Anti-patterns to avoid in MongoDB - Step-by-Step Execution

Concept Flow - Anti-patterns to avoid

Start Query Design

↓

Identify Data Model

↓

Avoid Embedding Too Much

↓

Avoid Large Documents

↓

Avoid Frequent Joins

↓

Avoid Unindexed Queries

↓

Test Performance

↓

Refine Model

↓

End

This flow shows the steps to design MongoDB queries while avoiding common anti-patterns that hurt performance and maintainability.

Execution Sample

MongoDB

db.users.find({"profile.address.city": "New York"})
// Avoid unindexed queries on nested fields

This query searches users by city in their address but can be slow if no index exists on the nested field.

Execution Table

Step	Action	Query/Operation	Effect	Notes
1	Start designing data model	N/A	Plan schema	Consider embedding vs referencing
2	Embed too much data	User document with large embedded arrays	Large document size	Can cause slow reads/writes
3	Create large documents	Documents >16MB	Fails insert/update	MongoDB limit exceeded
4	Perform frequent joins	Using $lookup on large collections	Slow queries	Joins are costly in MongoDB
5	Run unindexed queries	Find on unindexed nested field	Full collection scan	Very slow for big data
6	Test query performance	Explain plan	Identify slow parts	Use indexes or redesign
7	Refine model	Add indexes, limit embedding	Improved speed	Better scalability
8	End	N/A	Optimized queries	Avoided anti-patterns

💡 Stopped after refining model to avoid common MongoDB anti-patterns

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 5	Final
Document Size	Small	Large (due to embedding)	Too Large (>16MB)	N/A	Optimized (smaller)
Query Speed	N/A	Slower	Fails	Very Slow (unindexed)	Fast (indexed)
Index Usage	None	None	None	None	Indexes added

Key Moments - 3 Insights

Why is embedding too much data in one document a problem?

Embedding too much data makes documents large, which slows down reads and writes as shown in execution_table step 2.

What happens if a document exceeds 16MB in MongoDB?

MongoDB rejects documents larger than 16MB, causing insert or update failures as shown in execution_table step 3.

Why should queries on nested fields be indexed?

Without indexes, queries scan the whole collection, making them very slow as shown in execution_table step 5.

Visual Quiz - 3 Questions

Look at the execution_table, at which step does the document size become too large?

A Step 2

B Step 3

C Step 5

D Step 7

💡 Hint

Check the 'Document Size' variable in variable_tracker after Step 3

According to variable_tracker, what happens to query speed after embedding too much data?

A It becomes faster

B It stays the same

C It becomes slower

D It fails immediately

💡 Hint

Look at 'Query Speed' after Step 2 in variable_tracker

If we add indexes as in Step 7, what is the expected effect on query speed?

A Query speed improves

B Query speed becomes slower

C No change in query speed

D Queries fail

💡 Hint

Refer to 'Query Speed' and 'Index Usage' in variable_tracker at the final stage

Concept Snapshot

MongoDB Anti-patterns to Avoid:
- Avoid embedding too much data in one document (causes large documents)
- Never exceed 16MB document size limit
- Avoid frequent joins ($lookup) on large collections
- Always index fields used in queries, especially nested fields
- Test query performance and refine schema accordingly

Full Transcript

This visual execution shows common MongoDB anti-patterns to avoid. Starting from data model design, embedding too much data leads to large documents that slow operations. Documents larger than 16MB are rejected by MongoDB. Frequent joins using $lookup on big collections cause slow queries. Queries on unindexed nested fields result in full collection scans and poor performance. Testing query plans helps identify slow parts. Adding indexes and limiting embedding improves speed and scalability. Variables like document size and query speed change through these steps, highlighting the impact of anti-patterns and their fixes.

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