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Design a multi-level API rate limiter

xAISoftware Engineer · Technical Screen
EasySoftware EngineerTechnical ScreenSystem Design

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This question assesses the ability to design a scalable, multi-tenant rate limiting service that supports hierarchical policies and global traffic coordination.

Problem Statement

Design a distributed API rate limiting service that enforces traffic policies across three levels: global (per API key), regional (per datacenter), and endpoint-specific (per path).

Requirements

  • Functional: Support different windows (e.g., 100 requests per minute, 5,000 requests per hour). Reject requests exceeding limits with a 429 Too Many Requests status code.
  • Non-Functional: High availability and low latency (rate limiting checks should add < 10ms to the request path). The system must handle sudden spikes in traffic without failing closed.
  • Scale: Handle 500,000 requests per second across a global infrastructure.

Discussion Points

Your solution should cover:

  1. Data Model: How to store counters and configuration for fast lookups.
  2. Architecture: Where the decision-making logic resides (sidecar, middleware, or centralized gateway) and how it handles high throughput.
  3. Consistency: How to manage concurrency for counters in a distributed environment (e.g., avoiding race conditions during atomic increments).
  4. Failure Handling: Strategies for when the rate-limiting storage (e.g., Redis) is unreachable.

Hints

Hint 1

Consider using a distributed caching layer like Redis with atomic increment operations (INCR) to track counters across different request nodes.

Hint 2

Think about the trade-off between strict consistency and performance; could you use an 'eventual consistency' model where counters are buffered and synced periodically?

Hint 3

Evaluate the benefits of a token bucket algorithm versus a fixed-window counter in terms of memory usage and burst handling.

Follow-up questions

  • How would you handle a 'noisy neighbor' issue where a single API key consumes all available capacity, affecting other users?
  • If the rate limiter is implemented as a sidecar, how would you push updated rate-limit configurations to thousands of instances in near real-time?
  • How would you measure the accuracy of your rate limiter, and what observability metrics would you prioritize to detect false positives?