Toybox should be simple, small, fast, and full featured. Often, these things need to be balanced off against each other. In general, keeping the code simple the most important (and hardest) goal, and small is slightly more important than fast. Features are the reason we write code in the first place but this has all been implemented before so if we can't do a better job why bother? It should be possible to get 80% of the way to each goal before they really start to fight.

Here they are in reverse order of importance:

The roadmap has the list of features we're trying to implement, and the reasons for them. After the 1.0 release some of that material may get moved here.

Some things are simply outside the scope of the project: even though posix defines commands for compiling and linking, we're not going to include a compiler or linker (and support for a potentially infinite number of hardware targets). And until somebody comes up with a ~30k ssh implementation, we're going to point you at dropbear or polarssl.

Environmental dependencies are a type of complexity, so needing other packages to build or run is a big downside. For example, we don't use curses when we can simply output ansi escape sequences and trust all terminal programs written in the past 30 years to be able to support them. (A common use case is to download a statically linked toybox binary to an arbitrary Linux system, and use it in an otherwise unknown environment; being self-contained helps support this.)

It's easy to say lots about optimizing for speed (which is why this section is so long), but at the same time it's the optimization we care the least about. The essence of speed is being as efficient as possible, which means doing as little work as possible. A design that's small and simple gets you 90% of the way there, and most of the rest is either fine-tuning or more trouble than it's worth (and often actually counterproductive). Still, here's some advice:

First, understand the darn problem you're trying to solve. You'd think I wouldn't have to say this, but I do. Trying to find a faster sorting algorithm is no substitute for figuring out a way to skip the sorting step entirely. The fastest way to do anything is not to have to do it at all, and _all_ optimization boils down to avoiding unnecessary work.

Speed is easy to measure; there are dozens of profiling tools for Linux (although personally I find the "time" command a good starting place). Don't waste too much time trying to optimize something you can't measure, and there's no much point speeding up things you don't spend much time doing anyway.

Understand the difference between throughput and latency. Faster processors improve throughput, but don't always do much for latency. After 30 years of Moore's Law, most of the remaining problems are latency, not throughput. (There are of course a few exceptions, like data compression code, encryption, rsync...) Worry about throughput inside long-running loops, and worry about latency everywhere else. (And don't worry too much about avoiding system calls or function calls or anything else in the name of speed unless you are in the middle of a tight loop that's you've already proven isn't running fast enough.)

"Locality of reference" is generally nice, in all sorts of contexts. It's obvious that waiting for disk access is 1000x slower than doing stuff in RAM (and making the disk seek is 10x slower than sequential reads/writes), but it's just as true that a loop which stays in L1 cache is many times faster than a loop that has to wait for a DRAM fetch on each iteration. Don't worry about whether "&" is faster than "%" until your executable loop stays in L1 cache and the data access is fetching cache lines intelligently. (To understand DRAM, L1, and L2 cache, read Hannibal's marvelous ram guid at Ars Technica: part one, part two, part three, plus this article on cacheing, and this one on bandwidth and latency. And there's more where that came from.) Running out of L1 cache can execute one instruction per clock cycle, going to L2 cache costs a dozen or so clock cycles, and waiting for a worst case dram fetch (round trip latency with a bank switch) can cost thousands of clock cycles. (Historically, this disparity has gotten worse with time, just like the speed hit for swapping to disk. These days, a _big_ L1 cache is 128k and a big L2 cache is a couple of megabytes. A cheap low-power embedded processor may have 8k of L1 cache and no L2.)

Learn how virtual memory and memory managment units work. Don't touch memory you don't have to. Even just reading memory evicts stuff from L1 and L2 cache, which may have to be read back in later. Writing memory can force the operating system to break copy-on-write, which allocates more memory. (The memory returned by malloc() is only a virtual allocation, filled with lots of copy-on-write mappings of the zero page. Actual physical pages get allocated when the copy-on-write gets broken by writing to the virtual page. This is why checking the return value of malloc() isn't very useful anymore, it only detects running out of virtual memory, not physical memory. Unless you're using a NOMMU system, where all bets are off.)

Don't think that just because you don't have a swap file the system can't start swap thrashing: any file backed page (ala mmap) can be evicted, and there's a reason all running programs require an executable file (they're mmaped, and can be flushed back to disk when memory is short). And long before that, disk cache gets reclaimed and has to be read back in. When the operating system really can't free up any more pages it triggers the out of memory killer to free up pages by killing processes (the alternative is the entire OS freezing solid). Modern operating systems seldom run out of memory gracefully.

Also, it's better to be simple than clever. Many people think that mmap() is faster than read() because it avoids a copy, but twiddling with the memory management is itself slow, and can cause unnecessary CPU cache flushes. And if a read faults in dozens of pages sequentially, but your mmap iterates backwards through a file (causing lots of seeks, each of which your program blocks waiting for), the read can be many times faster. On the other hand, the mmap can sometimes use less memory, since the memory provided by mmap comes from the page cache (allocated anyway), and it can be faster if you're doing a lot of different updates to the same area. The moral? Measure, then try to speed things up, and measure again to confirm it actually _did_ speed things up rather than made them worse. (And understanding what's really going on underneath is a big help to making it happen faster.)

