A leaky bucket counter is a number that stands for how full a imaginary bucket currently is. The level drains steadily as time passes at a configured rate, and it rises each time the system admits a unit of work, such as a packet or a request. If admitting new work would push the level past the bucket’s limit, the work is delayed, dropped or rejected, depending on the implementation.
What the counter actually represents
The counter is not simply “requests seen in the last minute.” It models accumulated content that leaks away continuously. Two requests a millisecond apart raise the level almost twice as much as one request, while a pause lets the level fall back toward zero. That is why a leaky bucket counter has no sharp window boundaries: there is no moment when the count resets.
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Three pieces of state or configuration define it:
- The level (the counter itself), usually with a timestamp of when it was last updated.
- The leak rate, which sets how fast the level falls between arrivals.
- The capacity or tolerance, the ceiling the level must not exceed.
Implementations rarely run a real timer to drain the bucket. They store the last update time and, on each arrival, calculate how much would have leaked out since then.
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RFC 7415, Session Initiation Protocol (SIP) Rate Control (February 2015), gives a precise version. It states: “Conceptually, the leaky bucket algorithm can be viewed as a finite capacity bucket whose real-valued content drains out at a continuous rate of 1 unit of content per time unit and whose content increases by the increment T for each forwarded SIP request.”
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In that document:
- The bucket’s content is real-valued and drains at one unit per unit of time.
- T is the increment added per forwarded request, calculated as the inverse of the configured request rate. At 10 requests per second, T is 0.1 second.
- TAU is the tolerance, the limit the counter is checked against.
- On each request, the implementation computes an adjusted counter from the previous counter and the time elapsed since the last conforming request. If the result is within TAU, the request is forwarded and the counter and last-conformance time are updated.
- If the calculated value exceeds TAU, the request is rejected and the stored counter and last-conformance time stay unchanged. A rejected request therefore does not add to the bucket.
This is one standards-based example. Other systems that use the phrase “leaky bucket counter” may use different units (bytes instead of requests, for instance), different state, or a different order of check and update.
Worked example
Take a rate of 10 requests per second, so T = 0.1 s, and a tolerance TAU = 0.2 s. The counter starts at 0. The sketch below uses the common formulation in which the drained value is compared with TAU before T is added; it illustrates the idea rather than reproducing the RFC’s full pseudo-code.
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| Arrival time (s) | Drained value | Within TAU (0.2)? | Counter after |
|---|---|---|---|
| 0.00 | 0.00 | Yes, forward | 0.10 |
| 0.01 | 0.09 | Yes, forward | 0.19 |
| 0.02 | 0.18 | Yes, forward | 0.28 |
| 0.03 | 0.25 | No, reject | 0.28 (unchanged) |
| 0.20 | 0.10 | Yes, forward | 0.20 |
Three near-simultaneous requests get through because the tolerance allows a small burst. The fourth is rejected, and after a short pause the bucket has drained enough to accept traffic again. Over the long run, the admitted rate cannot exceed the configured 10 per second.
Leaky bucket counter versus token bucket
The two are related but not synonyms. They describe conformance with different state.
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| Aspect | Leaky bucket | Token bucket |
|---|---|---|
| What the state holds | Accumulated content that drains over time | Accumulated transmission credits (tokens) that build up over time |
| Effect of an admitted unit | Raises the level | Consumes tokens |
| Typical goal | Smooth, controlled output | Bounded bursts above a sustainable rate |
| Burst limit set by | Capacity or tolerance (TAU in RFC 7415) | Bucket size b |
RFC 2216 gives the token-bucket bound formally: over any interval of length T, traffic cannot exceed rT + b, where r is the token rate and b is the bucket size. That is a protocol definition, not a measured figure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Timing caveat in traffic shaping
When a leaky bucket is used to shape network traffic, it aims to regulate the output flow. RFC 3290’s discussion of shaping notes that clock granularity in an implementation can affect when packets are released and can introduce jitter. A coarse timer means the real output will be a little burstier or less even than the idealised model.
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Reading the term correctly
- It measures modeled bucket content or outstanding work, not a raw tally in a fixed time window.
- Elapsed time and the leak rate drain it between arrivals.
- The configured rate and the tolerance together decide how large a burst passes.
- Always check what a given product’s counter is in: requests, bytes, or time units.
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