I am trying to calculate total value if that value exits. But query is not working 100%. So can somebody help me to solve this problem. Here my sample document. I have attached two documents. Please these documents & find out best solution
Document : 1
{
"_id" : 1"),
"message_count" : 4,
"messages" : {
"data" : [
{
"id" : "11",
"saleValue": 1000
},
{
"id" : "112",
"saleValue": 1400
},
{
"id" : "22",
},
{
"id" : "234",
"saleValue": 111
}
],
},
"createdTime" : ISODate("2018-03-18T10:18:48.000Z")
}
Document : 2
{
"_id" : 444,
"message_count" : 4,
"messages" : {
"data" : [
{
"id" : "444",
"saleValue" : 2060
},
{
"id" : "444",
},
{
"id" : 234,
"saleValue" : 260
},
{
"id" : "34534",
}
]
},
"createdTime" : ISODate("2018-03-18T03:11:50.000Z")
}
Needed Output:
{
total : 4831
}
My query :
db.getCollection('myCollection').aggregate([
{
"$group": {
"_id": "$Id",
"totalValue": {
$sum: {
$sum: "$messages.data.saleValue"
}
}
}
}
])
So please if possible help me to solve this problem. Thanks in advance
It's not working correctly because it is aggregating all the documents in the collection; you are grouping on a constant "_id": "tempId", you just need to reference the correct key by adding the $ as:
db.getCollection('myCollection').aggregate([
{ "$group": {
"_id": "$tempId",
"totalValue": {
"$sum": { "$sum": "$messages.data.saleValue" }
}
} }
])
which in essence is a single stage pipeline version of an aggregate operation with an extra field that holds the sum expression before the group pipeline then calling that field as the $sum operator in the group.
The above works since $sum from MongoDB 3.2+ is available in both the $project and $group stages and when used in the $project stage, $sum returns the sum of the list of expressions. The expression "$messages.data.value" returns a list of numbers [120, 1200] which are then used as the $sum expression:
db.getCollection('myCollection').aggregate([
{ "$project": {
"values": { "$sum": "$messages.data.value" },
"tempId": 1,
} },
{ "$group": {
"_id": "$tempId",
"totalValue": { "$sum": "$values" }
} }
])
You can add a $unwind before your $group, in that way you will deconstructs the data array, and then you can group properly:
db.myCollection.aggregate([
{
"$unwind": "$messages.data"
},
{
"$group": {
"_id": "tempId",
"totalValue": {
$sum: {
$sum: "$messages.data.value"
}
}
}
}
])
Output:
{ "_id" : "tempId", "totalValue" : 1320 }
db.getCollection('myCollection').aggregate([
{
$unwind: "$messages.data",
$group: {
"_id": "tempId",
"totalValue": { $sum: "$messages.data.value" }
}
}
])
$unwind
According to description as mentioned into above question, as a solution please try executing following aggregate query
db.myCollection.aggregate(
// Pipeline
[
// Stage 1
{
$unwind: {
path: '$messages.data'
}
},
// Stage 2
{
$group: {
_id: {
pageId: '$pageId'
},
total: {
$sum: '$messages.data.saleValue'
}
}
},
// Stage 3
{
$project: {
pageId: '$_id.pageId',
total: 1,
_id: 0
}
}
]
);
You can do it without using $group. Grouping made other data to be managed and addressed. So, I prefer using $sum and $map as shown below:
db.getCollection('myCollection').aggregate([
{
$addFields: {
total: {
$sum: {
$map: {
input: "$messages.data",
as: "message",
in: "$$message.saleValue",
},
},
},
},
},
}
])
I want to group all the documents according to a field but to restrict the number of documents grouped for each value.
Each message has a conversation_ID. I need to get 10 or lesser number of messages for each conversation_ID.
