Software Engineering

I often like to have streams of trials playing in the background when I am working, and the recent trial of interest was the trial of Karen Read, who was accused of murder. One issue in this case was a conflict between two timestamps logged by different apps on a potential suspect’s phone. Apple health had logged the person climbing flights of stairs at a given time, and at the same timestamp, a log from the app Waze indicated that the person was driving. This seems impossible, but these apps used different time sources on the phone.

A guilty pleasure of mine is the pursuit of perfection. It is certainly a vice in most contexts, but there are some problems whose solutions demand a measure of perfection. These are problems that I will refer to as “5-9 problems”: problems whose solutions need five 9’s (or more) in some dimension. Usually, those nines are correctness of some kind, but they can also be availability or for some systems, speed.

This account comes from several publicly available sources as well as accounts from insiders who worked at Knight Capital Group at the time of the issue. I am telling it second- or third-hand.

On August 1, 2012, Knight Capital fell on its sword. It experienced a software glitch that literally bankrupted the company. Between 9:30 am and 10:15 am EST, the employees of Knight capital watched in disbelief and scrambled to figure out what went wrong as the company acquired massive long and short positions, largely concentrated in 154 stocks, totaling 397 million shares and $7.65 billion. At 10:15, the kill switch was flipped, stopping the company’s trading operations for the day. By early afternoon, many of Knight Capital’s employees had already sent out resumes, expecting to be unemployed by the end of the week.

As you build a computer system, little things start to show up: maybe that database query is awkward for the feature you are building, or you find your server getting bogged down transferring gigabytes of data in hexadecimal ASCII, or your app translates itself to Japanese on the fly for hundreds of thousands of separate users. These are places where your abstractions are misaligned - your app would be quantitatively better if it had a better DB schema, a way to transfer binary data, or native internationalization for your Japanese users. Each of these misalignments carries a cost.

A modern CPU is an incredible machine. It can execute many instructions at the same time, it can re-order instructions to ensure that memory accesses and dependency chains don’t impact performance too much, it contains hundreds of registers, and it has huge areas of silicon devoted to predicting which branches your code will take. However, if you have a tight loop and you are interested in optimizing the hell out of it, the same mechanisms that make your code run fast can make your job very difficult. They add a lot of complexity that can make it hard to figure out how to optimize a function, and they can also create local optima that trap you into a less efficient solution.

A lot of ink is spent on the “monoliths vs. microservices” debate, but the real issue behind this debate is about whether distributed system architecture is worth the developer time and cost overheads. By thinking about the real operational considerations of our systems, we can get some insight into whether we actually need distributed systems for most things.

We have all gotten so familiar with virtualization and abstractions between our software and the servers that run it. These days, “serverless” computing is all the rage, and even “bare metal” is a class of virtual machine. However, every piece of software runs on a server. Since we now live in a world of virtualization, most of these servers are a lot bigger and a lot cheaper than we actually think.

Python and Assembly have one thing in common: as a professional software engineer, they are both languages that you probably should know how to read, but be terrified to write. These languages seem to be (and are) at opposite ends of the spectrum: One is almost machine code, and the other is almost a scripting language. One is beginner-friendly and the other is seen as hostile to experts. One is viciously versatile with tons of libraries and ports, and the other is ridiculously limited in its capabilities. However, when you are creating production software, both are the wrong tool for the job.