Design a distributed rate limiter for an API gateway
Reported in Flipkart interview loops. System design testing token bucket, sliding window, and Redis coordination.
Interview scenario
Often asked in Flipkart technical or coding rounds. Prepare a clear spoken answer plus key trade-offs.
Model answer
Try answering aloud first
Cover trade-offs, structure, and a concrete example before revealing the baseline response.
How to frame this at Flipkart: Connect your answer to measurable impact, clarity of thought, and trade-offs the team cares about. Below is a strong baseline response you can adapt with your own project examples.
Goal: cap requests per user/API key (e.g., 1000/min) across many stateless gateway nodes with minimal latency.
Algorithms: Token bucket (smooth burst allowance), fixed window (simple but boundary spikes), sliding window log (accurate, memory heavy), sliding window counter (hybrid).
Architecture: Gateway checks local cache → Redis central store for cross-node counts. Use Lua scripts or Redis Cell for atomic increment+TTL. Fail open vs closed is a product decision during Redis outages.
Return HTTP 429 with Retry-After header. Sharded Redis by user hash for scale. Mention global vs per-endpoint limits, tiered quotas, and async enforcement for background jobs.
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