I am able to group according to the following command but can't figure out how to restrict the
number of grouped documents apart from slicing the results
Message.aggregate({'$group':{_id:'$conversation_ID',msgs:{'$push':{msgid:'$_id'}}}})
How to limit the length of msgs array for each conversation_ID to 10?
Modern
From MongoDB 3.6 there is a "novel" approach to this by using $lookup to perform a "self join" in much the same way as the original cursor processing demonstrated below.
Since in this release you can specify a "pipeline" argument to $lookup as a source for the "join", this essentially means you can use $match and $limit to gather and "limit" the entries for the array:
db.messages.aggregate([
{ "$group": { "_id": "$conversation_ID" } },
{ "$lookup": {
"from": "messages",
"let": { "conversation": "$_id" },
"pipeline": [
{ "$match": { "$expr": { "$eq": [ "$conversation_ID", "$$conversation" ] } }},
{ "$limit": 10 },
{ "$project": { "_id": 1 } }
],
"as": "msgs"
}}
])
You can optionally add additional projection after the $lookup in order to make the array items simply the values rather than documents with an _id key, but the basic result is there by simply doing the above.
There is still the outstanding SERVER-9277 which actually requests a "limit to push" directly, but using $lookup in this way is a viable alternative in the interim.
NOTE: There also is $slice which was introduced after writing the original answer and mentioned by "outstanding JIRA issue" in the original content. Whilst you can get the same result with small result sets, it does involve still "pushing everything" into the array and then later limiting the final array output to the desired length.
So that's the main distinction and why it's generally not practical to $slice for large results. But of course can be alternately used in cases where it is.
There are a few more details on mongodb group values by multiple fields about either alternate usage.
Original
As stated earlier, this is not impossible but certainly a horrible problem.
Actually if your main concern is that your resulting arrays are going to be exceptionally large, then you best approach is to submit for each distinct "conversation_ID" as an individual query and then combine your results. In very MongoDB 2.6 syntax which might need some tweaking depending on what your language implementation actually is:
var results = [];
db.messages.aggregate([
{ "$group": {
"_id": "$conversation_ID"
}}
]).forEach(function(doc) {
db.messages.aggregate([
{ "$match": { "conversation_ID": doc._id } },
{ "$limit": 10 },
{ "$group": {
"_id": "$conversation_ID",
"msgs": { "$push": "$_id" }
}}
]).forEach(function(res) {
results.push( res );
});
});
But it all depends on whether that is what you are trying to avoid. So on to the real answer:
The first issue here is that there is no function to "limit" the number of items that are "pushed" into an array. It is certainly something we would like, but the functionality does not presently exist.
The second issue is that even when pushing all items into an array, you cannot use $slice, or any similar operator in the aggregation pipeline. So there is no present way to get just the "top 10" results from a produced array with a simple operation.
But you can actually produce a set of operations to effectively "slice" on your grouping boundaries. It is fairly involved, and for example here I will reduce the array elements "sliced" to "six" only. The main reason here is to demonstrate the process and show how to do this without being destructive with arrays that do not contain the total you want to "slice" to.
Given a sample of documents:
{ "_id" : 1, "conversation_ID" : 123 }
{ "_id" : 2, "conversation_ID" : 123 }
{ "_id" : 3, "conversation_ID" : 123 }
{ "_id" : 4, "conversation_ID" : 123 }
{ "_id" : 5, "conversation_ID" : 123 }
{ "_id" : 6, "conversation_ID" : 123 }
{ "_id" : 7, "conversation_ID" : 123 }
{ "_id" : 8, "conversation_ID" : 123 }
{ "_id" : 9, "conversation_ID" : 123 }
{ "_id" : 10, "conversation_ID" : 123 }
{ "_id" : 11, "conversation_ID" : 123 }
{ "_id" : 12, "conversation_ID" : 456 }
{ "_id" : 13, "conversation_ID" : 456 }
{ "_id" : 14, "conversation_ID" : 456 }
{ "_id" : 15, "conversation_ID" : 456 }
{ "_id" : 16, "conversation_ID" : 456 }
You can see there that when grouping by your conditions you will get one array with ten elements and another with "five". What you want to do here reduce both to the top "six" without "destroying" the array that only will match to "five" elements.
