Concurrent Programming with Ruby and Tuple Spaces Luc Castera Founder / messagepub.com

The Free Lunch is Over: A Fundamental Turn Toward Concurrency in Software Source: http://www.gotw.ca/publications/concurrency-ddj.htm “ The major processor manufacturers and architectures have run out of room with most of their traditional approaches to boosting CPU performance. Instead of driving clock speeds ands straight-line instruction throughput ever higher, they are instead turning en masse to hyperthreading and multicore architectures. […] And that puts us at a fundamental turning point in software development, at least for the next few years...” – Herb Sutter – March 2005

Outline 1. The problem with Ruby Threads 2. Multiple Ruby Processes 3. Inter-process Communication with TupleSpaces

PART 1 The Problem With Threads A closer look at the Ruby threading model

3 Types of Threading Models: 1 : N 1 : 1 M : N

3 Types of Threading Models: 1 : N 1 : 1 M : N Kernel Threads User-Space Threads

1 : N -> Green Threads One kernel thread for N user threads aka “lightweight threads”

10 ms

10 ms

10 ms

10 ms

10 ms

10 ms

10 ms

10 ms

RUBY 1.8

Pros and Cons Pros: Thread creation, execution, and cleanup are cheap Lots of threads can be created Cons: Not really parallel because kernel scheduler doesn't know about threads and can't schedule them across CPUs or take advantage of SMP Blocking I/O operation can block all green threads Example: C Extension Example: mysql gem (solution: NeverBlock mysqlplus)

Pros:

Thread creation, execution, and cleanup are cheap

Lots of threads can be created

Cons:

Not really parallel because kernel scheduler doesn't know about threads and can't schedule them across CPUs or take advantage of SMP

Blocking I/O operation can block all green threads

Example: C Extension

Example: mysql gem (solution: NeverBlock mysqlplus)

blocking

1 : 1 -> Native Threads 1 kernel thread for each user thread

Pros and Cons Pros: Threads can execute on different CPUs (truly parallel) Threads do not block each other Cons: Setup Overhead Low limit on number of threads Linux kernel bug with lots of threads

Pros:

Threads can execute on different CPUs (truly parallel)

Threads do not block each other

Cons:

Setup Overhead

Low limit on number of threads

Linux kernel bug with lots of threads

RUBY 1.9

I lied.

Global Interpreter Lock A Global Interpreter Lock (GIL) is a mutual exclusion lock held by a programming language interpreter thread to avoid sharing code that is not thread-safe with other threads. There is always one GIL for one interpreter process. Usage of a Global Interpreter Lock in a language effectively limits concurrency of a single interpreter process with multiple threads – there is no or very little increase in speed when running the process on a multiprocessor machine. Source: Wikipedia

A person (male or female) who intentionally or unintentionally stops the progress of two others getting their game on.

“ Concurrency is a myth in Ruby” – Ilya Grigorik

Unless you are using JRuby.

A note on Fibers Ruby 1.9 introduces fibers. Fibers are green threads, but scheduling must be done by the programmer and not the VM. Faster and cheaper then native threads. Implemented for Ruby 1.8 by Aman Gupta. Learn More: http://tinyurl.com/rubyfibers http://all-thing.net/fibers http://all-thing.net/fibers-via-continuations

Ruby 1.9 introduces fibers.

Fibers are green threads, but scheduling must be done by the programmer and not the VM.

Faster and cheaper then native threads.

Implemented for Ruby 1.8 by Aman Gupta.

