In a MongoDB collection, I have documents with a "position" field for ordering and an optional "date" field, e.g.
[
{
"_id": "doc1",
"position": 1
},
{
"_id": "doc2",
"position": 2,
"date": "2021-05-20T08:00:00.000Z"
},
{
"_id": "doc3",
"position": 3
},
{
"_id": "doc4",
"position": 4,
"date": "2021-05-20T08:00:00.000Z"
}
]
I would like the query this collection to get the documents "before" a specified date, in position order. The algorithm would be:
find the first element whose date is "after" the specified date
return all the documents whose position is less than the position of the element found, sorted by "position"
I have implemented this algorithm naïvely with 2 independent queries. However, I suspect it can be done with a single call to the database, but I have no idea how to proceed. Maybe with an aggregation pipeline?
Can someone give me a clue how this can be done?
EDIT: Here are the current queries I use (roughly):
limit_element = db.getCollection('collection').find({
"date": { "$gte": ISODate("2021-05-20T08:00:00.000Z") }
}).sort({
"position": 1
}).limit(1)
position = limit_element['position']
elements = db.getCollection('collection').find({
"position": { "$lt": position }
}).sort({
"position": 1
})
You can use an aggregation pipeline with two match clauses. Essentially its the same thing as you do now but within one DB access so a bit faster. With aggregation you can acess results from the previus stage to use in the next stage. If that is worth it you have to decide. I think your naive approach is sensible. In any case this a conditional problem so you will have to first find one and then do the other. Difference is just where you do the steps.
Related
Hi I am trying to increment the count of the matching requirement in an array. My sample collection looks like the following:
{
"_id": ObjectId("60760ba2e870fa518f2ae48b"),
"userId": "6075f7289822d94dca8066b4",
"requirements": [
{
"searchText": "zee5",
"planType": "basic",
"mode": "PRIVATE",
"count": 32.0
},
{
"searchText": "sony",
"planType": "standard",
"mode": "PUBLIC",
"count": 12.0
},
{
"searchText": "prime",
"planType": "premium",
"mode": "PRIVATE",
"count": 2
}
]
}
If a user searches for prime, with filter premium and PRIVATE, then the count of the last requirement should be updated. If he searches for prime, with filter standard and PRIVATE, then the new requirement will be inserted with count 1.
I am doing in two steps. First I fire an update with the following query and then if no update, I fire a push query with count 1:
db.getCollection('userProfile').update({ "$and" : [{ "requirements.searchText" : {$eq:"prime"}}, {"requirements.mode" : {$eq: "PUBLIC"}}, {"requirements.planType": {$eq: "standard"}}, { "userId" : "6075f7289822d94dca8066b4"}]}, {$inc: {"requirements.$.count" : 1}})
I was expecting that the above query will not update any requirement, since there is no exact match. Interestingly, it increments the count of the second requirement with (sony, standard, public). What is wrong with the query? How can I get it right?
Demo - with Update - https://mongoplayground.net/p/-ISXaAayxxv
Demo No update - https://mongoplayground.net/p/88bTj3lz7U_
Use $elemMatch to make sure all properties are present in the same object inside the array
The $elemMatch operator matches documents that contain an array field with at least one element that matches all the specified query criteria.
db.collection.update(
{
"requirements": {
$elemMatch: { "searchText": "prime","mode": "PUBLIC", "planType": "standard" }
},
"userId": "6075f7289822d94dca8066b4"
},
{ $inc: { "requirements.$.count": 1 } }
)
Problem -
Your current query will match any document with all these fields in
requirements array in any object, if they match 1 property in 1 index of the array and another match in the next index query will find the document valid.
