Schema design for write-heavy workloads in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to insert a new document into the collection.

MongoDB

db.collection.[1]({ name: "Alice", age: 30 })

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

AinsertOne

BfindOne

CupdateOne

DdeleteOne

Attempts:

3 left

💡 Hint

Common Mistakes

Using findOne instead of insertOne will only retrieve data.

updateOne requires an existing document to update.

deleteOne removes documents, not adds.

The insertOne method adds a new document to the collection, which is essential for write-heavy workloads.

2fill in blank

medium

Complete the code to create an index that improves write performance by avoiding duplicates.

MongoDB

db.collection.createIndex({ email: 1 }, { [1]: true })

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

Asparse

Bbackground

Cunique

DexpireAfterSeconds

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sparse' does not enforce uniqueness.

Background indexing affects build time, not uniqueness.

expireAfterSeconds is for TTL indexes, unrelated here.

The unique option ensures no duplicate emails, which helps maintain data integrity in write-heavy workloads.

3fill in blank

hard

Fix the error in the update operation to increment the 'count' field by 1.

MongoDB

db.collection.updateOne({ _id: 1 }, { [1]: { count: 1 } })

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

A$inc

B$addToSet

C$push

D$set

Attempts:

3 left

💡 Hint

Common Mistakes

Using $set replaces the value instead of incrementing.

$push and $addToSet are for arrays, not numbers.

The $inc operator increments a numeric field by the specified value, perfect for counters in write-heavy workloads.

4fill in blank

hard

Fill both blanks to create a schema design that embeds comments inside posts for faster writes.

MongoDB

const commentSchema = new Schema({ text: String, date: [2] }); const postSchema = new Schema({ title: String, comments: [[1]] });

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

AcommentSchema

BDate

CString

DNumber

Attempts:

3 left

💡 Hint

Common Mistakes

Using String instead of commentSchema for comments loses structure.

Using Number for date is not appropriate.

Embedding commentSchema inside posts allows fast writes by storing related data together. The Date type is used for the comment date.

5fill in blank

hard

Fill all three blanks to write a bulk insert operation for multiple user documents.

MongoDB

db.users.[1]([{ name: "John" }, { name: "Jane" }], { [2]: true, [3]: 1000 })

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

AinsertMany

Bordered

Cw

Dupsert

Attempts:

3 left

💡 Hint

Common Mistakes

Using insertOne inserts only one document.

Upsert is for update operations, not inserts.

insertMany inserts multiple documents at once. The ordered option controls if operations stop on error. w sets acknowledgment level for writes, important in write-heavy workloads.

Practice

1. Which schema design approach is best for a MongoDB collection with very frequent writes?

easy

A. Use many small documents instead of one large nested document

B. Use one large document with deeply nested arrays

C. Use a single document for all data to reduce queries

D. Use only embedded documents without references

Solution

1. Step 1: Understand write-heavy workload needs

   Write-heavy workloads benefit from smaller documents to reduce update size and locking.

2. Step 2: Compare schema designs

   Large nested documents cause bigger writes and slower updates, while many small documents keep writes fast and simple.

3. Final Answer:

   Use many small documents instead of one large nested document -> Option A

4. Quick Check:

   Write-heavy = small docs [OK]

Hint: Choose small documents for fast writes [OK]

Common Mistakes:

• Thinking one big document is faster for writes
• Ignoring update size impact on performance
• Assuming embedded documents always improve speed

2. Which of the following is the correct way to define a MongoDB schema optimized for frequent writes?

easy

A. Embedding large arrays inside documents for all related data

B. Splitting data into multiple small collections with references

C. Using a single document with nested objects for all user data

D. Storing all data in one collection without indexes

Solution

1. Step 1: Identify schema syntax for write optimization

   Splitting data into small collections with references reduces document size and write contention.

2. Step 2: Evaluate options

   Embedding large arrays or single large documents slows writes; no indexes hurt query but not write speed.

3. Final Answer:

   Splitting data into multiple small collections with references -> Option B

4. Quick Check:

   Small collections + references = write-friendly [OK]

Hint: Split data, use references for write speed [OK]

Common Mistakes:

• Embedding large arrays causing slow writes
• Ignoring indexing impact on queries
• Using one big document for all data

3. Given this schema design for a write-heavy app:

{ _id: 1, user: "Alice", posts: [ {id: 1, text: "Hello"}, {id: 2, text: "World"} ] }

What is the main drawback when updating the text of a single post?

medium

A. Only the single post is updated, so writes are fast

B. MongoDB automatically splits the document for faster writes

C. The entire document must be rewritten, slowing writes

D. Updates fail because nested arrays are not allowed

Solution

1. Step 1: Understand document update behavior in MongoDB

   MongoDB rewrites the whole document on update, even if only one nested element changes.

2. Step 2: Analyze impact on write-heavy workloads

   Large documents with nested arrays cause slower writes because the entire document is rewritten.

3. Final Answer:

   The entire document must be rewritten, slowing writes -> Option C

4. Quick Check:

   Nested arrays cause full document rewrite [OK]

Hint: Nested arrays cause full document rewrite [OK]

Common Mistakes:

• Assuming only changed nested element updates
• Believing MongoDB splits documents automatically
• Thinking updates fail on nested arrays

4. You designed a schema with large embedded arrays for a write-heavy collection. Writes are slow. What is the best fix?

medium

A. Split embedded arrays into separate collections with references

B. Add more indexes to speed up writes

C. Increase document size limit to hold more data

D. Use a single large document to reduce queries

Solution

1. Step 1: Identify cause of slow writes

   Large embedded arrays cause big document rewrites, slowing writes.

2. Step 2: Apply schema design fix

   Splitting arrays into separate collections reduces document size and speeds up writes.

3. Final Answer:

   Split embedded arrays into separate collections with references -> Option A

4. Quick Check:

   Split large arrays to speed writes [OK]

Hint: Split large arrays into collections [OK]

Common Mistakes:

• Adding indexes to fix write speed
• Increasing document size limit
• Using one big document for all data

5. You have a write-heavy social app where users post comments frequently. Which schema design best balances fast writes and easy reads?

hard

A. Use a single document for all users and comments combined

B. Embed all comments inside each user document as a large array

C. Store all comments in one document per post with nested arrays

D. Store each comment as a separate document with a user reference

Solution

1. Step 1: Analyze write-heavy app needs

   Frequent writes require small documents to avoid large rewrites and locking.

2. Step 2: Evaluate schema options for balance

   Storing comments as separate documents with user references keeps writes fast and allows easy querying.

3. Final Answer:

   Store each comment as a separate document with a user reference -> Option D

4. Quick Check:

   Separate docs + references = fast writes + easy reads [OK]

Hint: Use separate documents with references for comments [OK]

Common Mistakes:

• Embedding all comments causing slow writes
• Using one big document for all data
• Nesting comments deeply inside posts