I've got a bunch of documents like this in my collection.
{
"success": true,
"timestamp": 1519296206,
"base": "EUR",
"date": "2018-07-04",
"rates": {
"AUD": 1.566015,
"CAD": 1.560132,
"CHF": 1.154727,
"CNY": 7.827874,
"GBP": 0.882047,
"JPY": 132.360679,
"USD": 1.23396,
}
}
I would like to only get date and the entire rates subdocument like below. I know I could add rates.AUD, rates.CAD etc. to the projection but that would make the projection extremely big and just unbearable to read and hard to maintain as a new field (or currency in this case) might get added in the future.
{
"date": "2018-07-04",
"rates": {
"AUD": 1.566015,
"CAD": 1.560132,
"CHF": 1.154727,
"CNY": 7.827874,
"GBP": 0.882047,
"JPY": 132.360679,
"USD": 1.23396,
}
}
Is there any projection similar to {date: 1, "rates.*". 1} that works like described above?
Maybe this?
db.col.aggregate([ {
$project: {
date: 1,
rates: 1
}
}])
Related
I am beginner in MongoDB and struck at a place I am trying to fetch data from nested array but is it taking so long time as data is around 50K data, also it is not much accurate data, below is schema structure please see once -
{
"_id": {
"$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.
Hello Good Developers,
I am facing a situation in MongoDB where I've JSON Data like this
[{
"id": "GLOBAL_EDUCATION",
"general_name": "GLOBAL_EDUCATION",
"display_name": "GLOBAL_EDUCATION",
"profile_section_id": 0,
"translated": [
{
"con_lang": "US-EN",
"country_code": "US",
"language_code": "EN",
"text": "What is the highest level of education you have completed?",
"hint": null
},
{
"con_lang": "US-ES",
"country_code": "US",
"language_code": "ES",
"text": "\u00bfCu\u00e1l es su nivel de educaci\u00f3n?",
"hint": null
}...
{
....
}
]
I am projecting result using the following query :
db.collection.find({
},
{
_id: 0,
id: 1,
general_name: 1,
translated: {
$elemMatch: {
con_lang: "US-EN"
}
}
})
here's a fiddle for the same: https://mongoplayground.net/p/I99ZXBfXIut
I want those records who don't match $elemMatch don't get returned at all.
In the fiddle output, you can see that the second item doesn't have translated attribute, In this case, I don't want the second Item at all to be returned.
I am using Laravel as Backend Tech, I can filter out those records using PHP, but there are lots of records returned, and I think filtering using PHP is not the best option.
You need to use $elemMatch in the first parameter
db.collection.find({
translated: {
$elemMatch: {
con_lang: "IT-EN"
}
}
})
MongoPlayground
I have another problem to solve here. Thinking in arrays sometimes could be very challenging. Here is what I am lined up with. This is what my data looks like: -
{
"_id": { "Firm": "ABC", "year": 2014 },
"Headings": [
{
"costHead": "MNF",
"amount": 500000
},
{
"costHead": "SLS",
"amount": 25000
},
{
"costHead": "OVRHD",
"amount": 100
}
]
}
{
"_id": { "Firm": "CDF", "year": 2015 },
"Headings": [
{
"costHead": "MNF",
"amount": 15000
},
{
"costHead": "SLS",
"amount": 100500
},
{
"costHead": "MNTNC",
"amount": 7500
}
]
}
As you can see, I have a list that has a whole bunch of sub-documents.
Here is what I want to do .. I need to add more elements to this "Headings" list which should be : -
{
"costHead": "FxdCost",
"amount": "$Headings.amount (for costhead MFC) + $Headings.amount (for costhead OVRHD)"
}
I am unsure as to how to produce the above. Here are some challenges: -
I can addToSet the new subdocument I wish to add but the problem is addToSet can only be used in the group stage - which would be expensive (unless of course there is no other way).
Even if I use addToSet, I always have to use the $ operator to refer to elements that I read from my JSON file. Now the element I am trying to add here (costHead: FxdCost) is not present in my JSON file and hence I cannot use the $ operator.
Does anyone have any advice on how to go about this. This is after all basic ETL.
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.
The document is like below.
