I have a collection with array countries values like this. I want to sum the values of the countries.
{
"_id": ObjectId("54cd5e7804f3b06c3c247428"),
"country_json": {
"AE": NumberLong("13"),
"RU": NumberLong("16"),
"BA": NumberLong("10"),
...
}
},
{
"_id": ObjectId("54cd5e7804f3b06c3c247429"),
"country_json": {
"RU": NumberLong("12"),
"ES": NumberLong("28"),
"DE": NumberLong("16"),
"AU": NumberLong("44"),
...
}
}
How to sum the values of countries to get a result like this?
{
"AE": 13,
"RU": 28,
..
}
This can simply be done using aggregation
> db.test.aggregate([
{$project: {
RU: "$country_json.RU",
AE: "$country_json.AE",
BA: "$country_json.BA"
}},
{$group: {
_id: null,
RU: {$sum: "$RU"},
AE: {$sum: "$AE"},
BA: {$sum: "$BA"}
}
])
Output:
{
"_id" : null,
"RU" : NumberLong(28),
"AE" : NumberLong(13),
"BA" : NumberLong(10)
}
This isn't a very good document structure if you intend to aggregate statistics across the "keys" like this. Not really a fan of "data as key names" anyway, but the main point is it does not "play well" with many MongoDB query forms due to the key names being different everywhere.
Particularly with the aggregation framework, a better form to store the data is within an actual array, like so:
{
"_id": ObjectId("54cd5e7804f3b06c3c247428"),
"countries": [
{ "key": "AE", "value": NumberLong("13"),
{ "key": "RU", "value": NumberLong("16"),
{ "key": "BA", "value": NumberLong("10")
]
}
With that you can simply use the aggregation operations:
db.collection.aggregate([
{ "$unwind": "$countries" },
{ "$group": {
"_id": "$countries.key",
"value": { "$sum": "$countries.value" }
}}
])
Which would give you results like:
{ "_id": "AE", "value": NumberLong(13) },
{ "_id": "RU", "value": NumberLong(28) }
That kind of structure does "play well" with the aggregation framework and other MongoDB query patterns because it really is how it's "expected" to be done when you want to use the data in this way.
Without changing the structure of the document you are forced to use JavaScript evaluation methods in order to traverse the keys of your documents because that is the only way to do it with MongoDB:
db.collection.mapReduce(
function() {
var country = this.country_json;
Object.keys(country).forEach(function(key) {
emit( key, country[key] );
});
},
function(key,values) {
return values.reduce(function(p,v) { return NumberLong(p+v) });
},
{ "out": { "inline": 1 } }
)
And that would produce exactly the same result as shown from the aggregation example output, but working with the current document structure. Of course, the use of JavaScript evaluation is not as efficient as the native methods used by the aggregation framework so it's not going to perform as well.
Also note the possible problems here with "large values" in your cast NumberLong fields, since the main reason they are represented that way to JavaScipt is that JavaScipt itself has limitations on the size of that value than can be represented. Likely your values are just trivial but simply "cast" that way, but for large enough numbers as per the intent, then the math will simply fail.
So it's generally a good idea to consider changing how you structure this data to make things easier. As a final note, the sort of output you were expecting with all the keys in a single document is similarly counter intuitive, as again it requires traversing keys of a "hash/map" rather than using the natural iterators of arrays or cursors.
Related
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.
Assuming we have a document in my MongoDB collection like the following:
{
"_id": "coffee",
"orders": [ "espresso", "cappuccino", "espresso", ... ],
}
How do I use a single update statement that ensures there are exactly say 2 espressos in this document, without knowing how many there are to begin with?
I know that using 2 consecutive statements I can do
db.test.update(
{ _id: "coffee" },
{ "$pull": { "orders": "espresso" } }
);
followed by
db.test.update(
{ "_id": "coffee" },
{ "$push": { "orders": { "$each": ["espresso", "espresso"] } } }
);
But when combining both into a single statement, MongoDB balks with an error 40, claiming Updating the path 'orders' would create a conflict at 'orders' (understandable enough - how does MongoDB what to do first?).
So, how can I do the above in a single statement? Please note that since I'll be using the above in the context of a larger unordered bulk operation, combining the above in an ordered bulk operation won't work.
Thanks for your help!
I am new to Mongodb and wish to implement search on field in mongo collection.
