Anti-corruption layer in Microservices - System Design Exercise

Design: Anti-corruption Layer for Microservices Integration

Design the anti-corruption layer component between two microservices with different domain models and communication protocols. Out of scope: internal implementation of each microservice business logic.

Functional Requirements

FR1: Allow two or more microservices with different data models and protocols to communicate without corrupting each other's domain logic

FR2: Translate requests and responses between incompatible interfaces

FR3: Prevent changes in one service from forcing changes in others

FR4: Support synchronous and asynchronous communication

FR5: Handle data validation and transformation

FR6: Log translation errors for debugging

Non-Functional Requirements

NFR1: Must handle up to 10,000 requests per second

NFR2: API response latency p99 under 150ms

NFR3: Availability target 99.9% uptime

NFR4: Must be scalable horizontally

NFR5: Should not introduce tight coupling between services

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

❓ Question 6

Key Components

Translator/Adapter modules for data transformation

Facade or Gateway to expose unified interface

Validation components to check data correctness

Error handling and logging mechanisms

Communication protocols (REST, gRPC, message queues)

Caching if needed for performance

Design Patterns

Adapter pattern to convert interfaces

Facade pattern to provide simplified interface

Anti-corruption Layer pattern from Domain-Driven Design

Message translation in asynchronous messaging

Circuit breaker for fault tolerance

Reference Architecture

Client Service A  <--->  Anti-Corruption Layer  <--->  Service B

+----------------+       +-------------------------+       +----------------+
| Service A      |       | Anti-Corruption Layer    |       | Service B      |
| (Domain Model) | <---> | - Translator/Adapter     | <---> | (Different     |
|                |       | - Validator             |       |  Domain Model) |
|                |       | - Facade/Gateway        |       |                |
+----------------+       +-------------------------+       +----------------+

Components

Translator/Adapter

Custom code in microservice language (e.g., Java, Node.js)

Convert data formats and models between Service A and Service B

Facade/Gateway

REST API or gRPC endpoint

Expose a unified interface to Service A and hide complexity of Service B

Validator

Validation libraries or custom logic

Ensure data integrity and correctness before forwarding

Error Logger

Centralized logging system (e.g., ELK stack, Splunk)

Record translation and communication errors for troubleshooting

Communication Layer

REST/gRPC clients or message queue clients

Handle protocol-specific communication with Service B

Request Flow

1. Service A sends a request to the Anti-Corruption Layer facade.

2. Facade receives the request and passes it to the Translator.

3. Translator converts Service A's data model to Service B's model.

4. Validator checks the transformed data for correctness.

5. If valid, Communication Layer sends the request to Service B.

6. Service B processes and sends a response back.

7. Communication Layer receives response and passes it to Translator.

8. Translator converts response back to Service A's model.

9. Facade returns the translated response to Service A.

10. Errors during translation or communication are logged.

Database Schema

No dedicated database for the anti-corruption layer is required. It acts as a stateless translator and proxy. If caching is added, a simple key-value store schema may be used to cache frequent translations.

Scaling Discussion

Bottlenecks

Translator component CPU usage under high request load

Communication Layer latency due to network or Service B slowness

Error logging system overload

Single instance of Anti-Corruption Layer causing a bottleneck

Solutions

Scale Anti-Corruption Layer horizontally with load balancer

Optimize translation logic and use efficient serialization formats

Implement circuit breaker and retries to handle Service B slowness

Use asynchronous messaging to decouple and buffer requests

Use scalable logging infrastructure with backpressure handling

Interview Tips

Time: Spend 10 minutes clarifying requirements and constraints, 20 minutes designing the architecture and data flow, 10 minutes discussing scaling and trade-offs, 5 minutes summarizing.

Explain the purpose of the anti-corruption layer to protect domain integrity

Discuss how translation and validation prevent corruption

Describe synchronous and asynchronous communication handling

Highlight scalability and fault tolerance considerations

Mention patterns like Adapter and Facade to simplify integration

Practice

1. What is the main purpose of an Anti-corruption layer in microservices architecture?

easy

A. To translate and isolate differences between two systems to prevent corruption

B. To speed up database queries between microservices

C. To store user session data securely

D. To monitor network traffic between services

Solution

1. Step 1: Understand the role of the anti-corruption layer

   The anti-corruption layer acts as a translator between two systems with different models or rules.

2. Step 2: Identify its main goal

   Its goal is to prevent the internal system from being affected or corrupted by external system differences.

