MongoDB efficient dealing with embedded documents - performance

Question

I have serious trouble finding anything useful in Mongo documentation about dealing with embedded documents. Let's say I have a following schema:
{
_id: ObjectId,
...
data: [
{
_childId: ObjectId // let's use custom name so we can distinguish them
...
}
]
}
What's the most efficient way to remove everything inside data for
particular _id?
What's the most efficient way to remove embedded document with
particular _childId inside given _id? What's the performance
here, can _childId be indexed in order to achieve logarithmic (or
similar) complexity instead of linear lookup? If so, how?
What's the most efficient way to insert a lot of (let's say a 1000)
documents into data for given _id? And like above, can we get
O(n log n) or similar complexity with proper indexing?
What's the most efficient way to get the count of documents inside data for given _id?

Answer 1

The other two answers give sensible advice on your questions 1-4, but I want to address your question by interrogating the basis for asking it in the first place. The terminology of "embedded document" in the context of MongoDB storing "documents" confuses people. You should not think of an embedded document as another document in MongoDB that you search for, index, or update as its own document, because that's not what it is. It's a grouped collection of fields inside a document; it's a BSON field of type Object. To quote the embedded document docs,
Embedded data models allow applications to store related pieces of information in the same database record. As a result, applications may need to issue fewer queries and updates to complete common operations.
Starting from knowledge about your use case, you should pick your documents and document structure to make your common operations easier. If you are so concerned about 1-4, you probably want to unwind your data array of childIds into separate documents. A concrete example of this common "antipattern" is a blog with many authors - you could have a user document with a large, changing array of posts embedded inside, or a post document with user information replicated in each. I can't say for sure what is or isn't wrong with your data model as you've given no specific details about it, but struggling to understand why 1-4 seem hard or undocumented or slow in MongoDB is a good sign that you should rethink the data model so the equivalent of 1-4 are fun and easy! Or at least easier and more fun.

Answer 2

I can't find anything on speed so I will go with the ways found in the documentation in the hope that they made the most efficient ways the one they documented:
If you want to remove all subdocuments in data you can just update data to []
The official way to remove a document with a specific _childId from data would be $pull:
db.collection.update(
{ },
{ $pull: { data: { _childId: id } } },
)
might need to add { multi: true } if _childId is not unique (multipart subdocuments)
On indexing on subdocuments I would refer you to this question. Short answer yes you can index fields in subdocuments for faster lookup just like you would index normal fields by
db.collection.ensureIndex({"data._childId" : 1})
If you want to search for a subdocument in only one specific document you can use aggregation i.e.
db.collection.aggregate({$match:{_id : _id},
{$unwind:'$data'},
{$match:{data._childId: _childID})
which will first match for _id and only then for _childId. It will return the parent document with data only containing the subdocument(s) with _childId.
There is $push for that although for 1000 subdocument you might not want to do it in one query anyways

Answer 3

Trudbert is right: db.collection.update({_id:yourId},{$set:{data:[]}})
Two points for Trudbert. However, I would like to add that if you have the whole document available in your app, it might be reasonable to simply replace the contents of the whole document if suitable for your use case.
I have made good experience with bulk updates performance wise. You might want to try it.
I don't know how you come to the idea that an aggregate wouldn't use indices, but since _id is unique, it would make much more sense to use db.collection.findOne({_id:yourId},{"data._childId":1,_id:0}).data.length or use it's equivalent as a raw command in the driver of choice. Since the connection is already established, unless the array is very big, it should be faster to simply return the data instead of having the calculations done on a possibly (over)loaded server.
As per your comments to Trudberts answer: _id is unique. So exactly one doc will need to be modified for a known _id: db.collection.update({_id:theId},{$pull..... It does not get more efficient. For an unknown id, create an index on childId and do the same pull operation with a match on childId instead of id with the multi option set to remove all references to a specific childId.
I strongly second Trudberts suggestion of using the aggregation framework to create documents when needed out of optimized data. Currently, I have an aggregation pipeline which analyses 5M records with more than 7 million relations to each other in some 6 seconds. On a non sharded standalone instance. With spinning disks, crappy IO and not even optimized. With careful planning the aggregations (an early match limiting the documents passed to the ones not processed so far) and merging them with earlier results (adapt the _id in the group phase can achieve that), you can even optimize this for some mere fractions of seconds, if absolutely necessary.