MongoDB as a document database supports embedding fields and whole documents in nested complex hierarchies inside the same document. Naturally, it also allows us to index these fields.

In our books collection example, we can have documents such as the following:

{
"_id" : ObjectId("5969ccb614ae9238fe76d7f1"),
"name" : "MongoDB Indexing Cookbook",
"isbn" : "1001",
"available" : 999,
"meta_data" : {
"page_count" : 256,
"average_customer_review" : 4.8
}
} 

Here, the meta_data field is a document itself, with page_count and average_customer_review fields.

Again, we can create an index on page_count as follows:

db.books.createIndex( { "meta_data.page_count": 1 } )

This can answer queries on equality and range comparison around the meta_data.page_count field.

> db.books.find({"meta_data.page_count": { $gte: 200 } })
> db.books.find({"meta_data.page_count": 256 })