MongoDB Indexes createIndex Performance

Why Indexes Matter

Without an index, MongoDB performs a collection scan - reading every document to find matches. On large collections this is extremely slow. An index stores a small, ordered subset of data that MongoDB can traverse quickly, similar to a book's index. The trade-off is slightly slower writes and additional storage.

Creating Indexes

Single Field, Compound, and Unique Indexes

// Single field index (1 = ascending, -1 = descending)
db.users.createIndex({ email: 1 })
db.users.createIndex({ age: -1 })

// Compound index - covers queries on multiple fields
db.users.createIndex({ role: 1, age: -1 })

// Unique index - enforces uniqueness
db.users.createIndex({ email: 1 }, { unique: true })

// Unique compound index
db.products.createIndex({ category: 1, sku: 1 }, { unique: true })

// Named index
db.users.createIndex({ email: 1 }, { name: "idx_email_unique", unique: true })

// List all indexes on a collection
db.users.getIndexes()

// Drop a specific index by name
db.users.dropIndex("idx_email_unique")

// Drop all indexes except _id
db.users.dropIndexes()

Multikey, Text, and Geospatial Indexes

Multikey, Text, Geospatial, and Hashed Indexes

// Multikey index - automatically created when indexing an array field
db.products.createIndex({ tags: 1 })
// Now queries like db.products.find({ tags: "mongodb" }) use the index

// Text index - for full-text search
db.articles.createIndex({ title: "text", body: "text" })
// Search using $text
db.articles.find({ $text: { $search: "mongodb tutorial" } })
// With relevance score
db.articles.find(
  { $text: { $search: "mongodb" } },
  { score: { $meta: "textScore" } }
).sort({ score: { $meta: "textScore" } })

// Geospatial index - for location-based queries
db.places.createIndex({ location: "2dsphere" })
// Find places within 5km of a point
db.places.find({
  location: {
    $near: {
      $geometry: { type: "Point", coordinates: [-73.97, 40.77] },
      $maxDistance: 5000
    }
  }
})

// Hashed index - for hash-based sharding
db.users.createIndex({ userId: "hashed" })

Sparse and TTL Indexes

Sparse Indexes and TTL (Time-To-Live) Indexes

// Sparse index - only indexes documents that have the field
// Useful for optional fields to save space
db.users.createIndex({ phone: 1 }, { sparse: true })

// TTL index - automatically deletes documents after a time period
// Expire sessions after 1 hour (3600 seconds)
db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 })

// Expire at a specific date stored in the document
db.events.createIndex({ expiresAt: 1 }, { expireAfterSeconds: 0 })
// Document: { event: "sale", expiresAt: ISODate("2024-12-31T23:59:59Z") }

Analyzing Index Usage with explain()

Using explain() to Verify Index Usage

// Basic explain - shows query plan
db.users.find({ email: "alice@example.com" }).explain()

// executionStats - shows actual execution metrics
db.users.find({ email: "alice@example.com" }).explain("executionStats")

// Key fields to check in explain output:
// winningPlan.stage: "IXSCAN" = index used, "COLLSCAN" = no index (slow!)
// executionStats.totalDocsExamined: should be close to nReturned
// executionStats.executionTimeMillis: query execution time

// Example output snippet:
// {
//   "winningPlan": { "stage": "FETCH", "inputStage": { "stage": "IXSCAN", "indexName": "email_1" } },
//   "executionStats": { "nReturned": 1, "totalDocsExamined": 1, "executionTimeMillis": 0 }
// }

// Hint - force MongoDB to use a specific index
db.users.find({ email: "alice@example.com" }).hint({ email: 1 })

Index Types Summary

// Single field:  { field: 1 } or { field: -1 }
// Compound:      { field1: 1, field2: -1 }
// Multikey:      { arrayField: 1 }  (auto-detected)
// Text:          { field: "text" }
// Geospatial:    { location: "2dsphere" } or { location: "2d" }
// Hashed:        { field: "hashed" }
// Unique:        { field: 1 }, { unique: true }
// Sparse:        { field: 1 }, { sparse: true }
// TTL:           { dateField: 1 }, { expireAfterSeconds: N }
// Partial:       { field: 1 }, { partialFilterExpression: { active: true } }
// Wildcard:      { "$**": 1 }  (indexes all fields)

Before you move on

MongoDB Indexes createIndex Performance Mastery Check

5 checks

Without an index, MongoDB performs a collection scan - reading every document to find matches.
On large collections this is extremely slow.
An index stores a small, ordered subset of data that MongoDB can traverse quickly, similar to a book's index.
The trade-off is slightly slower writes and additional storage.
The most common trap here is building the index before checking the query pattern.

MongoDB Questions Learners Ask

MongoDB must update the index whenever indexed fields change.

An index automatically created over array values, with restrictions on some compound-array combinations.

Run explain(\"executionStats\") and inspect the winning plan and examined counts.

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