im having trouble query this type of data
[
{ev:1,
image:[{id:24,name:'ads1'},{id:25,name:'ads2'},{id:26,name:'ads4'},{id:27,name:'ads3'}]
},
{ev:1,
image:[{id:29,name:'ads1'},{id:23,name:'ads2'},{id:34,name:'ads4'},{id:50,name:'ads3'}]
}
]
this is my query
var data = schema.find( {
$and: [
{
ev: 1
},
{
'image.id': {
$in: [26,29,50,34]
}
}
]
} , 'ev image' )
what result i need is
[
{ev:1,
image:[{id:26,name:'ads4'}]
},
{ev:1,
image:[{id:29,name:'ads1'},{id:34,name:'ads4'}, {id:50,name:'ads3'}]
}
]
Im using moongose
but i keep getting back the whole image array
i basically need the image object filtered with only the image.id i want
PS there are more ev 1,2,3.... hence i need to only find in the ev i provide
could someone please help me with this
im new to mongodb
Find is used to keep or reject complete documents(find has a project also but in general for transformations we use aggregate operators).
The bellow is aggregation that keeps only part of the array.
Query
match the ev=1
filter the image and keep only those that are contained in the the array [26, 29, 50, 34]
if you want you can add another match to filter out the documents with empty array (no id was member of the array)
PlayMongo
aggregate(
[{"$match": {"ev": {"$eq": 1}}},
{"$set":
{"image":
{"$filter":
{"input": "$image",
"cond": {"$in": ["$$this.id", [26, 29, 50, 34]]}}}}}])
I am trying to create a CouchDB Mango Query with an index with the hope that the query runs faster. At the moment I have the following Mango Query which returns what I am looking for but it's slow. Therefore, I assume, I need to create an index to make it faster. I need help figuring out how to create that index.
selector: {
categoryIds: {
$in: categoryIds,
},
},
sort: [{ publicationDate: 'desc' }],
You can assume that my documents are let say news articles from different categories. Therefore in each document I have a field that contains one or more categories that the news article belongs to. For that I have an array of categoryIds for each document. My query needs to be optimized for queries like "Give me all news that have categoryId1 in their array of categoryIds sorted by publicationDate". What I don't know how to do is 1. How to define an index 2. What that index should be 3. How to use that index in "use_index" field of the Mango Query. Any help is appreciated.
Update after "Alexis Côté" answer:
If I define the index like this:
{
"_id": "_design/0f11ca4ef1ea06de05b31e6bd8265916c1bbe821",
"_rev": "6-adce50034e870aa02dc7e1e075c78361",
"language": "query",
"views": {
"categoryIds-json-index": {
"map": {
"fields": {
"categoryIds": "asc"
},
"partial_filter_selector": {}
},
"reduce": "_count",
"options": {
"def": {
"fields": [
"categoryIds"
]
}
}
}
}
}
And run the Mango Query like this:
{
"selector": {
"categoryIds": {
"$in": [
"e0bd5f97ac35bdf6893351337d269230"
]
}
},
"use_index": "categoryIds-json-index"
}
It still does return the results but they are not sorted in the order I want by publicationDate. So I am not clear what you are suggesting the solution is.
You can create an index as documented here
In your case, you will need an index on the "categoryIds" field.
You can specify the index using "use_index": "_design/<name>"
Note:The query planner should automatically pick this index if it's compatible.
Is there a direct way to project a new field if a value matches one in a huge sub array. I know i can use the $elemMatch or $ in the $match condition, but that would not allow me to get the rest of the non matching values (users in my case).
Basically i want to list all type 1 items and show all the users while highlighting the subscribed user. The reason i want to do this through mongodb is to avoid iterating over multiple thousand users for every item. Infact that is the part 2 of my question, can i limit the number of user's array that would be returned, i just need around 10 array values to be returned not thousands.
The collection structure is
{
name: "Coke",
type: 2,
users:[{user: 13, type:1},{ user:2: type:2}]
},
{
name: "Adidas",
type: 1,
users:[{user:31, type:3},{user: 51, type:1}]
},
{
name: "Nike",
type: 1,
users:[{user:21, type:3},{user: 31, type:1}]
}
Total documents are 200,000+ and growing...
