Problems / Caching, hard
5 million products, 2 KB each, in a 256 MB cache whose entries expire after 10 seconds. The database behind it has 2 vCPUs.
Free to open. Runs in your browser, no sign-up.
Success ≥ 99%, p99 ≤ 300 ms.
3,000 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 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. | 256 MB memory |
| 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. | 2 vCPU · 40 connections |
Try it first. Open one hint at a time if you get stuck.
Open the cache: what limits its hit rate right now, memory or TTL?
Fix one limit and the other shows up next.
Memory for the working set, a TTL of minutes, then see what the database still needs.
Give the cache a few GB of memory and a TTL of minutes ("set the cache memory to 4 GB", "cache TTL 10 minutes"); add a read replica if the database is still hot.