Distance outside of maps

Distance is seriously strange.

Yesterday on the southern coast of Spain, at an Italian restaurant run by a German, I had tiramisu, because I’ve never had tiramisu this close to Italy. People laughed,
because Spain is farther from Italy than Germany or Poland (geographically) – but for food purposes it’s closer, right?

Geographic distance is so nice, on a map, so clear and measurable.
And it’s almost never relevant.

Sydney is farther from SF than SF is from Sydney, by 2 hours of flying, because of wind.
St Louis is farther than San Francisco from Europe, because there are direct flights to SF.

Today in Frankfurt I went from A gates to Z gates. Sounds far! … except in the map, Z is right on top of A. Which does not make it close, because the path from A to Z goes through passport control.


Forget maps. They’re satisfying, fun, and deceptive, because they give us the feeling we understand distance.

Distance is fluid, inconstant. Gates are closer when the sidewalk is moving, farther when people are bunched up and slow.

In software systems, distance is all kinds of inconsistent. Networks get slow and computers get farther apart. WiFi goes down and suddenly they’re on another planet.

And here’s the thing about distance: it’s crucial to our understanding of time.
One thing distributed systems can teach us about the real world: there is no time outside of place. There is no ordering of events across space. There is only what we see in a particular spot.

Roland Kuhn spoke at J on the Beach about building reliable systems in the face of fluctuating distance like this. The hardest part is coming up with a consistent (necessarily fictional) ordering of events, so programs can make decisions based on those events.

Humans deal all the time with ambiguity, with “yeah this person said stop over here, and also this machine kept going over there.” We don’t expect the world to
have perfect consistency. Yet we wish it did, so we create facsimiles of certainty in our software.  

Distributed systems teach us how expensive that is. how limiting.

Martin Thompson’s talk about protocols had real-life advice for collaborating over fluctuating distance. Think carefully about how we will interact, make decisions locally, deal with feedback and recover.

Distance is a thing, and it is not simple or constant. Time is not universal, it is always located in space. Humans are good at putting ambiguous situations together, at sensemaking. This is really hard to do in a computer.
Software, in its difficulty, teaches us to appreciate our own skills.