I have the following structure for my test collection:-
{
'key': <unique key>,
'val_arr': [
['laptop', 'macbook pro', '16gb', 'i9', 'spacegrey'],
['cellphone', 'iPhone', '4gb', 't2', 'rose gold'],
['laptop', 'macbook air', '8gb', 'i5', 'black'],
['router', 'huawei', '10x10', 'white'],
['laptop', 'macbook', '8gb', 'i5', 'silve'],
}
And I wish to find them based on index number and value, i.e.
Find the entry where first element in any of the val_arr is laptop and 3rd element's value is 8gb.
I tried looking at composite indexes in mongodb, but they have a limit of 32 keys to be indexed. Any help in this direction is appreciated.
There is a limit on indexes here but it really should not matter. In your case you actually say 'key': <unique key>. So if that really is "unique" then it's the only thing in the collection that need be indexed, as long as you actually include that "key" as part of every query you make since this will determine you to select a document.
Indexes on arrays "within" a document really don't matter that much unless you actually intend to search directly for those elements within a document. That might be the case, but this actually has no bearing on matching your values by numbered index positions:
db.collection.find(
{
"val_arr": {
"$elemMatch": { "0": "laptop", "2": "8gb" }
}
},
{ "val_arr.$": 1 }
)
Which would return:
{
"val_arr" : [
[
"laptop",
"macbook air",
"8gb",
"i5",
"black"
]
]
}
The $elemMatch allows you to express "multiple conditions" on the same array element. This is needed over standard dot notation forms because otherwise the condition is simply looking for "any" array member which matches the value at the index. For instance:
db.collection.find({ "val_arr.0": "laptop", "val_arr.2": "4gb" })
Actually matches the given document even though that "combination" does not exist on a single "row", but both values are actually present in the array as a whole. But just in different members. Using those same values with $elemMatch makes sure the pair is matched on the same element.
Note the { "val_arr.$": 1 } in the above example, which is the projection for the "single" matched element. That is optional, but this is just to talk about identifying the matches.
Using .find() this is as much as you can do and is a limitation of the positional operator in that it can only identify one matching element. The way to do this for "multiple matches" is to use aggregate() with $filter:
db.collection.aggregate([
{ "$match": {
"val_arr": {
"$elemMatch": { "0": "laptop", "2": "8gb" }
}
}},
{ "$addFields": {
"val_arr": {
"$filter": {
"input": "$val_arr",
"cond": {
"$and": [
{ "$eq": [ { "$arrayElemAt": [ "$$this", 0 ] }, "laptop" ] },
{ "$eq": [ { "$arrayElemAt": [ "$$this", 2 ] }, "8gb" ] }
]
}
}
}
}}
])
Which returns:
{
"key" : "k",
"val_arr" : [
[
"laptop",
"macbook air",
"8gb",
"i5",
"black"
],
[
"laptop",
"macbook",
"8gb",
"i5",
"silve"
]
]
}
The initial query conditions which actually select the matching document go into the $match and are exactly the same as the query conditions shown earlier. The $filter is applied to just get the elements which actually match it's conditions. Those conditions do a similar usage of $arrayElemAt inside the logical expression as to how the index values of "0" and "2" are applies in the query conditions itself.
Using any aggregation expression incurs an additional cost over the standard query engine capabilities. So it is always best to consider if you really need it before you dive and and use the statement. Regular query expressions are always better as long as they do the job.
Changing Structure
Of course whilst it's possible to match on index positions of an array, none of this actually helps in being able to actually create an "index" which can be used to speed up queries.
