Bringing Concurrency to Ruby - RubyConf India 2014

A talk on the why and how of high-concurrency Ruby, delivered at RubyConf India 2014 in Goa.

Transcript

1. Bringing Concurrency to Ruby Charles Oliver Nutter @headius
3. Me, about 40 weeks of the year
4. Me, the other 12 weeks of the year
10. Concurrency
11. Parallelism?
12. Concurrency • Two or more jobs • Making progress • Over a given time span
13. Parallelism • Two or more computations • Executing at the same moment in time
14. Examples • Thread APIs: concurrency • Actore APIs: concurrency • Native thread, process: parallelism • If the underlying system supports it • SIMD, GPU, vector operations: parallelism
18. You Need Both • Work that can split into concurrent jobs • Platform that runs those jobs in parallel • In an ideal world, scales with job count • In our world, each job adds overhead
19. Process-level Concurrency • Separate processes running concurrently • As parallel as OS/CPU can make them • Low risk due to isolated memory space • High memory requirements • High communication overhead
20. Thread-level Concurrency • Threads in-process running concurrently • As parallel as OS/CPU can make them • Higher risk due to shared memory space • Lower memory requirements • Low communication overhead
21. Popular Platforms Concurrency Parallelism GC Notes MRI 1.8.7 ✔ ✘ Single thread, stop- the-world Large C core would need much work MRI 1.9+ ✔ ✘ Single thread, stop- the-world Few changes since 1.9.3 JRuby (JVM) ✔ ✔ Many concurrent and parallel options JVM is the “best” platform for conc Rubinius ✔ ✔ Single thread, stop- the-world, partial concurrent old gen Promising, but a long road ahead Topaz ✘ ✘ Single thread, stop- the-world Incomplete impl Node.js (V8) ✘ ✘ Single thread, stop- the-world No threads in JS CPython ✔ ✘ Reference-counting Reference counting kills parallelism Pypy ✔ ✘ Single thread, stop- the-world Exploring STM to enable concurrency
22. Idle System
24. MRI 1.8.7, One Thread
25. MRI 1.8.7, Eight Threads
26. Timeslicing Thread 1 Thread 2 Thread 3 Thread 4 Native thread Native thread Native thread Native thread “Green” or “virtual” or “userspace” threads share a single native thread. The CPU then schedules that thread on available CPUs. Time’s up Time’s up Time’s up
27. MRI 1.8.7, Eight Threads
28. MRI 1.9.3+, Eight Threads
29. GVL: Global VM Lock Thread 1 Thread 2 Thread 3 Thread 4 CPU CPU CPU CPU In 1.9+, each thread gets its own native thread, but a global lock prevents concurrent execution. Time slices are finer grained and variable, but threads still can’t run in parallel. Lock xfer CPU Lock xfer Lock xfer
30. GVL: Global Venue Lock
31. MRI 1.9.3+, Eight Threads
32. MRI 1.9.3+, Eight Threads
33. JRuby, One Thread
34. Why Do We See Parallelism? • Hotspot JVM has many background threads • GC with concurrent and parallel options • JIT threads • Signal handling • Monitoring and management
35. JRuby, One Thread
36. JRuby, Eight Threads
37. Time Matters Too 0 1.75 3.5 5.25 7 Time per iteration MRI 1.8.7 MRI 1.9.3 JRuby Nearly 10x faster than 1.9.3
38. Rules of Concurrency 1. Don’t do it, if you don’t have to. 2. If you must do it, don’t share data. 3. If you must share data, make it immutable. 4. If it must be mutable, coordinate all access.
39. #1: Don’t • Many problems won’t benefit • Explicitly sequential things, e.g • Bad code can get worse • Multiply perf, GC, alloc overhead by N • Fixes may not be easy (esp. in Ruby) • The risks can get tricky to address
44. I’m Not Perfect • Wrote a naive algorithm • Measured it taking N seconds • Wrote the concurrent version • Measured it taking roughly N seconds • Returned to original to optimize
45. Fix Single-thread First! Time in seconds 0 5 10 15 20 big_list time v1 v2 v3 v4 String slice instead of unpack/pack Simpler loops Stream from file
46. Time in seconds 0 17.5 35 52.5 70 Processing 23M word file Non-threaded Two threads Four threads
47. Before Conc Work • Fix excessive allocation (and GC) • Fix algorithmic complexity • Test on the runtime you want to target • If serial perf is still poor after optimization, the task, runtime, or system may not be appropriate for a concurrent version.
48. Concurrency won’t help code that’s using up all hardware resources.
49. #2: Don’t Share Data • Process-level concurrency • …have to sync up eventually, though • Threads with their own data objects • Rails request objects, e.g. • APIs with a “master” object, usually • Weakest form of concurrency
50. #3: Immutable Data • In other words… • Data can be shared • Threads can pass it around safely • Cross-thread view of data can’t mutate • Threads can’t see concurrent mutations as they happen, avoiding data races
51. Object#freeze • Simplest mechanism for immutability • For read-only: make changes, freeze • Read-mostly: dup, change, freeze, replace • Write-mostly: same, but O(n) complexity
52. Immutable Data Structure • Designed to avoid visible mutation but still have good performance characteristics • Copy-on-write is poor-man’s IDS • Better: persistent data structures like Ctrie http://en.wikipedia.org/wiki/Ctrie
53. Persistent? • Collection you have a reference to is guaranteed never to change • Modifications return a new reference • …and only duplicate affected part of trie
