Problems / Scaling, easy
Each API request validates its session with an auth service that has 40 slots. At 1,500 req/s it can’t keep up.
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Success ≥ 99%, p99 ≤ 500 ms.
1,500 requests a second for 30 seconds: 90% reads, 10% writes.
What each component does in this design, and what calls it. The simulator mimics each one from these settings.
| Component | Its job here | Settings |
|---|---|---|
| Load balancerLoad balancer | Traffic enters here.Calls API.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header. | – |
| APIApp service | Load balancer forwards to it.Calls Auth service, Redis cache (checked first), Postgres.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 200 slots · timeout 1.0 s |
| Redis cacheCache | API checks it first for reads.An in-memory cache like Redis usually answers in well under a millisecond inside one region. | 95% hit rate |
| PostgresSQL database | API reads it when Redis cache misses. API calls it for writes.Postgres runs one process per connection, which is why poolers like PgBouncer sit in front of busy databases. | 8 vCPU · 60 connections |
| Auth serviceAuth / identity service | API calls it.Short-lived access tokens plus refresh tokens limit the damage if a token leaks. | 40 slots |
Try it first. Open one hint at a time if you get stuck.
Find the component on every request’s path that turns red.
A shared dependency needs capacity for everyone’s traffic.
Give the auth service more capacity (or cache sessions).