When to revert to monolith in Microservices - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Identifying the right time to revert to a monolith

Which scenario best indicates that a microservices architecture should be reverted back to a monolith?

A When the system has too many small services causing high operational overhead and complex debugging.

B When the system requires strict isolation for security and compliance reasons.

C When different teams require full independence to deploy services separately.

D When the system needs to scale out rapidly to handle millions of users.

💡 Hint

Think about when microservices add more complexity than benefits.

Too many small services can increase operational overhead and make debugging difficult, which is a common reason to revert to a monolith for simplicity.

❓ Architecture

intermediate

Choosing architecture based on team size and product maturity

A startup has a small team and a product in early development. Which architecture choice is most suitable?

A Adopt microservices immediately to prepare for future scaling.

B Build multiple monoliths for different features.

C Start with a monolith to reduce complexity and speed up development.

D Use a serverless architecture to avoid managing infrastructure.

💡 Hint

Consider simplicity and speed for small teams.

Starting with a monolith helps small teams move faster and avoid the overhead of managing many services.

❓ scaling

advanced

Scaling challenges indicating a monolith reversion

Which scaling challenge is a strong signal to consider reverting from microservices to a monolith?

A When the system requires horizontal scaling of stateless services.

B When network latency between services causes significant performance degradation.

C When individual services can be scaled independently without issues.

D When the deployment pipeline supports continuous integration for all services.

💡 Hint

Think about communication overhead between services.

High network latency between many microservices can degrade performance, suggesting a monolith might be more efficient.

❓ tradeoff

advanced

Tradeoffs in reverting to monolith from microservices

What is a major tradeoff when reverting from microservices back to a monolith?

A Simplified deployment but reduced team autonomy and slower feature releases.

B Increased operational complexity but faster debugging.

C Improved fault isolation but harder to scale individual components.

D Better security isolation but increased network overhead.

💡 Hint

Consider how deployment and team workflows change.

Reverting to a monolith simplifies deployment but reduces the ability for teams to deploy independently, potentially slowing releases.

❓ estimation

expert

Estimating operational overhead in microservices vs monolith

A system has 50 microservices, each requiring 2 hours of daily maintenance. If reverted to a monolith requiring 10 hours daily maintenance, what is the daily operational overhead saved?

A 50 hours saved daily

B 100 hours saved daily

C 40 hours saved daily

D 90 hours saved daily

💡 Hint

Calculate total hours before and after, then find the difference.

50 services * 2 hours = 100 hours before; monolith = 10 hours; saved = 100 - 10 = 90 hours.

Practice

1. Which of the following is a common reason to revert from microservices back to a monolith?

easy

A. When you want to increase the number of services for better modularity

B. When the system needs to handle more users simultaneously

C. When you want to add more independent teams to work on the project

D. When microservices cause too much complexity and slow down development

Solution

1. Step 1: Understand microservices complexity

   Microservices can add overhead in communication and deployment, increasing complexity.

2. Step 2: Identify when to simplify

   If complexity slows development or causes performance issues, reverting to monolith helps.

3. Final Answer:

   When microservices cause too much complexity and slow down development -> Option D

4. Quick Check:

   Complexity and slow development = revert to monolith [OK]

Hint: Choose option mentioning complexity or slow development [OK]

Common Mistakes:

• Confusing scalability needs with reverting reasons
• Thinking more services always improve modularity
• Assuming more teams mean revert to monolith

2. Which syntax correctly describes a scenario to revert to monolith in a system design document?

easy

A. If (microservices_complexity > threshold) then revert_to_monolith()

B. while (services_count < max) { add_service(); }

C. deploy(microservices) if performance is good

D. scale(monolith) when load increases

Solution

1. Step 1: Analyze the condition for reverting

   The condition to revert is when microservices complexity exceeds a limit.

2. Step 2: Match syntax to scenario

   If (microservices_complexity > threshold) then revert_to_monolith() correctly uses a conditional to revert when complexity is high.

3. Final Answer:

   If (microservices_complexity > threshold) then revert_to_monolith() -> Option A

4. Quick Check:

   Condition on complexity triggers revert = If (microservices_complexity > threshold) then revert_to_monolith() [OK]

Hint: Look for condition checking complexity before revert [OK]

Common Mistakes:

• Choosing loops or unrelated deployment commands
• Ignoring the revert condition in syntax
• Confusing scaling with reverting

3. Given a microservices system with high network latency causing slow responses, what is the likely output of reverting to a monolith?

medium

A. Reduced network overhead and faster response times

B. Increased network calls and slower response times

C. More complex deployment pipelines

D. Increased service discovery failures

Solution

1. Step 1: Understand network latency impact

   Microservices communicate over network, causing latency and slow responses.

2. Step 2: Effect of reverting to monolith

   Combining services reduces network calls, lowering latency and improving speed.

3. Final Answer:

   Reduced network overhead and faster response times -> Option A

4. Quick Check:

   Less network calls = faster responses [OK]

Hint: Revert reduces network calls, so responses get faster [OK]

Common Mistakes:

• Thinking reverting increases network calls
• Confusing deployment complexity with runtime latency
• Assuming service discovery issues increase after revert

4. You have a microservices system with many small services causing deployment failures. Which fix correctly reverts to a monolith?

medium

A. Split services further to isolate failures

B. Combine services into a single deployable unit

C. Add more network retries for service calls

D. Increase the number of service instances

Solution

1. Step 1: Identify deployment failure cause

   Many small services increase deployment complexity and failure risk.

2. Step 2: Correct revert action

   Combining services into one unit simplifies deployment and reduces failures.

3. Final Answer:

   Combine services into a single deployable unit -> Option B

4. Quick Check:

   Combine services to simplify deployment [OK]

Hint: Fix deployment by combining services into one unit [OK]

Common Mistakes:

• Splitting services more instead of combining
• Adding retries doesn't fix deployment complexity
• Scaling instances doesn't solve deployment failures

5. A company has 20 microservices but faces slow feature delivery and high operational costs. What is the best approach to decide if reverting to a monolith is suitable?

hard

A. Add more microservices to distribute workload evenly

B. Immediately merge all services into one monolith to reduce costs

C. Evaluate team size, deployment complexity, and performance bottlenecks before deciding

D. Ignore operational costs and focus only on scaling microservices

Solution

1. Step 1: Analyze factors affecting delivery and costs

   Team size, deployment complexity, and performance issues impact delivery speed and costs.

2. Step 2: Make informed decision

   Evaluating these factors helps decide if reverting to monolith improves simplicity and efficiency.

3. Final Answer:

   Evaluate team size, deployment complexity, and performance bottlenecks before deciding -> Option C

4. Quick Check:

   Balanced evaluation guides revert decision [OK]

Hint: Assess team and complexity before reverting [OK]

Common Mistakes:

• Rushing to merge without analysis
• Adding more services without solving issues
• Ignoring costs and focusing only on scaling