Learn More:

http://tinyurl.com/rubyfibers

http://all-thing.net/fibers

http://all-thing.net/fibers-via-continuations

M : N -> Hybrid Model M kernel threads for N user threads “ best of both worlds”

Pros and Cons Pros: Take advantage of multiple CPUs Not all threads are blocked by blocking system calls Cheap creation, execution, and cleanup Cons: Need scheduler in userland and kernel to work with each other Green threads doing blocking I/O operations will block all other green threads sharing same kernel thread Difficult to write, maintain, and debug code

Pros:

Take advantage of multiple CPUs

Not all threads are blocked by blocking system calls

Cheap creation, execution, and cleanup

Cons:

Need scheduler in userland and kernel to work with each other

Green threads doing blocking I/O operations will block all other green threads sharing same kernel thread

Difficult to write, maintain, and debug code

“ Writing multi-threaded code is really, really hard. And it is hard because of Shared Memory.” – Jim Weirich The Other Problem with Threads http://rubyconf2008.confreaks.com/what-all-rubyist-should-know-about-threads.html

Multi-Threaded Code is Hard + Concurrency is a myth = FAIL!

Stop thinking in threads Design your application to use multiple processes

PART 2 Multiple Ruby Processes

Pros and Cons Pros: No longer sharing memory Take advantage of multiple CPUs (Performance) Not all threads are blocked by blocking system calls. Scalability Fault-Tolerance Cons: Process creation, execution and cleanup is expensive Uses a lot of memory (loading Ruby VM for every process) Need a way for processes to communicate!

Pros:

No longer sharing memory

Take advantage of multiple CPUs (Performance)

Not all threads are blocked by blocking system calls.

Scalability

Fault-Tolerance

Cons:

Process creation, execution and cleanup is expensive

Uses a lot of memory (loading Ruby VM for every process)

Need a way for processes to communicate!

Latency Starting/Stopping Fault-Tolerance Monitoring

but we will focus on...

How do the processes communicate?

Options DRB Sockets Queues RabbitMQ ActiveMQ Key-Value Databases Redis Tokyo Cabinet Memcached Relational Databases XMPP TupleSpaces

DRB

Sockets

Queues

RabbitMQ

ActiveMQ

Key-Value Databases

Redis

Tokyo Cabinet

Memcached

Relational Databases

XMPP

TupleSpaces

Examples

Rails + Mongrel/Thin Cluster of application servers (Mongrel, Thin...) Communication between processes is done via the database.

Cluster of application servers (Mongrel, Thin...)

Communication between processes is done via the database.

Nanite A self-assembling fabric of Ruby daemons http://github.com/ezmobius/nanite Uses RabbitMQ/AMQP for IPC

A self-assembling fabric of Ruby daemons

http://github.com/ezmobius/nanite

Uses RabbitMQ/AMQP for IPC

Revactor Uses the actor model Actors are kinda like threads, with messaging baked-in. Each Actor has a mailbox. It's like coding erlang in Ruby. Messages are passed between actors using TCP sockets. Good Documentation http://revactor.org/ “ Erlang provides a sledgehammer for the problems of concurrent programming. But, sometimes you don't need a sledgehammer... just a flyswatter will do.” – Tony Arcieri Discontinued for Reia

Uses the actor model

Actors are kinda like threads, with messaging baked-in.

Each Actor has a mailbox.

It's like coding erlang in Ruby.

Messages are passed between actors using TCP sockets.

Good Documentation

http://revactor.org/

“ Erlang provides a sledgehammer for the problems of concurrent programming. But, sometimes you don't need a sledgehammer... just a flyswatter will do.” – Tony Arcieri

Discontinued for Reia

Journeta Journeta is a dirt simple library for peer discovery and message passing between Ruby applications on a LAN Uses UDP Sockets for IPC “ Uses the fucked up Ruby socket API” -> from their RDOC Demo(?)

Journeta is a dirt simple library for peer discovery and message passing between Ruby applications on a LAN

Uses UDP Sockets for IPC

“ Uses the fucked up Ruby socket API”

-> from their RDOC

Demo(?)

PART 3 TupleSpaces I nterprocess Communication with TupleSpaces

A tuple space provides a repository of tuples that can be accessed concurrently.

