Hello I'd like to know if it's possible to create pseudo properties on Mondodb. This is, currently I have a collections users like this:
{_id: (_1), name: "user1", secret: "1"}
{_id: (_2), name: "user2", secret: "2"}
When I query the database. I do something like:
function getuser(objectId) {
db.users.find({_id : objectId}).toArray(function(err, result) {
x = result[0];
x.pseudoField1 = hash(secret);
return x;
});
}
Then I do some operations on the x object, and return to put on the database, but before I have to filter the not needed properties, so I do:
y = {}
y._id = x._id
y.name = x.name
y.secret = x.secret
db.users.update({_id: y._id}, y);
What I'd like to do is know if there is any way to make the databse automaticaly return an object with the pseudoField1 with the function I want, and furthermore, when I issue an update with x, only the fields _id, name and secret get updated.
When you want to calculate fields on the database, you can use an aggregation pipeline with a $project stage. The aggregation frameworks offers some simple arithmetic operators to create fields which are derived from values of other fields, but implementing a complex hash function is likely far too complicated to do on the database.
Your second requirement - telling MongoDB to ignore a certain field when inserting - isn't possible out-of-the-box. But what you can do is remove the field from the document before saving it. You can also use an object-document wrapper like Mongoose which allows you to define schemas and exclude certain fields from storing them in the database.
Related
Let's say I have a Schema with a couple fields foo and bar;
I then want to retrieve all Documents using a projection. I want to retrieve all foos and bars with aliases and then "create" another field for my result based on some conditional logic of what bar is. If the condition is true, I simply want to tack on a leading '0' char to this new field, otherwise, I just want to set it to whatever barAlias is.
So, something like
const pipeline = [
{ $match: {} },
{
$project: {
fooAlias: "$foo",
barAlias: "$bar",
newField: (if some condition with barAlias) ? '0' + barAlias : barAlias
}
}
];
const docs = await Collection.aggregate(pipeline);
I know how to use $cond and $concat, but my issue here is that I'm trying to base my logic on the alias fields. Is this possible. Thanks!
I am trying to check If a field exists in a sub-document of an array and if it does, it will only provide those documents in the callback. But every time I log the callback document it gives me all values in my array instead of ones based on the query.
I am following this tutorial
And the only difference is I am using the findOne function instead of find function but it still gives me back all values. I tried using find and it does the same thing.
I am also using the same collection style as the example in the link above.
Example
In the image above you can see in the image above I have a document with a uid field and a contacts array. What I am trying to do is first select a document based on the inputted uid. Then after selecting that document then I want to display the values from the contacts array where contacts.uid field exists. So from the image above only values that would be displayed is contacts[0] and contacts[3] because contacts1 doesn't have a uid field.
Contact.contactModel.findOne({$and: [
{uid: self.uid},
{contacts: {
$elemMatch: {
uid: {
$exists: true,
$ne: undefined,
}
}
}}
]}
You problems come from a misconception about data modeling in MongoDB, not uncommon for developers coming from other DBMS. Let me illustrate this with the example of how data modeling works with an RDBMS vs MongoDB (and a lot of the other NoSQL databases as well).
With an RDBMS, you identify your entities and their properties. Next, you identify the relations, normalize the data model and bang your had against the wall for a few to get the UPPER LEFT ABOVE AND BEYOND JOIN™ that will answer the questions arising from use case A. Then, you pretty much do the same for use case B.
With MongoDB, you would turn this upside down. Looking at your use cases, you would try to find out what information you need to answer the questions arising from the use case and then model your data so that those questions can get answered in the most efficient way.
Let us stick with your example of a contacts database. A few assumptions to be made here:
Each user can have an arbitrary number of contacts.
Each contact and each user need to be uniquely identified by something other than a name, because names can change and whatnot.
Redundancy is not a bad thing.
With the first assumption, embedding contacts into a user document is out of question, since there is a document size limit. Regarding our second assumption: the uid field becomes not redundant, but simply useless, as there already is the _id field uniquely identifying the data set in question.
The use cases
Let us look at some use cases, which are simplified for the sake of the example, but it will give you the picture.