54. Hamster • Pure-Ruby persistent data structures • Set, List, Stack, Queue, Vector, Hash • Based on Clojure’s Ctrie collections • https://github.com/hamstergem/hamster
55. person = Hamster.hash(! :name => “Simon",! :gender => :male)! # => {:name => "Simon", :gender => :male}! ! person[:name]! # => "Simon"! person.get(:gender)! # => :male! ! friend = person.put(:name, "James")! # => {:name => "James", :gender => :male}! person! # => {:name => "Simon", :gender => :male}! friend[:name]! # => "James"! person[:name]! # => "Simon"
56. Coming Soon • Reimplementation by Smit Shah • Mostly “native” impl of Ctrie • Considerably better perf than Hamster • https://github.com/Who828/persistent_data_structures
57. Other Techniques • Known-immutable data like Symbol, Fixnum • Mutate for a while, then freeze • Hand-off: if you pass mutable data, assume you can’t mutate it anymore • Sometimes enforced by runtime, e.g. “thread-owned objects”
58. #4: Synchronize Mutation • Trickiest to get right; usually best perf • Fully-immutable generates lots of garbage • Locks, atomics, and specialized collections
59. Locks • Avoid concurrent operations • Read + write, in general • Many varieties: reentrant, read/write • Many implementations
60. Mutex • Simplest form of lock • Acquire, do work, release • Not reentrant semaphore = Mutex.new! ...! a = Thread.new {! semaphore.synchronize {! # access shared resource! }! }
61. ConditionVariable • Release mutex temporarily • Signal others waiting on the mutex • …and be signaled • Similar to wait/notify/notifyAll in Java
62. resource = ConditionVariable.new! ! a = Thread.new {! mutex.synchronize {! # Thread 'a' now needs the resource! resource.wait(mutex)! # 'a' can now have the resource! }! }! ! b = Thread.new {! mutex.synchronize {! # Thread 'b' has finished using the resource! resource.signal! }! }!
63. Monitor • Reentrancy • “try” acquire • Mix-in for convenience • Java synchronization = CondVar + Monitor
64. Monitor require 'monitor'! ! lock = Monitor.new! lock.synchronize do! # exclusive access! end
65. Monitor require 'monitor'! ! class SynchronizedArray < Array! ! include MonitorMixin! ! alias :old_shift :shift! ! def shift(n=1)! self.synchronize do! self.old_shift(n)! end! end! ...
66. Atomics • Without locking… • …replace a value only if unchanged • …increment, decrement safely • Thread-safe code can use atomics instead of locks, usually with better performance
67. atomic • Atomic operations for Ruby • https://github.com/headius/ruby-atomic
68. require 'atomic'! ! my_atomic = Atomic.new(0)! my_atomic.value! # => 0! my_atomic.value = 1! my_atomic.swap(2)! # => 1! my_atomic.compare_and_swap(2, 3)! # => true, updated to 3! my_atomic.compare_and_swap(2, 3)! # => false, current is not 2
69. Specialized Collections • thread_safe gem • Fully-synchronized Array and Hash • Atomic-based hash impl (“Cache”) • java.util.concurrent • Numerous tools for concurrency
70. Queues • Thread-safe Queue and SizedQueue • Pipeline data to/from threads • Standard in all Ruby impls
71. thread_count = (ARGV[2] || 1).to_i! queue = SizedQueue.new(thread_count * 4)! ! word_file.each_line.each_slice(50) do |words|! queue << words! end! queue << nil # terminating condition
72. threads = thread_count.times.map do |i|! Thread.new do! while true! words = queue.pop! if words.nil? # terminating condition! queue.shutdown! break! end! words.each do |word|! # analyze the word
73. Putting It All Together • These are a lot of tools to sort out • Others have sorted them out for you
74. Celluloid • Actor model implementation • OO/Ruby sensibilities • Normal classes, normal method calls • Async support • Growing ecosystem • Celluloid-IO and DCell (distributed actors) • https://github.com/celluloid/celluloid
75. class Sheen! include Celluloid! ! def initialize(name)! @name = name! end! ! def set_status(status)! @status = status! end! ! def report! "#{@name} is #{@status}"! end! end
76. >> charlie = Sheen.new "Charlie Sheen"! => #<Celluloid::Actor(Sheen:0x00000100a312d0) @name="Char >> charlie.set_status "winning!"! => "winning!"! >> charlie.report! => "Charlie Sheen is winning!"! >> charlie.async.set_status "asynchronously winning!"! => nil! >> charlie.report! => "Charlie Sheen is asynchronously winning!"
77. Sidekiq • Simple, efficient background processing • Think Resque or DelayedJob but better • Normal-looking Ruby class is the job • Simple call to start it running in background • http://mperham.github.io/sidekiq/
78. class HardWorker! include Sidekiq::Worker! ! def perform(name, count)! puts 'Doing hard work'! end! end! ! ...later, in a controller...! ! HardWorker.perform_async('bob', 5)
79. Concurrent Ruby • Grab bag of concurrency patterns • Actor, Agent, Channel, Future, Promise, ScheduledTask, TimerTask, Supervisor • Thread pools, executors, timeouts, conditions, latches, atomics • May grow into a central lib for conc stuff • https://github.com/jdantonio/concurrent-ruby
80. …all the examples I’ve shown you and more
81. Recap • The future of Ruby is concurrent • The tools are there to help you • Let’s all help move Ruby forward
82. Thank you! • Charles Oliver Nutter • headius@headius.com • @headius