And the following query:
db.messages.aggregate([
{ "$group": {
"_id": "$conversation_ID",
"first": { "$first": "$_id" },
"msgs": { "$push": "$_id" },
}},
{ "$unwind": "$msgs" },
{ "$project": {
"msgs": 1,
"first": 1,
"seen": { "$eq": [ "$first", "$msgs" ] }
}},
{ "$sort": { "seen": 1 }},
{ "$group": {
"_id": "$_id",
"msgs": {
"$push": {
"$cond": [ { "$not": "$seen" }, "$msgs", false ]
}
},
"first": { "$first": "$first" },
"second": { "$first": "$msgs" }
}},
{ "$unwind": "$msgs" },
{ "$project": {
"msgs": 1,
"first": 1,
"second": 1,
"seen": { "$eq": [ "$second", "$msgs" ] }
}},
{ "$sort": { "seen": 1 }},
{ "$group": {
"_id": "$_id",
"msgs": {
"$push": {
"$cond": [ { "$not": "$seen" }, "$msgs", false ]
}
},
"first": { "$first": "$first" },
"second": { "$first": "$second" },
"third": { "$first": "$msgs" }
}},
{ "$unwind": "$msgs" },
{ "$project": {
"msgs": 1,
"first": 1,
"second": 1,
"third": 1,
"seen": { "$eq": [ "$third", "$msgs" ] },
}},
{ "$sort": { "seen": 1 }},
{ "$group": {
"_id": "$_id",
"msgs": {
"$push": {
"$cond": [ { "$not": "$seen" }, "$msgs", false ]
}
},
"first": { "$first": "$first" },
"second": { "$first": "$second" },
"third": { "$first": "$third" },
"forth": { "$first": "$msgs" }
}},
{ "$unwind": "$msgs" },
{ "$project": {
"msgs": 1,
"first": 1,
"second": 1,
"third": 1,
"forth": 1,
"seen": { "$eq": [ "$forth", "$msgs" ] }
}},
{ "$sort": { "seen": 1 }},
{ "$group": {
"_id": "$_id",
"msgs": {
"$push": {
"$cond": [ { "$not": "$seen" }, "$msgs", false ]
}
},
"first": { "$first": "$first" },
"second": { "$first": "$second" },
"third": { "$first": "$third" },
"forth": { "$first": "$forth" },
"fifth": { "$first": "$msgs" }
}},
{ "$unwind": "$msgs" },
{ "$project": {
"msgs": 1,
"first": 1,
"second": 1,
"third": 1,
"forth": 1,
"fifth": 1,
"seen": { "$eq": [ "$fifth", "$msgs" ] }
}},
{ "$sort": { "seen": 1 }},
{ "$group": {
"_id": "$_id",
"msgs": {
"$push": {
"$cond": [ { "$not": "$seen" }, "$msgs", false ]
}
},
"first": { "$first": "$first" },
"second": { "$first": "$second" },
"third": { "$first": "$third" },
"forth": { "$first": "$forth" },
"fifth": { "$first": "$fifth" },
"sixth": { "$first": "$msgs" },
}},
{ "$project": {
"first": 1,
"second": 1,
"third": 1,
"forth": 1,
"fifth": 1,
"sixth": 1,
"pos": { "$const": [ 1,2,3,4,5,6 ] }
}},
{ "$unwind": "$pos" },
{ "$group": {
"_id": "$_id",
"msgs": {
"$push": {
"$cond": [
{ "$eq": [ "$pos", 1 ] },
"$first",
{ "$cond": [
{ "$eq": [ "$pos", 2 ] },
"$second",
{ "$cond": [
{ "$eq": [ "$pos", 3 ] },
"$third",
{ "$cond": [
{ "$eq": [ "$pos", 4 ] },
"$forth",
{ "$cond": [
{ "$eq": [ "$pos", 5 ] },
"$fifth",
{ "$cond": [
{ "$eq": [ "$pos", 6 ] },
"$sixth",
false
]}
]}
]}
]}
]}
]
}
}
}},
{ "$unwind": "$msgs" },
{ "$match": { "msgs": { "$ne": false } }},
{ "$group": {
"_id": "$_id",
"msgs": { "$push": "$msgs" }
}}
])
You get the top results in the array, up to six entries:
{ "_id" : 123, "msgs" : [ 1, 2, 3, 4, 5, 6 ] }
{ "_id" : 456, "msgs" : [ 12, 13, 14, 15 ] }
As you can see here, loads of fun.
