I have a JSON in MongoDB and I am trying to check if at least one of the items in the JSON doesn't contain a specific field.
{
"_id" : 12345,
"orderItems" : [
{
"itemId" : 45678,
"isAvailable" : true,
"isEligible" " false
},
{
"itemId" : 87653,
"isAvailable" : true
}
]
}
So in the above JSON, since the 2nd one under order items doesn't contain iseligible field, I need to get this _id.
I tried the below query so far, which didnt work:
db.getCollection('orders').find({"orderItems.iseligible":{$exists:false})
You can use $elemMatch to evaluate the presence of the nested key. Once that's accomplished, project out the _id value.
db.orders.find({
orderItems: {
$elemMatch: {
"isEligible": {
$exists: false
}
}
}
},
{
_id: 1
})
Here is a Mongo playground with the finished code, and a similar SO answer.
I would like to update each elements (object) in an array of a company.
Here my actual data :
{
_id: ObjectId("60d31024860ce0400b586111")
contracts:
[
{
name: 1.pdf
url: "https://someurl"
createdAt: 2021-06-23T10:42:44.594+00:00
}
{
name: 2.pdf
url: "https://someurl"
}
{
name: 3.pdf
url: "https://someurl"
}
]
}
I would like to add a defined date on each object (in contracts) that has no "updatedAt" key.
Here what I tried :
db.companies.update({ _id: ObjectId("60d31024860ce0400b586111"),"contracts.createdAt": { $exists: false } },{ $set: { "contracts.$.createdAt": "test" } })
but I got this error :
"The positional operator did not find the match needed from the query."
I have also tried this and it works, but I don't wanna query by file name. I just wanna add "createdAt" on each elements found that has no "createdAt"
db.companies.update({ "contracts.name": "2.pdf" },{ $set: { "contracts.$.createdAt": "atest" } })
I think you need to use the filtered position operator:
$ - updates the first matched array element
$[] - updates all the matched elements with a specific condition
The specific condition is mentioned in the arrayFilters key.
db.students.update(
{ },
{ $set: { "contracts.$[element].createdAt" : "atest"} },
{ multi: true,
arrayFilters: [ { "element.createdAt": { $exists: false } } ]
}
)
multi - true is to apply the operation on all the matching documents.
Also notice, how the first query parameter is empty, which means the query runs for all documents. I used it based on the second query you wrote but you can also add in an ObjectID query there.
I have a document like this:
{
"_id" : "4mDYgt6gID",
...
"MultipleChoiceQuestions" : [
{
...
"LeadInFile" : null,
...
},
{
...
"LeadInFile" : 'some string',
...
}
],
...
}
How do I query for any documents that have a non-null value in LeadInFile?
I'm trying different things, currently something like this
db.getCollection('QuizTime:Quizzes').find({"MultipleChoiceQuestions": [{ "LeadInFile": { $ne: null}}]});
Is returning 0 records.
The current form of the query is saying:
Find documents where MultipleChoiceQuestions is [{ "LeadInFile": { $ne: null}}]
Try using dot notation; this is used to access elements of an array or fields in an embedded document. For example:
db.getCollection('QuizTime:Quizzes').find({
"MultipleChoiceQuestions.LeadinFile" : { "$ne" : null }
})
Suppose I have sample db structure
[
{ name: 'hello world', description: { key: 'something' } },
{ name: 'user', description: { key: 'hello world' } },
]
with index
db.fulltext.createIndex({ name: 'text', '$**': 'text' }, { weights: { name: 10, '$**': 5 } })
I am finding documents with the query
db.fulltext.find({ $text: { $search: 'hello world' } }, { score: { $meta: 'textScore' } })
But... It gives me 15.0 score for both documents... It's impossible to add weight to wildcard operator? Why second document multiply score from name key?
The wildcard index "$**" includes all the string fields in the document in the text index. In the above scenario, name is a string attribute for which weight was given as 10 and in general all string fields weight was assigned as 5 (including name field because wild card is used). So, the weight is overridden.
