Sparse indexes in MongoDB

Introduction

Sparse indexes help speed up searches by only indexing documents that have a specific field. This saves space and makes queries faster when some documents don't have that field.

When you have a collection where some documents do not have a certain field.

When you want to index only documents that contain a specific field to save storage.

When you want to speed up queries that filter on a field that is not present in all documents.

When you want to avoid indexing documents with null or missing values for a field.

Syntax

MongoDB

db.collection.createIndex({ field: 1 }, { sparse: true })

The sparse: true option tells MongoDB to only index documents where the field exists and is not null.

The 1 means ascending order for the index.

Examples

This creates a sparse index on the email field in the users collection. Only users with an email will be indexed.

MongoDB

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

This creates a sparse index on the discount field in the products collection. Only products with a discount will be indexed.

MongoDB

db.products.createIndex({ discount: 1 }, { sparse: true })

Sample Program

This example inserts items where some have a price field and some do not. Then it creates a sparse index on price. Finally, it finds and sorts items that have a price.

MongoDB

db.inventory.insertMany([
  { item: "apple", category: "fruit" },
  { item: "banana", category: "fruit", price: 1 },
  { item: "carrot", category: "vegetable", price: 2 },
  { item: "donut" }
])

// Create sparse index on price
 db.inventory.createIndex({ price: 1 }, { sparse: true })

// Find all items with price
 db.inventory.find({ price: { $exists: true } }).sort({ price: 1 })

Important Notes

Sparse indexes do not include documents where the indexed field is missing or null.

Using sparse indexes can reduce index size and improve query speed for fields not present in all documents.

Be careful: queries that look for documents without the field will not use the sparse index.

Summary

Sparse indexes only index documents that have the indexed field.

This saves space and speeds up queries on fields that are not in every document.

Use sparse indexes when many documents might not have the field you want to index.

Practice

1. What is the main purpose of a sparse index in MongoDB?

easy

A. To index only documents that contain the indexed field

B. To index all documents regardless of field presence

C. To create a backup of the database

D. To speed up writes by disabling indexing

Solution

1. Step 1: Understand sparse index behavior

   Sparse indexes only include documents that have the indexed field present.

2. Step 2: Compare with other index types

   Unlike regular indexes, sparse indexes skip documents missing the field, saving space and improving query speed on sparse data.

3. Final Answer:

   To index only documents that contain the indexed field -> Option A

4. Quick Check:

   Sparse index = indexes only documents with the field [OK]

Hint: Sparse indexes skip documents missing the field [OK]

Common Mistakes:

• Thinking sparse indexes index all documents
• Confusing sparse indexes with backups
• Assuming sparse indexes disable indexing

2. Which of the following is the correct way to create a sparse index on the field email in MongoDB?

easy

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

B. db.collection.createIndex({ email: 1 }, { background: true })

C. db.collection.createIndex({ email: 1 }, { sparse: true })

D. db.collection.createIndex({ email: 1 }, { expireAfterSeconds: 3600 })

Solution

1. Step 1: Identify sparse index syntax

   The option { sparse: true } is used to create a sparse index in MongoDB.

2. Step 2: Verify the correct command

   The command db.collection.createIndex({ email: 1 }, { sparse: true }) correctly creates a sparse index on the email field.

3. Final Answer:

   db.collection.createIndex({ email: 1 }, { sparse: true }) -> Option C

4. Quick Check:

   Sparse index creation uses { sparse: true } [OK]

Hint: Use { sparse: true } option to create sparse indexes [OK]

Common Mistakes:

• Using unique instead of sparse option
• Confusing background with sparse
• Using expireAfterSeconds for sparse index

3. Given a collection with documents:

{ _id: 1, name: "Alice", age: 30 }

{ _id: 2, name: "Bob" }

{ _id: 3, name: "Charlie", age: 25 }

and a sparse index on age, what documents will be returned by db.collection.find({ age: { $gte: 25 } })?

medium

A. Documents with _id 1 and 3 only

B. Documents with _id 1, 2, and 3

C. Documents with _id 2 only

D. No documents returned

Solution

1. Step 1: Understand sparse index effect on query

   The sparse index on age only indexes documents that have the age field. Document with _id 2 has no age field, so it is not indexed.

2. Step 2: Evaluate the query filter

   The query { age: { $gte: 25 } } matches documents where age is 25 or more. Documents with _id 1 (age 30) and _id 3 (age 25) match. Document _id 2 is excluded because it has no age field.

3. Final Answer:

   Documents with _id 1 and 3 only -> Option A

4. Quick Check:

   Sparse index excludes docs missing field, so only _id 1 and 3 match [OK]

Hint: Sparse index excludes docs missing field, so only those with field match [OK]

Common Mistakes:

• Including documents without the indexed field
• Assuming all documents are returned
• Confusing sparse index with regular index

4. You created a sparse index on phone field but your queries on phone are slow. What is a likely cause?

medium

A. Sparse indexes index all documents regardless of field presence

B. Sparse indexes always slow down queries

C. You forgot to create the index

D. Many documents do not have the phone field, so index is small and not used

Solution

1. Step 1: Analyze sparse index behavior

   Sparse indexes only index documents with the field. If many documents lack the phone field, the index is small.

2. Step 2: Understand query performance impact

   If the index is small or not selective, MongoDB may choose not to use it, causing slower queries.

3. Final Answer:

   Many documents do not have the phone field, so index is small and not used -> Option D

4. Quick Check:

   Sparse index small if field missing in many docs, may not speed queries [OK]

Hint: Sparse index small if many docs lack field, may not improve speed [OK]

Common Mistakes:

• Assuming sparse indexes always slow queries
• Forgetting to create the index
• Thinking sparse indexes index all documents

5. You want to create an index on the status field, but only for documents where status exists and is not null. Which MongoDB index type should you use and why?

hard

A. TTL index, because it removes documents after a time

B. Sparse index, because it indexes only documents with the field present

C. Regular index, because it indexes all documents regardless of field

D. Unique index, because it enforces unique values

Solution

1. Step 1: Identify index requirement

   You want to index only documents where status exists and is not null, skipping documents without status.

2. Step 2: Match index type to requirement

   Sparse indexes index only documents that have the indexed field, which fits the requirement perfectly.

3. Final Answer:

   Sparse index, because it indexes only documents with the field present -> Option B

4. Quick Check:

   Use sparse index to index only docs with the field [OK]

Hint: Use sparse index to index only documents with the field present [OK]

Common Mistakes:

• Choosing regular index which indexes all documents
• Confusing TTL or unique indexes with sparse
• Not understanding sparse index skips missing fields