bringing concurrency to ruby - rubyconf india 2014
TRANSCRIPT
Bringing Concurrency to Ruby
Charles Oliver Nutter @headius
Me, about 40 weeks of the year
Me, the other 12 weeks of the year
Concurrency
Parallelism?
Concurrency
• Two or more jobs
• Making progress
• Over a given time span
Parallelism
• Two or more computations
• Executing at the same moment in time
Examples
• Thread APIs: concurrency
• Actore APIs: concurrency
• Native thread, process: parallelism
• If the underlying system supports it
• SIMD, GPU, vector operations: parallelism
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
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
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
Popular PlatformsConcurrency 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
Idle System
MRI 1.8.7, One Thread
MRI 1.8.7, Eight Threads
Timeslicing
Thread 1
Thread 2Thread 3Thread 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 upTime’s up Time’s up
MRI 1.8.7, Eight Threads
MRI 1.9.3+, Eight Threads
GVL: Global VM Lock
Thread 1
Thread 2Thread 3Thread 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
CPULock xfer Lock xfer
GVL: Global Venue Lock
MRI 1.9.3+, Eight Threads
MRI 1.9.3+, Eight Threads
JRuby, One Thread
Why Do We See Parallelism?
• Hotspot JVM has many background threads
• GC with concurrent and parallel options
• JIT threads
• Signal handling
• Monitoring and management
JRuby, One Thread
JRuby, Eight Threads
Time Matters Too
0
1.75
3.5
5.25
7
Time per iterationMRI 1.8.7 MRI 1.9.3 JRuby
Nearly 10x faster than
1.9.3
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.
#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
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
Fix Single-thread First!T
ime
in s
econ
ds
0
5
10
15
20
big_list timev1 v2 v3 v4
String slice instead of unpack/packSimpler loops
Stream from file
Tim
e in
sec
onds
0
17.5
35
52.5
70
Processing 23M word fileNon-threaded Two threads Four threads
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.
Concurrency won’t help code that’s using up all hardware
resources.
#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
#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
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
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
Persistent?
• Collection you have a reference to is guaranteed never to change
• Modifications return a new reference
• …and only duplicate affected part of trie
Hamster
• Pure-Ruby persistent data structures
• Set, List, Stack, Queue, Vector, Hash
• Based on Clojure’s Ctrie collections
• https://github.com/hamstergem/hamster
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"
Coming Soon
• Reimplementation by Smit Shah
• Mostly “native” impl of Ctrie
• Considerably better perf than Hamster
• https://github.com/Who828/persistent_data_structures
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”
#4: Synchronize Mutation
• Trickiest to get right; usually best perf
• Fully-immutable generates lots of garbage
• Locks, atomics, and specialized collections
Locks
• Avoid concurrent operations
• Read + write, in general
• Many varieties: reentrant, read/write
• Many implementations
Mutex
• Simplest form of lock
• Acquire, do work, release
• Not reentrant
semaphore = Mutex.new!...!a = Thread.new {! semaphore.synchronize {! # access shared resource! }!}
ConditionVariable
• Release mutex temporarily
• Signal others waiting on the mutex
• …and be signaled
• Similar to wait/notify/notifyAll in Java
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! }!}!
Monitor
• Reentrancy
• “try” acquire
• Mix-in for convenience
• Java synchronization = CondVar + Monitor
Monitor
require 'monitor'! !lock = Monitor.new!lock.synchronize do! # exclusive access!end
Monitorrequire 'monitor'! !class SynchronizedArray < Array! ! include MonitorMixin! ! alias :old_shift :shift! ! def shift(n=1)! self.synchronize do! self.old_shift(n)! end! end!...
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
atomic
• Atomic operations for Ruby
• https://github.com/headius/ruby-atomic
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
Specialized Collections
• thread_safe gem
• Fully-synchronized Array and Hash
• Atomic-based hash impl (“Cache”)
• java.util.concurrent
• Numerous tools for concurrency
Queues
• Thread-safe Queue and SizedQueue
• Pipeline data to/from threads
• Standard in all Ruby impls
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
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
Putting It All Together
• These are a lot of tools to sort out
• Others have sorted them out for you
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
class Sheen! include Celluloid! ! def initialize(name)! @name = name! end! ! def set_status(status)! @status = status! end! ! def report! "#{@name} is #{@status}"! end!end
>> charlie = Sheen.new "Charlie Sheen"! => #<Celluloid::Actor(Sheen:0x00000100a312d0) @name="Charlie Sheen">!>> 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!"
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/
class HardWorker! include Sidekiq::Worker! ! def perform(name, count)! puts 'Doing hard work'! end!end!!...later, in a controller...! !HardWorker.perform_async('bob', 5)
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
…all the examples I’ve shown you and more
Recap
• The future of Ruby is concurrent
• The tools are there to help you
• Let’s all help move Ruby forward
