It seems easier to scale the Heroku dynos or AWS servers with a slider, but is it really required? When developers see that their application is slow, their first reflex is to increase the dyno size. They hardly ever look at the application code. I've seen a lots of Ruby applications and most of them are over-scaled and wasting money. I intend to start by focusing on areas where Ruby takes a performance hit, such as Garbage collection and memory management. Then I will talk about the solutions to fix them in Ruby applications. Further I will showcase how why its better to use one function in Ruby over the other and the performance hit the application is taking because of them. These will be backed by the benchmarking suite I have created from my experience.

1. PERFORMANCE
OPTIMIZATION IN RUBYPRATHMESH RANAUT
© Prathmesh Ranaut, 2017

2. © Prathmesh Ranaut, 2017

3. ☃ -> "
© Prathmesh Ranaut, 2017

4. WHO AM I
> Computer Science Undergrad
> Helped ! of ruby apps improve
performance
© Prathmesh Ranaut, 2017

5. > Celluloid - Concurrent ruby framework
> Improved performance by 300%
© Prathmesh Ranaut, 2017

6. WHAT IS PERFORMANCE
OPTIMIZATION? !
© Prathmesh Ranaut, 2017

7. DOING
MOREWITH
LESS
© Prathmesh Ranaut, 2017

8. HOW?
© Prathmesh Ranaut, 2017

9. TIP #1
© Prathmesh Ranaut, 2017

10. ALWAYS HAVE A BENCHMARKING SUITE
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11. BECHMARK-IPS
© Prathmesh Ranaut, 2017

12. require "benchmark/ips"
Benchmark.ips do |ips|
ips.report("addition") { 1 + 2 }
end
© Prathmesh Ranaut, 2017

13. require "benchmark/ips"
Benchmark.ips do |ips|
ips.report("addition") { 1 + 2 }
end
Warming up --------------------------------------
addition 325.015k i/100ms
Calculating -------------------------------------
addition 12.544M (± 8.5%) i/s - 62.403M in 5.012765s
© Prathmesh Ranaut, 2017

14. class Worker
include Celluloid
def hashed(hash, key)
hash[key]
end
end
# Worker pool of different sizes
pool_10 = Worker.pool(size: 10)
pool_100 = Worker.pool(size: 100)
hash = {}
ENTRIES = 10_000
TESTS = 100_000
# Using a constant key, to derive consistent benchmarking results
KEY = 500
# Populate hash array
ENTRIES.times do |i|
hash[i] = i
end
Benchmark.bmbm do |ips|
puts "Finding the key : #{KEY}"
ips.report("pool - 10") do
TESTS.times do
pool_10.async.hashed(hash, KEY)
end
end
ips.report("pool - 100") do
TESTS.times do
pool_100.async.hashed(hash, KEY)
end
end
end
© Prathmesh Ranaut, 2017

15. TIP #2
© Prathmesh Ranaut, 2017

16. UPGRADE RUBY
© Prathmesh Ranaut, 2017

17. RUBY 1.9
> YARV
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18. RUBY 1.9
> YARV
RUBY 2.0
> Copy-on-Write
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19. RUBY 2.1
> Generation GC
RUBY 2.4
> Improves the speed to access an instance variable
© Prathmesh Ranaut, 2017

20. TIP #3
© Prathmesh Ranaut, 2017

21. TRY ANOTHER IMPLEMENTATION OF
RUBY
© Prathmesh Ranaut, 2017

22. RUBY 2.3 VS JRUBY
RUBY 2.3
Calculating -------------------------------------
addition 12.544M (± 8.5%) i/s - 62.403M
JRUBY
Calculating -------------------------------------
addition 22.929M (± 9.3%) i/s - 113.127M
© Prathmesh Ranaut, 2017

23. TIP #4
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24. PROFILE YOUR METHODS
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25. TIP #5
© Prathmesh Ranaut, 2017

26. FIGURE OUT THE BOTTLENECK
I/O OR CPU OR MEMORY
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27. TIP #6
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28. SWITCH TO ELIXIR
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29. TRY MULTITHREADING
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30. TOOLS
THAT MAKE MY LIFE EASIER
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31. METHOD PROFILER
HTTPS://GITHUB.COM/CHANGE/METHOD_PROFILER
© Prathmesh Ranaut, 2017

32. profiler = MethodProfiler.observe(BazingaClass)
# Do stuff with the BazingaClass
puts profiler.report
© Prathmesh Ranaut, 2017

33. © Prathmesh Ranaut, 2017

34. KCACHEGRIND
HTTPS://KCACHEGRIND.GITHUB.IO/
© Prathmesh Ranaut, 2017

35. © Prathmesh Ranaut, 2017

36. RUBYPROF
HTTPS://GITHUB.COM/RUBY-PROF/RUBY-PROF
© Prathmesh Ranaut, 2017

37. require 'ruby-prof'
# profile the code
result = RubyProf.profile do
# ... code to profile ...
end
# print a graph profile to text
printer = RubyProf::GraphPrinter.new(result)
printer.print(STDOUT, {})
© Prathmesh Ranaut, 2017

38. © Prathmesh Ranaut, 2017

39. ObjectSpace.count_objects
© Prathmesh Ranaut, 2017

40. {
:TOTAL=>30165,
:FREE=>2216,
:T_OBJECT=>928,
:T_CLASS=>574,
:T_MODULE=>38,
:T_FLOAT=>4,
:T_STRING=>13182,
:T_REGEXP=>77,
:T_ARRAY=>2715,
:T_HASH=>88,
:T_STRUCT=>3,
:T_BIGNUM=>2,
:T_FILE=>30,
:T_DATA=>363,
:T_MATCH=>233,
:T_COMPLEX=>1,
:T_SYMBOL=>6,
:T_IMEMO=>4530,
:T_NODE=>5138,
:T_ICLASS=>37
}
© Prathmesh Ranaut, 2017

41. DTRACE
© Prathmesh Ranaut, 2017

42. THANK YOU
&
QUESTIONSHELLO@PRATHMESHRANAUT.COM
HTTPS://TWITTER.COM/PRATHMESHRANAUT
© Prathmesh Ranaut, 2017