On 02/09/2012 11:40 PM, Steve Miner wrote:
filter is lazy so it won't actually do the work unless the values are needed.
To get a reasonable time, you need to use the result for some computation. Try
something like this:
(defn sum-all [m] (reduce + (apply map + (vals m))))
(time (sum-all (separate-nums (range 10000))))
It was pretty simple. I forgot laziness :/
I ran several times in a row both functions on the same sequence, and I
obtained these timings (functional first, iterative second):
"Elapsed time: 3019.56405 msecs"
"Elapsed time: 621.744839 msecs"
"Elapsed time: 867.197906 msecs"
"Elapsed time: 551.287444 msecs"
"Elapsed time: 314.490382 msecs"
"Elapsed time: 647.862119 msecs"
"Elapsed time: 328.403288 msecs"
"Elapsed time: 621.69671 msecs"
"Elapsed time: 334.29854 msecs"
"Elapsed time: 839.599691 msecs"
"Elapsed time: 272.061383 msecs"
"Elapsed time: 499.008063 msecs"
The patterns seems to be this: initially the functional one is slower,
but quickly begins to run about twice as fast as the iterative one.
Using instead a sequence of random numbers, I got a more stable result:
in average the functional approach is slower than the iterative one.
I think the lesson here is this: use a functional approach, that way the
code is easier to write, read, compose and reason about. If and when you
need to optimize, one option is to rewrite some core functions in an
iterative style. A plus here is that functional code is easier to profile.
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