"searchText": "prime",
"mode": "PUBLIC",
"planType": "standard"
I am beginner in MongoDB and struck at a place I am trying to fetch data from nested array but is it taking so long time as data is around 50K data, also it is not much accurate data, below is schema structure please see once -
{
"_id": {
"$oid": "6001df3312ac8b33c9d26b86"
},
"City": "Los Angeles",
"State":"California",
"Details": [
{
"Name": "Shawn",
"age": "55",
"Gender": "Male",
"profession": " A science teacher with STEM",
"inDate": "2021-01-15 23:12:17",
"Cars": [
"BMW","Ford","Opel"
],
"language": "English"
},
{
"Name": "Nicole",
"age": "21",
"Gender": "Female",
"profession": "Law student",
"inDate": "2021-01-16 13:45:00",
"Cars": [
"Opel"
],
"language": "English"
}
],
"date": "2021-01-16"
}
Here I am trying to filter date with date and Details.Cars like
db.getCollection('news').find({"Details.Cars":"BMW","date":"2021-01-16"}
it is returning details of other persons too which do not have cars- BMW , Only trying to display details of person like - Shawn which have BMW or special array value and date too not - Nicole, rest should not appear but is it not happening.
Any help is appreciated. :)
A combination of $match on the top-level fields and $filter on the array elements will do what you seek.
db.foo.aggregate([
{$match: {"date":"2021-01-16"}}
,{$addFields: {"Details": {$filter: {
input: "$Details",
as: "zz",
cond: { $in: ['BMW','$$zz.Cars'] }
}}
}}
,{$match: {$expr: { $gt:[{$size:"$Details"},0] } }}
]);
Notes:
$unwind is overly expensive for what is needed here and it likely means "reassembling" the data shape later.
We use $addFields where the new field to add (Details) already exists. This effectively means "overwrite in place" and is a common idiom when filtering an array.
The second $match will eliminate docs where the date matches but not a single entry in Details.Cars is a BMW i.e. the array has been filtered down to zero length. Sometimes you want to know this info so if this is the case, do not add the final $match.
I recommend you look into using real dates i.e. ISODate instead of strings so that you can easily take advantage of MongoDB date math and date formatting functions.
Is a common mistake think that find({nested.array:value}) will return only the nested object but actually, this query return the whole object which has a nested object with desired value.
The query is returning the whole document where value BMW exists in the array Details.Cars. So, Nicole is returned too.
To solve this problem:
To get multiple elements that match the criteria you can do an aggregation stage using $unwind to separate the different objects into array and match by the criteria you want.
db.collection.aggregate([
{
"$match": { "Details.Cars": "BMW", "date": "2021-01-26" }
},
{
"$unwind": "$Details"
},
{
"$match": { "Details.Cars": "BMW" }
}
])
This query first match by the criteria to avoid $unwind over all collection.
Then $unwind to get every document and $match again to get only the documents you want.
Example here
To get only one element (for example, if you match by _id and its unique) you can use $elemMatch in this way:
db.collection.find({
"Details.Cars": "BMW",
"date": "2021-01-16"
},
{
"Details": {
"$elemMatch": {
"Cars": "BMW"
}
}
})
Example here
You can use $elemenMatch into query or projection stage. Docs here and here
Using $elemMatch into query the way is this:
db.collection.find({
"Details": {
"$elemMatch": {
"Cars": "BMW"
}
},
"date": "2021-01-16"
},
{
"Details.$": 1
})
Example here
The result is the same. In the second case you are using positional operator to return, as docs says:
The first element that matches the query condition on the array.
That is, the first element where "Cars": "BMW".
You can choose the way you want.
I have a mongoDB collection of documents like this:
{
"_id": 1,
"size": 10,
"name": "ABCD"
}
I would like to:
Sort them by "name" in ascending order
Return however many first documents from the result, where their cumulative "size" will be greater or equal to 100
I have briefly looked into $redact stage of aggregation framework, but I can't figure out whether I can store the cumulative sum outside the document. What would be the best approach to solve this problem?
EDIT:
An example collection:
{ "name": "AAAA", "size": 2}
{ "name": "BBBB", "size": 4}
{ "name": "CCCC", "size": 3}
So the query would be designed to return the first X documents, in order of their appearance, when their cumulative size reaches 6.
