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API Rate Limiting & Scalability Patterns · Lesson

Testing and Monitoring Your Rate Limiter

Verify a rate limiter behaves correctly under load and observe it in production with the right metrics, load tests, and alerts.

Build It, Then Trust It

You have designed and implemented rate limiters. The final discipline is proving they work: testing them under realistic conditions and monitoring them once live so you catch regressions and abuse.

Unit Testing the Logic

Start with deterministic unit tests of the core algorithm. Inject a fake clock so you can advance time precisely and assert exactly when requests are allowed or denied.

def test_token_bucket_refills():
    clock = FakeClock(0)
    rl = TokenBucket(rate=1, capacity=2, clock=clock)
    assert rl.allow() and rl.allow()
    assert not rl.allow()
    clock.advance(1)
    assert rl.allow()

All lessons in this course

  1. In-Memory Rate Limiter Design
  2. Distributed Rate Limiting with Redis
  3. Handling Rate Limit Exceedance
  4. Testing and Monitoring Your Rate Limiter
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