Problems / Scaling, medium
Every page calls a search index with 32 query slots and 4 ms of CPU per query on 4 vCPUs, at 1,200 req/s.
Free to open. Runs in your browser, no sign-up.
Success ≥ 99%, p99 ≤ 500 ms.
1,200 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, Search index.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 |
| Search indexSearch index | API calls it for reads.Search engines build an inverted index: each word points to the documents that contain it. | 32 slots |
Try it first. Open one hint at a time if you get stuck.
The API and database are fine.
Search is CPU-heavy: rate × CPU per query.
Give the search index more capacity (slots and vCPU), e.g. more nodes or shards.