The best course here is to actually use meaningful property names instead of plain arrays:
{
'key': "k",
'val_arr': [
{
'type': 'laptop',
'name': 'macbook pro',
'memory': '16gb',
'processor': 'i9',
'color': 'spacegrey'
},
{
'type': 'cellphone',
'name': 'iPhone',
'memory': '4gb',
'processor': 't2',
'color': 'rose gold'
},
{
'type': 'laptop',
'name': 'macbook air',
'memory': '8gb',
'processor': 'i5',
'color': 'black'
},
{
'type':'router',
'name': 'huawei',
'size': '10x10',
'color': 'white'
},
{
'type': 'laptop',
'name': 'macbook',
'memory': '8gb',
'processor': 'i5',
'color': 'silve'
}
]
}
This does allow you "within reason" to include the paths to property names within the array as part of a compound index. For example:
db.collection.createIndex({ "val_arr.type": 1, "val_arr.memory": 1 })
And then actually issuing queries looks far more descriptive in the code than cryptic values of 0 and 2:
db.collection.aggregate([
{ "$match": {
"val_arr": {
"$elemMatch": { "type": "laptop", "memory": "8gb" }
}
}},
{ "$addFields": {
"val_arr": {
"$filter": {
"input": "$val_arr",
"cond": {
"$and": [
{ "$eq": [ "$$this.type", "laptop" ] },
{ "$eq": [ "$$this.memory", "8gb" ] }
]
}
}
}
}}
])
Expected results, and more meaningful:
{
"key" : "k",
"val_arr" : [
{
"type" : "laptop",
"name" : "macbook air",
"memory" : "8gb",
"processor" : "i5",
"color" : "black"
},
{
"type" : "laptop",
"name" : "macbook",
"memory" : "8gb",
"processor" : "i5",
"color" : "silve"
}
]
}
The common reason most people arrive at a structure like you have in the question is typically because they think they are saving space. This is not simply not true, and with most modern optimizations to the storage engines MongoDB uses it's basically irrelevant over any small gains that might have been anticipated.
Therefore, for the sake of "clarity" and also in order to actually support indexing on the data within your "arrays" you really should be changing the structure and use named properties here instead.
And again, if your entire usage pattern of this data is not using the key property of the document in queries, then it probably would be better to store those entries as separate documents to begin with instead of being in an array at all. That also makes getting results more efficient.
So to break that all down your options here really are:
You actually always include key as part of your query, so indexes anywhere else but on that property do not matter.
You change to using named properties for the values on the array members allowing you to index on those properties without hitting "Multikey limitations"
You decide you never access this data using the key anyway, so you just write all the array data as separate documents in the collection with proper named properties.
Going with one of those that actually suits your needs best is essentially the solution allowing you to efficiently deal with the sort of data you have.
N.B Nothing to do with the topic at hand really ( except maybe a note on storage size ), but it would generally be recommended that things with an inherent numeric value such as the memory or "8gb" types of data actually be expressed as numeric rather than "strings".
The simple reasoning is that whilst you can query for "8gb" as an equality, this does not help you with ranges such as "between 4 and 12 gigabytes.
Therefore it usually makes much more sense to use numeric values like 8 or even 8000. Note that numeric values will actually have an impact on storage in that they will typically take less space than strings. Which given that the omission of property names may have been attempting to reduce storage but does nothing, does show an actual area where storage size can be reduced as well.
I have a collection containing objects with the following structure
{
"dep_id": "some_id",
"departament": "dep name",
"employees": [{
"name": "emp1",
"age": 31
},{
"name": "emp2",
"age": 35
}]
}
I would like to sort and save the array of employees for the object with id "some_id", by employees.age, descending. The best outcome would be to do this atomically using mongodb's query language. Is this possible?
If not, how can I rearrange the subdocuments without affecting the parent's other data or the data of the subdocuments? In case I have to download the data from the database and save back the sorted array of children, what would happen if something else performs an update to one of the children or children are added or removed in the meantime?
In the end, the data should be persisted to the database like this:
{
"dep_id": "some_id",
"departament": "dep name",
"employees": [{
"name": "emp2",
"age": 35
},{
"name": "emp1",
"age": 31
}]
}
The best way to do this is to actually apply the $sort modifier as you add items to the array. As you say in your comment "My actual objects have a "rank" and 'created_at'", which means that you really should have asked that in your question instead of writing a "contrived" case ( don't know why people do that ).
So for "sorting" by multiple properties, the following reference would adjust like this:
db.collection.update(
{ },
{ "$push": { "employees": { "$each": [], "$sort": { "rank": -1, "created_at": -1 } } } },
{ "multi": true }
)
But to update all the data you presently have "as is shown in the question", then you would sort on "age" with:
db.collection.update(
{ },
{ "$push": { "employees": { "$each": [], "$sort": { "age": -1 } } } },
{ "multi": true }
)
Which oddly uses $push to actually "modify" an array? Yes it's true, since the $each modifier says we are not actually adding anything new yet the $sort modifier is actually going to apply to the array in place and "re-order" it.
