Building Software
Engineering Fundamentals for the Agent Era
Contents Section 4, Math and Algorithms
A design that needs tens of thousands of writes per second from a single database node sized for a few thousand.
An agent pipeline that calls a model once per row across a million rows, with nobody having estimated the bill.
Logging or telemetry volume that costs more than the service it describes.
Quick order-of-magnitude math for storage, throughput, latency, cost and time, done before building so bad designs fail on paper.
An agent's design stores every page view as a 2 KB JSON row in one database table and makes one model call per view to classify it, for a site with 20 million views a day. Estimate the yearly storage and the daily model cost, and find the part of the design that fails first.
Watch
Demonstrates estimating throughput, storage and cost from a small set of memorized base rates, then checking the result against the real system, which is the whole method of this subsection.
The talk where Jeff Dean presents his "numbers everyone should know" and uses back-of-the-envelope calculations to pick between designs before building them.
Eskildsen describes how napkin-math cost and latency estimates shaped the architecture of a database company, connecting estimation to real design and cost decisions.
Column 7, "The Back of the Envelope", is the canonical short text on order-of-magnitude estimates, the Rule of 72, safety factors and checking an answer against a second method.
Its methodology and capacity-planning material shows how to turn request rates into load on CPU, memory, disk and network and find which resource saturates first.
Teaches dimensional analysis, rounding and sanity checks for quick order-of-magnitude answers, the habits behind carrying units through a calculation.
Reference
A maintained table of measured base rates for memory, disk, network, serialization and cloud cost, with the benchmark code that produced them, to use as reference points for any estimate.