Problems / Scaling, medium

Autoscaling hits its ceiling

The API runs on a container platform that autoscales from 2 to 3 instances. Traffic needs more.

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Your goals

Success ≥ 99%, p99 ≤ 500 ms.

Test cases

Like a coding problem, a solution has to pass every case, not just the one it was built for. Submit in the lab runs all 3; 1 of them are hidden until then.

  1. As given. The problem’s own traffic, scheduled failures and seed.
  2. A slow dependency. The main data store or model answers twice as slowly for part of the run.

The traffic

3,000 requests a second for 30 seconds: 90% reads, 10% writes.

The system

What each component does in this design, and what calls it. The simulator mimics each one from these settings.

ComponentIts job hereSettings
Load balancerLoad balancerTraffic enters here.Calls API (containers).Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header.–
API (containers)Container platformLoad balancer forwards to it.Calls Redis cache (checked first), Postgres.Kubernetes places pods on nodes by their CPU and memory requests, and only replaces a lost node's pods after about 5 minutes.300 slots · 2–3 instances
Redis cacheCacheAPI (containers) 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 databaseAPI (containers) reads it when Redis cache misses. API (containers) 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

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