OAuth 2.0 for microservices - Scalability & System Analysis

Scalability Analysis - OAuth 2.0 for microservices

Growth Table: OAuth 2.0 for Microservices

Users	What Changes?
100 users	Single authorization server handles token issuance; microservices validate tokens locally or via introspection; low latency.
10,000 users	Authorization server load increases; token cache needed; microservices may use local token validation libraries; introspection calls optimized.
1,000,000 users	Authorization server becomes bottleneck; need horizontal scaling; token revocation and refresh token management complex; distributed cache for tokens; microservices use JWT validation to reduce introspection.
100,000,000 users	Massive authorization server cluster with load balancing; global token cache/CDN; token revocation via blacklist with distributed storage; microservices rely on stateless JWT validation; network bandwidth and latency critical.

First Bottleneck

The authorization server is the first bottleneck. It handles token issuance, validation (if introspection is used), and revocation. As user count and token requests grow, CPU and database load on this server increase, causing latency and failures.

Scaling Solutions

• Horizontal scaling: Run multiple authorization server instances behind a load balancer to distribute token requests.
• Token caching: Use distributed caches (e.g., Redis) to store token introspection results to reduce repeated DB calls.
• Stateless tokens: Use JWT access tokens with embedded claims and signature verification to avoid introspection calls.
• Token revocation: Implement token blacklist with efficient distributed storage or short-lived tokens with refresh tokens.
• Microservice validation: Microservices validate tokens locally using public keys to reduce network calls.
• CDN and geo-distribution: Deploy authorization servers and caches closer to users to reduce latency.

Back-of-Envelope Cost Analysis

• Assuming 1M users, each making 1 request per second -> 1M QPS token validations.
• Authorization server can handle ~5,000 QPS per instance -> need ~200 instances for token issuance/introspection.
• Using JWT reduces introspection calls by 90%, lowering load to ~100,000 QPS -> ~20 instances needed.
• Storage for token revocation lists depends on token lifetime; short-lived tokens reduce storage needs.
• Network bandwidth: 1M QPS x ~1KB token data = ~1GB/s bandwidth needed for token validation traffic.

Interview Tip

Start by identifying the main components: authorization server, microservices, token types. Discuss how token validation scales and where bottlenecks appear. Propose stateless tokens and caching to reduce load. Mention trade-offs like token revocation complexity. Always connect scaling steps to specific bottlenecks.

Self Check

Your authorization server handles 1,000 QPS token introspection. Traffic grows 10x to 10,000 QPS. What do you do first?

Answer: Introduce stateless JWT tokens so microservices can validate tokens locally without introspection calls, reducing load on the authorization server.

Key Result

The authorization server is the first bottleneck as user and token validation requests grow; using stateless JWT tokens and caching reduces load and enables horizontal scaling.