Time and Complexity

2020 being a leap year, it’s a good opportunity to talk about how we track time. We’ll start with that, but this post is as a reflection on the inherent complexity of the physical world and human societies.

There is a famous blog post, Falsehoods programmers believe about time, which covers some well known pitfalls like years have 365 days or February is always 28 days long. Unfortunately, a lot of software has such assumptions hardcoded and things go awry.

But before talking about software, let’s step back and look at how we measure time.

Atomic Clocks

Atomic clocks provide an extremely precise measure of the passage of time. These clock don’t gain or lose a single second over hundreds of millions of years. These devices are beautiful: they can provide a monotonic count of the passage of time with an incredible precision.

In fact, we have the International Atomic Time standard, or TAI, which is defined, according to Wikipedia, by the weighted average of 400 atomic clocks from over 50 laboratories across the world. This is an extremely precise measure of time passing on Earth.

It is also not practical enough to be used as the basis of our calendars. Even with a very accurate, high-resolution, atomic clock, we still need to account for the fact that the Earth orbits around the Sun (so we get seasons), and spins around its axis (so we get day and night). Let’s see why there isn’t a simple mathematical function from atomic clock tick to year-month-day-hour-minute-second.

Leap Years

Leap years were introduced to account for the fact that Earth’s orbit around the Sun is slightly longer than exactly 365 days. Without adjusting for this fact, the seasons would gradually shift around the calendar. But a leap year doesn’t necessarily occur every 4 years. Turns out Earth’s orbit is slightly smaller than 365 days and 6 hours, so adding one day every 4 years would cause seasons to drift the opposite direction. The leap year rule is actually

A leap year is every year divisible by 4, except for years divisible by 100, unless they are also divisible by 400.

So years like 2020, 2024, 2028 and so on are leap years. But years like 1700, 1800, 1900, are not leap years, because they are divisible by 100. Except 1600, and 2000, which are not only divisible by 100, but also by 400.

We started with a simple model of a precise, monotonic atomic clock measuring ticks, but when get to user-friendly time, we end up with complex business rules that aim to account for the physical world. But it gets more complicated.

Leap Seconds and Standards

If leap years were all there is to it, we could’ve easily mapped an atomic clock tick to a precise date time value. But it gets more complicated. Turns out Earth’s rotation is not constant - it is irregular, and trends towards slow down. Major earthquakes can affect the momentum of the rotation. Tidal interaction with the moon is also slowing down the speed of rotation over millions of years.

UT1, the Universal Time standard based on Earth’s rotation, is drifting from the UTC, the Coordinated Universal Time, which uses atomic clocks to measure time. Because of this, UTC had to introduce leap seconds. A leap second aims to bring UTC time (based on atomic clock measurements) back in sync with UT1 time (time as observed astronomically), so they are not more than 1 second apart.

A leap second adds one second to a day, so we end up with a 61 seconds-long minute. Leap seconds are usually added at the end of the month, UTC time. For example, on June 30th 2015, the UTC time was, at some point, 23:59:60. This effectively makes a day 1 second longer.

There is no formula for this: as we measure time both astronomically and atomically, a standards body decides when a leap second is introduced and notifies the world every 6 months. In fact, the standard UTC time we use is, as of the time of this writing, 37 seconds behind the TAI.

Other Requirements

Besides the physical realities of measuring time atomically and astronomically, we have multiple other requirements.

We have daylight saving time, which moves the clocks forward 1 hour in spring and 1 hour backward in the fall. This creates a 23 hour-long day in the spring and a 25 hour-long day in the fall (falsehood programmers believe about time: all days have 24 hours). This is also not standard across the world: some countries observe daylight saving while others don’t.

We have time zones, which don’t neatly divide the earth in 24 equal-width slices, rather are set at geopolitical boundaries. Time zones aren’t even necessarily multiples of 1 hour: India is 5 hour and 30 minutes ahead of UTC.

Also note that daylight saving and time zones get updated: Russia recently stopped observing daylight saving while China went from 5 different time zones to a single one, even though its geography hasn’t changed.

Inherent Complexity

Even with an exact atomic clock, when taking into account year length and day and night cycles, we have to introduce additional rules to determine the date and time, like leap years and daylight saving. Not only that, international standards bodies determine when leap seconds occur, while countries are free to decide which time zone or time zones they are using.

I believe this is typical of any non-trivial problem space we tackle with software. When we try to model the physical world, things get messy. They get messier with humans in the system: laws, standards, and expectations introduce other arbitrary rules. Software needs to be complex to handle the real world.

Accidental Complexity

The above conclusion might seem to stand against pretty much everything I wrote on this blog, where I try to argue for clean and simple code. Why bother if any real world piece of software is destined to grow complex? The reason is that there is enough complexity inherent in dealing with the real world, without us having to introduce more. We don’t need to make things worse than they are. To quote a couple of lines from The Zen of Python:

Simple is better than complex.

Complex is better than complicated.

We try to keep things simple. Sometime simple is not enough, we need complex solutions to complex problems. But at least let’s not make them complicated. A clean, well-crafted system, with rules properly encapsulated can still be fairly easy to work with. If developers introduce additional complexity which stems not from the problem domain but from coding practices, then the ability to reason about and maintain the system drops precipitously. This is called accidental complexity.

We should always ask ourselves whether the complexity we are dealing with is inherent or accidental. The former is unavoidable, the latter should be avoided at all cost.