MongoDB Indexing & Searching

Indexes in MongoDB improve query performance by allowing the database to find data faster instead of scanning every document (COLLSCAN). Searching relies heavily on indexes for speed and efficiency.

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1. What is an Index in MongoDB?

An index is a data structure (usually a B-Tree) that stores references to documents based on specific fields, improving query performance.

• Without Index → MongoDB scans all documents.

• With Index → MongoDB jumps directly to matching documents.

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2. Creating Indexes

Syntax

db.collection.createIndex({ field: 1 })  // Ascending
db.collection.createIndex({ field: -1 }) // Descending

Example

db.products.createIndex({ price: 1 })

This creates an ascending index on the price field.

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3. Types of Indexes

A. Single Field Index

• Indexes one field.

• Best for simple queries.

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

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B. Compound Index

• Indexes multiple fields.

• Useful when filtering/sorting on multiple fields.

db.orders.createIndex({ customerId: 1, orderDate: -1 })

Query Example:

db.orders.find({ customerId: 123 }).sort({ orderDate: -1 })

Tip: Order of fields in compound indexes matters.

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C. Unique Index

• Ensures no duplicate values.

db.users.createIndex({ email: 1 }, { unique: true })

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D. Text Index (For Full-Text Search)

Used to search words or phrases inside string fields.

db.articles.createIndex({ title: "text", content: "text" })

Example Query:

db.articles.find({ $text: { $search: "mongodb indexing" } })

Features:

• Supports phrase search:

  db.articles.find({ $text: { $search: "\"mongodb indexing\"" } })
  

• Exclude words:

  db.articles.find({ $text: { $search: "mongodb -charts" } })
  

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E. Wildcard Index (For Dynamic Fields)

Used when documents have unknown or changing fields.

db.logs.createIndex({ "$**": 1 })

This creates an index on all fields.

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F. TTL (Time-To-Live) Index

Automatically deletes documents after a set time.

db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 })

• Deletes documents 1 hour after createdAt.

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G. Partial Index

Indexes only matching documents to save space.

db.orders.createIndex(
  { status: 1 },
  { partialFilterExpression: { status: "pending" } }
)

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H. Sparse Index

Indexes only documents that contain the field.

db.users.createIndex({ phone: 1 }, { sparse: true })

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4. Searching in MongoDB

MongoDB supports different search methods:

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A. Basic Search

db.products.find({ price: 500 })

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B. Range Queries (Works Best with Indexes)

db.products.find({ price: { $gte: 500, $lte: 1000 } })

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C. Regex Search (Pattern Matching)

db.users.find({ name: { $regex: "^A", $options: "i" } })

• ^A → Starts with A

• $options: "i" → Case-insensitive

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D. Text Search (Full-Text Search)

First, create a text index:

db.blogs.createIndex({ title: "text", content: "text" })

Then search:

db.blogs.find({ $text: { $search: "MongoDB charts" } })

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E. Atlas Search (Recommended for Advanced Search)

MongoDB Atlas Search provides fast, relevant, ranked search.

Step 1 — Enable Atlas Search

• Go to Atlas → Database → Search → Create Index.

Step 2 — Example Query

db.products.aggregate([
  {
    $search: {
      index: "productSearch",
      text: {
        query: "laptop",
        path: ["title", "description"]
      }
    }
  }
])

Features:

• Fuzzy matching (laptp → laptop)

• Ranking & scoring

• Supports synonyms

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5. Checking Indexes

List All Indexes

db.products.getIndexes()

Check Query Performance

db.products.find({ price: 500 }).explain("executionStats")

Look for:

• IXSCAN → Uses index (Good)

• COLLSCAN → Collection scan (Slow)

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6. Deleting Indexes

db.products.dropIndex("price_1")

Or drop all indexes:

db.products.dropIndexes()

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7. Performance Best Practices

• Always index fields used in:

  • find() filters

  • sort()

  • group() in aggregations

• Use compound indexes for multi-field queries.

• Use partial indexes for sparse data.

• Avoid too many indexes — they slow down writes.

• Prefer Atlas Search over $regex for large collections.

• Monitor performance with:

  db.collection.explain("executionStats")
  

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8. Indexing + Searching Example

Scenario: E-commerce products collection:

{
  "name": "iPhone 16",
  "category": "Mobile",
  "price": 1200,
  "description": "Latest Apple flagship phone"
}

Step 1 — Create Index

db.products.createIndex({ name: "text", description: "text", category: 1, price: 1 })

Step 2 — Search Products

db.products.find({ $text: { $search: "Apple phone" } }).sort({ price: 1 })

Step 3 — Check Query Plan

db.products.find({ $text: { $search: "Apple" } }).explain("executionStats")

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