Incremental migration plan in Microservices - Scalability & System Analysis

Scalability Analysis - Incremental migration plan

Growth Table: Incremental Migration Plan

Users	System State	Changes & Challenges
100 users	Monolith with initial microservices started	Basic microservices deployed; low traffic; minimal integration issues
10,000 users	Partial migration; some services fully independent	Increased inter-service communication; need for service discovery and API gateways
1,000,000 users	Majority services migrated; microservices communicate at scale	Database bottlenecks appear; need caching, read replicas; monitoring critical
100,000,000 users	Fully migrated; global distributed microservices	Network bandwidth and data partitioning bottlenecks; advanced sharding and CDN usage

First Bottleneck

At early scale (up to 10,000 users), the first bottleneck is the database because the monolith and microservices share the same database or have tightly coupled data. As traffic grows, database queries increase, causing latency and contention.

Scaling Solutions

• Database Decoupling: Gradually migrate data ownership to microservices with separate databases.
• Read Replicas & Caching: Use read replicas and caching layers (e.g., Redis) to reduce DB load.
• Service Discovery & API Gateway: Manage service communication efficiently.
• Horizontal Scaling: Add more instances of microservices behind load balancers.
• Sharding & Partitioning: Split databases by user or data type to handle large scale.
• CDN Usage: Cache static content closer to users to reduce bandwidth.

Back-of-Envelope Cost Analysis

• At 1M users, assume 10 requests/user/day -> ~10M requests/day ≈ 115 QPS average.
• Peak QPS can be 5x average -> ~575 QPS, within a few DB replicas' capacity.
• Storage grows with data; plan for TBs of data with backups and archiving.
• Network bandwidth must support inter-service calls and user traffic; consider 1 Gbps links for data centers.

Interview Tip

Start by describing the current system state and traffic. Identify the first bottleneck clearly. Then explain incremental steps to migrate and scale, focusing on decoupling, data ownership, and gradual rollout. Highlight monitoring and fallback plans to reduce risk.

Self Check

Your database handles 1000 QPS. Traffic grows 10x. What do you do first?

Answer: Implement read replicas and caching to reduce load on the primary database before considering more complex sharding or service scaling.

Key Result

Incremental migration starts with database bottlenecks at low scale; scaling solutions focus on decoupling data, caching, and horizontal scaling to handle millions of users smoothly.