ZipsPerCity" ] }
}}
])
That is a little contrived and I am well aware that the actual result set has more than two cities, but the point it to illustrate that the arrays/sets sent to the "set operators" such as $setDifference need to be in the same document.
The result here compares the "left" array with the "right" array, returning the members from the "left" that are different to the "right". Both sets are unique here with no overlap so the results should be expected:
{
"_id" : null,
"A" : {
"city" : "BETHLEHEM",
"ZipsPerCity" : [
"18018",
"18015"
]
},
"B" : {
"city" : "BEAVER SPRINGS",
"ZipsPerCity" : [
"17843",
"17812"
]
},
"C" : [
"18018",
"18015"
]
}
This is really better illustrated with actual "sets" with common members. So this document:
{ "A" : [ "A", "A", "B", "C", "D" ], "B" : [ "B", "C" ] }
Responds to $setDifference:
{ "C" : [ "A", "D" ] }
And $setEquals:
{ "C" : false }
$setIntersection:
{ "C" : [ "B", "C" ] }
$setUnion:
{ "C" : [ "B", "D", "C", "A" ] }
$setIsSubSet reversing the order to $B, $A:
{ "C" : true }
The other set operators $anyElementTrue and $allElementsTrue are likely most useful when used along with the $map operator which can re-shape arrays and evaluate conditions against each element.
A very good usage of $map is alongside $setDifference, where you can "filter" array contents without using $unwind:
db.arrays.aggregate([
{ "$project": {
"A": {
"$setDifference": [
{
"$map": {
"input": "$A",
"as": "el",
"in": {
"$cond": [
{ "$eq": [ "$$el", "A" ] },
"$$el",
false
]
}
}
},
[false]
]
}
}}
])
That can be very handy when you have a lot of results in the pipeline and you do not want to "expand" out all of those results by "unwinding" the array. But note that this is a "set" and as such only one element matching "A" is returned:
{ "A" : ["A"] }
So the things to keep in mind here are that you:
Operate only within the "same" document at a time
The results are generally "sets" and that means they are both "unique" and "un-ordered" as a result.
Overall that should be a decent run-down on what the set operators are and how you use them.
I have a collection in mongodb like this:
db.country_list.find().pretty()
{
"_id" : ObjectId("53917321ccbc96175d7a808b"),
"countries" : [
{
"countryName" : "Afghanistan",
"iso3" : "AFG",
"callingCode" : "93"
},
{
"countryName" : "Aland Islands",
"iso3" : "ALA",
"callingCode" : "358"
},
{
"countryName" : "Albania",
"iso3" : "ALB",
"callingCode" : "355"
}
]
}
like that i have 100 country details
i want to retrieve a country name where the calling code is 355.
I have tried like this
db.country_list.find({countries: {$elemMatch :{ 'callingCode':'355'} } } )
and like this
db.country_list.find({'countries.callingCode':'355'}).pretty()
but i am getting all records.How to get a specific record .Thanks in advance
What you want is the positional $ operator:
db.country_list.find(
{ "countries": { "$elemMatch" :{ "callingCode":"355"} } }
{ "countries.$": 1 }
)
Or even with the other syntax you tried:
db.country_list.find(
{ "countries.callingCode": "355"}
{ "countries.$": 1 }
)
This is because a "query" matches documents and is not a filter for the array contained in those documents. So the second argument there projects the field with the "position" that was matched on the query side.
If you need to match more than one array element, then you use the aggregation framework which has more flexibility:
db.country_list.aggregate([
// Matches the documents that "contain" the match
{ "$match": {
"countries.callingCode": "355"
}},
// Unwind the array to de-normalize as documents
{ "$unwind": "$countries" },
// Match to "filter" the array content
{ "$match": {
"countries.callingCode": "355"
}},
// Group back if you want an array
{ "$group": {
"_id": "$_id",
"countries": { "$push": "$countries" }
}}
])
Or with MongoDB 2.6 or greater you can do this without the $unwind and $group:
db.country_list.aggregate([
// Matches the documents that "contain" the match
{ "$match": {
"countries.callingCode": "355"
}},
// Project with "$map" to filter
{ "$project": {
"countries": {
"$setDifference": [
{ "$map": {
"input": "$countries",
"as": "el",
"in": {
"$cond": [
{ "$eq": [ "$$el.callingCode", "355" ] }
"$$el",
false
]
}
}},
[false]
]
}
}}
])