MongoDB Covered Query

A covered query in MongoDB is a query that uses an index and doesn’t need to scan the actual documents. An index covers a query if the following conditions are met:

All fields used in the query are part of an index.

All fields returned in the result are from the same index.

Example:
Suppose you have a collection named example with indexes on the fields type and item:

db.example.createIndex({ type: 1, item: 1 })

This index will cover queries that operate on the type and item fields and return only the item field:

db.example.find(  { type: "game", item: /^c/ },  { item: 1, _id: 0 } )

Note: In earlier versions, you had to explicitly exclude the _id field to cover the query. Since MongoDB 3.6, an index can also cover queries on fields inside embedded documents.

Example 1

Consider a collection called tutorials with documents like:

{ _id: 1, user: { login: "student" } }

And an index:

{ "user.login": 1 }

A covered query example would be:

db.tutorial.find(  { "user.login": "student" },  { "user.login": 1, _id: 0 } )

Example 2

Let's take another case:

Document in the examples collection:

{  "_id": ObjectId("53402597d852426020000002"),  "contact": "1234567809",  "dob": "01-01-1991",  "gender": "M",  "name": "ABC",  "user_name": "abcuser" }

Create a compound index:

db.examples.ensureIndex({ gender: 1, user_name: 1 })

Now, this query will be covered:

db.examples.find({ gender: "M" }, { user_name: 1, _id: 0 })

From these examples, we understand that MongoDB can fetch data directly from the index without accessing the actual documents, making queries faster and more efficient.

Important: An index cannot cover a query if:

Any indexed field is an array.

Any indexed field is a subdocument.

MongoDB Analyzing Query

Analyzing queries is crucial for evaluating database performance and query design. In MongoDB, we mainly use two operations: $explain and $hint.

1. $explain

The $explain operator provides detailed information about how MongoDB executed a query — such as which indexes were used, the number of documents scanned, and overall performance stats.

Example:

Using the same examples collection:

db.examples.ensureIndex({ gender: 1, user_name: 1 })

Running an $explain:

db.examples.find({ gender: "M" }, { user_name: 1, _id: 0 }).explain()

The output would look something like this:

{  "cursor": "BtreeCursor gender_1_user_name_1",  "isMultiKey": false,  "n": 1,  "nscannedObjects": 0,  "nscanned": 1,  "nscannedObjectsAllPlans": 0,  "nscannedAllPlans": 1,  "scanAndOrder": false,  "indexOnly": true,  "nYields": 0,  "nChunkSkips": 0,  "millis": 0,  "indexBounds": {    "gender": [      [ "M", "M" ]    ],    "user_name": [      [ { "$minElement": 1 }, { "$maxElement": 1 } ]    ]  } }

Understanding the output:

indexOnly: true indicates that the index was sufficient to fulfill the query.

cursor: "BtreeCursor" shows the type of cursor used (index-based).

n shows the number of documents matched.

nscannedObjects is the total number of documents scanned.

nscanned shows the number of index entries scanned.

2. $hint

The $hint operator forces MongoDB to use a specific index for query execution. This is helpful when you want to manually test or optimize queries with different indexes.

Example using $hint:

db.examples.find({ gender: "M" }, { user_name: 1, _id: 0 }).hint({ gender: 1, user_name: 1 })

Summary

In this Debugshala tutorial, we learned about MongoDB Covered Queries and Analyzing Queries with examples. We explored how $explain helps understand query execution and how $hint can enforce index usage for performance testing.