After you have initially grouped you basically want to "pop" the $first value off of the stack for the array results. To make this process simplified a little, we actually do this in the initial operation. So the process becomes:
$unwind the array
Compare to the values already seen with an $eq equality match
$sort the results to "float" false unseen values to the top ( this still retains order )
$group back again and "pop" the $first unseen value as the next member on the stack. Also this uses the $cond operator to replace "seen" values in the array stack with false to help in the evaluation.
The final action with $cond is there to make sure that future iterations are not just adding the last value of the array over and over where the "slice" count is greater than the array members.
That whole process needs to be repeated for as many items as you wish to "slice". Since we already found the "first" item in the initial grouping, that means n-1 iterations for the desired slice result.
The final steps are really just an optional illustration of converting everything back into arrays for the result as finally shown. So really just conditionally pushing items or false back by their matching position and finally "filtering" out all the false values so the end arrays have "six" and "five" members respectively.
So there is not a standard operator to accommodate this, and you cannot just "limit" the push to 5 or 10 or whatever items in the array. But if you really have to do it, then this is your best approach.
You could possibly approach this with mapReduce and forsake the aggregation framework all together. The approach I would take ( within reasonable limits ) would be to effectively have an in-memory hash-map on the server and accumulate arrays to that, while using JavaScript slice to "limit" the results:
db.messages.mapReduce(
function () {
if ( !stash.hasOwnProperty(this.conversation_ID) ) {
stash[this.conversation_ID] = [];
}
if ( stash[this.conversation_ID.length < maxLen ) {
stash[this.conversation_ID].push( this._id );
emit( this.conversation_ID, 1 );
}
},
function(key,values) {
return 1; // really just want to keep the keys
},
{
"scope": { "stash": {}, "maxLen": 10 },
"finalize": function(key,value) {
return { "msgs": stash[key] };
},
"out": { "inline": 1 }
}
)
So that just basically builds up the "in-memory" object matching the emitted "keys" with an array never exceeding the maximum size you want to fetch from your results. Additionally this does not even bother to "emit" the item when the maximum stack is met.
The reduce part actually does nothing other than essentially just reduce to "key" and a single value. So just in case our reducer did not get called, as would be true if only 1 value existed for a key, the finalize function takes care of mapping the "stash" keys to the final output.
The effectiveness of this varies on the size of the output, and JavaScript evaluation is certainly not fast, but possibly faster than processing large arrays in a pipeline.
Vote up the JIRA issues to actually have a "slice" operator or even a "limit" on "$push" and "$addToSet", which would both be handy. Personally hoping that at least some modification can be made to the $map operator to expose the "current index" value when processing. That would effectively allow "slicing" and other operations.
Really you would want to code this up to "generate" all of the required iterations. If the answer here gets enough love and/or other time pending that I have in tuits, then I might add some code to demonstrate how to do this. It is already a reasonably long response.