In general, being simple is better than being clever. Optimization strategies change with time. For example, decades ago precalculating a table of results (for things like isdigit() or cosine(int degrees)) was clearly faster because processors were so slow. Then processors got faster and grew math coprocessors, and calculating the value each time became faster than the table lookup (because the calculation fit in L1 cache but the lookup had to go out to DRAM). Then cache sizes got bigger (the Pentium M has 2 megabytes of L2 cache) and the table fit in cache, so the table became fast again... Predicting how changes in hardware will affect your algorithm is difficult, and using ten year old optimization advice and produce laughably bad results. But being simple and efficient is always going to give at least a reasonable result.

The famous quote from Ken Thompson, "When in doubt, use brute force", applies to toybox. Do the simple thing first, do as little of it as possible, and make sure it's right. You can always speed it up later.

Again, simple gives you most of this. An algorithm that does less work is generally smaller. Understand the problem, treat size as a cost, and get a good bang for the byte.

Understand the difference between binary size, heap size, and stack size. Your binary is the executable file on disk, your heap is where malloc() memory lives, and your stack is where local variables (and function call return addresses) live. Optimizing for binary size is generally good: executing fewer instructions makes your program run faster (and fits more of it in cache). On embedded systems, binary size is especially precious because flash is expensive (and its successor, MRAM, even more so). Small stack size is important for nommu systems because they have to preallocate their stack and can't make it bigger via page fault. And everybody likes a small heap.

Measure the right things. Especially with modern optimizers, expecting something to be smaller is no guarantee it will be after the compiler's done with it. Binary size isn't the most accurate indicator of the impact of a given change, because lots of things get combined and rounded during compilation and linking. Matt Mackall's bloat-o-meter is a python script which compares two versions of a program, and shows size changes in each symbol (using the "nm" command behind the scenes). To use this, run "make baseline" to build a baseline version to compare against, and then "make bloatometer" to compare that baseline version against the current code.

Avoid special cases. Whenever you see similar chunks of code in more than one place, it might be possible to combine them and have the users call shared code. (This is the most commonly cited trick, which doesn't make it easy. If seeing two lines of code do the same thing makes you slightly uncomfortable, you've got the right mindset.)

Some specific advice: Using a char in place of an int when doing math produces significantly larger code on some platforms (notably arm), because each time the compiler has to emit code to convert it to int, do the math, and convert it back. Bitfields have this problem on most platforms. Because of this, using char to index a for() loop is probably not a net win, although using char (or a bitfield) to store a value in a structure that's repeated hundreds of times can be a good tradeoff of binary size for heap space.

Complexity is a cost, just like code size or runtime speed. Treat it as a cost, and spend your complexity budget wisely. (Sometimes this means you can't afford a feature because it complicates the code too much to be worth it.)

Simplicity has lots of benefits. Simple code is easy to maintain, easy to port to new processors, easy to audit for security holes, and easy to understand. (Comments help, but they're no substitute for simple code.)

Prioritizing simplicity tends to serve our other goals: simplifying code generally reduces its size (both in terms of binary size and runtime memory usage), and avoiding unnecessary work makes code run faster. Smaller code also tends to run faster on modern hardware due to CPU cacheing: fitting your code into L1 cache is great, and staying in L2 cache is still pretty good.

Joel Spolsky argues against throwing code out and starting over, and he has good points: an existing debugged codebase contains a huge amount of baked in knowledge about strange real-world use cases that the designers didn't know about until users hit the bugs, and most of this knowledge is never explicitly stated anywhere except in the source code.

That said, the Mythical Man-Month's "build one to throw away" advice points out that until you've solved the problem you don't properly understand it, and about the time you finish your first version is when you've finally figured out what you _should_ have done. (The corrolary is that if you build one expecting to throw it away, you'll actually wind up throwing away two. You don't understand the problem until you _have_ solved it.)

Joel is talking about what closed source software can afford to do: Code that works and has been paid for is a corporate asset not lightly abandoned. Open source software can afford to re-implement code that works, over and over from scratch, for incremental gains. Before toybox, the unix command line has already been reimplemented from scratch several times in a row (the original AT&T Unix command line in assembly and then in C, the BSD versions, the GNU tools, BusyBox...) but maybe toybox can do a better job. :)

P.S. How could I resist linking to an article about why programmers should strive to be lazy and dumb?

Toybox should run on Android (alas, with bionic), and every other hardware platform Linux runs on. Other posix/susv4 environments (perhaps MacOS X or newlib+libgloss) are vaguely interesting but only if they're easy to support; I'm not going to spend much effort on them.

I don't do windows.

Toybox should work on both 32 bit and 64 bit systems. By the end of 2008 64 bit hardware will be the new desktop standard, but 32 bit hardware will continue to be important in embedded devices for years to come.

Toybox relies on the fact that on any Unix-like platform, pointer and long are always the same size (on both 32 and 64 bit). Pointer and int are _not_ the same size on 64 bit systems, but pointer and long are.

This is guaranteed by the LP64 memory model, a Unix standard (which Linux and MacOS X both implement). See the LP64 standard and the LP64 rationale for details.

Note that Windows doesn't work like this, and I don't care. The insane legacy reasons why this is broken on Windows are explained here.

On platforms like x86, variables of type char default to unsigned. On platforms like arm, char defaults to signed. This difference can lead to subtle portability bugs, and to avoid them we specify which one we want by feeding the compiler -funsigned-char.

The reason to pick "unsigned" is that way we're 8-bit clean by default.