Given a user, I want to find a single contact.
Given a user, I want to find all of his contacts.
Given a user, I want to find the details of his contact "John Doe"
Given a contact, I want to edit it.
Given a contact, I want to delete it.
The data models
User
{
"_id": new ObjectId(),
"name": new String(),
"whatever": {}
}
Contact
{
"_id": new ObjectId(),
"contactOf": ObjectId(),
"name": new String(),
"phone": new String()
}
Obviously, contactOf refers to an ObjectId which must exist in the User collection.
The implementations
Given a user, I want to find a single contact.
If I have the user object, I have it's _id, and the query for a single contact becomes as easy as
db.contacts.findOne({"contactOf":self._id})
Given a user, I want to find all of his contacts.
Equally easy:
db.contacts.find({"contactOf":self._id})
Given a user, I want to find the details of his contact "John Doe"
db.contacts.find({"contactOf":self._id,"name":"John Doe"})
Now we have the contact one way or the other, including his/her/undecided/choose not to say _id, we can easily edit/delete it:
Given a contact, I want to edit it.
db.contacts.update({"_id":contact._id},{$set:{"name":"John F Doe"}})
I trust that by now you get an idea on how to delete John from the contacts of our user.
Notes
Indices
With your data model, you would have needed to add additional indices for the uid fields - which serves no purpose, as we found out. Furthermore, _id is indexed by default, so we make good use of this index. An additional index should be done on the contact collection, however:
db.contact.ensureIndex({"contactOf":1,"name":1})
Normalization
Not done here at all. The reasons for this are manifold, but the most important is that while John Doe might have only have the mobile number of "Mallory H Ousefriend", his wife Jane Doe might also have the email address "janes_naughty_boy#censored.com" - which at least Mallory surely would not want to pop up in John's contact list. So even if we had identity of a contact, you most likely would not want to reflect that.
Conclusion
With a little bit of data remodeling, we reduced the number of additional indices we need to 1, made the queries much simpler and circumvented the BSON document size limit. As for the performance, I guess we are talking of at least one order of magnitude.
In the tutorial you mentioned above, they pass 2 parameters to the method, one for filter and one for projection but you just passed one, that's the difference. You can change your query to be like this:
Contact.contactModel.findOne(
{uid: self.uid},
{contacts: {
$elemMatch: {
uid: {
$exists: true,
$ne: undefined,
}
}
}}
)
The agg framework makes filtering for existence of a field a little tricky. I believe the OP wants all docs where a field exists in an array of subdocs and then to return ONLY those subdocs where the field exists. The following should do the trick:
var inputtedUID = "0"; // doesn't matter
db.foo.aggregate(
[
// This $match finds the docs with our input UID:
{$match: {"uid": inputtedUID }}
// ... and the $addFields/$filter will strip out those entries in contacts where contacts.uid does NOT exist. We wish we could use {cond: {$zz.name: {$exists:true} }} but
// we cannot use $exists here so we need the convoluted $ifNull treatment. Note we
// overwrite the original contacts with the filtered contacts:
,{$addFields: {contacts: {$filter: {
input: "$contacts",
as: "zz",
cond: {$ne: [ {$ifNull:["$$zz.uid",null]}, null]}
}}
}}
,{$limit:1} // just get 1 like findOne()
]);
show(c);
{
"_id" : 0,
"uid" : 0,
"contacts" : [
{
"uid" : "buzz",
"n" : 1
},
{
"uid" : "dave",
"n" : 2
}
]
}
I want to update all the fields in a MongoDB document and I have a Javascript object that contains all these fields. I could easily type out each field to update but this seems like a lot of manual work and not reusable. I wanted to do something like below but this creates an object containing all the new field data within the document called newData.
I've tried JSON.stringify on the variable but the format isn't appropriate for update.
var newData = {
_id:ObjectId("53245234..."),
id: 88888,
firstData: "someData",
secondData: 787855,
thirdData: [ 45,17,12,234]
};
var collection = db.get('CollectionToUpdate');
//strip out dB id so as not to overwrite it, possibly not needed
if ("_id" in newData) {
delete newData["_id"];
}
//find the correct document based on program generated id and update
collection.update({id: newData.id}, {
newData
})
If you trust newData will not have any keys you don't intend (like update operators) this should work:
var collection = db.get('CollectionToUpdate');
collection.update({id: newData.id}, newData)
Note that this replaces the document. I assume that is what you meant by "update all the fields". update does not replace "_id".
