Why gradual migration reduces risk in Microservices - Design It to Understand It

Design: Gradual Migration in Microservices

Focus on migration strategy and risk reduction techniques. Out of scope: detailed microservice implementation or specific technology stacks.

Functional Requirements

FR1: Migrate a monolithic application to microservices step-by-step

FR2: Ensure system remains functional during migration

FR3: Minimize downtime and user impact

FR4: Allow rollback of changes if issues occur

FR5: Enable testing of new microservices independently

Non-Functional Requirements

NFR1: Support at least 10,000 concurrent users during migration

NFR2: API response latency p99 under 300ms

NFR3: Availability target 99.9% uptime during migration

NFR4: Data consistency must be maintained between old and new systems

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

API Gateway or Router for traffic splitting

Service Registry for discovery

Message queues for asynchronous communication

Database synchronization or dual writes

Monitoring and logging tools

Design Patterns

Strangler Fig pattern

Canary releases

Blue-Green deployment

Feature toggles

Circuit breaker pattern

Reference Architecture

Monolith System
   |
   | Gradual Migration
   v
+-------------------+       +-------------------+
| Old Monolith      |<----->| API Gateway       |
+-------------------+       +-------------------+
                                |          |
                                |          v
                                |    +-------------------+
                                |    | New Microservice 1 |
                                |    +-------------------+
                                |
                                v
                           +-------------------+
                           | New Microservice 2 |
                           +-------------------+

Components

API Gateway

Nginx, Kong, or custom proxy

Route requests to old monolith or new microservices based on migration progress

Old Monolith

Existing monolithic application

Serve requests not yet migrated

New Microservices

Docker containers, Kubernetes

Handle migrated functionality independently

Database Synchronization

Dual writes, change data capture

Keep data consistent between old and new systems

Monitoring & Logging

Prometheus, ELK stack

Track system health and detect issues early

Request Flow

1. Client sends request to API Gateway.

2. API Gateway decides to route request to old monolith or new microservice based on migration phase.

3. If routed to new microservice, it processes request independently.

4. Both old and new systems write to synchronized databases to maintain data consistency.

5. Monitoring tools collect logs and metrics from all components.

6. If issues detected, rollback to old monolith by changing routing rules in API Gateway.

Database Schema

Entities remain consistent across old and new systems. Dual writes or event sourcing ensure data synchronization. Key entities include User, Order, Product with relationships preserved. Migration may involve splitting monolithic tables into microservice-specific schemas.

Scaling Discussion

Bottlenecks

API Gateway becomes a single point of failure or bottleneck.

Data synchronization overhead increases latency.

Rollback complexity if data diverges.

Monitoring large distributed systems is challenging.

Solutions

Use multiple API Gateway instances with load balancing and failover.

Implement eventual consistency with conflict resolution strategies.

Design rollback procedures with data reconciliation tools.

Use centralized logging and distributed tracing for observability.

Interview Tips

Time: Spend 10 minutes understanding migration goals and risks, 15 minutes designing gradual migration architecture, 10 minutes discussing scaling and rollback strategies, 10 minutes answering questions.

Explain why migrating gradually reduces risk by limiting blast radius.

Discuss routing strategies to split traffic safely.

Highlight importance of data consistency and rollback plans.

Mention monitoring to detect issues early.

Show awareness of scaling challenges and solutions.