Of course this would then explain how "new" updates to the array should be written in order to apply that $sort and ensure that the "largest age" is always "first" in the array:
db.collection.update(
{ "dep_id": "some_id" },
{ "$push": {
"employees": {
"$each": [{ "name": "emp": 3, "age": 32 }],
"$sort": { "age": -1 }
}
}}
)
So what happens here is as you add the new entry to the array on update, the $sort modifier is applied and re-positions the new element between the two existing ones since that is where it would sort to.
This is a common pattern with MongoDB and is typically used in combination with the $slice modifier in order to keep arrays at a "maximum" length as new items are added, yet retain "ordered" results. And quite often "ranking" is the exact usage.
So overall, you can actually "update" your existing data and re-order it with "one simple atomic statement". No looping or collection renaming required. Furthermore, you now have a simple atomic method to "update" the data and maintain that order as you add new array items, or remove them.
In order to get what you want you can use the following query:
db.collection.aggregate({
$unwind: "$employees" // flatten employees array
}, {
$sort: {
"employees.name": -1 // sort all documents by employee name (descending)
}
}, {
$group: { // restore the previous structure
_id: "$_id",
"dep_id": {
$first: "$dep_id"
},
"departament": {
$first: "$departament"
},
"employees": {
$push: "$employees"
},
}
}, {
$out: "output" // write everything out to a separate collection
})
After this step you would want to drop your source table and rename the "output" collection to match your source table name.
This solution will, however, not deal with the concurrency issue. So you should remove write access from the collection first so nobody modifies it during the process and then restore it once you're done with the migration.
You could alternatively query all data first, then sort the employees array on the client side and then use either single update queries or - faster but more complicated - a bulk write operation with all the individual update calls in order to update the existing documents. Here, you could use the entire document that you've initially read as a filter for the update operation. So if an individual update does not modify any document you'd know straight away, that some other change must have modified the document you read before. Those cases you'd need to retry later (or straight away until the update does actually modify a document).
I'm trying to get MongoDB to aggregate for me over an array with different key-value pairs, without knowing keys (Just a simple sum would be ok.)
Example docs:
{data: [{a: 3}, {b: 7}]}
{data: [{a: 5}, {c: 12}, {f: 25}]}
{data: [{f: 1}]}
{data: []}
So basically each doc (or it's array really) can have 0 or many entries, and I don't know the keys for those objects, but I want to sum and average the values over those keys.
Right now I'm just loading a bunch of docs and doing it myself in Node, but I'd like to offload that work to MongoDB.
I know I can unwind those first, but how to proceed from there? How to sum/avg/min/max the values if I don't know the keys?
If you do not know the keys or cannot make a reasonable educated guess then you are basically stuck from going any further with the aggregation framework. You could supply "all of the keys" for consideration, but I supect your acutal data looks more like this:
{ "data": [{ "film": 10 }, { "televsion": 5 },{ "boardGames": 1 }] }
So there would be little point here findin out all the "key names" and then throwing that at an aggregation statement.
For the record though, "this is why you do not structure your data storage like this". Information like "film" here should not be used as a "key" name, because it is useful "data" that could be searched upon and most importantly "indexed" in a database system.
So your data should really look like this:
{
"data": [
{ "type": "film", "value": 10 },
{ "type": "televsion", "valule": 5 },
{ "type": "boardGames", "value": 1 }
]
}
Then the aggregation statement is simple, as are many other things:
db.collection.aggregate([
{ "$unwind": "$data" },
{ "$group": {
"_id": null,
"sum": { "$sum": "$data.value" },
"avg": { "$avg": "$data.value" }
}}
])
But since the key names are constantly changing in documents and do not have a uniform structure, then you need JavaScript processing on the server to traverse the keys, and that meand mapReduce:
db.collection.mapReduce(
function() {
this.data.forEach(function(data) {
Object.keys(data).forEach(function(key) {
emit(null,data[key]); // emit the value regardless of key name
});
});
},
function(key,values) {
return Array.sum(values); // Just summing for example
},
{ "out": { "inline": 1 } }
)
And of course the JavaScript execution here will work much more slowly than the native coded operators available to the aggregation framework.
So this should be an abject lesson as to why you don not use "data" as "key names" when storing data in a database. The aggregation framework works with standard structres and is fast, falling back to JavaScript processing is more flexible, but the cost is mostly in speed and other features.