Anti-patterns to avoid in MongoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the 'One Big Collection' anti-pattern in MongoDB?

It is when all data is stored in a single collection without proper structure, causing slow queries and difficulty in managing data.

beginner

Why should you avoid embedding large arrays inside documents in MongoDB?

Because large arrays can cause document size to grow too big, leading to performance issues and hitting MongoDB's document size limit.

beginner

What problem arises from using unindexed queries frequently in MongoDB?

Unindexed queries cause full collection scans, which slow down performance and increase resource usage.

intermediate

Explain the 'Overusing Joins' anti-pattern in MongoDB.

MongoDB is not designed for heavy join operations; overusing $lookup or manual joins can degrade performance and complicate queries.

intermediate

Why is ignoring schema design considered an anti-pattern in MongoDB?

Ignoring schema design leads to inconsistent data, harder maintenance, and inefficient queries, even though MongoDB is schema-flexible.

Which of the following is a common MongoDB anti-pattern?

A Using indexes on frequently queried fields

B Embedding small related documents

C Storing all data in one large collection

D Designing a clear schema

Storing all data in one large collection without structure is an anti-pattern that causes slow queries.

What happens if you frequently run queries without indexes in MongoDB?

A Full collection scans slow down performance

B Queries run faster

C Data gets deleted automatically

D Indexes are created automatically

Without indexes, MongoDB scans the entire collection, which slows down queries.

Why should large arrays inside documents be avoided in MongoDB?

A They improve query speed

B They can exceed document size limits and reduce performance

C They automatically create indexes

D They simplify schema design

Large arrays can make documents too big, hitting size limits and hurting performance.

What is a downside of overusing $lookup for joins in MongoDB?

A It can degrade performance and complicate queries

B It automatically indexes joined fields

C It reduces data duplication

D It speeds up queries

$lookup joins are costly and can slow down queries if overused.

Ignoring schema design in MongoDB can lead to:

A Automatic schema enforcement

B Easier maintenance

C Consistent and efficient data

D Inconsistent data and inefficient queries

Without schema design, data can become inconsistent and queries inefficient.

Describe three common anti-patterns to avoid when designing MongoDB databases.

Think about how data is stored, queried, and indexed.

Explain why ignoring schema design in MongoDB can cause problems, even though it is schema-flexible.

Consider the benefits of planning your data structure.

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