I'm currently learning MongoDB why do we use .populate(). What is the difference between .populate() and .virtual()?
Can anyone tell me how they differ and when one should be used over the other?
Both serves a different purpose and one cannot be a replacement of other.
Virtual: If you want attributes that you can get and set but that are not themselves persisted to mongodb, virtual attributes is the Mongoose feature for you.Remember, only non-virtual properties work as part of queries and for field selection.See Virtual
Populate: ObjectIds can refer to another document in a collection within our database and be populate()d when querying.It gives you DBRef-like behavior. See Populate
Related
I am trying to read from a Mongo database using mongoose where the models make use of the discriminator inheritance functionality, but the documents in the DB are all inserted by another service (using the Java Mongo driver) which does not use mongoose nor its discriminators. All of my queries using subclass models (those which use the discriminator function) return empty arrays when I try to read from the DB. I think it's because mongoose is expecting those documents to contain a discriminator key, however the service which is inserting the documents has no knowledge of discriminator keys, and thus isn't setting them on the mongoDB documents.
How can I create my models and use the discriminator function such that they can still query for these documents inserted by another service?
For more context, I want to use discriminators because inheritance allows me to cleanly structure the fields of the models I'm creating and define model-specific static methods, and it lets me not write duplicate code. If there is a better way to accomplish these goals without using mongoose's built-in discriminator pattern, please share!
According to the documentation:
The way mongoose tells the difference between the different
discriminator models is by the 'discriminator key', which is __t by
default. Mongoose adds a String path called __t to your schemas that
it uses to track which discriminator this document is an instance of.
Also mongoose saves documents with discriminators to the single collections.
So, in order to have access to the documents you need to save __t parameter, and check if you save schemas with the same discriminators to a single collection
I'm trying to build an ionic application which retrieves data from Cloudant using pouchdb. Cloudant allows creating only databases and documents.
How can I create some collections in Cloudant?
Two part answer:
A set of documents that meet certain criteria can be considered a collection in Cloudant/CouchDB. You can create views to fetch those documents. Such a view might check for the existence of a property in a document ("all documents with a property named type"), the value of a property ("all documents with a property named type having the value of book") or any other condition that makes sense for your application and return the appropriate documents.
You basically have to follow a three step process:
determine how you can identify documents in your database that you consider to be part of the collection
create a view based on your findings in the previous step
query the view to retrieve those documents
Above documentation link provides more details.
Properties in your document can represent collections as well, as in the following example, which defines a simple array of strings.
{
"mycollectionname": [
"element1",
"element2",
...
]
}
How you implement collections really depends on your use-case scenario.
Long post, but hope that helps.
I would like to explain this with a RDBMS analogy.
In any RDBMS, a new database would mean a different connection with different set of credentials.
A collection would mean the set of tables in that particular database.
A record would mean a row in a table.
Similarly, you can look at a single Cloudant service instance as a database(RDBMS terminology).
A collection would be a "database" in that service instance in Cloudant's terminology.
A document would correpond to a single row.
Hence, Cloudant has no concept of collection as such. If you need to store your related documents in a separate collection you must do it with multiple databases within the same service instance.
If you want to use only a single database, you could create a field like "record_index" to differentiate between the different documents. While querying these documents, you could use an index. For. e.g. I have a student database. But I do not want to store the records for Arts, Commerce, Science branches in different databases. I will add a field "record_type": "arts", etc. in the records. Create an index,
{ selector: {record_type: "arts"}}
Before doing any operation on the arts records, you can use this index and query the documents. In this way, you will be able to logically group your documents.
I am trying to come up with a rough design for an application we're working on. What I'd like to know is, if there is a way to directly map a one to many relation in mongo.
My schema is like this:
There are a bunch of Devices.
Each device is known by it's name/ID uniquely.
Each device, can have multiple interfaces.
These interfaces can be added by a user in the front end at any given
time.
An interface is known uniquely by it's ID, and can be associated with
only one Device.
A device can contain at least an order of 100 interfaces.
I was going through MongoDB documentation wherein they mention things relating to Embedded document vs. multiple collections. By no means am I having a detailed clarity over this as I've just started with Mongo and meteor.
Question is, what could seemingly be a better approach? Having multiple small collections or having one big embedded collection. I know this question is somewhat subjective, I just need some clarity from folks who have more expertise in this field.
Another question is, suppose I go with the embedded model, is there a way to update only a part of the document (specific to the interface alone) so that as and when itf is added, it can be inserted into the same device document?
It depends on the purpose of the application.
