A rate limiter decides whether a request may proceed by checking how many requests an identity has made over a defined period or how much capacity it has left. Start by choosing what counts as an identity—such as a user, API key, tenant, or IP address—then choose an algorithm that matches your needs for burst tolerance, boundary accuracy, and traffic shaping. For one application process, in-memory state may be enough. Across multiple servers, the instances need coordinated state and atomic updates, often through a shared store such as Redis.
Define the quota before choosing an algorithm
A limiter is only meaningful once you define both the counted event and the identity that owns the quota. A key scoped to a user ID enforces a per-user limit; a key scoped to an IP address makes callers behind that address share a limit. API keys and tenant IDs are other common choices. Redis’s rate-limiter documentation describes keys scoped to dimensions including user, IP, API key, tenant, or model: Redis rate limiter documentation.
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For a given identity key, the core invariant is simple: allow a request only if the selected algorithm’s current state permits it, then update that state as one operation. A separate “read count, check limit, write count” sequence is unsafe under concurrent requests: two requests can read the same old value and both pass.
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The algorithms differ in how they handle bursts and window boundaries, how much state they retain, and whether excess traffic is rejected or shaped. The descriptions below reflect Redis’s documented implementation comparison; they are not universal performance guarantees for every implementation: Redis token-bucket and rate-limiter comparison.
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| Algorithm | Burst and boundary behavior | Accuracy and state | Typical behavior |
|---|---|---|---|
| Fixed window | A limit is enforced within discrete time windows; a caller can use nearly a full quota just before a boundary and another just after it. Redis characterizes its fixed-window approach as approximate, with possible bursts up to twice the configured limit around a boundary. | One string key in Redis’s comparison. | Simple counter that rejects requests above the window limit. |
| Sliding-window log | Tracks requests over a moving interval, avoiding the fixed-window boundary jump. | Exact in Redis’s comparison, with O(n) entries for the requests retained in the interval. | Rejects requests when the exact rolling count reaches the limit. |
| Sliding-window counter | Blends current and prior window counts to smooth the boundary effect. | Near-exact in Redis’s comparison, using two keys. | Rejects excess requests with less state than a per-request log. |
| Token bucket | Allows a controlled burst up to bucket capacity while enforcing a refill rate over time. | Redis describes its implementation as exact for this behavior, with one hash key. | Allows a request when enough tokens remain; otherwise rejects it. |
| Leaky-bucket policing | Redis’s comparison describes no bursts. | Exact in that comparison. | Polices traffic by rejecting requests that exceed the permitted rate. Other leaky-bucket designs can shape traffic by queueing requests instead. |
Use a fixed window for a straightforward quota
A fixed window suits simple rules such as “no more than N requests per minute” when ease of implementation matters more than perfectly even behavior at the boundary. Its boundary burst is the trade-off: the counter resets at the window change, so requests concentrated on either side can exceed the intended rate over a short span.
Use a sliding window when boundary fairness matters
A sliding-window log gives a precise rolling count but maintains an entry for each request still inside the interval, so state grows with request volume. A sliding-window counter uses less state and smooths the reset effect, at the cost of an approximation. Choose between them based on how important exact rolling counts are relative to memory and update cost.
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Use a token bucket for a rate with a bounded burst
A token bucket combines a steady refill rate with a maximum capacity. Conceptually, store the capacity, refill rate, last-refill time, and remaining tokens. Refill according to elapsed time, capped at capacity; allow a request only if enough tokens remain, then deduct the request’s cost. This suits APIs that should tolerate short bursts while controlling sustained use.
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Use leaky-bucket behavior when output should be steadier
“Leaky bucket” can mean policing, which rejects traffic arriving too quickly, or shaping, which queues and releases traffic at a steadier pace. These are different behaviors despite sharing a name. If the system must not pass bursts unchecked, policing may fit; if requests can wait and should leave at a more constant rate, shaping is the closer match. Confirm the semantics of the specific implementation before relying on its label.
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Start with one process, then decide whether state must be shared
Single-process limiter
An in-memory map or counter is the simplest option when one process handles all requests for the quota. It avoids a network call on every decision, but its state exists only in that process. If a load balancer can send the same identity to different application instances, each local counter sees only part of that caller’s traffic; a local limiter does not enforce one consistent global quota.
Distributed limiter with Redis
When several instances must enforce the same quota, put the relevant state in a shared store and make the read, decision, and update atomic. Redis documents INCR and EXPIRE as basic primitives for a fixed-window counter, and Lua scripts for keeping a read-decide-update sequence together atomically: Redis rate limiter documentation. For time-based Lua examples, Redis’s Go documentation uses the Redis server clock, which avoids relying on application servers whose clocks may differ: Redis token-bucket documentation.
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Atomicity matters because multiple instances may process requests for the same identity at once. Without an atomic update, concurrent requests can all observe available capacity and exceed the limit. The shared store gives instances a common view, but it also adds synchronous latency and makes the store an additional dependency in the request path. Microsoft’s throttling guidance treats throttling as an architectural pattern whose implementation and response depend on workload and service requirements: Microsoft Azure Architecture Center: Throttling Pattern.
Implement a safe fixed-window counter
- Choose the key scope. Build a key from the quota dimension, such as a user ID or API key, and the window identifier. This determines which requests share a counter.
- Increment and set expiry atomically. Use Redis’s documented
INCRandEXPIREapproach so the count advances and the key is cleaned up after its window. Do not implement this as a separate application-side read, conditional check, and write. - Decide based on the returned count. Allow requests up to the configured threshold and reject later requests in the same window. Return an appropriate throttling response in the API’s protocol, and, where useful, indicate when the caller may retry.
- Check Redis version before using newer commands. Redis’s general rate-limiter documentation identifies
HEXPIREas a Redis 8 capability; do not assume it exists on earlier deployments.
The Redis fixed-window example is a useful starting point, but production key design should also account for the duration and uniqueness of windows, cleanup, and the application’s response to storage errors: Redis rate limiter documentation.
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Decide what happens when the shared store fails
A Redis timeout forces a policy choice. Fail-open means allowing traffic when the limiter cannot be checked, preserving availability but potentially bypassing the quota during the outage. Fail-closed means rejecting requests whose limit status cannot be verified, protecting strict enforcement but also blocking otherwise valid traffic. Redis’s Go example demonstrates a fail-open choice for a Redis timeout; it does not make that choice right for every service: Redis token-bucket documentation.
Choose based on what the limit protects. If it guards a scarce or abuse-sensitive resource, strict enforcement may outweigh availability; if the limiter is an advisory safeguard and service continuity is more important, bypassing it temporarily may be acceptable. Make the policy explicit, monitor limiter errors, and ensure operators can distinguish a rejected request from a store failure.
Make the decision by prioritizing the constraint
- Need the simplest quota: fixed window, accepting its boundary burst.
- Need a precise rolling count: sliding-window log, accepting per-request state.
- Need smoother boundaries with modest state: sliding-window counter.
- Need steady replenishment plus short bursts: token bucket.
- Need a steadier output rate: leaky-bucket shaping; use policing instead when excess traffic must be rejected rather than queued.
- Need a quota shared across application instances: centralize state and make updates atomic, while accounting for added latency and store availability.
Redis’s tutorial covers fixed-window, sliding-window, token-bucket, and leaky-bucket implementations, with examples and distinctions among their behaviors: Redis rate-limiter tutorial.
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