Index intersection behavior in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find documents where age is 25 using an index.

MongoDB

db.users.find({ age: [1] })

Drag options to blanks, or click blank then click option'

A"25"

B{ $eq: 25 }

C25

D{ age: 25 }

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around numbers causes type mismatch.

Using nested objects incorrectly in the query.

The query needs the value 25 directly to match the age field.

2fill in blank

medium

Complete the query to find documents where age is 25 and status is 'A' using index intersection.

MongoDB

db.users.find({ age: 25, status: [1] })

Drag options to blanks, or click blank then click option'

A{ status: "A" }

B"A"

C{ $eq: "A" }

DA

Attempts:

3 left

💡 Hint

Common Mistakes

Not quoting string values causes errors.

Using nested objects unnecessarily.

The status value must be a string, so it needs quotes.

3fill in blank

hard

Fix the error in the query to use index intersection for age and status.

MongoDB

db.users.find({ age: [1], status: "A" })

Drag options to blanks, or click blank then click option'

A"25"

B{ age: 25 }

C{ $eq: 25 }

D25

Attempts:

3 left

💡 Hint

Common Mistakes

Using string '25' instead of number 25.

Using nested objects incorrectly.

Age should be a number, not a string or nested object.

4fill in blank

hard

Fill both blanks to create a query that uses index intersection for age and status.

MongoDB

db.users.find({ age: [1], status: [2] })

Drag options to blanks, or click blank then click option'

A25

B"A"

C30

D"B"

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up numbers and strings.

Using wrong values for age or status.

The query filters age by 25 (number) and status by "A" (string).

5fill in blank

hard

Fill all three blanks to create a query that uses index intersection for age, status, and score.

MongoDB

db.users.find({ age: [1], status: [2], score: [3] })

Drag options to blanks, or click blank then click option'

A25

B"A"

C85

D"B"

Attempts:

3 left

💡 Hint

Common Mistakes

Quoting numbers or not quoting strings.

Using wrong values for fields.

The query filters age by 25 (number), status by "A" (string), and score by 85 (number).

Practice

1. What does index intersection in MongoDB allow the database to do?

easy

A. Combine multiple indexes to answer a single query

B. Create a new index automatically

C. Merge two collections into one

D. Delete unused indexes to save space

Solution

1. Step 1: Understand the purpose of index intersection

   Index intersection allows MongoDB to use more than one index together to satisfy a query efficiently.

2. Step 2: Differentiate from other index operations

   It does not create or delete indexes, nor merge collections; it only combines existing indexes for query optimization.

3. Final Answer:

   Combine multiple indexes to answer a single query -> Option A

4. Quick Check:

   Index intersection = combining indexes [OK]

Hint: Index intersection means using multiple indexes together [OK]

Common Mistakes:

• Thinking index intersection creates new indexes
• Confusing index intersection with collection merging
• Assuming it deletes indexes automatically

2. Which of the following is the correct way to check if MongoDB used index intersection for a query?

easy

A. Check the size of the indexes with db.collection.indexSize()

B. Run db.collection.stats() to see index usage

C. Use db.collection.aggregate() without any options

D. Use db.collection.find().explain() and look for indexIntersection in the plan

Solution

1. Step 1: Identify the method to analyze query plans

   The explain() method shows how MongoDB executes a query, including index usage.

2. Step 2: Look for index intersection details

   Within the explain output, the indexIntersection field indicates if multiple indexes were combined.

3. Final Answer:

   Use db.collection.find().explain() and look for indexIntersection in the plan -> Option D

4. Quick Check:

   Explain() shows index intersection [OK]

Hint: Use explain() to see index intersection in query plan [OK]

Common Mistakes:

• Using stats() which shows collection info, not query plan
• Running aggregate() without explain() does not show index use
• Checking index size does not reveal intersection

3. Given a collection with indexes on { age: 1 } and { city: 1 }, what will MongoDB do for this query?

db.users.find({ age: 30, city: "NY" })

medium

A. Use index intersection combining both age and city indexes

B. Use only the index on city

C. Use only the index on age

D. Perform a full collection scan ignoring indexes

Solution

1. Step 1: Analyze the query filter fields

   The query filters on both age and city, and there are separate indexes on each field.

2. Step 2: Understand MongoDB's index intersection behavior

   MongoDB can combine these two indexes to efficiently find documents matching both conditions using index intersection.

3. Final Answer:

   Use index intersection combining both age and city indexes -> Option A

4. Quick Check:

   Query on two indexed fields = index intersection [OK]

Hint: Multiple single-field indexes on query fields trigger index intersection [OK]

Common Mistakes:

• Assuming MongoDB uses only one index per query
• Thinking MongoDB always does full scan if multiple indexes exist
• Confusing index intersection with compound indexes

4. You wrote this query:

db.products.find({ category: "books", price: { $lt: 20 } })

Indexes exist on { category: 1 } and { price: 1 }. But explain() shows no index intersection used. What is a likely cause?

medium

A. MongoDB does not support index intersection on range queries

B. MongoDB prefers a single index that covers both fields if available

C. The query syntax is incorrect for index intersection

D. Index intersection only works with compound indexes

Solution

1. Step 1: Understand MongoDB's index selection logic

   MongoDB chooses the most efficient index plan. If a single index covers both fields, it prefers that over intersection.

2. Step 2: Consider the indexes present

   Since only single-field indexes exist, MongoDB may pick one index if it estimates it is more efficient than combining both.

3. Final Answer:

   MongoDB prefers a single index that covers both fields if available -> Option B

4. Quick Check:

   MongoDB picks best index, not always intersection [OK]

Hint: MongoDB picks best index plan, not always intersection [OK]

Common Mistakes:

• Believing index intersection requires compound indexes
• Thinking query syntax disables index intersection
• Assuming range queries never use index intersection

5. You have a collection with indexes:

{ author: 1 }, { year: 1 }, { genre: 1 }

To optimize this query:

db.books.find({ author: "Alice", year: 2020, genre: "fiction" })

Which index strategy will most likely improve performance using index intersection?

hard

A. Create a compound index on { author: 1, year: 1, genre: 1 }

B. Keep single-field indexes and let MongoDB intersect them

C. Create compound indexes on pairs like { author: 1, year: 1 } and { genre: 1 }

D. Drop all indexes and create a single index on { author: 1 }

Solution

1. Step 1: Understand index intersection limits

   MongoDB can combine multiple single-field indexes, but compound indexes on multiple fields are often more efficient.

2. Step 2: Evaluate index strategies

   A full compound index on all three fields is best but costly to maintain; compound indexes on pairs plus a single index can help MongoDB intersect efficiently.

3. Step 3: Compare options

   Create compound indexes on pairs like { author: 1, year: 1 } and { genre: 1 } creates compound indexes on pairs, enabling better intersection than only single-field indexes, improving query speed without full compound index overhead.

4. Final Answer:

   Create compound indexes on pairs like { author: 1, year: 1 } and { genre: 1 } -> Option C

5. Quick Check:

   Compound pairs + intersection = better performance [OK]

Hint: Compound indexes on pairs plus single indexes improve intersection [OK]

Common Mistakes:

• Assuming only single-field indexes are enough
• Dropping indexes reduces performance
• Believing full compound index is always best