[:add, 1, 2] [:result, 79] [:add, 60, 5] [:token] [:search, “linda”] [:where_is, :waldo [:subtract, 10, 2] [:save, 7864] The Blackboard Metaphor

[:add, 1, 2] [:result, 79] [:add, 60, 5] [:token] [:search, “linda”] [:where_is, :waldo [:subtract, 10, 2] [:save, 7864] The Blackboard Metaphor [:add, nil, nil]

[:add, 1, 2] [:result, 79] [:add, 60, 5] [:token] [:search, “linda”] [:where_is, :waldo [:subtract, 10, 2] [:save, 7864] The Blackboard Metaphor [nil]

[:add, 1, 2] [:result, 79] [:add, 60, 5] [:token] [:search, “linda”] [:where_is, :waldo [:subtract, 10, 2] [:save, 7864] The Blackboard Metaphor [:where_is, :waldo]

About Tuple Spaces First implementation was Linda. Linda was developed by David Gelernter and Nicholas Carriero at Yale University. Implementations exists for most languages. The Ruby implementation is Rinda. Rinda is a built-in library, so no need to install.

First implementation was Linda.

Linda was developed by David Gelernter and Nicholas Carriero at Yale University.

Implementations exists for most languages.

The Ruby implementation is Rinda.

Rinda is a built-in library, so no need to install.

5 Basic Operations read read_all write take notify

read

read_all

write

take

notify

5 Basic Operations read read_all write take notify Reads tuple, but does not remove it. Blocking, by default, but takes an additional timeout argument.

read

read_all

write

take

notify

5 Basic Operations read read_all write take notify Returns all tuples matching tuple. Does not remove the found tuples.

read

read_all

write

take

notify

5 Basic Operations read read_all write take notify Adds Tuple Takes an optional timeout parameter

read

read_all

write

take

notify

5 Basic Operations read read_all write take notify Atomic Read + Delete Blocking, by default, but takes an additional timeout argument.

read

read_all

write

take

notify

5 Basic Operations read read_all write take notify Registers for notifications of events: Write Take Delete

read

read_all

write

take

notify

Write

Take

Delete

Key Features Spaces are shared Space handles details of concurrent access Spaces are persistent If agent process dies, data is still in space However, if space process dies, data is lost (?) Spaces are associative Associative lookups rather than memory location or identifier Spaces are transactionally secure Atomic Operations Spaces allow us to exchange executable content

Spaces are shared

Space handles details of concurrent access

Spaces are persistent

If agent process dies, data is still in space

However, if space process dies, data is lost (?)

Spaces are associative

Associative lookups rather than memory location or identifier

Spaces are transactionally secure

Atomic Operations

Spaces allow us to exchange executable content

A Rinda tuple can be an array or a hash

A Rinda tuple can be an array or a hash ( But let's stick with the array, I like that better! )

Start a Tuple Space on port 1234

Clients/Agents

DEMO Rinda

RingServer

This is also a TupleSpace

SPOF

Rinda is not persistent... If it crashes while you have tuples in the space, you lose them all.

Only Ruby

Introducing Blackboard TupleSpace implementation on top of Redis -> Persistent Redis is a really fast key-value database. Like memcached but data is not volatile. Same API -> Plug & Play For now, only supports: take, read, and write http://github.com/dambalah/blackboard

TupleSpace implementation on top of Redis

-> Persistent

Redis is a really fast key-value database.

Like memcached but data is not volatile.

Same API -> Plug & Play

For now, only supports: take, read, and write

http://github.com/dambalah/blackboard

Server Just start the redis-server: $ redis-server

Client/Agents

DEMO Blackboard

Blackboard Benchmarks

Blackboard: Future Move from Redis to a custom based Erlang blackboard implementation. I would like that Erlang implementation to be easily used from other programming languages also. So it's really two projects: Blackboard in erlang Ruby-library to talk to blackboard in erlang

Move from Redis to a custom based Erlang blackboard implementation.

I would like that Erlang implementation to be easily used from other programming languages also.

