Sparse indexes in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Effect of sparse index on documents without the indexed field

Consider a MongoDB collection with documents where some have the field age and others do not. A sparse index is created on the age field.

What will be the result of the following query?

db.collection.find({ age: { $exists: false } }).hint({ age: 1 })

Assuming the sparse index is used as a hint, what will the query return?

A It returns no documents because sparse indexes exclude documents without the indexed field.

B It returns all documents where the <code>age</code> field does not exist.

C It returns all documents regardless of the <code>age</code> field existence.

D It causes a query error because sparse indexes cannot be used with $exists queries.

💡 Hint

Think about what documents are included in a sparse index.

Sparse indexes only include documents that have the indexed field. Documents without the field are not indexed and thus cannot be found using the sparse index. Using the sparse index as a hint for a query looking for documents without the field will return no results.

🧠 Conceptual

intermediate

Purpose of sparse indexes in MongoDB

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

A To index all documents including those with null or missing values for the indexed field.

B To automatically update the index when documents are deleted.

C To create a unique index that enforces uniqueness only on documents with the indexed field.

D To index only documents that contain the indexed field, saving space and improving performance for sparse data.

💡 Hint

Think about how sparse data affects indexing.

Sparse indexes only include documents that have the indexed field. This reduces index size and improves performance when many documents lack that field.

📝 Syntax

advanced

Correct syntax to create a sparse index

Which of the following commands correctly creates a sparse index on the email field in a MongoDB collection?

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

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

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

D db.collection.createIndex({ email: 1 }, { sparse: 'yes' })

💡 Hint

Check the exact option name and value type for sparse indexes.

The correct option to create a sparse index is { sparse: true }. Other options are invalid or incorrect.

❓ optimization

advanced

When to prefer sparse indexes over regular indexes

You have a MongoDB collection where only 10% of documents have the phoneNumber field. You want to index this field to speed up queries filtering by phoneNumber.

Which indexing strategy is best to optimize storage and query performance?

A Create a sparse index on <code>phoneNumber</code> to index only documents that have this field.

B Create a regular index on <code>phoneNumber</code> to index all documents regardless of field presence.

C Create a compound index including <code>phoneNumber</code> and another field to cover more queries.

D Do not create any index because sparse fields are not suitable for indexing.

💡 Hint

Consider how many documents have the field and how sparse indexes work.

A sparse index indexes only documents with the field, saving space and improving query speed when the field is missing in many documents.

🔧 Debug

expert

Unexpected query result with sparse index

A developer created a sparse index on the status field in a MongoDB collection. They run this query:

db.collection.find({ status: null })

But the query returns documents where status does not exist, even though they expected only documents with status set to null.

Why does this happen?

A Because the sparse index causes documents with <code>status</code> missing to be indexed as null.

B Because in MongoDB, querying <code>{ status: null }</code> matches documents where <code>status</code> is null or missing.

C Because sparse indexes include documents with missing fields as null values.

D Because the query syntax is invalid and returns all documents.

💡 Hint

Think about how MongoDB treats null in queries.

In MongoDB, a query for { field: null } matches documents where the field is either null or does not exist. This is independent of the sparse 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