System design interviews evaluate your capability to architect large-scale distributed applications capable of servicing millions of concurrent requests with low latency and high availability.

Key System Design Concepts Covered

• Rate Limiting Algorithms: Token Bucket, Leaky Bucket, Sliding Window Log vs Counter.
• Consistent Hashing: Minimizing key redistribution during node additions/failures in distributed caches.
• Distributed Transactions: Two-Phase Commit (2PC) vs Saga Pattern (Orchestration vs Choreography).
• Database Sharding & Replication: Read-replicas, write quorum, and partition key strategy.

Question 1: Designing a Distributed Rate Limiter

When designing a rate limiter for microservices, you must prevent API abuse while ensuring sub-millisecond overhead. Below is an example implementation using Redis Lua scripting for atomic execution:

-- Atomic Redis Rate Limiter Script
local key = KEYS[1]
local limit = tonumber(ARGV[1])
local window = tonumber(ARGV[2])

local current = redis.call("INCR", key)
if current == 1 then
    redis.call("EXPIRE", key, window)
end

if current > limit then
    return 0 -- Rate limit exceeded
else
    return 1 -- Request allowed
end

Architecture Checklist

Always verify latency metrics, failover strategy, data persistence guarantees, and monitoring alerts (Prometheus/Grafana) when presenting your design.