Asynchronous Processing with Ruby on Rails (RailsConf 2008)

Asynchronous Processing Jonathan Dahl (and RailSpikes, Slantwise, Zencoder, etc.)

I. What is it, and why should I care?

Wife: What are you talking about at RailsConf this year? Jon: Asynchronous Processing Wife: [changes subject]

important tool

(ahem...)

Related Concepts • Background Processing • Parallel Processing • Distributed Processing

has_attachment :storage => :s3

Image Upload ( 15 seconds) Send to S3 Browser Response

Image Upload Send to S3 ( 15 seconds) Browser Response

Image Upload Send to S3 Browser ( 3 seconds) Response

Image Upload Send to S3 Browser Response

has_attachment :storage => :file_system

Image Upload ( 15 seconds) Browser Response Send to S3

Image Upload Browser ( 3 seconds) Response Send to S3 ( who cares?)

has_attachment :storage => :s3, :thumbnails => { :thumb => '100x100!', :small => '240x180>', :medium => '500x500>' }

Image Upload Generate 3 Thumbnails Send Thumbnail A to S3 Send Thumbnail B to S3 Send Thumbnail C to S3 Send Original to S3 Browser Response

II. When do I need it?

Time

Request • Method (GET, POST) • URI (host, port, path) • Parameters

Response • Status (200, 404, 500) • Metadata (content type, server info, etc.) • Body (xml, html, ﬁle)

Resources

Trigger

HTTP trigger - browser request

HTTP trigger - API request GET /photos/1.xml HTTP/1.1 Host: example.com:80

Human trigger - capistrano cap staging deploy

Human trigger - rake rake db:migrate

Human trigger - console $ script/console production Loading production environment (Rails 2.0.2) >> Photo.destroy_all

No trigger? - Send email in 2 hours - Sync data at 3:00am PST - Notify admin when disk is 90% full - Expire sessions that are inactive - Archive records that exceed quota

1. Time 2. Resources 3. Trigger

Concrete examples • Sending mail • Transcoding video/audio • Storing images on S3 • Receiving email • Synching with outside database • Complex computations

class Emailer < ActionMailer::ARMailer

Zencoder User Video Sharing Website Data Storage (Amazon S3) Zencoder Manager Worker Worker Worker Worker

class Photo < ActiveRecord::Base after_create :background_s3_upload def background_s3_upload Bj.submit \"./script/runner ./jobs/send_to_s3.rb #{self.id}\" end end

III. So how do you decide what to use?

be seamless

how reliable?

when should it run?

dependencies and system complexity

scaling and/or performance

IV. The simple solution: fork or thread

Parallel vs. Background

1. Stay within one request

2. thread.join

3. ActiveRecord ActiveRecord::Base.allow_concurrency = true

ﬁre and forget

Spawn spawn(:method => :fork) do # do something end

1. Time 2. Resources 3. Trigger

V. More robust solutions

Task Storage Task Trigger

Task Storage • task details (what happens?) • priority • when to run

Task Trigger • worker pulling jobs • time-based • execute immediately

Task Storage Task Trigger Database daemon Message Queue cron

create_table \"jobs\" do |t| t.text \"command\" t.integer \"priority\" t.integer \"pid\" t.datetime \"submitted_at\" t.datetime \"started_at\" t.datetime \"finished_at\" t.text \"result\" end

create_table \"photos\" do |t| t.string \"filename\" t.datetime \"created_at\" t.datetime \"processed_at\" end

• Amazon SQS • Websphere MQ • Starling • JMS • beanstalkd

queue = SQS.get_queue(\"task_list\")

put message queue.send_message \"process:2872\"

receive message message = queue.receive_message

Starling starling -h 192.168.1.1 -d

require 'memcache' starling = MemCache.new('192.168.1.1:22122') # Put messages onto a queue: starling.set('my_queue', 12345) # Get message from the queue: starling.get('my_queue')

storage choice? • queue: optimized for performance • database: you’ve already got one

daemon #!/usr/bin/env ruby class JobRequester < SimpleDaemon::Base def self.start loop { Job.process_next } end end JobRequester.daemonize

0 6 * * * script/runner jobs/send_emails.rb

cronedit require 'cronedit' CronEdit::Crontab.Add \"send-emails\", { :minute => 0, :hour => 6, :command => \"script/runner jobs/send_emails.rb\" } CronEdit::Crontab.Remove 'old-task'

trigger choice • process: always running • cron: as reliable as your operating system

BackgroundDRb class BillingWorker < BackgrounDRb::MetaWorker set_worker_name :billing_worker def create(args = nil) # this method is called when worker is loaded for the first time end def charge_customer(customer_id = nil) logger.info 'charging customer now' end end MiddleMan.worker(:billing_worker).charge_customer(current_customer.id)

:backgroundrb: :ip: 0.0.0.0 :development: :backgroundrb: :port: 11111 :log: foreground :production: :backgroundrb: :port: 22222 :lazy_load: true :debug_log: false ./script/backgroundrb start

AP4R def MyController def queue ap4r.async_to({:action => 'download'}, {:story => story.id, :url => params[:url]}) end def download # long-running task end end

Bj Acronym

create_table \"bj_job\", :primary_key => \"bj t.text \"command\" t.text \"state\" t.integer \"priority\" t.text \"tag\" t.integer \"is_restartable\" t.text \"submitter\" t.text \"runner\" t.integer \"pid\" t.datetime \"submitted_at\" t.datetime \"started_at\" t.datetime \"finished_at\" t.text \"env\"

Bj.submit \"./script/runner ./jobs/task.rb\"

after_create :bj_send_to_s3 def bj_send_to_s3 Bj.submit \"./script/runner ./jobs/send.rb #{id}\" end

Workling # environment config Workling::Remote.dispatcher = Workling::Remote::Runners::StarlingRunner.new

# task class class ImageWorker < Workling::Base def send_to_s3(options = {}) # put file to S3 end end

# trigger asynchronous job ImageWorker.asynch_send_to_s3(:image_id => 2927)

script/workling_starling_client start

Pitfalls

race conditions

alive, but stalled

VI. some recommendations

general purpose Bj

distributed processing SQS (+ custom worker)

time-scheduled cron (+ rake or script)

speed + scalability Starling/Workling

Thanks! Jonathan Dahl Slides at RailSpikes http://railspikes.com

Asynchronous Processing with Ruby on Rails (RailsConf 2008)

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