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Ruby Performance - The Last Mile - RubyConf Ind...

headius
March 20, 2016

Ruby Performance - The Last Mile - RubyConf India 2016

Talk on how JRuby is bringing performance and concurrency to Ruby, delivered at RubyConf India 2016 in Kochi, Kerala, India.

headius

March 20, 2016
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  1. What you love from Ruby • Latest Ruby language features

    • Mostly-same Ruby standard library • Pure-Ruby gems work great • Native gems with JRuby support • It walks like Ruby, talks like Ruby • It is Ruby!
  2. With the power of the JVM • Fast JIT to

    native code • Fully parallel threading • Leading-edge garbage collectors • Access to Java, Scala, Clojure, ... • But it's still Ruby!
  3. What do you optimize for? • Easy to develop with:

    short time until first deploy • Fast startup: good response cycle at command line • Straight-line performance, many operations per second • Parallelism: utilize many cores to get more done
  4. Production Quality? • Support for 99%+ of Ruby language features

    • Important parts of standard library • Runs typical Ruby applications and libraries • Healthy extension ecosystem • CRuby, JRuby are the only real options right now
  5. CRuby (MRI) • Up until 1.9, AST interpreter • YARV

    bytecode VM introduced for 1.9.0 • GC and performance improvements through 2.x series • Ruby 2.3 is latest, released in December • Future work on JIT, GC, happening now
  6. JRuby • Many redesigns since creation in 2001 • AST

    interpreter until 2007 • Simple AST-to-bytecode JIT until JRuby 9000 • Optimizing compiler with JIT for 9k • JRuby 9.0.5 is current, 9.1 in a couple weeks • Next-gen Truffle runtime in the works
  7. Block Pass-through loop {
 puts Benchmark.measure {
 i = 0


    while i < 1_000_000
 i+=1
 foo { }; foo { }; foo { }; foo { }; foo { }
 end
 }
 }
  8. csv.rb Converters Converters = {
 integer: lambda { |f|
 Integer(f.encode(ConverterEncoding))

    rescue f
 },
 float: lambda { |f|
 Float(f.encode(ConverterEncoding)) rescue f
 },
  9. Concurrency? Parallelism? • Parallelism happens on the harder, e.g. multi-core

    • Concurrency happens in the software, e.g. Thread API • You can have concurrency without parallelism • You can have both with JRuby
  10. Parallelism in Ruby • On CRuby, usually process-level • Ruby

    threads prevented from running in parallel • Extensions, IO can opt to release lock • On JRuby, usually thread-level • Ruby thread == JVM thread == OS thread • Single-process, shared memory
  11. A Mailing Queue • A simple example of concurrency •

    For each job, construct an email to send • Some computation added to make processing heavier • "Ruby Concurrency and Parallelism: A Practical Tutorial"
 https://www.toptal.com/ruby/ruby-concurrency-and-parallelism-a- practical-primer
  12. require "./lib/mailer"
 require "benchmark"
 
 puts Benchmark.measure{
 (ARGV[0] || 10_000).times

    do |i|
 Mailer.deliver do
 from "eki_#{i}@eqbalq.com"
 to "jill_#{i}@example.com"
 subject "Threading and Forking (#{i})"
 body "Some content"
 end
 end
 }
  13. POOL_SIZE = (ARGV[0] || 10).to_i
 
 jobs = Queue.new
 


    (ARGV[1] || 10_000).to_i.times{|i| jobs.push i}
 
 workers = (POOL_SIZE).times.map do
 Thread.new do
 begin
 while x = jobs.pop(true)
 Mailer.deliver do
 ...
 end
 end
 rescue ThreadError
 end
 end
 end
 
 workers.map(&:join)
  14. CRuby: mailer * 1000 Time in Seconds 0 37.5 75

    112.5 150 synchronous 4 threads 4 forks
  15. JRuby vs MRI Times Improvement 0 0.85 1.7 2.55 3.4

    CRuby Forks JRuby Threads 3.37x 3.09x
  16. Great Features, Hidden Costs • Blocks are expensive to create,

    slower than method calls • case/when is an O(n) cascade of calls • Singleton classes/methods are costly and hurt method cache • Literal arrays, hashes, strings have to be constructed, GCed • Flow-control exceptions can be very expensive and hard to find
  17. CRuby Tooling • Basic GC stats built in • Simple

    profilers in standard library • Some third-party tools • stackprof, ruby-prof, perftools.rb, ...
  18. JVM Tooling • Wide range of GCs: parallel, concurrent, realtime,

    pauseless • Built-in tools for analyzing GC, JIT, thread, IO, heap • Built-in remote monitoring via JMX • Dozens of tools out there for profiling, management, and more
  19. VisualVM • Graphical console into your application • Monitor GC,

    threads, CPU usage • Sampled or full profiling with GUI browser • Live memory dumping, heap inspection • Ships with every OpenJDK install
  20. Java Mission Control • Extremely low-overhead application recording • Analyze

    results offline in JMC • GC, CPU, heap events, IO operation all browsable • Commercial feature, free for development use
  21. More on GC • JVM GCs are incredibly tunable with

    sensible defaults • Tools like http://gceasy.io and JClarity give you a deeper view • These are the best GCs and the best tools in the world
  22. Profiling Tools • Command line options: --profile, --sample • JVM

    command-line profilers like prof • Many graphical sampling/complete profiling options • Flame graphs, stack profilers, you name it!