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A rate limiter decides whether a request may proceed by checking the caller’s quota and updating its state. To build one, define who shares a quota, choose an algorithm that fits your burst and accuracy needs, and make each decision-and-update atomic. A counter in one process is enough for a single instance; when multiple servers must enforce the same limit, they need coordinated state, often in a shared store such as Redis.
Define the quota before choosing an algorithm
Start with the invariant: for a given identity key, allow a request only if the algorithm’s current state permits it, then update that state as one operation. Decide what counts as a request and which callers share a quota. A key might represent a user ID, API key, tenant, IP address, or model. That choice determines whether two callers draw from one limit or have separate limits. See Redis’s rate-limiter documentation for examples of quota dimensions and implementation primitives.
Also decide what the limiter does when a caller exceeds its quota: reject the request, delay it, or queue it for later. Rejection and traffic shaping solve different problems. An API that returns an error for excess requests is not the same as a system that buffers work and releases it at a controlled pace.
Choose an algorithm for your traffic pattern
These designs differ in burst allowance, behavior at window boundaries, state cost, and whether they simply police requests or shape their delivery. Redis’s comparison describes the behavior of its implementations; those characterizations are not universal performance guarantees.
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| Algorithm | Burst and boundary behavior | Accuracy and state | Typical behavior |
|---|---|---|---|
| Fixed window | Can admit a boundary burst approaching twice the configured limit across adjacent windows. | Approximate; one string key in the Redis comparison. | Counts requests in a fixed interval and rejects those over the limit. |
| Sliding-window log | Does not have the fixed-window boundary jump when it tracks request timestamps precisely. | Exact in the Redis comparison; O(n) entries for requests in the window. | Checks the request history in the active interval and rejects excess requests. |
| Sliding-window counter | Smooths the boundary effect without recording every request. | Near-exact in the Redis comparison; two keys. | Uses current and previous-window counts to estimate the rolling total. |
| Token bucket | Allows bursts up to bucket capacity while constraining the long-run rate through refill. | Exact with controlled bursts in the Redis comparison; one hash key. | Accepts a request when sufficient tokens remain; rejects it otherwise. |
| Leaky-bucket policing | Does not pass bursts unchecked in the Redis comparison. | Exact in that comparison; state cost is not stated there. | Polices excess traffic. A shaping variant can queue requests and release them at a steadier rate. |
For the comparison details and implementation examples, see Redis’s Go token-bucket guide and its rate-limiter tutorial.
Use fixed windows for simple quotas
A fixed window counts requests in intervals such as each minute. It is easy to reason about and inexpensive to represent, but a caller can use its full allowance just before a window ends and again just after the next one begins. That makes it a poor fit when a sharp boundary burst would overload the protected service.
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Use a sliding window when boundary fairness matters
A sliding-window log evaluates requests over a rolling interval, but its per-request history costs more state as traffic grows. A sliding-window counter approximates a rolling total with less state by combining adjacent window counts. Choose between them based on how much boundary precision the protected operation needs and how much state you can afford.
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Use token bucket for bounded bursts
A token bucket holds up to a configured capacity. Tokens refill at a chosen rate; each accepted request consumes tokens. If the bucket has no tokens, the limiter rejects or delays the request, depending on the surrounding design. Capacity sets the maximum burst the bucket can absorb, while refill rate governs sustained traffic. This is a useful model when short bursts are acceptable but prolonged traffic must stay within a rate.
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Use leaky bucket when output should be steadier
“Leaky bucket” can describe policing or shaping. Policing rejects excess requests; shaping queues them and releases them at a more constant pace. If requests must not reach a downstream service in unchecked bursts, a shaping design may fit better than a token bucket. Account for queue capacity and waiting time if you queue: those are application-level choices, not properties implied by the name alone.
Start with local state for one process
For a service running as one process, an in-memory counter or bucket can be the simplest implementation. Keep state per identity key, apply the chosen algorithm, and ensure concurrent requests cannot both observe the same allowance and exceed it. Local state is not a global quota, however: if a load balancer can send the same caller to different instances, each instance may maintain a separate count.
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That separation may be acceptable when limits are intentionally per instance or approximate. It is not sufficient when all servers must enforce one shared allowance.
Coordinate multiple servers with shared state
When independently handling instances need a common quota, they need shared state and atomic updates. Redis is one coordination option: each instance evaluates and updates the same identity-scoped state. This gives them a common view, but adds a synchronous datastore dependency and latency to the request path. Microsoft’s throttling pattern guidance also discusses throttling as a way to protect services and manage demand.
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Implement a fixed-window counter atomically
Redis documents INCR and EXPIRE as basic primitives for fixed-window counters: increment the key and arrange for it to expire when the window ends. Do not implement enforcement as separate read, check, and write calls; concurrent requests can race between those steps and both be admitted. Make the counter update and its expiry handling safe as a unit. Redis’s general documentation also mentions HEXPIRE as a Redis 8 capability, so check the deployed version before relying on it; it is not available on older deployments merely because the documentation describes it.
Represent token-bucket state in Redis
A token-bucket record needs enough information to determine the current token balance, capacity, refill rate, and when the balance was last updated. On each request, calculate elapsed time, add the corresponding refill without exceeding capacity, decide whether a token can be spent, and persist the resulting state. The read-decide-update sequence must be atomic so simultaneous requests cannot spend the same token.
Redis’s Go examples use Lua scripts for atomic limiter operations. For time-based scripts, the guide obtains time from Redis rather than relying on each application server’s clock, avoiding drift between instances. See the Redis Go guide for its implementation details.
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A datastore timeout forces a policy choice. Fail-open allows requests through when the limiter cannot check state: availability is preserved, but the limit can be bypassed during the outage. Fail-closed rejects requests it cannot verify: enforcement remains strict, but otherwise valid traffic may be denied. The right choice depends on what the limiter protects and the service’s availability requirements. Redis’s Go example demonstrates a fail-open choice for a Redis timeout; it is an example, not a universal recommendation.
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