Documentation for update
The internet is full of resources for dealing with arrays, but often objects are a more natural fit for data and seemingly more efficient.
I want to store key-value objects under dynamic field names like this:
project['en-US'] = { 'nav-back': 'Go back', ... }
project['pt-BR'] = { 'nav-back': 'Volte', ... }
Doing this seems like it would be more efficient than keeping an array of all languages and having to filter it to get all language entries for a given language.
My question is: How can I insert a key-value pair into an object with a dynamic name using mongoose? And would the object need to exist or can I create it if it doesn't in one operation?
I tried this:
await Project.update(
{ _id: projectId },
{
$set: {
[`${language}.${key}`]: value,
},
});
But no luck regardless of if I have an empty object there to begin with or not: { ok: 0, n: 0, nModified: 0 }.
Bonus: Should I index these objects and how? (I will want to update single items)
Thanks!
In mongoose, the schema is everything. It describe the data you gonna read/store from the database. If you wanna add dynamically a new key in the schema it's gonna be hard.
In this particulary case I would recommend to use the mongodb-native-driver which is way more permissive about the data manipulation. So you could read the data in a specific format and dynamically add your field into it.
To resume my thought, how should your dynamic change happen :
Use mongodb-native-driver to insert the new key into the database data
Modify the mongoose schema you have in the code (push a new key into it)
Use mongoose to manipulate the data afterward
Do not forget to dynamically update your mongoose model or you won't read the new key at the next find.
I solved this using the original code snippet unchanged, but adding { strict: false } to the schema:
const projectSchema = new Schema({ ...schema... }, { strict: false });
I've run into a bit of an issue with some data that I'm storing in my MongoDB (Note: I'm using mongoose as an ODM). I have two schemas:
mongoose.model('Buyer',{
credit: Number,
})
and
mongoose.model('Item',{
bid: Number,
location: { type: [Number], index: '2d' }
})
Buyer/Item will have a parent/child association, with a one-to-many relationship. I know that I can set up Items to be embedded subdocs to the Buyer document or I can create two separate documents with object id references to each other.
The problem I am facing is that I need to query Items where it's bid is lower than Buyer's credit but also where location is near a certain geo coordinate.
To satisfy the first criteria, it seems I should embed Items as a subdoc so that I can compare the two numbers. But, in order to compare locations with a geoNear query, it seems it would be better to separate the documents, otherwise, I can't perform geoNear on each subdocument.
Is there any way that I can perform both tasks on this data? If so, how should I structure my data? If not, is there a way that I can perform one query and then a second query on the result from the first query?
Thanks for your help!
There is another option (besides embedding and normalizing) for storing hierarchies in mongodb, that is storing them as tree structures. In this case you would store Buyers and Items in separate documents but in the same collection. Each Item document would need a field pointing to its Buyer (parent) document, and each Buyer document's parent field would be set to null. The docs I linked to explain several implementations you could choose from.
If your items are stored in two separate collections than the best option will be write your own function and call it using mongoose.connection.db.eval('some code...');. In such case you can execute your advanced logic on the server side.
You can write something like this:
var allNearItems = db.Items.find(
{ location: {
$near: {
$geometry: {
type: "Point" ,
coordinates: [ <longitude> , <latitude> ]
},
$maxDistance: 100
}
}
});
var res = [];
allNearItems.forEach(function(item){
var buyer = db.Buyers.find({ id: item.buyerId })[0];
if (!buyer) continue;
if (item.bid < buyer.credit) {
res.push(item.id);
}
});
return res;
After evaluation (place it in mongoose.connection.db.eval("...") call) you will get the array of item id`s.
Use it with cautions. If your allNearItems array will be too large or you will query it very often you can face the performance problems. MongoDB team actually has deprecated direct js code execution but it is still available on current stable release.