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Add benchmark for Array#new vs. Fixnum#times + map #91
Add benchmark for Array#new vs. Fixnum#times + map #91
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When you need to map the result of a block invoked a fixed amount of times, you have an option between: ``` Array.new(n) { ... } ``` and: ``` n.times.map { ... } ``` The latter one is about 60% slower.
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require "benchmark/ips" | |||
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ELEMENTS = 9 |
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How does this change with the number of elements?
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@nateberkopec: It tapers off a bit, and stabilizes around 40% slower at 1_000
elements and up. Note: logarithmic scale!
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Thank you so much for the graph! I'm just getting started with this repo, so someone else should comment, but whenever there's an Array or Enumerable benchmark I'm always curious with how it scales with # of elements. You may want to add a comment explaining that this behaves pretty much the same way at 1 million elements as it does at 10.
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No problems @nateberkopec. :-) Is a comment in the PR sufficient, or do you want me to add it to the README
?
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shrug let's see what someone else thinks
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I vote for link to this PR in README.md
.
This'll make info available for everyone, not only for people who reviewing PR's. Also, link will share that cool graph you have here:)
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@nateberkopec, @deniskorobicyn, @JuanitoFatas: Thanks for the kind words. 😃 I added the graph to the PR description, and linked to the PR from the README
.
As suggested by @deniskorobicyn, adding a link from the `README.md` to the PR, which has more information on performance characteristics.
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There's a faster way: require 'benchmark/ips'
LIMIT = 9
def fastest
[].fill(0, LIMIT, &:itself)
end
def fast
Array.new(LIMIT, &:itself)
end
def slow
(0...LIMIT).map(&:itself)
end
def slowest
LIMIT.times.map(&:itself)
end
Benchmark.ips do |bm|
bm.report('Array#fill') { fastest }
bm.report('Array#new') { fast }
bm.report('Range#map') { slow }
bm.report('Fixnum#times') { slowest }
bm.compare!
end
|
Let me add a reference to this. @JuanitoFatas We can merge this. |
Ping @JuanitoFatas. 😘 |
When you need to map the result of a block invoked a fixed amount of times, you have an option between:
and:
The latter one is about 60% slower for
n = 10
, which goes down to around 40% forn > 1_000.
Note: logarithmic scale!