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My name is Marc Brooker. I've been writing code, reading code, and living vicariously through computers for as long as I can remember. I like to build things that work. I also dabble in machining, welding, cooking and skiing.

I'm currently an engineer at Amazon Web Services (AWS) in Seattle, where I work on databases, serverless, and serverless databases. Before that, I worked on EC2 and EBS.
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Telling Stories About Little鈥檚 Law

Building Up Intuition with Narrative

Little鈥檚 Law is widely used as a tool for understanding the behavior of distributed systems. The law says that the mean concurrency in the system (饾惪) is equal to the mean rate at which requests arrive (位) multiplied by the mean time that each request spends in the system (饾憡):

饾惪 = 位饾憡

As I鈥檝e written about before, Little鈥檚 law is useful because it gives us a clear way to reason about the capacity of a system, which is often difficult to observe directly, based on quantities like arrival rate (requests per second) and latency which are easier to measure directly. Concurrency is a useful measure of capacity in real systems, because it directly measures consumption of resources like threads, memory, connections, file handles and anything else that鈥檚 numerically limited. It also provides an indirect way to think about contention: if the concurrency in a system is high, then it鈥檚 likely that contention is also high.

I like Little鈥檚 Law as a mathematical tool, but also as a narrative tool. It provides a powerful way to frame stories about system behavior.


The way Little鈥檚 Law is written, each of the terms are long-term averages, and 位 and 饾憡 are independent. In the real world, distributed systems don鈥檛 tend to actually behave this nicely.

Request time (饾憡) tends to increase as concurrency (饾惪) increases. Amdahl鈥檚 Law provides the simplest model of this: each request has some portion of work which is trivially parallelizable, and some portion of work that is forced to be serialized in some way. Amdahl鈥檚 law is also wildly optimistic: most real-world systems don鈥檛 see throughput level out under contention, but rather see throughput drop as contention rises beyond some limit. The universal scalability law captures one model of this behavior. The fundamental reason for this is that contention itself has a cost.

Even in the naive, beautiful, Amdahl world, latency increases as load increases because throughput starts to approach some maximum. In the USL world, this increase can be dramatically non-linear. In both cases 饾憡 is a function of 饾惪.

Arrival rate (位) also depends on request time (饾憡), and typically in a non-linear way. There are three ways to see this relationship:

The combination of these effects tends to be that the dynamic behavior of distributed systems has scary cliffs. Systems have plateaus where they behave well where 饾憡, 饾惪 and 位 are either close-to-independent or inversely proportional, and cliffs where direct proportionality kicks in and they spiral down to failure. Throttling, admission control, back pressure, backoff and other mechanisms can play a big role in avoiding these cliffs, but they still exist.

Arrival Processes and Spiky Behavior

The mean, like all descriptive statistics, doesn鈥檛 tell the whole story about data. The mean is very convenient in the mathematics of Little鈥檚 law, but tends to hide effects caused by high-percentile behavior. Little鈥檚 law鈥檚 use of long-term means also tends to obscure the fact that real-world statistical processes are frequently non-stationary: they include trends, cycles, spikes and seasonality which are not well-modeled as a single stationary time series. Non-stationary behavior can affect 饾憡, but is most noticeable in the arrival rate 位.

There are many causes for changes in 位. Seasonality is a big one: the big gift-giving holidays, big sporting events, and other large correlated events can significantly increase arrival rate during some period of time. Human clients tend to exhibit significant daily, weekly, and yearly cycles. People like to sleep. For many systems, though, the biggest cause of spikes is the combination of human biases and computer precision: cron jobs. When humans pick a time for a task to be done (backup once a day, ping once a minute), they don鈥檛 tend to pick a uniformly random time. Instead, they cluster the work around the boundaries of months, days, hours, minutes and seconds. This leads to significant spikes of traffic, and pushes the distribution of arrival time away from the Poisson process ideal1.

Depending on how you define long term mean, these cyclic changes in 位 can either show up in the distribution of 位 as high percentiles, or show up in 位 being non-stationary. Depending on the data and the size of the spikes it鈥檚 still possible to get useful results out of Little鈥檚 law, but they will be less precise and potentially more misleading.

Telling Stories

Somewhat inspired by Little鈥檚 law, we can build up a difference equation that captures more of real-world behavior:

Wn+1 = 饾憡(Ln, 位n, t)

n+1 = 位(Ln, Wn, t)

Ln+1 = 位n+1 饾憡n+1

I find that this is a powerful mental model, even if it鈥檚 lacking some precision and is hard to use for clean closed-form results. Breaking the behavior of the system down into time steps provides a way to tell a story about the way the system behaves in the next time step, and how the long-term behavior of the system emerges. It鈥檚 also useful for building simple simulations of the dynamics of systems.

Telling stories about our systems, for all its potential imprecision, is a powerful way to build and communicate intuition.

The system was ticking along nicely, then just after midnight a spike of requests from arrived from a flash sale. This caused latency to increase because of increased lock contention on the database, which in turn caused 10% of client calls to time-out and be retried. A bug in backoff in our client meant that this increased call rate to 10x the normal for this time of day, further increasing contention. And so on鈥

Each step in the story evolves by understanding the relationship between latency, concurrency and arrival rate. The start of the story is almost always some triggering event that increases latency or arrival rate, and the end is some action or change that breaks the cycle. Each step in the story offers an opportunity to identify something to make the system more robust. Can we reduce the increase in 饾憡 when 位 increases? Can we reduce the increase in 位 when 饾憡 exceeds a certain bound? Can we break the cycle without manual action?

The typical resiliency tools, like backoff, backpressure and throttling, are all answers to these types of questions, but are far from the full set of answers. Telling the stories allows us to look for more answers.


  1. Network engineers have long known that the Poisson model is less bursty than many real systems. An Empirical Workload Model for Driving Wide-Area TCP/IP Network Simulations and Wide Area Traffic: The Failure of Poisson Modeling are classics in that genre. I鈥檓 not aware of good research on this problem in microservice or SoA architectures, but I鈥檓 sure there are some interesting results to be found there.