Code to generate pipeline:
var key = "$conversation_ID";
var val = "$_id";
var maxLen = 10;
var stack = [];
var pipe = [];
var fproj = { "$project": { "pos": { "$const": [] } } };
for ( var x = 1; x <= maxLen; x++ ) {
fproj["$project"][""+x] = 1;
fproj["$project"]["pos"]["$const"].push( x );
var rec = {
"$cond": [ { "$eq": [ "$pos", x ] }, "$"+x ]
};
if ( stack.length == 0 ) {
rec["$cond"].push( false );
} else {
lval = stack.pop();
rec["$cond"].push( lval );
}
stack.push( rec );
if ( x == 1) {
pipe.push({ "$group": {
"_id": key,
"1": { "$first": val },
"msgs": { "$push": val }
}});
} else {
pipe.push({ "$unwind": "$msgs" });
var proj = {
"$project": {
"msgs": 1
}
};
proj["$project"]["seen"] = { "$eq": [ "$"+(x-1), "$msgs" ] };
var grp = {
"$group": {
"_id": "$_id",
"msgs": {
"$push": {
"$cond": [ { "$not": "$seen" }, "$msgs", false ]
}
}
}
};
for ( n=x; n >= 1; n-- ) {
if ( n != x )
proj["$project"][""+n] = 1;
grp["$group"][""+n] = ( n == x ) ? { "$first": "$msgs" } : { "$first": "$"+n };
}
pipe.push( proj );
pipe.push({ "$sort": { "seen": 1 } });
pipe.push(grp);
}
}
pipe.push(fproj);
pipe.push({ "$unwind": "$pos" });
pipe.push({
"$group": {
"_id": "$_id",
"msgs": { "$push": stack[0] }
}
});
pipe.push({ "$unwind": "$msgs" });
pipe.push({ "$match": { "msgs": { "$ne": false } }});
pipe.push({
"$group": {
"_id": "$_id",
"msgs": { "$push": "$msgs" }
}
});
That builds the basic iterative approach up to maxLen with the steps from $unwind to $group. Also embedded in there are details of the final projections required and the "nested" conditional statement. The last is basically the approach taken on this question:
Does MongoDB's $in clause guarantee order?
Starting Mongo 4.4, the $group stage has a new aggregation operator $accumulator allowing custom accumulations of documents as they get grouped, via javascript user defined functions.
Thus, in order to only select n messages (for instance 2) for each conversation:
// { "conversationId" : 3, "messageId" : 14 }
// { "conversationId" : 5, "messageId" : 34 }
// { "conversationId" : 3, "messageId" : 39 }
// { "conversationId" : 3, "messageId" : 47 }
db.collection.aggregate([
{ $group: {
_id: "$conversationId",
messages: {
$accumulator: {
accumulateArgs: ["$messageId"],
init: function() { return [] },
accumulate:
function(messages, message) { return messages.concat(message).slice(0, 2); },
merge:
function(messages1, messages2) { return messages1.concat(messages2).slice(0, 2); },
lang: "js"
}
}
}}
])
// { "_id" : 5, "messages" : [ 34 ] }
// { "_id" : 3, "messages" : [ 14, 39 ] }
The accumulator:
accumulates on the field messageId (accumulateArgs)
is initialised to an empty array (init)
accumulates messageId items in an array and only keeps a maximum of 2 (accumulate and merge)
Starting in Mongo 5.2, it's a perfect use case for the new $topN aggregation accumulator:
// { "conversationId" : 3, "messageId" : 14 }
// { "conversationId" : 5, "messageId" : 34 }
// { "conversationId" : 3, "messageId" : 39 }
// { "conversationId" : 3, "messageId" : 47 }
db.collection.aggregate([
{ $group: {
_id: "$conversationId",
messages: { $topN: { n: 2, output: "$messageId", sortBy: { _id: 1 } } }
}}
])
// { "_id" : 5, "messages" : [ 34 ] }
// { "_id" : 3, "messages" : [ 14, 39 ] }
This applies a $topN group accumulation that:
takes for each group the top 2 (n: 2) elements
and for each grouped record extracts the field value (output: "$messageId")
the choice of the "top 2" is defined by sortBy: { _id: 1 } (that I chose to be _id since you didn't specify an order).
