Anti-patterns to avoid in MongoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find all documents in the collection.

MongoDB

db.collection.[1]()

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

Afind

Binsert

Cupdate

Ddelete

Attempts:

3 left

💡 Hint

Common Mistakes

Using insert() instead of find() to get data.

Trying to use update() to retrieve documents.

The find() method retrieves documents from a MongoDB collection. Using other methods like insert() or update() will not return documents.

2fill in blank

medium

Complete the code to avoid the anti-pattern of fetching all documents when only one is needed.

MongoDB

db.collection.[1]({})

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

AfindOne

Bfind

Caggregate

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using find() when only one document is needed.

Using aggregate() unnecessarily for simple queries.

Using findOne() fetches only one document, which is efficient when you need a single result. Using find() fetches all documents, which can be slow and waste resources.

3fill in blank

hard

Fix the error in the query to avoid the anti-pattern of unindexed queries.

MongoDB

db.collection.find({ [1]: 'value' })

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

AunindexedField

BnonexistentField

CrandomField

DindexedField

Attempts:

3 left

💡 Hint

Common Mistakes

Querying on fields without indexes causing slow performance.

Using fields that do not exist in the collection.

Queries should use indexed fields to avoid slow scans. Using indexedField ensures the query uses an index, improving performance.

4fill in blank

hard

Fill both blanks to avoid the anti-pattern of large documents by projecting only needed fields.

MongoDB

db.collection.find({}, { [1]: 1, [2]: 1 })

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

Aname

Bpassword

Cemail

DlargeData

Attempts:

3 left

💡 Hint

Common Mistakes

Including sensitive fields like password in projections.

Fetching large fields unnecessarily.

Projecting only necessary fields like name and email avoids fetching large unnecessary data such as password or largeData, improving query speed and reducing memory use.

5fill in blank

hard

Fill all three blanks to avoid the anti-pattern of inefficient updates by using the correct update operator and filter.

MongoDB

db.collection.updateOne({ [1]: '123' }, { [2]: { [3]: 'newValue' } })

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

A_id

B$set

CfieldToUpdate

D$push

Attempts:

3 left

💡 Hint

Common Mistakes

Using $push instead of $set for single field updates.

Filtering by a non-unique field causing multiple updates.

Replacing the whole document instead of updating a field.

Use _id to filter the document uniquely. Use the $set operator to update only the specified field fieldToUpdate. This avoids replacing the whole document and improves efficiency.

Practice

1. Which of the following is an anti-pattern to avoid in MongoDB when designing your data model?

easy

A. Splitting large data into separate collections

B. Storing huge arrays inside a single document

C. Using indexes to speed up queries

D. Keeping documents shallow with minimal nesting

Solution

1. Step 1: Understand data model best practices

   MongoDB recommends avoiding very large arrays inside one document because it can slow down queries and updates.

2. Step 2: Identify the anti-pattern

   Options A, B, and D are good practices, but storing huge arrays in one document is an anti-pattern.

3. Final Answer:

   Storing huge arrays inside a single document -> Option B

4. Quick Check:

   Huge arrays in one document = anti-pattern [OK]

Hint: Large arrays in one document slow performance [OK]

Common Mistakes:

• Thinking indexes are bad
• Believing deep nesting is good
• Ignoring document size limits

2. Which of the following MongoDB document structures is syntactically correct, uses a structured object for address, and avoids the deep nesting anti-pattern?

easy

A. { name: "Alice", address: { city: "NY", zip: 10001 } }

B. { name: "Bob", address: { city: { name: "LA", code: { zip: 90001 } } } }

C. { name: "Carol", address: { city: { name: { code: { zip: 12345 } } } } }

D. { name: "Dave", address: "New York" }

Solution

1. Step 1: Check document nesting levels

   { name: "Alice", address: { city: "NY", zip: 10001 } } has shallow nesting: address contains city and zip directly.

2. Step 2: Identify deep nesting anti-pattern

   Options A and C have multiple nested objects inside city, which is deep nesting. { name: "Dave", address: "New York" } uses a string for address, which is valid but less structured.

3. Final Answer:

   { name: "Alice", address: { city: "NY", zip: 10001 } } -> Option A

4. Quick Check:

   Shallow nesting = correct syntax and good practice [OK]

Hint: Avoid multiple nested objects inside one field [OK]

Common Mistakes:

• Confusing deep nesting with valid nested objects
• Using strings instead of objects for structured data
• Ignoring syntax errors in nested documents

3. Given a MongoDB collection with documents containing a large array field, what is the likely result of querying without indexes?

medium

A. Automatic splitting of large arrays into smaller parts

B. Fast query response even with large arrays

C. Syntax error in query execution

D. Slow query performance due to collection scan

Solution

1. Step 1: Understand query execution without indexes

   Without indexes, MongoDB performs a collection scan, checking every document.

2. Step 2: Impact of large arrays on query speed

   Large arrays increase document size, making scans slower and more resource-intensive.

3. Final Answer:

   Slow query performance due to collection scan -> Option D

4. Quick Check:

   No index + large arrays = slow queries [OK]

Hint: No index means full scan, slow with big arrays [OK]

Common Mistakes:

• Assuming queries are always fast
• Expecting automatic array splitting
• Confusing syntax errors with performance issues

4. You have a MongoDB document with a very large array that is frequently updated. What is the best way to fix performance issues?

medium

A. Split the large array into multiple documents in a separate collection

B. Keep the large array inside the same document and update it directly

C. Remove indexes to speed up updates

D. Nest the array deeper inside the document

Solution

1. Step 1: Identify the problem with large arrays and frequent updates

   Large arrays inside one document cause slow updates and can hit document size limits.

2. Step 2: Choose the best solution to improve performance

   Splitting the array into multiple documents in a separate collection reduces document size and update cost.

3. Final Answer:

   Split the large array into multiple documents in a separate collection -> Option A

4. Quick Check:

   Split large data for frequent updates [OK]

Hint: Split big arrays into separate documents [OK]

Common Mistakes:

• Updating large arrays in one document
• Removing indexes which slows queries
• Increasing nesting depth unnecessarily

5. You want to design a MongoDB schema for a social media app where users have many posts and comments. Which design avoids anti-patterns?

hard

A. Store all posts and comments as large arrays inside each user document

B. Nest comments deeply inside each post inside the user document

C. Use separate collections for users, posts, and comments with references

D. Keep all data in one collection with mixed document types

Solution

1. Step 1: Analyze the data size and update frequency

   Users can have many posts and comments, which can grow large and update often.

2. Step 2: Choose schema design to avoid large arrays and deep nesting

   Using separate collections with references avoids huge arrays and deep nesting, improving performance and scalability.

3. Final Answer:

   Use separate collections for users, posts, and comments with references -> Option C

4. Quick Check:

   Separate collections avoid large arrays and deep nesting [OK]

Hint: Separate collections for related large data sets [OK]

Common Mistakes:

• Putting all data in one document
• Deep nesting of comments inside posts
• Mixing document types in one collection