When the text search is done, equal weightage is given for all String fields. So, the score is same for both the documents as there is no relative significance to the other indexed fields (i.e. because the wild card was used while creating the index).
The $text operator assigns a score to each document that contains the
search term in the indexed fields. The score represents the relevance
of a document to a given text search query.
When different weight is need for different fields, you need to provide the field names specifically while creating the index. In other words, you should not provide a weight for a String field and include wild card weight for all string fields. Obviously, one weight will override the other.
If you can change the index as mentioned below, you can see the difference.
Create Index:-
db.fulltext.createIndex({ name: 'text', 'description.key' : 'text' }, { weights: { name: 10, 'description.key' : 5 } })
Search:-
db.fulltext.find({ $text: { $search: 'hello world' } }, { score: { $meta: 'textScore' } })
Result:-
{
"_id" : ObjectId("57e119cbf522cc85b5595797"),
"name" : "hello world",
"description" : {
"key" : "something"
},
"score" : 15
}
{
"_id" : ObjectId("57e119cbf522cc85b5595798"),
"name" : "user",
"description" : {
"key" : "hello world"
},
"score" : 7.5
}
Is there a way to conditionally $addToSet based on a specific key field in a subdocument on an array?
Here's an example of what I mean - given the collection produced by the following sample bootstrap;
cls
db.so.remove();
db.so.insert({
"Name": "fruitBowl",
"pfms" : [
{
"n" : "apples"
}
]
});
n defines a unique document key. I only want one entry with the same n value in the array at any one time. So I want to be able to update the pfms array using n so that I end up with just this;
{
"Name": "fruitBowl",
"pfms" : [
{
"n" : "apples",
"mState": 1111234
}
]
}
Here's where I am at the moment;
db.so.update({
"Name": "fruitBowl",
},{
// not allowed to do this of course
// "$pull": {
// "pfms": { n: "apples" },
// },
"$addToSet": {
"pfms": {
"$each": [
{
"n": "apples",
"mState": 1111234
}
]
}
}
}
)
Unfortunately, this adds another array element;
db.so.find().toArray();
[
{
"Name" : "fruitBowl",
"_id" : ObjectId("53ecfef5baca2b1079b0f97c"),
"pfms" : [
{
"n" : "apples"
},
{
"n" : "apples",
"mState" : 1111234
}
]
}
]
I need to effectively upsert the apples document matching on n as the unique identifier and just set mState whether or not an entry already exists. It's a shame I can't do a $pull and $addToSet in the same document (I tried).
What I really need here is dictionary semantics, but that's not an option right now, nor is breaking out the document - can anyone come up with another way?
FWIW - the existing format is a result of language/driver serialization, I didn't choose it exactly.
further
I've gotten a little further in the case where I know the array element already exists I can do this;
db.so.update({
"Name": "fruitBowl",
"pfms.n": "apples",
},{
$set: {
"pfms.$.mState": 1111234,
},
}
)
But of course that only works;
for a single array element
as long as I know it exists
The first limitation isn't a disaster, but if I can't effectively upsert or combine $addToSet with the previous $set (which of course I can't) then it the only workarounds I can think of for now mean two DB round-trips.
The $addToSet operator of course requires that the "whole" document being "added to the set" is in fact unique, so you cannot change "part" of the document or otherwise consider it to be a "partial match".
You stumbled on to your best approach using $pull to remove any element with the "key" field that would result in "duplicates", but of course you cannot modify the same path in different update operators like that.
So the closest thing you will get is issuing separate operations but also doing that with the "Bulk Operations API" which is introduced with MongoDB 2.6. This allows both to be sent to the server at the same time for the closest thing to a "contiguous" operations list you will get:
var bulk = db.so.initializeOrderedBulkOp();
bulk.find({ "Name": "fruitBowl", "pfms.n": "apples": }).updateOne({
"$pull": { "pfms": { "n": "apples" } }
});
bulk.find({ "Name": "fruitBowl" }).updateOne({
"$push": { "pfms": { "n": "apples", "state": 1111234 } }
})
bulk.execute();
That pretty much is your best approach if it is not possible or practical to move the elements to another collection and rely on "upserts" and $set in order to have the same functionality but on a collection rather than array.
