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Why do fixed-window rate limiters fail?
A fixed-window limiter counts requests in discrete, non-overlapping intervals and resets the counter at each boundary. If the quota is L requests per W seconds, one request just before the boundary and another just after it belong to different windows. That makes the configured quota a per-bucket limit, not a strict cap on every rolling interval of length W.
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For example, with a limit of 100 requests per minute, a client could send 100 requests just before the minute changes and another 100 just after. As many as 200 requests may pass in an arbitrarily short interval around that boundary. Redis describes this as a possible 2x boundary burst; it is a consequence of the algorithm, not a benchmark of a particular service. Redis’s rate-limiter comparison explains the behavior.
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Boundary timeline: 100 requests per minute
- Before the boundary: The current minute’s counter admits requests 1–100.
- At the boundary: The fixed-window counter resets.
- After the boundary: The next minute’s counter admits another 100 requests.
The reset timestamp can be epoch-aligned or anchored to the first request, but either choice leaves a discontinuity if the algorithm still uses separate, non-overlapping buckets. Moving the boundary moves the burst opportunity; it does not remove it.
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Which rate-limiting algorithm fits the policy?
Choose the policy before choosing the data structure. Decide whether the endpoint needs a strict rolling cap, a smoothed count, a sustained rate that permits bursts, or a queue that smooths downstream work. The trade-offs below are described in Redis’s algorithm guide and its rate-limiter implementation documentation.
| Algorithm | Boundary and burst behavior | State and accuracy | Good fit |
|---|---|---|---|
| Fixed-window counter | Its abrupt reset can permit up to a 2x burst across adjacent boundaries. | One counter; approximate for a rolling-window policy. | Simple quotas where a boundary burst is acceptable. |
| Sliding-window log | Counts request timestamps in the rolling interval; no boundary burst in the algorithmic model. | Exact rolling count, with O(n) timestamp entries proportional to requests in the window. | High-value limits where exact rolling enforcement justifies the state cost. |
| Sliding-window counter | Combines adjacent window counts to smooth the boundary. | Near-exact rolling count using two counters rather than storing every request timestamp. | A practical balance of state use and accuracy. |
| Token bucket | Allows controlled bursts up to configured capacity as tokens replenish over time. | Bucket state; the policy must set refill rate and capacity. | APIs that allow some burst traffic while enforcing a longer-term rate. |
| Leaky bucket | Can reject bursts in policing mode or smooth output in shaping mode. | Bucket state; the choice is whether to reject excess traffic or queue and shape it. | Strict no-burst limits or steadier downstream traffic. |
When is a fixed window still reasonable?
Use it when the goal is a simple quota and a short boundary burst is harmless or separately controlled. Its low state cost and straightforward counting may be more valuable than exact rolling enforcement. Do not rely on it alone when even a brief burst could overload an expensive endpoint or violate the intended policy.
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When should you use a sliding window instead?
Choose a sliding-window log if every rolling interval must obey an exact cap and storing individual request timestamps is affordable. Choose a sliding-window counter when a smoothed, near-exact count is sufficient and the timestamp log’s state cost is not. These algorithms address the boundary discontinuity by accounting for neighboring windows or request times rather than merely shifting when a discrete bucket resets.
When is a token or leaky bucket better?
A token bucket is the natural fit when clients should be able to send a controlled burst, provided they respect a refill rate over time. A leaky bucket can reject bursts or queue and smooth them, depending on whether the policy is policing or shaping. Specify the capacity, refill rate, and excess-traffic behavior as part of the policy; the algorithm name alone does not determine them.
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How do you implement a distributed rate limiter with Redis?
A limiter deployed on multiple service instances must use state shared across those instances if the quota is intended to apply across them. Redis documents INCR and EXPIRE for fixed-window counters and Lua EVAL for making the decision and update atomic. Redis’s rate-limiter documentation describes these patterns.
1. Define the quota scope
Decide whether the limit is per user, IP address, API key, tenant, model, route, or global. A process-local counter enforces only a per-process limit. If the same caller can reach multiple instances, independent local counters can each admit traffic and collectively exceed the intended quota.
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2. Define window and time semantics
For a fixed window, choose how intervals are aligned, what happens exactly at the boundary, and how counter expiration relates to the key’s window. For a rolling algorithm, specify time units and use a consistent time source. Redis’s time-based example gets the current timestamp inside the Lua script using Redis server time, avoiding independently supplied application-instance clocks.
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A separate check followed by an increment is unsafe under concurrency: several requests may all read that quota remains and then all proceed. Treat each request’s read, admission decision, and state update as one indivisible operation for the relevant key. Redis documents Lua scripting with EVAL as a way to keep that operation atomic.
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4. Use state shared at the intended deployment scope
Store the counter or bucket in a shared system reachable by the instances that must enforce the same quota. Verify that the state is actually shared across the relevant service or edge locations; a centralized-looking configuration does not by itself prove that every location uses the same counter.
5. Return a useful rejection response
When a request is rejected, return a clear status and, when supported, a retry delay so clients can back off instead of retrying immediately. Cloudflare documents Ratelimit, Ratelimit-Policy, and retry-after headers for its APIs. Header names and semantics vary by service, so check the API’s own documentation rather than assuming these are universal.
What does counter scope mean at an edge provider?
A shared counter’s scope depends on the provider’s architecture. Cloudflare says it maintains counters for configured characteristic combinations and does not share counters across all data centers, except for data centers in the same geographic location. Therefore, a limit enforced at one location should not automatically be treated as a single global limit. See Cloudflare’s explanation of request-rate counting.
Rate-limit thresholds are provider-specific and can change. Cloudflare’s API limits page, updated August 25, 2026, lists a global limit of 1,200 requests per five-minute period per user; it says exceeding that limit blocks API calls for the next five minutes with HTTP 429. The same page documents reset and quota information in Ratelimit and Ratelimit-Policy, plus retry-after when the limit is exceeded. Treat these as Cloudflare API behavior, not a general rule for other APIs. Cloudflare’s API rate-limit documentation has the provider’s current details.
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