Every document has 10,000~50,000 users..
expected return
{
isUser: true,
name: "Adidas",
type: 1,
users:[{user:31, type:3},{user: 51, type:1}]
},
{
isUser: false,
name: "Nike",
type: 1,
users:[{user:21, type:3},{user: 31, type:1}]
}
and i've been trying this
.aggregate([
{$match:{type:1}},
{$project:
{
isUser:{$elemMatch:["users.user",51]},
users: 1,
type:1,
name: 1
}
}
])
this fails, i get an error "Maximum Stack size exceeded". Ive tried alot of combinations and none seem to work. I really want to avoid running multiple calls to mongodb. Can this be done in a single call?
I've been told to use unwind, but i am bit worried that it might lead to memory issues.
If i was using mysql, a simple subquery would have done the job... i hope i am overlooking a similar simple solution in mongodb.
Process the conditions for the array elements and match the result by using a combination of the $anyElementTrue which evaluates an array as a set and returns true if any of the elements are true and false otherwise, the $ifNull operator will act as a safety net that evaluates the following $map expression and returns the value of the expression if the expression evaluates to a non-null value. The $map in the $ifNull operator is meant to apply the conditional statement expression to each item in the users array and returns an array with the applied results. The resulting array will then be used evaluated by the $anyElementTrue and this will ultimately calculate and return the isUser field for each document:
db.collection.aggregate([
{ "$match": { "type": 1} },
{
"$project": {
"name": 1, "type": 1,
"isUser": {
"$anyElementTrue": [
{
'$ifNull': [
{
"$map": {
"input": "$users",
"as": "el",
"in": { "$eq": [ "$$el.user",51] }
}
},
[false]
]
}
]
}
}
}
])
I'm fairly good with sql queries, but I can't seem to get my head around grouping and getting sum of mongo db documents,
With this in mind, I have a job model with schema like below :
{
name: {
type: String,
required: true
},
info: String,
active: {
type: Boolean,
default: true
},
all_service: [
price: {
type: Number,
min: 0,
required: true
},
all_sub_item: [{
name: String,
price:{ // << -- this is the price I want to calculate
type: Number,
min: 0
},
owner: {
user_id: { // <<-- here is the filter I want to put
type: Schema.Types.ObjectId,
required: true
},
name: String,
...
}
}]
],
date_create: {
type: Date,
default : Date.now
},
date_update: {
type: Date,
default : Date.now
}
}
I would like to have a sum of price column, where owner is present, I tried below but no luck
Job.aggregate(
[
{
$group: {
_id: {}, // not sure what to put here
amount: { $sum: '$all_service.all_sub_item.price' }
},
$match: {'not sure how to limit the user': given_user_id}
}
],
//{ $project: { _id: 1, expense: 1 }}, // you can only project fields from 'group'
function(err, summary) {
console.log(err);
console.log(summary);
}
);
Could someone guide me in the right direction. thank you in advance
Primer
As is correctly noted earlier, it does help to think of an aggregation "pipeline" just as the "pipe" | operator from Unix and other system shells. One "stage" feeds input to the "next" stage and so on.
The thing you need to be careful with here is that you have "nested" arrays, one array within another, and this can make drastic differences to your expected results if you are not careful.
Your documents consist of an "all_service" array at the top level. Presumably there are often "multiple" entries here, all containing your "price" property as well as "all_sub_item". Then of course "all_sub_item" is an array in itself, also containg many items of it's own.
You can think of these arrays as the "relations" between your tables in SQL, in each case a "one-to-many". But the data is in a "pre-joined" form, where you can fetch all data at once without performing joins. That much you should already be familiar with.
However, when you want to "aggregate" accross documents, you need to "de-normalize" this in much the same way as in SQL by "defining" the "joins". This is to "transform" the data into a de-normalized state that is suitable for aggregation.