So output will be (because 2+4 is 6):
{ "name": "AAAA", "size": 2}
{ "name": "BBBB", "size": 4}
The only thing I can think of is to use the Cursor on the application level, and keep adding documents to result set, incrementing the "size" counter by value in the document. But is there a way to do that using Aggregation framework, for example?
EDIT2:
I also came across the 'rolling sum' terminology and using map-reduce. Sadly, in my case I would want the map-reduce operation to terminate when a global scope variable gets to or over a certain value, and I don't think it's possible (mapReduce will go over all documents fed to it at the outset).
I have startTime and endTime for all records like this:
{
startTime : 21345678
endTime : 31345678
}
I am trying to find number of all the conflicts. For example if there are two records and they overlap the number of conflict is 1. If there are three records and two of them overlap the conflict is 1. If there are three records and all three overlap the conflicts is 3 i.e [(X1, X2), (X1, X3), (X2, X3)]
As an algorithm I am thinking of sorting the data by start time and for each sorted record checking the end time and finding the records with start time less than the end time. This will be O(n2) time. A better approach will be using interval tree and inserting each record into the tree and finding the counts when overlaps occur. This will be O(nlgn) time.
I have not used mongoDB much so what kind of query can I use to achieve something like this?
As you correctly mention, there are different approaches with varying complexity inherent to their execution. This basically covers how they are done and which one you implement actually depends on which your data and use case is best suited to.
Current Range Match
MongoDB 3.6 $lookup
The most simple approach can be employed using the new syntax of the $lookup operator with MongoDB 3.6 that allows a pipeline to be given as the expression to "self join" to the same collection. This can basically query the collection again for any items where the starttime "or" endtime of the current document falls between the same values of any other document, not including the original of course:
db.getCollection('collection').aggregate([
{ "$lookup": {
"from": "collection",
"let": {
"_id": "$_id",
"starttime": "$starttime",
"endtime": "$endtime"
},
"pipeline": [
{ "$match": {
"$expr": {
"$and": [
{ "$ne": [ "$$_id", "$_id" },
{ "$or": [
{ "$and": [
{ "$gte": [ "$$starttime", "$starttime" ] },
{ "$lte": [ "$$starttime", "$endtime" ] }
]},
{ "$and": [
{ "$gte": [ "$$endtime", "$starttime" ] },
{ "$lte": [ "$$endtime", "$endtime" ] }
]}
]},
]
},
"as": "overlaps"
}},
{ "$count": "count" },
]
}},
{ "$match": { "overlaps.0": { "$exists": true } } }
])
The single $lookup performs the "join" on the same collection allowing you to keep the "current document" values for the "_id", "starttime" and "endtime" values respectively via the "let" option of the pipeline stage. These will be available as "local variables" using the $$ prefix in subsequent "pipeline" of the expression.
Within this "sub-pipeline" you use the $match pipeline stage and the $expr query operator, which allows you to evaluate aggregation framework logical expressions as part of the query condition. This allows the comparison between values as it selects new documents matching the conditions.
The conditions simply look for the "processed documents" where the "_id" field is not equal to the "current document", $and where either the "starttime"
$or "endtime" values of the "current document" falls between the same properties of the "processed document". Noting here that these as well as the respective $gte and $lte operators are the "aggregation comparison operators" and not the "query operator" form, as the returned result evaluated by $expr must be boolean in context. This is what the aggregation comparison operators actually do, and it's also the only way to pass in values for comparison.
Since we only want the "count" of the matches, the $count pipeline stage is used to do this. The result of the overall $lookup will be a "single element" array where there was a count, or an "empty array" where there was no match to the conditions.
An alternate case would be to "omit" the $count stage and simply allow the matching documents to return. This allows easy identification, but as an "array embedded within the document" you do need to be mindful of the number of "overlaps" that will be returned as whole documents and that this does not cause a breach of the BSON limit of 16MB. In most cases this should be fine, but for cases where you expect a large number of overlaps for a given document this can be a real case. So it's really something more to be aware of.
