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Techniques and numbers for estimating system's performance from first-principles
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src io_uring, syscalls, fadvise, madvise, format Feb 1, 2020
Cargo.lock io_uring, syscalls, fadvise, madvise, format Feb 1, 2020
Cargo.toml readme: round tcp to more memorable number Feb 3, 2020
LICENSE readme: license Oct 3, 2019 readme: add io_uring reading Feb 3, 2020

Napkin Math

The goal of this project is to collect software, numbers, and techniques to quickly estimate the expected performance of systems from first-principles. For example, how quickly can you read 1 GB of memory? By composing these resources you should be able to answer interesting questions like: how much storage cost should you expect to pay for a cloud application with 100,000 RPS?

The best introduction to this skill is through my talk at SRECON.

The best way to practise napkin math in the grand domain of computers is to work on your own problems. The second-best is to subscribe to this newsletter where you'll get a problem every few weeks to attack. It should only take you a few minutes to solve each one as your facility with these techniques improve.

The archive of problems to practise with are here. The solution will be in the following newsletter.


Here are the numbers from the program, run on my 2017 Macbook. The goal is to run this on more platform. Note that all numbers don't line up as they've been rounded to make them more memorable.

Operation Latency Throughput 1 MiB 1 GiB
Sequential Memory R/W (64 bytes) 5 ns 10 GiB/s 100 us 100 ms
Random Memory R/W (64 bytes) 50 ns 1 GiB/s 1 ms 1 s
System Call 500 ns N/A N/A N/A
Sequential SSD Read (8 KiB) 1 μs 4 GiB/s 200 us 200 ms
Sequential SSD write, -fsync (8KiB) 10 μs 1 GiB/s 1 ms 1 s
TCP Echo (TCP overhead) (64 bytes) 10 μs ? ? ?
Random SSD Seek (8 KiB) 100 μs 70 MiB/s 10 ms 15 s
Cloud us-east1 to us-east2 250 μs ? ? ?
Sequential SSD write, +fsync (8KiB) 5 ms 2 MiB/s 1 s 10 min
Mutex Lock/Unlock ? ? ? ?
{Snappy, Gzip, ..} Compression (? KiB) ? ? ? ?
Hashing (? bytes) ? ? ? ?
{MySQL, Memcached, Redis, ..} Query ? ? ? ?
Envoy/Nginx Overhead ? ? ? ?
{JSON, Protobuf, ..} Serializee (?) ? ? ? ?
Cloud us-east to us-central ? ? ? ?

You can run this with RUSTFLAGS='-C target-cpu=native' cargo run --release -- -h. You won't get the right numbers when you're compiling in debug mode. You can help this project by adding new suites and filling out the blanks.

I am aware of some inefficiencies in this suite. I intend to improve my skills in this area, in order to ensure the numbers are the upper-bound of performance you may be able to squeeze out in production. I find it highly unlikely any of them will be more than 2-3x off, which shouldn't be a problem for most users.

Cost Numbers

Approximate numbers that should be consistent between Cloud providers.

What Amount $ / Month
CPU 1 $10
Memory 1 GB $1
SSD 1 GB $0.1
Disk 1 GB $0.01
S3, GCS, .. 1 GB $0.01
Network 1 GB $0.01


  • Don't overcomplicate. If you are basing your calculation on more than 6 assumptions, you're likely making it harder than it should be.
  • Keep the units. They're good checksumming. Wolframalpha has terrific support if you need a hand in converting e.g. KiB to TiB.
  • Calculate with exponents. A lot of back-of-the-envelope calculations are done with just coefficients and exponents, e.g. c * 10^e. Your goal is to get within an order of magnitude right--that's just e. c matters a lot less. Only worrying about single-digit coefficients and exponents makes it much easier on a napkin (not to speak of all the zeros you avoid writing).
  • Perform Fermi decomposition. Write down things you can guess at until you can start to hint at an answer. When you want to know the cost of storage for logging, you're going to want to know how big a log line is, how many of those you have per second, what that costs, and so on.


  • "How to get consistent results when benchamrking on Linux?". Great compilation of various Kernel and CPU features to toggle for reliable bench-marking, e.g. CPU affinity, disabling turbo boost, etc. It also has resources on proper statistical methods for benchmarking.
  • LLVM benchmarking tips. Similar to the above in terms of dedicating CPUs, disabling address space randomization, etc.
  • Top-Down performance analysis methodology. Useful post about using toplev to find the bottlenecks. This is particularly useful for the benchmarking suite we have here, to ensure the programs are correctly written (I have not taken them through this yet, but plan to).
  • Godbolt's compiler explorer. Fantastic resource for comparing assembly between Rust and e.g. C with Clang/GCC.
  • cargo-asm. Cargo extension to allow disassembling functions. Unfortunately the support for closure is a bit lacking, which requires some refactoring. It's also very slow on even this simple program.
  • Agner's Assembly Guide. An excellent resource on writing optimum assembly, which will be useful to inspect the various functions for inefficiencies in our suite.
  • Agner's Instruction Tables. Thorough resource on the expected throughput for various instructions which is helpful to inspect the assembly.
  • Useful resource for low-level performance by the author of toplev.
  • Systems Performance (book). Fantastic book about analyzing system performance, finding bottlenecks, and understanding operating systems.
  • io_uring. Best summary, it links to many resources.
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