The $slice operator is not an aggregation operator so you can't do this (like I suggested in this answer, before the edit):
db.messages.aggregate([
{ $group : {_id:'$conversation_ID',msgs: { $push: { msgid:'$_id' }}}},
{ $project : { _id : 1, msgs : { $slice : 10 }}}]);
Neil's answer is very detailed, but you can use a slightly different approach (if it fits your use case). You can aggregate your results and output them to a new collection:
db.messages.aggregate([
{ $group : {_id:'$conversation_ID',msgs: { $push: { msgid:'$_id' }}}},
{ $out : "msgs_agg" }
]);
The $out operator will write the results of the aggregation to a new collection. You can then use a regular find query project your results with the $slice operator:
db.msgs_agg.find({}, { msgs : { $slice : 10 }});
For this test documents:
> db.messages.find().pretty();
{ "_id" : 1, "conversation_ID" : 123 }
{ "_id" : 2, "conversation_ID" : 123 }
{ "_id" : 3, "conversation_ID" : 123 }
{ "_id" : 4, "conversation_ID" : 123 }
{ "_id" : 5, "conversation_ID" : 123 }
{ "_id" : 7, "conversation_ID" : 1234 }
{ "_id" : 8, "conversation_ID" : 1234 }
{ "_id" : 9, "conversation_ID" : 1234 }
The result will be:
> db.msgs_agg.find({}, { msgs : { $slice : 10 }});
{ "_id" : 1234, "msgs" : [ { "msgid" : 7 }, { "msgid" : 8 }, { "msgid" : 9 } ] }
{ "_id" : 123, "msgs" : [ { "msgid" : 1 }, { "msgid" : 2 }, { "msgid" : 3 },
{ "msgid" : 4 }, { "msgid" : 5 } ] }
Edit
I assume this would mean duplicating the whole messages collection.
Isn't that overkill?
Well, obviously this approach won't scale with huge collections. But, since you're considering using large aggregation pipelines or large map-reduce jobs you probably won't use this for "real-time" requests.
There are many cons of this approach: 16 MB BSON limit if you're creating huge documents with aggregation, wasting disk space / memory with duplication, increased disk IO...
The pros of this approach: its simple to implement and thus easy to change. If your collection is rarely updated you can use this "out" collection like a cache. This way you wouldn't have to perform the aggregation operation multiple times and you could then even support "real-time" client requests on the "out" collection. To refresh your data, you can periodically do aggregation (e.g. in a background job that runs nightly).
Like it was said in the comments this isn't an easy problem and there isn't a perfect solution for this (yet!). I showed you another approach you can use, it's up to you to benchmark and decide what's most appropriate for your use case.
I hope this will work as you wanted:
db.messages.aggregate([
{ $group : {_id:'$conversation_ID',msgs: { $push: { msgid:'$_id' }}}},
{ $project : { _id : 1, msgs : { $slice : ["$msgid",0,10] }}}
]);
I am trying to aggregate in MongoDB.
I have a collection with some items. Each item has an array rows and each object in rows has fields quantity and price.
I want to multiply quantity and price, but I don't know how to specify the fields correctly.
I have tried
const pipeline = [
{
$group: {
_id: {
number: '$number',
},
total: {
$sum: {
$multiply: [
'$rows.quantity',
'$rows.price'
]
}
},
}
}
];
but it says that $multiply only supports numeric types and not arrays.
So it seems it doesn't understand that $rows.quantity is the numeric type field quantity in each object in the array.
I guess I should probably use $each or something else in order to iterate through the objects in the array.
From Using multiply aggregation with MongoDB I see that I am specifying the fields correctly; however, in that example it is a nested object instead of an array, so maybe I have to use https://docs.mongodb.org/v3.0/reference/operator/aggregation/unwind/?
