When to revert to monolith in Microservices - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a monolith in software architecture?

A monolith is a single unified software application where all components are tightly integrated and run as one process.

intermediate

Name one common reason to revert from microservices back to a monolith.

When the complexity of managing many microservices outweighs the benefits, such as increased operational overhead or difficulty in debugging.

beginner

How can a monolith improve development speed compared to microservices?

A monolith allows developers to work within a single codebase and deployment unit, reducing coordination and integration effort.

intermediate

What operational challenge might push a team to revert to a monolith?

Difficulties in monitoring, logging, and tracing across many microservices can make troubleshooting slow and error-prone.

intermediate

True or False: Reverting to a monolith means giving up scalability benefits of microservices.

False. While microservices can scale independently, a well-designed monolith can still scale vertically and sometimes horizontally with less complexity.

Which of the following is a valid reason to revert from microservices to a monolith?

A When service communication overhead is too high

B When you want to add more services

C When you need more independent deployments

D When you want to increase fault isolation

High communication overhead between many microservices can reduce performance and increase complexity, making a monolith simpler.

What is a key operational benefit of a monolith compared to microservices?

A Simpler monitoring and logging

B Independent scaling of components

C Faster independent deployments

D Better fault isolation

A monolith has a single process, making monitoring and logging simpler than across many microservices.

Which scenario suggests microservices might be better than a monolith?

A Small team with simple app

B Need for independent scaling of features

C Low operational complexity

D Tight integration between components

Microservices allow independent scaling of features, which is useful for large or complex apps.

What is a common drawback of microservices that might lead to reverting to a monolith?

A Too simple to manage

B High deployment speed

C Single codebase

D Increased debugging difficulty

Microservices increase debugging difficulty due to distributed nature and many moving parts.

Reverting to a monolith is often considered when:

A You want to add more microservices

B The app is growing rapidly in complexity

C The team is small and prefers simplicity

D You need more fault tolerance

Small teams may prefer monoliths for simplicity and easier coordination.

Explain three reasons why a team might choose to revert from microservices back to a monolith.

Think about what makes microservices hard to manage.

Describe how reverting to a monolith can impact scalability and deployment speed.

Consider trade-offs between simplicity and flexibility.

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