Why advanced indexing matters in MongoDB - Challenge Your Understanding

Challenge - 5 Problems

❓ query_result

intermediate

Find documents using a compound index

Given a MongoDB collection users with a compound index on { age: 1, city: 1 }, what is the output of this query?

db.users.find({ age: 30, city: "New York" }).explain("executionStats")

Which option correctly describes the query plan's index usage?

A The query scans the entire collection because the index is not used for equality matches.

B The query uses only the index on age and ignores the city field.

C The query uses the compound index to efficiently find documents matching both age and city.

D The query uses a text index instead of the compound index.

💡 Hint

Think about how compound indexes work with multiple fields in the query.

A compound index on { age: 1, city: 1 } allows efficient queries that specify both fields. The query uses the index to quickly find matching documents without scanning the whole collection.

🧠 Conceptual

intermediate

Why use multikey indexes?

In MongoDB, which situation requires a multikey index to improve query performance?

A When indexing a field with only numeric values.

B When indexing a single string field with unique values.

C When indexing a field that is always null.

D When indexing a field that contains an array of values.

💡 Hint

Think about how MongoDB indexes array fields.

Multikey indexes are used when a field contains arrays. MongoDB indexes each element of the array separately to allow efficient queries on array contents.

📝 Syntax

advanced

Identify the correct syntax for creating a partial index

Which option shows the correct MongoDB command to create a partial index on the orders collection for documents where status is "pending"?

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

B db.orders.createIndex({ orderDate: 1 }, { partialFilterExpression: { status: { $eq: "pending" } } })

C db.orders.createIndex({ orderDate: 1 }, { filter: { status: "pending" } })

D db.orders.createIndex({ orderDate: 1 }, { partialFilter: { status: "pending" } })

💡 Hint

Check the exact option name and syntax for partialFilterExpression.

The correct syntax uses the option name 'partialFilterExpression' with a valid query expression like { status: { $eq: "pending" } }.

❓ optimization

advanced

Choosing the best index for a frequent query

You have a collection products with fields category, price, and stock. The most frequent query is:

db.products.find({ category: "electronics", price: { $lt: 500 } })

Which index will optimize this query best?

A A compound index on { category: 1, price: 1 }

B A single field index on category only

C A single field index on price only

D A compound index on { price: 1, stock: 1 }

💡 Hint

Think about which fields appear in the query filter and their order in the index.

A compound index on { category: 1, price: 1 } supports queries filtering by category and price efficiently. The order matters: category first, then price.

🔧 Debug

expert

Why does this index not improve query performance?

A developer created this index:

db.sales.createIndex({ date: 1, total: 1 })

But the query

db.sales.find({ total: { $gt: 100 } })

still performs a collection scan. Why?

A Because the query does not specify the first field of the compound index (date), the index cannot be used.

B Because the index fields are in ascending order and the query needs descending order.

C Because the query uses a range operator on total, which is the second field, so the index is ignored.

D Because the index was created incorrectly and needs a unique constraint.

💡 Hint

Remember how MongoDB uses the prefix of compound indexes.

MongoDB can only use a compound index if the query filters on the first field in the index. Since the query filters only on total (the second field), the index is not used.

Practice

1. Why is advanced indexing important in MongoDB?

easy

A. It encrypts data for security.

B. It slows down data retrieval to save memory.

C. It deletes duplicate data automatically.

D. It makes data searches faster and more efficient.

Solution

1. Step 1: Understand the role of indexing

   Indexing helps MongoDB find data quickly without scanning the entire collection.

2. Step 2: Recognize the benefit of advanced indexing

   Advanced indexing techniques improve search speed and efficiency beyond basic indexes.

3. Final Answer:

   It makes data searches faster and more efficient. -> Option D

4. Quick Check:

   Advanced indexing = faster searches [OK]

Hint: Indexing speeds up searches by organizing data smartly [OK]

Common Mistakes:

• Thinking indexing slows down queries
• Confusing indexing with data encryption
• Assuming indexing deletes duplicates

2. Which of the following is the correct way to create a compound index on fields age and name in MongoDB?

easy

A. db.collection.createIndex({age: 1, name: 1})

B. db.collection.createIndex([age, name])

C. db.collection.createIndex('age', 'name')

D. db.collection.createIndex({age & name})

Solution

1. Step 1: Recall MongoDB syntax for compound indexes

   Compound indexes use an object with field names as keys and sort order as values.

2. Step 2: Match syntax to options

   db.collection.createIndex({age: 1, name: 1}) correctly uses an object with age: 1 and name: 1 to create ascending indexes.

3. Final Answer:

   db.collection.createIndex({age: 1, name: 1}) -> Option A

4. Quick Check:

   Compound index syntax = object with fields [OK]

Hint: Use object with fields and 1 or -1 for compound indexes [OK]

Common Mistakes:

• Using array instead of object for fields
• Passing field names as strings without keys
• Using invalid syntax like '&' operator

3. Given a collection with a unique index on email, what happens if you try to insert two documents with the same email value?

medium

A. The second insert fails with a duplicate key error.

B. Both documents are inserted without error.

C. MongoDB automatically changes the second email to be unique.

D. The first document is deleted and replaced by the second.

Solution

1. Step 1: Understand unique index behavior

   A unique index prevents duplicate values in the indexed field.

2. Step 2: Predict insertion result

   Inserting a duplicate value causes MongoDB to reject the second insert with an error.

3. Final Answer:

   The second insert fails with a duplicate key error. -> Option A

4. Quick Check:

   Unique index blocks duplicates = error on second insert [OK]

Hint: Unique index stops duplicates, second insert errors [OK]

Common Mistakes:

• Assuming MongoDB auto-fixes duplicates
• Thinking both inserts succeed
• Believing first document gets replaced

4. You created an index on username but your queries on username are still slow. What could be a possible reason?

medium

A. Indexes always slow down queries.

B. MongoDB does not support indexing on strings.

C. The index was created with the wrong field name.

D. You need to restart MongoDB to activate indexes.

Solution

1. Step 1: Check index correctness

   If the index field name is wrong, queries won't use the index and remain slow.

2. Step 2: Evaluate other options

   MongoDB supports string indexes, indexes speed up queries, and no restart is needed.

3. Final Answer:

   The index was created with the wrong field name. -> Option C

4. Quick Check:

   Wrong index field = no speedup [OK]

Hint: Verify index field matches query field exactly [OK]

Common Mistakes:

• Believing MongoDB can't index strings
• Thinking indexes slow queries
• Restarting MongoDB unnecessarily

5. You have a collection with millions of documents. You want to speed up queries filtering by status and createdAt. Which indexing strategy is best?

hard

A. Create separate single-field indexes on status and createdAt.

B. Create a compound index on {status: 1, createdAt: -1}.

C. Create a unique index on status.

D. Do not create any index to save disk space.

Solution

1. Step 1: Understand query filtering needs

   Queries filter by both status and createdAt, so a compound index helps.

2. Step 2: Compare indexing options

   A compound index on both fields supports queries filtering on both efficiently, better than separate indexes.

3. Step 3: Consider uniqueness and disk space

   Unique index on status is not suitable since status likely repeats; skipping indexes slows queries.

4. Final Answer:

   Create a compound index on {status: 1, createdAt: -1}. -> Option B

5. Quick Check:

   Compound index matches multi-field queries [OK]

Hint: Use compound index for multi-field query filters [OK]

Common Mistakes:

• Using unique index on non-unique field
• Relying on separate indexes for multi-field queries
• Avoiding indexes to save space but hurting speed