Sample document
{
number: 2,
rows: [
{
quantity: 10,
price: 312
},
{
quantity: 10,
price: 312
},
{
quantity: 10,
price: 312
},
]
}
Using the .aggregate() method.
Starting in version 3.2 you can use the $sum accumulator operator in the $project stage to calculates and returns the sum of array of quantity * price. Of course to get the array you need to use the $map operator. The $ifNull operator evaluates the value of "quantity" and "price" then returns 0 if they evaluate to a null value. The last stage in the pipeline is the $group stage where you group your document by "number" and return the "total" for each each group.
db.collection.aggregate([
{ "$project": {
"number": 1,
"total": {
"$sum": {
"$map": {
"input": "$rows",
"as": "row",
"in": { "$multiply": [
{ "$ifNull": [ "$$row.quantity", 0 ] },
{ "$ifNull": [ "$$row.price", 0 ] }
]}
}
}
}
}},
{ "$group": {
"_id": "$number",
"total": { "$sum": "$total" }
}}
])
If you are not on version 3.2 you will need to denormalize the "rows" array before the $project stage using the $unwind operator.
db.collection.aggregate([
{ "$unwind": "$rows" },
{ "$project": {
"number": 1,
"value": { "$multiply": [
{ "$ifNull": [ "$rows.quantity", 0 ] },
{ "$ifNull": [ "$rows.price", 0 ] }
]}
}},
{ "$group": {
"_id": "$number",
"total": { "$sum": "$value" }
}}
])
I have an article collection:
{
_id: 9999,
authorId: 12345,
coAuthors: [23456,34567],
title: 'My Article'
},
{
_id: 10000,
authorId: 78910,
title: 'My Second Article'
}
I'm trying to figure out how to get a list of distinct author and co-author ids out of the database. I have tried push, concat, and addToSet, but can't seem to find the right combination. I'm on 2.4.6 so I don't have access to setUnion.
Whilst $setUnion would be the "ideal" way to do this, there is another way that basically involved "switching" between a "type" to alternate which field is picked:
db.collection.aggregate([
{ "$project": {
"authorId": 1,
"coAuthors": { "$ifNull": [ "$coAuthors", [null] ] },
"type": { "$const": [ true,false ] }
}},
{ "$unwind": "$coAuthors" },
{ "$unwind": "$type" },
{ "$group": {
"_id": {
"$cond": [
"$type",
"$authorId",
"$coAuthors"
]
}
}},
{ "$match": { "_id": { "$ne": null } } }
])
And that is it. You may know the $const operation as the $literal operator from MongoDB 2.6. It has always been there, but was only documented and given an "alias" at the 2.6 release.
Of course the $unwind operations in both cases produce more "copies" of the data, but this is grouping for "distinct" values so it does not matter. Just depending on the true/false alternating value for the projected "type" field ( once unwound ) you just pick the field alternately.
Also this little mapReduce does much the same thing:
db.collection.mapReduce(
function() {
emit(this.authorId,null);
if ( this.hasOwnProperty("coAuthors"))
this.coAuthors.forEach(function(id) {
emit(id,null);
});
},
function(key,values) {
return null;
},
{ "out": { "inline": 1 } }
)
For the record, $setUnion is of course a lot cleaner and more performant:
db.collection.aggregate([
{ "$project": {
"combined": {
"$setUnion": [
{ "$map": {
"input": ["A"],
"as": "el",
"in": "$authorId"
}},
{ "$ifNull": [ "$coAuthors", [] ] }
]
}
}},
{ "$unwind": "$combined" },
{ "$group": {
"_id": "$combined"
}}
])
So there the only real concerns are converting the singular "authorId" to an array via $map and feeding an empty array where the "coAuthors" field is not present in the document.
Both output the same distinct values from the sample documents:
{ "_id" : 78910 }
{ "_id" : 23456 }
{ "_id" : 34567 }
{ "_id" : 12345 }