The $lookup pipeline stage in this context will "always" return an array in result, even if empty. The name of the output property "merging" into the existing document will be "overlaps" as specified in the "as" property to the $lookup stage.
Following the $lookup, we can then do a simple $match with a regular query expression employing the $exists test for the 0 index value of output array. Where there actually is some content in the array and therefore "overlaps" the condition will be true and the document returned, showing either the count or the documents "overlapping" as per your selection.
Other versions - Queries to "join"
The alternate case where your MongoDB lacks this support is to "join" manually by issuing the same query conditions outlined above for each document examined:
db.getCollection('collection').find().map( d => {
var overlaps = db.getCollection('collection').find({
"_id": { "$ne": d._id },
"$or": [
{ "starttime": { "$gte": d.starttime, "$lte": d.endtime } },
{ "endtime": { "$gte": d.starttime, "$lte": d.endtime } }
]
}).toArray();
return ( overlaps.length !== 0 )
? Object.assign(
d,
{
"overlaps": {
"count": overlaps.length,
"documents": overlaps
}
}
)
: null;
}).filter(e => e != null);
This is essentially the same logic except we actually need to go "back to the database" in order to issue the query to match the overlapping documents. This time it's the "query operators" used to find where the current document values fall between those of the processed document.
Because the results are already returned from the server, there is no BSON limit restriction on adding content to the output. You might have memory restrictions, but that's another issue. Simply put we return the array rather than cursor via .toArray() so we have the matching documents and can simply access the array length to obtain a count. If you don't actually need the documents, then using .count() instead of .find() is far more efficient since there is not the document fetching overhead.
The output is then simply merged with the existing document, where the other important distinction is that since theses are "multiple queries" there is no way of providing the condition that they must "match" something. So this leaves us with considering there will be results where the count ( or array length ) is 0 and all we can do at this time is return a null value which we can later .filter() from the result array. Other methods of iterating the cursor employ the same basic principle of "discarding" results where we do not want them. But nothing stops the query being run on the server and this filtering is "post processing" in some form or the other.
Reducing Complexity
So the above approaches work with the structure as described, but of course the overall complexity requires that for each document you must essentially examine every other document in the collection in order to look for overlaps. Therefore whilst using $lookup allows for some "efficiency" in reduction of transport and response overhead, it still suffers the same problem that you are still essentially comparing each document to everything.
A better solution "where you can make it fit" is to instead store a "hard value"* representative of the interval on each document. For instance we could "presume" that there are solid "booking" periods of one hour within a day for a total of 24 booking periods. This "could" be represented something like:
{ "_id": "A", "booking": [ 10, 11, 12 ] }
{ "_id": "B", "booking": [ 12, 13, 14 ] }
{ "_id": "C", "booking": [ 7, 8 ] }
{ "_id": "D", "booking": [ 9, 10, 11 ] }
With data organized like that where there was a set indicator for the interval the complexity is greatly reduced since it's really just a matter of "grouping" on the interval value from the array within the "booking" property:
db.booking.aggregate([
{ "$unwind": "$booking" },
{ "$group": { "_id": "$booking", "docs": { "$push": "$_id" } } },
{ "$match": { "docs.1": { "$exists": true } } }
])
And the output:
{ "_id" : 10, "docs" : [ "A", "D" ] }
{ "_id" : 11, "docs" : [ "A", "D" ] }
{ "_id" : 12, "docs" : [ "A", "B" ] }
That correctly identifies that for the 10 and 11 intervals both "A" and "D" contain the overlap, whilst "B" and "A" overlap on 12. Other intervals and documents matching are excluded via the same $exists test except this time on the 1 index ( or second array element being present ) in order to see that there was "more than one" document in the grouping, hence indicating an overlap.