So the same visualization applies. A master document's entries are replicated by the number of child documents, and a "join" to an "inner-child" will replicate both the master and initial "child" accordingly. In a "nutshell", this:
{
"a": 1,
"b": [
{
"c": 1,
"d": [
{ "e": 1 }, { "e": 2 }
]
},
{
"c": 2,
"d": [
{ "e": 1 }, { "e": 2 }
]
}
]
}
Becomes this:
{ "a" : 1, "b" : { "c" : 1, "d" : { "e" : 1 } } }
{ "a" : 1, "b" : { "c" : 1, "d" : { "e" : 2 } } }
{ "a" : 1, "b" : { "c" : 2, "d" : { "e" : 1 } } }
{ "a" : 1, "b" : { "c" : 2, "d" : { "e" : 2 } } }
And the operation to do this is $unwind, and since there are multiple arrays then you need to $unwind both of them before continuing any processing:
db.collection.aggregate([
{ "$unwind": "$b" },
{ "$unwind": "$b.d" }
])
So there the "pipe" first array from "$b" like so:
{ "a" : 1, "b" : { "c" : 1, "d" : [ { "e" : 1 }, { "e" : 2 } ] } }
{ "a" : 1, "b" : { "c" : 2, "d" : [ { "e" : 1 }, { "e" : 2 } ] } }
Which leaves a second array referenced by "$b.d" to further be de-normalized into the the final de-normalized result "without any arrays". This allows other operations to process.
Solving
With just about "every" aggregation pipeline, the "first" thing you want to do is "filter" the documents to only those that contain your results. This is a good idea, as especially when doing operations such as $unwind, then you don't want to be doing that on documents that do not even match your target data.
So you need to match your "user_id" at the array depth. But this is only part of getting the result, since you should be aware of what happens when you query a document for a matching value in an array.
Of course, the "whole" document is still returned, because this is what you really asked for. The data is already "joined" and we haven't asked to "un-join" it in any way.You look at this just as a "first" document selection does, but then when "de-normalized", every array element now actualy represents a "document" in itself.
So not "only" do you $match at the beginning of the "pipeline", you also $match after you have processed "all" $unwind statements, down to the level of the element you wish to match.
Job.aggregate(
[
// Match to filter possible "documents"
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// De-normalize arrays
{ "$unwind": "$all_service" },
{ "$unwind": "$all_service.all_subitem" },
// Match again to filter the array elements
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// Group on the "_id" for the "key" you want, or "null" for all
{ "$group": {
"_id": null,
"total": { "$sum": "$all_service.all_sub_item.price" }
}}
],
function(err,results) {
}
)
Alternately, modern MongoDB releases since 2.6 also support the $redact operator. This could be used in this case to "pre-filter" the array content before processing with $unwind:
Job.aggregate(
[
// Match to filter possible "documents"
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// Filter arrays for matches in document
{ "$redact": {
"$cond": {
"if": {
"$eq": [
{ "$ifNull": [ "$owner", given_user_id ] },
given_user_id
]
},
"then": "$$DESCEND",
"else": "$$PRUNE"
}
}},
// De-normalize arrays
{ "$unwind": "$all_service" },
{ "$unwind": "$all_service.all_subitem" },
// Group on the "_id" for the "key" you want, or "null" for all
{ "$group": {
"_id": null,
"total": { "$sum": "$all_service.all_sub_item.price" }
}}
],
function(err,results) {
}
)
That can "recursively" traverse the document and test for the condition, effectively removing any "un-matched" array elements before you even $unwind. This can speed things up a bit since items that do not match would not need to be "un-wound". However there is a "catch" in that if for some reason the "owner" did not exist on an array element at all, then the logic required here would count that as another "match". You can always $match again to be sure, but there is still a more efficient way to do this:
Job.aggregate(
[
// Match to filter possible "documents"
{ "$match": {
"all_service.all_sub_item.owner": given_user_id
}},
// Filter arrays for matches in document
{ "$project": {
"all_items": {
"$setDifference": [
{ "$map": {
"input": "$all_service",
"as": "A",
"in": {
"$setDifference": [
{ "$map": {
"input": "$$A.all_sub_item",
"as": "B",
"in": {
"$cond": {
"if": { "$eq": [ "$$B.owner", given_user_id ] },
"then": "$$B",
"else": false
}
}
}},
false
]
}
}},
[[]]
]
}
}},
// De-normalize the "two" level array. "Double" $unwind
{ "$unwind": "$all_items" },
{ "$unwind": "$all_items" },
// Group on the "_id" for the "key" you want, or "null" for all
{ "$group": {
"_id": null,
"total": { "$sum": "$all_items.price" }
}}
],
function(err,results) {
}
)
That process cuts down the size of the items in both arrays "drastically" compared to $redact. The $map operator processes each elment of an array to the given statement within "in". In this case, each "outer" array elment is sent to another $map to process the "inner" elements.
