ProgramGuru

MongoDB - Getting StartedMongoDB - Getting Started1
❯

  1. 1MongoDB Course Index

Module 1: Introduction and SetupModule 1: Introduction and Setup6
❯

  1. 1What is MongoDB? (NoSQL vs SQL)
  2. 2MongoDB Use-cases and Features Overview
  3. 3Installing MongoDB on Windows
  4. 4Installing MongoDB on Linux
  5. 5Installing MongoDB on Mac
  6. 6Introduction to MongoDB Atlas (Cloud)

Module 2: MongoDB BasicsModule 2: MongoDB Basics6
❯

  1. 1Understanding Databases, Collections, and Documents in MongoDB
  2. 2JSON vs BSON in MongoDB
  3. 3MongoDB Data Types
  4. 4Creating a Database and Collection in MongoDB
  5. 5Using MongoDB Compass
  6. 6Using MongoDB Shell

Module 3: CRUD OperationsModule 3: CRUD Operations6
❯

  1. 1Insert Operations in MongoDB
  2. 2Read Operations in MongoDB
  3. 3Query Filtering with Operators
  4. 4Update Operations in MongoDB
  5. 5Delete Operations in MongoDB
  6. 6Using Projection and Sorting in Queries

Module 4: Advanced QueryingModule 4: Advanced Querying4
❯

  1. 1MongoDB Query Operators Overview
  2. 2Working with Arrays and Embedded Documents in MongoDB
  3. 3Querying Nested Fields in MongoDB
  4. 4Pagination using limit(), skip(), and sort() in MongoDB

Module 5: Indexing and PerformanceModule 5: Indexing and Performance4
❯

  1. 1What are Indexes and Why Use Them
  2. 2Creating Single Field and Compound Indexes in MongoDB
  3. 3Multikey Indexes for Arrays
  4. 4Using explain() for Query Optimization in MongoDB

Module 6: Aggregation FrameworkModule 6: Aggregation Framework5
❯

  1. 1Introduction to Aggregation
  2. 2Using $match, $group, $project, $sort in MongoDB Aggregation
  3. 3Aggregation Pipeline Stages Explained
  4. 4Common Use Cases: Grouping, Counting, Summing
  5. 5MapReduce vs Aggregation Framework in MongoDB

Module 7: Schema Design & ValidationModule 7: Schema Design & Validation4
❯

  1. 1Schema Design Patterns: Embedding vs Referencing
  2. 2One-to-One, One-to-Many, Many-to-Many Relationships in MongoDB
  3. 3MongoDB Schema Validation with JSON Schema
  4. 4Defining Custom Validation Rules in MongoDB

Module 8: Working with MongoDB in CodeModule 8: Working with MongoDB in Code2
❯

  1. 1MongoDB with Python using PyMongo
  2. 2MongoDB with Node.js using Mongoose

Module 9: MongoDB Atlas (Cloud)Module 9: MongoDB Atlas (Cloud)3
❯

  1. 1Setting Up a Free MongoDB Atlas Cluster
  2. 2Creating and Managing Databases in Atlas
  3. 3Connecting MongoDB Atlas to Local Applications

Module 10: Backup, Restore and SecurityModule 10: Backup, Restore and Security5
❯

  1. 1Backing Up MongoDB using mongodump
  2. 2Restoring Data using mongorestore
  3. 3Creating Users and Assigning Roles in MongoDB
  4. 4Role-Based Access Control and Authentication
  5. 5Basic MongoDB Security Best Practices

Module 11: Real-World ProjectsModule 11: Real-World Projects3
❯

  1. 1Project 1: Task Tracker with Node.js + MongoDB
  2. 2Project 2: Blog App with Express + Mongoose
  3. 3Project 3: Inventory System (Console-based App)

Using explain() for Query Optimization in MongoDB

Using explain() for Query Optimization in MongoDB

MongoDB provides the explain() method to help developers analyze how queries are executed internally. This is essential when optimizing slow queries or verifying whether indexes are being used effectively.

What is explain()?

explain() shows the execution plan of a query — how MongoDB selects documents, whether it uses indexes or performs a full collection scan, and how many documents are scanned or returned.

It helps you answer key questions like:

How to Use explain()

You can append .explain() to any query or cursor. MongoDB will then return metadata about the query's execution plan.


    db.collection.find({ field: value }).explain()

Step-by-Step Example

1. Create a Sample Collection


    db.users.insertMany([
      { name: "Alice", age: 30 },
      { name: "Bob", age: 25 },
      { name: "Charlie", age: 35 },
      { name: "David", age: 25 },
      { name: "Eve", age: 40 }
    ]);

2. Run a Query Without Index

Now query for users aged 25 without any index:


    db.users.find({ age: 25 }).explain("executionStats")

Output:

    {
      ...
      "executionStats": {
        "totalDocsExamined": 5,
        "totalKeysExamined": 0,
        "executionStages": {
          "stage": "COLLSCAN",
          ...
        }
      }
      ...
    }

Explanation: The output shows stage: "COLLSCAN" which means the query did a collection scan — it examined all 5 documents to find matches. This is inefficient for large datasets.

Intuition Check

Q: What does it mean if "stage": "COLLSCAN" appears in your explain output?

A: MongoDB is scanning the entire collection because it doesn’t have an index on the field you're querying.

3. Create an Index


    db.users.createIndex({ age: 1 })

Output:

    {
      "createdCollectionAutomatically": false,
      "numIndexesBefore": 1,
      "numIndexesAfter": 2,
      "ok": 1
    }

4. Run the Same Query Again


    db.users.find({ age: 25 }).explain("executionStats")

Output:

    {
      ...
      "executionStats": {
        "totalDocsExamined": 2,
        "totalKeysExamined": 2,
        "executionStages": {
          "stage": "IXSCAN",
          ...
        }
      }
      ...
    }

Explanation: Now the query uses stage: "IXSCAN" — index scan. MongoDB only examined the 2 documents matching age = 25. Much faster and efficient!

5. Understanding explain() Output

Here are a few key fields to look at in explain("executionStats") output:

  • totalDocsExamined: Total documents scanned
  • totalKeysExamined: Index entries scanned
  • stage: COLLSCAN or IXSCAN
  • nReturned: Documents returned by the query

Intuition Check

Q: Should totalDocsExamined always equal nReturned?

A: No. If there’s a filter, MongoDB may examine more documents than it returns.

6. Forcing Index Use (Optional)

If you want MongoDB to use a specific index or test index behavior, you can use .hint():


    db.users.find({ age: 25 }).hint({ age: 1 }).explain("executionStats")

Summary

The explain() method is your window into MongoDB's query planner. It helps you:

  • Identify slow queries
  • Determine if and how indexes are used
  • Compare different query strategies

Using explain("executionStats") is an essential habit when optimizing MongoDB queries for performance, especially at scale.

Welcome to ProgramGuru

Sign up to start your journey with us

Support ProgramGuru.org❯

You can support this website with a contribution of your choice.

When making a contribution, mention your name, and programguru.org in the message. Your name shall be displayed in the sponsors list.

PayPal

UPI

PhonePe QR

MALLIKARJUNA M