This page has moved to

Tuesday, October 10, 2006

Human minds, programming, and the "caching problem"

When writing large software systems, rapid access to data is often important: what can be kept in memory or more slowly: processes on the same local network and on disk. In software development, we see the same effect: maximum speed and efficiently if a single person can understand the architecture and comprehend the entire system. Moving from a single developer to a very small team adds a little overhead: design notes and pencil and paper drawings turn into casual but more explicit short documents and conversation. The optimisation is minimising cost between two people talking and sharing information vs. maintaining documentation and reading time. Talking is almost always better because communication is a two way street, but if you have N developers, O(N^2) "talking overhead" is too expensive with a large N, so back to the one way street of documentation.

I like to view programming languages in terms of how they allow me to deal with complexity, keeping as much stuff in my head at once:
  • Lisp: great for building up the language from the bottom and extending towards an application domain. The new application "programming language" is higher level and the remaining part of a system is more concise code and easier to keep track of.
  • Ruby: concise, so programs are much shorter and easier to understand.
  • Java: the language does little for me as far as reducing complexity, but great IDEs like IntelliJ at least allow rapid code browsing, "who calls this" queries, etc.