This simply employs the $unwind aggregation pipeline stage to "deconstruct/denormalize" the array content so we can access the inner values for grouping. This is exactly what happens in the $group stage where the "key" provided is the booking interval id and the $push operator is used to "collect" data about the current document which was found in that group. The $match is as explained earlier.
This can even be expanded for alternate presentation:
db.booking.aggregate([
{ "$unwind": "$booking" },
{ "$group": { "_id": "$booking", "docs": { "$push": "$_id" } } },
{ "$match": { "docs.1": { "$exists": true } } },
{ "$unwind": "$docs" },
{ "$group": {
"_id": "$docs",
"intervals": { "$push": "$_id" }
}}
])
With output:
{ "_id" : "B", "intervals" : [ 12 ] }
{ "_id" : "D", "intervals" : [ 10, 11 ] }
{ "_id" : "A", "intervals" : [ 10, 11, 12 ] }
It's a simplified demonstration, but where the data you have would allow it for the sort of analysis required then this is the far more efficient approach. So if you can keep the "granularity" to be fixed to "set" intervals which can be commonly recorded on each document, then the analysis and reporting can use the latter approach to quickly and efficiently identify such overlaps.
Essentially, this is how you would implement what you basically mentioned as a "better" approach anyway, and the first being a "slight" improvement over what you originally theorized. See which one actually suits your situation, but this should explain the implementation and the differences.
with 2 documents like :
{
"name": "hello",
"family": 1
},
{
"name": "world",
"family": 1,
"category": 2
}
and a query like :
doc.find({$or: [{family: 1}, {category: 2}]})
how can i have results sorted with the one matching the 2 conditions ("world") as a first result but still have the doc matching only 1 condition as a last result ("hello") ?
i can't use the default $and operator as i would not see the "hello" document that do not match both conditions.
i saw how aggregation could help but for a more complex example than that it would be a lot of computation, i'm guessing this is common use case and there must be something obvious i'm missing
You cannot do that sort of query (pun not intended) with a simple .find() statement. What you are asking for involves "weighting", which is applying a "calculated precedence to values.
Anything with "calculation" basically conditions to be programmatically applied, and the particular assertion here to "sort" rules out the "JavaScript runner" options like mapReduce and simply leaves the Aggregation Framework or other handling of the results.
For the aggregation framework approach you would need to $project a calculated "weight" to each matched document based on the conditions:
db.collection.aggregate([
// Same match conditions to filter
{ "$match": { "$or": [{ "family": 1, }, { "category": 2 }] } },
// Assign the "weight" based on conditions
{ "$project": {
"name": 1,
"family": 1,
"weight": {
"$add": [
{ "$cond": {
"if": { "$eq": [ "$family", 1 ] },
"then": 1,
"else": 0
}},
{ "$cond": {
"if": { "$eq": [ "$category", 2 ] },
"then": 1,
"else": 0
}}
]
}
}},
// Then sort "descending" with highest "weight" on top
{ "$sort": { "weight": -1 } }
])
Basically you are using $cond to evaluate the condition that the returned document actually has data meeting your condition, since in the selection either field being present is a valid response. Where the condition is present we assign a value, and where not the value is 0.
When "both" conditions are present the $add operation combines the total in the weight. So here documents that met only one condition have a 1 and for both they have 2. If you waned for example "family" to have the greater preference, then you would assign 2 in the condition, leaving you with possible document scores of:
3 : For both category and family
2 : For family only
1 : For category only
You could shorten the syntax of the $project in MongoDB 3.4 or later with the $addFields pipeline operator instead, which is most useful when you have a "lot" of other document properties you want to return without needing to list them all in the $project.
Aside from this, the database services don't allow for "calculations" on the "sort". This is considered "manipulation", which is the purpose of the Aggregation Framework.
Whilst you can do the same sort of "weighting" by post processing the result set in client code, the issue here is of course where you want to "limit" the results to return in actions like "paging". This is where running the operations on the server comes into play, and the reason why you use the Aggregation Framework for this.