3. Final Answer:

   To translate and isolate differences between two systems to prevent corruption -> Option A

4. Quick Check:

   Anti-corruption layer = Translation and isolation [OK]

Hint: Think: 'translator' between systems to avoid confusion [OK]

Common Mistakes:

• Confusing it with caching or monitoring layers
• Thinking it speeds up queries directly
• Assuming it stores user data

2. Which of the following is the correct way to implement an anti-corruption layer between two microservices?

easy

A. Directly expose the legacy system's database schema to the new service

B. Allow the new system to write directly to the legacy system's tables

C. Use the same data model in both systems without changes

D. Create a translation interface that maps legacy data to the new system's model

Solution

1. Step 1: Review implementation best practices

   An anti-corruption layer should translate and map data between systems, not share schemas directly.

2. Step 2: Identify the correct approach

   Creating a translation interface that maps legacy data to the new system's model isolates differences and protects both systems.

3. Final Answer:

   Create a translation interface that maps legacy data to the new system's model -> Option D

4. Quick Check:

   Translation interface = Correct implementation [OK]

Hint: Map legacy data to new model, never share schemas directly [OK]

Common Mistakes:

• Exposing legacy database schema directly
• Using identical data models without translation
• Allowing direct writes to legacy tables

3. Given the following pseudo-code for an anti-corruption layer translating legacy user data, what will be the output?

legacyUser = {"fullName": "Jane Doe", "age": 30}

function translateUser(legacy) {
  return {
    name: legacy.fullName,
    isAdult: legacy.age >= 18
  }
}

newUser = translateUser(legacyUser)
console.log(newUser)

medium

A. {"name": "Jane Doe", "isAdult": false}

B. {"fullName": "Jane Doe", "isAdult": true}

C. {"name": "Jane Doe", "isAdult": true}

D. {"name": "Jane Doe"}

Solution

1. Step 1: Analyze the translation function

   The function creates a new object with 'name' from 'fullName' and 'isAdult' as true if age >= 18.

2. Step 2: Apply the function to the legacy user

   legacyUser has fullName 'Jane Doe' and age 30, so isAdult is true.

3. Final Answer:

   {"name": "Jane Doe", "isAdult": true} -> Option C

4. Quick Check:

   Translate fullName and check age >= 18 = true [OK]

Hint: Check property mapping and age condition carefully [OK]

Common Mistakes:

• Using legacy property names in output
• Incorrectly evaluating age condition
• Missing one of the output properties

4. A developer wrote this anti-corruption layer code snippet but it causes errors when legacy data is missing some fields:

function translateOrder(legacyOrder) {
  return {
    id: legacyOrder.orderId,
    total: legacyOrder.amount.value,
    status: legacyOrder.status.toUpperCase()
  }
}

What is the main issue and how to fix it?

medium

A. The function should return legacyOrder directly without changes

B. The code assumes nested fields exist; add checks to handle missing or undefined fields

C. Use lowercase for status instead of toUpperCase()

D. Remove the id field to avoid errors

Solution

1. Step 1: Identify the error cause

   The code accesses nested fields like legacyOrder.amount.value without checking if amount exists, causing errors if missing.

2. Step 2: Fix by adding safety checks

   Use conditional checks or optional chaining to safely access nested fields and avoid runtime errors.

3. Final Answer:

   The code assumes nested fields exist; add checks to handle missing or undefined fields -> Option B

4. Quick Check:

   Missing field checks cause errors = add safety checks [OK]

Hint: Always check nested fields exist before accessing [OK]

Common Mistakes:

• Ignoring null or undefined nested objects
• Returning legacy data without translation
• Changing case without reason
• Removing necessary fields

5. You need to integrate a legacy billing system with your new microservice. The legacy system uses different currency codes and date formats. How should you design the anti-corruption layer to handle this integration effectively?

hard

A. Build a translation layer that converts legacy currency codes to standard ISO codes and normalizes date formats before passing data to the new service

B. Modify the legacy system to use the new system's currency codes and date formats directly

C. Ignore currency and date differences and pass data as-is to the new service

D. Store all legacy data in the new system without any translation

Solution

1. Step 1: Identify integration challenges

   Legacy system uses different currency codes and date formats, which can cause data misinterpretation.

2. Step 2: Design translation in anti-corruption layer

   Create a layer that converts legacy currency codes to standard ISO codes and normalizes date formats to the new system's expected format.

3. Final Answer:

   Build a translation layer that converts legacy currency codes to standard ISO codes and normalizes date formats before passing data to the new service -> Option A

4. Quick Check:

   Translate legacy formats to standard before integration [OK]

Hint: Translate legacy formats to standard before integration [OK]

Common Mistakes:

• Trying to change legacy system directly
• Passing data without translation
• Storing legacy data without normalization