Burnout or health problem?

I experienced a lot of burnout last year. Usually, I can just outlast burnout and it goes away when I start a new project. This time it lasted most of the year and I didn’t really get back to normal until 2021. In my case the reason my ‘burnout’ lasted so long and was hard to get rid of is because it was actually a health problem. I got into this industry because I love coding but I didn’t really feel that way last year and even considered leaving the industry. I had a lot of troubling focusing with the single minded obsession you need to beat down tricky bugs. At the time I thought I had burnout, I was depressed, it had to be something like that right? 

Well in this case it wasn’t burnout or depression, but my diet. I’ve had issues with various foods making me sick in the past, but never really isolated the cause beyond ‘don’t buy bread with preservatives in it’. I eat out a lot and while I’d thought about doing an exclusion diet several years ago, and have built apps in the past to help people isolate food ingredients they have issues with, I’d never actually done an exclusion diet myself. 

In the end I started an exclusion diet in January after moving into my new apartment here in Phoenix. The effects in my case have been totally worth it, as I now know which foods I can and cannot eat if I want to think clearly. I can’t really say I understand the medical causes behind things, but a number of foods give me a combination of brain fog, headaches and stomach discomfort. It isn’t life threatening beyond making me suicidal on occasion. But I really can’t afford to eat anything with Sage in it without ruining an entire day. 

So far I’ve had to exclude Sage, preserved meats, and wheat or gluten products. I can eat most other foods without a problem.  

The meta programming problem with functional programming in software leviathans.

Few of the software leviathans are built in functional languages. Facebook uses PHP/Hack, Google Java, C++, Amazon Java, Netflix Java. The common consensus about functional languages is that they provide large benefits over object oriented and procedural languages like Java. One particular claim is that functional languages like Haskell can do the same work in 1/10th the lines of code. If functional languages really are better we would expect to see the big tech companies investing heavily in adopting functional languages. We might even expect them to create a functional programming language just for their use case, but instead Google created Go possibly the least functional programming language created in the 21st century. What is going on here? Why aren’t functional programming languages being adopted in the biggest software systems on the planet? 

People have argued that inertia is the explanation for the low adoption of functional programming languages in massive software projects, but I think the evidence is in the opposite direction. Google created an entire new language that was intentionally less functional than Java. Facebook started on PHP and then extended that language into Hack. They could have used that energy to completely adopt Haskell. 

My suspicion is that the real reason functional languages are not used in massive software leviathans is meta-programming. Meta-programming enables software developers to create custom domain specific languages, literally adding new programming syntax and expressions to the code base. This is an incredible power and can make a lot of problems much easier. But meta-programming does not scale.  

In a software project with 10,000 software engineers. At this scale the limiting factor is not our ability to write clean and concise code. The main issue is understanding the effects of changes to the code base. A change might take a month to research before changing 500 lines of code. Not doing your research upfront more likely then not will result in you starting the project than realizing 2 weeks in that your approach will never work. Then having to start over. 

Meta-programming falls under the set of programming constructs that are easier to write than they are to read. This is true for all code of course, but in large code bases reading Golang code is reliably easier than reading Lisp code. 

In a algorithmic metaphor, Golang code complexity scales at O(n^2) vs Lisp code scaling at O(n^3). 

Software Leviathans

Dis-economies of scale, why FAANG pays high salaries, the dominance of Java

The top end of software engineering jobs are dominated by what I’ve started thinking of as ‘Software Leviathans’, large software systems that are staffed by thousands of engineers. A few that come to mind are Amazon Alexa, Amazon.com, Google Search, Salesforce, Facebook.com. These are not “monoliths’ or large services that do everything. Instead they are the result of combining 100s of smaller ‘micro-services’ into one massive software product. 

These leviathans do many many things, few people on the planet can claim to know all of the features of facebook.com. It is quite possible that there exists no single list that enumerates every feature in that product. 

Similarly, development on these systems happens in parallel across many teams. It it is essentially impossible for any one person to keep track of everything that is being added to the system. 

Leviathans are too big for anyone to understand. It doesn’t matter what architecture or runtime choices are made. It could be one massive JVM, a million lambda functions, a hundred thousand docker containers or thousands of micro-services. Even if you work on the leviathan, you won’t have any real understanding of the total state of the system. Each engineer will be aware of and communicate with a tiny fraction of the total number of people working inside the leviathan. 

Leviathans are heterogeneous systems. The do not do ‘one thing well’. Leviathans do everything you can think of. Google.com is a search engine, but it’s also a calculator, an advertising system, a web scraper, a hotel booking tool, a flight booking tool, and many more. Leviathans grow in parallel, across myriad tentacles of functionality. New features emerge all the time usually to the surprise of other engineers on the project. 

Leviathans are difficult to work in. Despite appearing to be a sea of constant change from the outside. Any change made inside the Leviathan is extremely expensive in engineering hours. There are thousands of potential interactions each engineering team has to consider when evaluating changes to their system. The architecture must be constrained heavily to support parallel development in environments where coordination between different teams is impossible due to scale. Engineers working on a software leviathan spend a relatively small fraction of their time actually writing code as compared to debugging issues, research, coordinating changes, and documenting. 

Leviathans are interesting because they are the ‘core’ services powering the digital world these days. Their scale is at top of the chart in the software engineering world and as a result they expose the limitations of software engineering. 

Software diseconomies of scale are at their most evident in these software leviathans. They are massive projects with huge numbers of the best engineers working on them. But development is slow per engineer and code quality is not clearly superior to industry best practices. 

Why I stopped going on twitter, using time tracking apps to monitor your time with Qbserve

I’ve been an avid twitter user for years, but had to stop this winter. I have been listening to ‘Deep Work’ while driving cross country and have done a lot of thinking about how to do better work. One of the things recommended in the book is to quit social media or at least exclude it from the part of your day when you work. I’ve typically just blocked twitter from my network during the workday then used it as much as I wanted afterwards. 

Well another thing I did in the pursuit of ‘deep work’ is to review my Qbserve stats for the last few months. My twitter numbers were way higher than I expected. I have been spending thousands of dollars worth of time using Twitter producing fun content that twitter then monetizes. I could have gotten a part time job or learned to paint. 

Track your time. There are a bunch of apps that can do it. I use Qbserve because it stores data locally and felt like a less heavy weight solution. I have also used RescueTime, but found logging in again when I need to restart tracking to be a pain. 

Once you have tracking going it gives you a lot of insight into what you are doing on your computer. Some people might think “ah, if I’m on the computer I’m working, what else would I use it for” but for millennials and digital natives who spend most of their lives on a computer it can really help. 

For example I know how much clock time I spent reading Xianxia, translated chinese pulp fiction, on wuxiaworld.co this year, four whole days. That is nearly double the amount of time I spent on news.ycombinator.com which came in at 1 day and 13 hours. I also know how much time I spent writing, note taking and journaling this year, around 30 hours so far. Admittedly, I haven’t run the app 24/7 and didn’t start until March so I only have around 8~ months worth of data.

I don’t think I would have made the realization of how much time I was spending on twitter, without a time tracking app. It is a lot like Television for normal people, it is just on all the time when you are home, you don’t really think about it’s effects on your life. Most people underestimate how much time they spend watching television, but you don’t have to underestimate how much time you spend on Youtube, just get Qbserve and review the data occasionally. 

In the week or so since I quit, I’ve already read a couple books and started writing on my blog again.