Problems / AI products, medium

Recommendations: the online feature store

Every recommendation reads user and item features from an online feature store before the ranking model scores candidates. The store holds 6 reads at once at about 3 ms each; 2,500 requests arrive a second.

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Your goals

Success ≥ 99%, p99 ≤ 5 s.

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 5; 1 of them are hidden until then.

  1. As given. The problem’s own traffic, scheduled failures and seed.
  2. Peak hour. 25% more traffic than the problem states, with the same failures.
  3. A slow dependency. The main data store or model answers twice as slowly for part of the run.
  4. A server dies. One replica of the service with the most replicas is killed for part of the run.

The traffic

2,500 requests a second for 30 seconds: 100% request (reads).

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 AI app.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header.–
AI appApp serviceLoad balancer forwards to it.Calls Online feature store, Ranking model.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s.3 replicas × 500 slots · timeout 30 s
Online feature storeSession storeAI app calls it.A shared session store lets any app server handle a user's next request.6 slots
Ranking modelSelf-hosted serving poolAI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out.2000 slots

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