Optimizing Ruby’s JSON, Part 4
In the previous post, we established that as long as ruby/json wasn’t competitive on micro-benchmarks, public perception wouldn’t change. Since what made ruby/json appear so bad on micro-benchmarks was its setup cost, we had to find ways to reduce it further. Spot the Seven Differences So I decided to file this performance discrepancy as a bug, and investigate it as such and started profiling Step
Optimizing Ruby’s JSON, Part 3
In the previous post, I covered how I reimplemented JSON::Generator::State#configure in Ruby and some other changes. Unfortunately, it didn’t go as well as I initially thought. Mistakes Were Made The default gems that ship with Ruby are automatically copied inside ruby/ruby’s repo. In short, there’s a bot aptly named matzbot, that replicates all the commits from the various ruby/* gems, inside rub
Optimizing Ruby’s JSON, Part 2
In the previous post, I covered my motivations for improving ruby/json’s performance, and detailed the first 4 notable optimizations applied to speed up JSON generation. If I was to cover every single optimization applied, at this rate I’d end up with a dozen parts, so I’ll try to only focus on the one that made a significant difference or used an interesting pattern. Reducing Setup Cost - Argumen
Optimizing Ruby’s JSON, Part 1
I was recently made maintainer of the json gem, and aside from fixing some old bugs, I focused quite a bit on its performance, so that it is now the fastest JSON parser and generator for Ruby on most benchmarks. Contrary to what one might think, there wasn’t any black magic or deep knowledge involved. Most of the performance patches I applied were fairly simple optimizations driven by profiling. A
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#db/seeds.rb User.create!( name: "kamekame", email: "[email protected]", password: "password", password_confirmation: "password" ) 700.times do |n| Post.create!( title: Faker::Music.band, content: "example-#{n+1}", user_id: 1 ) end â‘¡benchmarkヘルパーをcontrollerã«åŸ‹ã‚込む 今回ã¯ã€ â‘ posts#indexã«ã‚¢ã‚¯ã‚»ã‚¹ã—ã€@posts = current_user.posts.order(created_at: :desc)ã®å‡¦ç†ã‚’終ãˆã‚‹ã¾ã§ã«ã‹ã‹ã‚‹æ™‚é–“ â‘¡posts#showã«ã‚¢ã‚¯ã‚»ã‚¹ã—ã€@post = current_user.p
Speeding up Ruby by rewriting C… in Ruby
There is a recent language comparison repo which has been getting shared a lot. In it, CRuby was the third slowest option, only beating out R and Python. The repo author, @BenjDicken, created a fun visualization of each language’s performance. Here’s one of the visualizations, which shows Ruby as the third slowest language benchmarked: The code for this visualization is from https://benjdd.com/lan
Ruby might be faster than you think - John Hawthorn
I saw a project a couple weeks back which allows writing and running Crystal methods inline inside a Ruby file. It’s a neat project, and I don’t want to take away from it but something in the README example looked off to me. require 'crystalruby' require 'benchmark' module Fibonnaci crystalize [n: :int32] => :int32 def fib_cr(n) a = 0 b = 1 n.times { a, b = b, a + b } a end module_function def fib
Is my host fast yet?
Real-world server response (Time to First Byte) latencies, as experienced by real-world users navigating the web. Leaderboard: May 2024 Sort by Host Client Websites Fast (p75 < 800ms) Average Slow (p75 >= 1800ms) See a missing hosting provider? Please help us identify how to surface them here. How do you measure real-world Time to First Byte? This report is powered by Chrome User Experience Report
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