A rate limiter is a request budget tied to an identity and replenished over time. A token bucket is a practical way to allow a defined burst while controlling the long-run request rate: capacity sets the burst allowance, refill rate restores budget, and each request consumes a chosen number of tokens. Before implementing one, decide who shares a budget, what traffic pattern to permit, and what clients should receive when the budget runs out.
What a rate limiter controls
A limiter decides whether a request may proceed based on a policy. The policy is more than a number of requests per second: it also defines which requests are counted together, how quickly the budget returns, whether requests have different costs, and how denial is communicated.
That identity choice is consequential. Requests with the same limiter key share one budget; requests with different keys are accounted separately. Depending on the application, the key could represent an authenticated principal, an API key, an account, or another identity. These are not interchangeable: for example, a shared network address may represent multiple callers, while an account-level key may intentionally make several credentials share a limit.
Why a fixed window can allow a boundary spike
A fixed-window counter counts requests during a clock-aligned interval and resets when the interval ends. If the limit is 100 requests per minute, a caller might use 100 just before the boundary and another 100 just after it. The caller can therefore send a burst of 200 requests in a short span even though neither individual minute exceeded its limit.
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This boundary behavior is the central lesson behind the token-bucket approach discussed in Timevolt’s “Building a Rate Limiter: Lessons from The Matrix”. A token bucket does not reset the entire budget at a clock boundary; it allows a bounded reserve and replenishes it continuously according to the configured policy.
How a token bucket works
Think of the bucket as stored permission to make requests. It can hold up to a maximum number of tokens. Refill adds tokens over time until the bucket reaches that capacity. Each incoming request consumes its configured token cost. If there are not enough tokens available, the request is denied.
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- Capacity: the maximum tokens the bucket can store, and therefore the maximum accumulated burst allowance.
- Refill rate: how quickly tokens return over time, which sets the replenishment pace.
- Request cost: how many tokens an operation consumes. A cost of one treats each request equally; a higher cost can account for operations that use more resources.
- Key: the identity used to select a bucket. All traffic sharing that key draws from the same budget.
Capacity and refill rate are separate policy choices. Raising capacity permits a larger burst when tokens have accumulated; it does not make the bucket replenish faster. Raising the refill rate restores tokens more quickly. A bucket whose capacity exceeds its refill rate can permit a temporary burst, followed by a wait while the budget refills.
A token bucket replaces fixed-window reset spikes with bounded bursts and ongoing replenishment. It should not be described as enforcing one exact universal limit across every arbitrary time interval: the observed behavior depends on capacity, refill rate, request cost, identity key, implementation, and how multiple instances coordinate.
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Define the policy before choosing implementation settings
- Choose the caller identity. Specify what shares a bucket—such as an authenticated principal or API credential—and consider whether multiple callers might unintentionally share a key.
- Set the sustained rate. Decide how quickly budget should return, based on the service’s capacity and the intended use of the API.
- Set burst tolerance. Choose the maximum accumulated capacity that can be served safely at once. Do not treat a high capacity as a higher sustained rate.
- Set request costs. If operations vary substantially in work, decide whether expensive operations should consume more than one token.
- Define denial behavior. Choose the client-facing response and any application-specific handling when the bucket lacks sufficient tokens.
Using Spring Cloud Gateway’s Redis limiter
Spring Cloud Gateway documents the RequestRateLimiter filter, which delegates decisions to a RateLimiter. Its Redis implementation uses token buckets and requires the reactive Redis starter. The Spring Cloud Reference Documentation’s current page, accessed October 5, 2026, says that when a request is denied, “a status of HTTP 429 - Too Many Requests (by default) is returned.” Because the reference is version-sensitive, check the documentation for the Spring Cloud release used by your application before copying configuration.
The Redis limiter’s documented settings map directly to the bucket model:
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replenishRateis the number of requests replenished per second.burstCapacityis the bucket’s maximum request capacity.requestedTokensis the token cost of each request; its default is 1.
The gateway uses a KeyResolver to choose the key for per-identity accounting. The documented default resolves the authenticated principal name. The reference also shows an illustrative resolver using a user query parameter and explicitly warns that it is “not recommended for production.” A caller-controlled query parameter should not be mistaken for a trustworthy identity merely because it appears in an example.
Spring’s examples, including settings such as a rate of 10 and a burst capacity of 20, are configuration illustrations, not universal recommendations or measured performance results. Choose values for the service and traffic you actually need to govern. See the Spring Cloud Reference Documentation for the filter and Redis limiter details.
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Questions to settle for a production design
- Can callers evade or unfairly share limits? Review how the key is authenticated and whether the chosen identity matches the policy’s intended scope.
- Are all requests equally costly? If not, consider whether the configured token cost should vary by operation.
- What should a client do after denial? Ensure the application’s response behavior is clear and consistent with its API contract.
- Will multiple gateway instances share accounting? A local in-process limiter and a shared limiter have different coordination and failure characteristics. The Spring documentation establishes a Redis-backed option, but the right coordination and failure behavior depend on system requirements; do not assume every implementation behaves identically.
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