{
"title": "Book1",
"dailyactiviescores":[
{
"date": 2013-06-05,
"score": 10,
},
{
"date": 2013-06-06,
"score": 21,
},
]
}
The daily active score is intended to increase once the book is opened by a reader. The first solution comes to mind is use "$" to find whether target date has a score or not, and deal with it.
err = bookCollection.Update(
{"title":"Book1", "dailyactivescore.date": 2013-06-06},
{"$inc":{"dailyactivescore.$.score": 1}})
if err == ErrNotFound {
bookCollection.Update({"title":"Book1"}, {"$push":...})
}
But I cannot help to think is there any way to return the index of an item inside array? If so, I could use one query to do the job rather than two. Like this.
index = bookCollection.Find(
{"title":"Book1", "dailyactivescore.date": 2013-06-06}).Select({"$index"})
if index != -1 {
incTarget = FormatString("dailyactivescore.%d.score", index)
bookCollection.Update(..., {"$inc": {incTarget: 1}})
} else {
//push here
}
Incrementing a field that's not present isn't the issue as doing $inc:1 on it will just create it and set it to 1 post-increment. The issue is when you don't have an array item corresponding to the date you want to increment.
There are several possible solutions here (that don't involve multiple steps to increment).
One is to pre-create all the dates in the array elements with scores:0 like so:
{
"title": "Book1",
"dailyactiviescores":[
{
"date": 2013-06-01,
"score": 0,
},
{
"date": 2013-06-02,
"score": 0,
},
{
"date": 2013-06-03,
"score": 0,
},
{
"date": 2013-06-04,
"score": 0,
},
{
"date": 2013-06-05,
"score": 0,
},
{
"date": 2013-06-06,
"score": 0
}, { etc ... }
]
}
But how far into the future to go? So one option here is to "bucket" - for example, have an activities document "per month" and before the start of a month have a job that creates the new documents for next month. Slightly yucky. But it'll work.
Other options involve slight changes in schema.
You can use a collection with book, date, activity_scores. Then you can use a simple upsert to increment a score:
db.books.update({title:"Book1", date:"2013-06-02", {$inc:{score:1}}, {upsert:true})
This will increment the score or insert new record with score:1 for this book and date and your collection will look like this:
{
"title": "Book1",
"date": 2013-06-01,
"score": 10,
},
{
"title": "Book1",
"date": 2013-06-02,
"score": 1,
}, ...
Depending on how much you simplified your example from your real use case, this might work well.
Another option is to stick with the array but switch to using the date string as a key that you increment:
Schema:
{
"title": "Book1",
"dailyactiviescores":{
{ "2013-06-01":10},
{ "2013-06-02":8}
}
}
Note it's now a subdocument and not an array and you can do:
db.books.update({title:"Book1"}, {"dailyactivityscores.2013-06-03":{$inc:1}})
and it will add a new date into the subdocument and increment it resulting in:
{
"title": "Book1",
"dailyactiviescores":{
{ "2013-06-01":10},
{ "2013-06-02":8},
{ "2013-06-03":1}
}
}
Note it's now harder to "add-up" the scores for the book so you can atomically also update a "subtotal" in the same update statement whether it's for all time or just for the month.
But here it's once again problematic to keep adding days to this subdocument - what happens when you're still around in a few years and these book documents grow hugely?
I suspect that unless you will only be keeping activity scores for the last N days (which you can do with capped array feature in 2.4) it will be simpler to have a separate collection for book-activity-score tracking where each book-day is a separate document than to embed the scores for each day into the book in a collection of books.
According to the docs:
The $inc operator increments a value of a field by a specified amount.
If the field does not exist, $inc sets the field to the specified
amount.
So, if there won't be a score field in the array item, $inc will set it to 1 in your case, like this:
{
"title": "Book1",
"dailyactiviescores":[
{
"date": 2013-06-05,
"score": 10,
},
{
"date": 2013-06-06,
},
]
}
bookCollection.Update(
{"title":"Book1", "dailyactivescore.date": 2013-06-06},
{"$inc":{"dailyactivescore.$.score": 1}})
will result into:
{
"title": "Book1",
"dailyactiviescores":[
{
"date": 2013-06-05,
"score": 10,
},
{
"date": 2013-06-06,
"score": 1
},
]
}
Hope that helps.