A logical test is performed here with $cond whereby if the "condiition" is met then the "inner" array elment is returned, otherwise the false value is returned.
The $setDifference is used to filter down any false values that are returned. Or as in the "outer" case, any "blank" arrays resulting from all false values being filtered from the "inner" where there is no match there. This leaves just the matching items, encased in a "double" array, e.g:
[[{ "_id": 1, "price": 1, "owner": "b" },{..}],[{..},{..}]]
As "all" array elements have an _id by default with mongoose (and this is a good reason why you keep that) then every item is "distinct" and not affected by the "set" operator, apart from removing the un-matched values.
Process $unwind "twice" to convert these into plain objects in their own documents, suitable for aggregation.
So those are the things you need to know. As I stated earlier, be "aware" of how the data "de-normalizes" and what that implies towards your end totals.
It sounds like you want to, in SQL equivalent, do "sum (prices) WHERE owner IS NOT NULL".
On that assumption, you'll want to do your $match first, to reduce the input set to your sum. So your first stage should be something like
$match: { all_service.all_sub_items.owner : { $exists: true } }
Think of this as then passing all matching documents to your second stage.
Now, because you are summing an array, you have to do another step. Aggregation operators work on documents - there isn't really a way to sum an array. So we want to expand your array so that each element in the array gets pulled out to represent the array field as a value, in its own document. Think of this as a cross join. This will be $unwind.
$unwind: { "$all_service.all_sub_items" }
Now you've just made a much larger number of documents, but in a form where we can sum them. Now we can perform the $group. In your $group, you specify a transformation. The line:
_id: {}, // not sure what to put here
is creating a field in the output document, which is not the same documents as the input documents. So you can make the _id here anything you'd like, but think of this as the equivalent to your "GROUP BY" in sql. The $sum operator will essentially be creating a sum for each group of documents you create here that match that _id - so essentially we'll be "re-collapsing" what you just did with $unwind, by using the $group. But this will allow $sum to work.
I think you're looking for grouping on just your main document id, so I think your $sum statement in your question is correct.
$group : { _id : $_id, totalAmount : { $sum : '$all_service.all_sub_item.price' } }
This will output documents with an _id field equivalent to your original document ID, and your sum.
I'll let you put it together, I'm not super familiar with node. You were close but I think moving your $match to the front and using an $unwind stage will get you where you need to be. Good luck!
If I had a schema that looked something like this:
var person = new Schema({
active: {type: Boolean},
otherSetting: {type: Boolean}
});
Would it be possible with just one query to get the entire total count of all people, total people active, total people inactive, as well as the total count for people with otherSetting set to true and other Setting set to false? Would otherSetting and active have to be broken into two queries?
I've been playing around with the aggregate framework on this problem and although this seems like a simple problem, I can't seem to do it with just one query.
Is it even possible? Thanks for any help.
The aggregation framework has logical operators such as $cond that work well with your boolean conition here:
db.collection.aggregate([
{ "$group": {
"_id": null,
"active": { "$sum": { "$cond": [ "$active", 1, 0 ] } },
"inActive": { "$sum": { "$cond": [ "$active", 0, 1 ] } },
"total": { "$sum": 1 }
}}
])
The $cond operator is a "ternary" operator ( if/then/else ) that allows the evaluation of a logical condition to return the true ( then ) or false ( else ) values.
The "boolean" is evaluated as true/false in the first argument to $cond which passes the appropriate value to $sum in order to get the conditional totals.
Everything works within a single $group pipeline stage with a grouping key _id of null since you want to add up the whole collection. If grouping on the value of another field then replace that null with the field you want.