So it's really two projects:

Blackboard in erlang

Ruby-library to talk to blackboard in erlang

Thank you! Luc Castera Founder / messagepub.com

Questions?Feedback? [email_address] www.speakerrate.com Luc Castera Founder / messagepub.com

Resources / References Part 1: Threading Models http://timetobleed.com/threading-models-so-many-different-ways-to-get-stuff-done/ http://envycasts.com/products/scaling-ruby http://www.infoq.com/news/2007/05/ruby-threading-futures http://thebogles.com/blog/2006/11/ruby-threading/ http://spec.ruby-doc.org/wiki/Ruby_Threading http://www.bitwiese.de/2007/09/on-processes-and-threads.html http://www.igvita.com/2008/11/13/concurrency-is-a-myth-in-ruby/ http://bartoszmilewski.wordpress.com/2008/08/24/threads-dont-scale-processes-do/ http://en.wikipedia.org/wiki/Global_Interpreter_Lock http://www.gotw.ca/publications/concurrency-ddj.htm http://tinyurl.com/rubyfibers

Part 1: Threading Models

http://timetobleed.com/threading-models-so-many-different-ways-to-get-stuff-done/

http://envycasts.com/products/scaling-ruby

http://www.infoq.com/news/2007/05/ruby-threading-futures

http://thebogles.com/blog/2006/11/ruby-threading/

http://spec.ruby-doc.org/wiki/Ruby_Threading

http://www.bitwiese.de/2007/09/on-processes-and-threads.html

http://www.igvita.com/2008/11/13/concurrency-is-a-myth-in-ruby/

http://bartoszmilewski.wordpress.com/2008/08/24/threads-dont-scale-processes-do/

http://en.wikipedia.org/wiki/Global_Interpreter_Lock

http://www.gotw.ca/publications/concurrency-ddj.htm

http://tinyurl.com/rubyfibers

Resources / References Part 2: Multiple Processes http://github.com/ezmobius/nanite http://erlang.org/ http://www.rabbitmq.com/ http://code.google.com/p/redis/ http://revactor.org/ http://journeta.rubyforge.org/ http://home.mindspring.com/~eric_rollins/ParallelRuby.html

Part 2: Multiple Processes

http://github.com/ezmobius/nanite

http://erlang.org/

http://www.rabbitmq.com/

http://code.google.com/p/redis/

http://revactor.org/

http://journeta.rubyforge.org/

http://home.mindspring.com/~eric_rollins/ParallelRuby.html

Resources / References Part 3: TupleSpaces http://c2.com/cgi/wiki?TupleSpace http://en.wikipedia.org/wiki/Tuplespace http://www.julianbrowne.com/article/viewer/space-based-architecture-example http://www.rubyagent.com/ http://segment7.net/projects/ruby/drb/ http://segment7.net/projects/ruby/drb/rinda/ringserver.html JavaSpaces Principles, Patterns, and Practice – Freeman, Hupfer, et. al. http://www.ruby-doc.org/stdlib/libdoc/rinda/rdoc/index.html

Part 3: TupleSpaces

http://c2.com/cgi/wiki?TupleSpace

http://en.wikipedia.org/wiki/Tuplespace

http://www.julianbrowne.com/article/viewer/space-based-architecture-example

http://www.rubyagent.com/

http://segment7.net/projects/ruby/drb/

http://segment7.net/projects/ruby/drb/rinda/ringserver.html

JavaSpaces Principles, Patterns, and Practice – Freeman, Hupfer, et. al.

http://www.ruby-doc.org/stdlib/libdoc/rinda/rdoc/index.html

Things I wish I had time to spend on MPI and Ruby-MPI http://github.com/abedra/mpi-ruby/tree/master Ruby forkoff: http://tinyurl.com/forkoff

MPI and Ruby-MPI

http://github.com/abedra/mpi-ruby/tree/master

Ruby forkoff:

http://tinyurl.com/forkoff