Big document
A good example on where you'd want a big embedded collection would be if you are not going to modify (normally) the data but you're going to query them a lot. In my application I use this for storing pre-processed trips with all the information. Therefore when someone wants to consult this trip, all the information is located in a single document. However if your query is based on a value that is embedded in a trip, inside a list this would be very slow. If that's the case I'd recommend creating another collection with a relation between both collections. Also for updating part of a document it would be slow since it would require you to fetch the whole document and then update it.
Small documents with relations
If you plan on modify the data a lot, I'd recommend you to stick to a reference to another collection. With small documents, this will allow you to update any collection quicker. If you want to model a unique relation you may consider using a unique index in mongo. This can be done using: db.members.createIndex( { "user_id": 1 }, { unique: true } ).
Therefore:
Big object: Great for querying data but slow for complex queries.
Small related collections: Great for updating but requires several queries on distinct collections.
I am currently evaluating mongodb for a project I have started but I can't find any information on what the equivalent of an SQL view in mongodb would be. What I need, that an SQL view provides, is to lump together data from different tables (collections) into a single collection.
I want nothing more than to clump some documents together and label them as a single document. Here's an example:
I have the following documents:
cc_address
us_address
billing_address
shipping_address
But in my application, I'd like to see all of my addresses and be able to manage them in a single document.
In other cases, I may just want a couple of fields from collections:
I have the following documents:
fb_contact
twitter_contact
google_contact
reddit_contact
each of these documents have fields that align, like firstname lastname and email, but they also have fields that don't align. I'd like to be able to compile them into a single document that only contains the fields that align.
This can be accomplished by Views in SQL correct? Can I accomplish this kind of functionality in MongoDb?
The question is quite old already. However, since mongodb v3.2 you can use $lookup in order to join data of different collections together as long as the collections are unsharded.
Since mongodb v3.4 you can also create read-only views.
There are no "joins" in MongoDB. As said by JonnyHK, you can either enormalize your data or you use embedded documents or you perform multiple queries
However, you could also use Map-Reduce.
or if you're prepared to use the development branch, you could test the new aggregation framework though maybe it's too much? This new framework will be in the soon-to-be-released 2.2, which is production-ready unlike 2.1.x.
Here's the SQL-Mongo chart also, which may be of some help in your learning.
Update: Based on your re-edit, you don't need Map-Reduce or the Aggregation Framework because you're just querying.
You're essentially doing joins, querying multiple documents and merging the results. The place to do this is within your application on the client-side.
MongoDB queries never span more than a single collection as there is no support for joins. So if you have related data you need available in the results of a query you must either add that related data to the collection you're querying (i.e. denormalize your data), or make a separate query for it from another collection.
I am currently evaluating mongodb for a project I have started but I
can't find any information on what the equivalent of an SQL view in
mongodb would be
In addition to this answer, mongodb now has on-demand materialized views. In a nutshell, this feature allows you to use aggregate and $merge (in 4.2) to create/update a quick view collection that you can query from faster. The strategy is used to update the quick view collection whenever the main collection has a record change. This has the side effect unlike SQL of increasing your data storage size. But the benefits can be huge depending on your querying needs.
I'd like to code a web app where most of the sections are dependent on the user profile (for example different to-do lists per person etc) and I'd love to use MongoDB. I was thinking of creating about 10 embedabble documents for the main profile document and keep everything related to one user inside his own document.
I don't see a clear way of using foreign keys for mongodb, the only way would be to create a field to_do_id with the type of ObjectId for example, but they would be totally unrelated internally, just happen to have the same Ids I'd have to query for.
Is there a limit on the number of embedded document types inside a top level document that could degrade performance?
How do you guys solve the issue of having a central profile document that most of the documents have to relate to in presenting a view per person?
Do you use semi foreign keys inside MongoDb and have fields with ObjectId types that would have some other document's unique Id instead of embedding them?
I cannot feel what approach should be taken when. Thank you very much!
There is no special limit with respect to performance. The larger the document, though, the longer it takes to transmit over the wire. The whole document is always retrieved.
I do it with references. You can choose between simple manual references and the database DBRef as per this page: http://www.mongodb.org/display/DOCS/Database+References
The link above documents how to have references in a document in a semi-foreign key way. The DBRef might be good for what you are trying to do, but the simple manual reference is very efficient.
I am not sure a general rule of thumb exists for which reference approach is best. Since I use Java or Groovy mostly, I like the fact that I get a DBRef object returned. I can check for this datatype and use that to decide how to handle the reference in a generic way.
So I tend to use a simple manual reference for references to different documents in the same collection, and a DBRef for references across collections.
I hope that helps.