I have faced the exact same scenario. I was inserting and removing likes from a post.
What I did is, using mongoose findOneAndUpdate function (which is similar to update or findAndModify function in mongodb).
The key concept is
Insert when the field is not present
Delete when the field is present
The insert is
findOneAndUpdate({ _id: theId, 'likes.userId': { $ne: theUserId }},
{ $push: { likes: { userId: theUserId, createdAt: new Date() }}},
{ 'new': true }, function(err, post) { // do the needful });
The delete is
findOneAndUpdate({ _id: theId, 'likes.userId': theUserId},
{ $pull: { likes: { userId: theUserId }}},
{ 'new': true }, function(err, post) { // do the needful });
This makes the whole operation atomic and there are no duplicates with respect to the userId field.
I hope this helpes. If you have any query, feel free to ask.
As far as I know MongoDB now (from v 4.2) allows to use aggregation pipelines for updates.
More or less elegant way to make it work (according to the question) looks like the following:
db.runCommand({
update: "your-collection-name",
updates: [
{
q: {},
u: {
$set: {
"pfms.$[elem]": {
"n":"apples",
"mState": NumberInt(1111234)
}
}
},
arrayFilters: [
{
"elem.n": {
$eq: "apples"
}
}
],
multi: true
}
]
})
In my scenario, The data need to be init when not existed, and update the field If existed, and the data will not be deleted. If the datas have these states, you might want to try the following method.
// Mongoose, but mostly same as mongodb
// Update the tag to user, If there existed one.
const user = await UserModel.findOneAndUpdate(
{
user: userId,
'tags.name': tag_name,
},
{
$set: {
'tags.$.description': tag_description,
},
}
)
.lean()
.exec();
// Add a default tag to user
if (user == null) {
await UserModel.findOneAndUpdate(
{
user: userId,
},
{
$push: {
tags: new Tag({
name: tag_name,
description: tag_description,
}),
},
}
);
}
This is the most clean and fast method in the scenario.
As a business analyst , I had the same problem and hopefully I have a solution to this after hours of investigation.
// The customer document:
{
"id" : "1212",
"customerCodes" : [
{
"code" : "I"
},
{
"code" : "YK"
}
]
}
// The problem : I want to insert dateField "01.01.2016" to customer documents where customerCodes subdocument has a document with code "YK" but does not have dateField. The final document must be as follows :
{
"id" : "1212",
"customerCodes" : [
{
"code" : "I"
},
{
"code" : "YK" ,
"dateField" : "01.01.2016"
}
]
}
// The solution : the solution code is in three steps :
// PART 1 - Find the customers with customerCodes "YK" but without dateField
// PART 2 - Find the index of the subdocument with "YK" in customerCodes list.
// PART 3 - Insert the value into the document
// Here is the code
// PART 1
var myCursor = db.customers.find({ customerCodes:{$elemMatch:{code:"YK", dateField:{ $exists:false} }}});
// PART 2
myCursor.forEach(function(customer){
if(customer.customerCodes != null )
{
var size = customer.customerCodes.length;
if( size > 0 )
{
var iFoundTheIndexOfSubDocument= -1;
var index = 0;
customer.customerCodes.forEach( function(clazz)
{
if( clazz.code == "YK" && clazz.changeDate == null )
{
iFoundTheIndexOfSubDocument = index;
}
index++;
})
// PART 3
// What happens here is : If i found the indice of the
// "YK" subdocument, I create "updates" document which
// corresponds to the new data to be inserted`
//
if( iFoundTheIndexOfSubDocument != -1 )
{
var toSet = "customerCodes."+ iFoundTheIndexOfSubDocument +".dateField";
var updates = {};
updates[toSet] = "01.01.2016";
db.customers.update({ "id" : customer.id } , { $set: updates });
// This statement is actually interpreted like this :
// db.customers.update({ "id" : "1212" } ,{ $set: customerCodes.0.dateField : "01.01.2016" });
}
}
}
});
Have a nice day !