SlideShare a Scribd company logo
1 of 56
Download to read offline
Big Bird.
(scaling twitter)
Rails Scales.
(but not out of the box)
First, Some Facts
• 600 requests per second. Growing fast.
• 180 Rails Instances (Mongrel). Growing fast.
• 1 Database Server (MySQL) + 1 Slave.
• 30-odd Processes for Misc. Jobs
• 8 Sun X4100s
• Many users, many updates.
Joy          Pain




Oct   Nov   Dec    Jan   Feb     March   Apr
IM IN UR RAILZ




     MAKIN EM GO FAST
It’s Easy, Really.
1. Realize Your Site is Slow
2. Optimize the Database
3. Cache the Hell out of Everything
4. Scale Messaging
5. Deal With Abuse
It’s Easy, Really.
1. Realize Your Site is Slow
2. Optimize the Database
3. Cache the Hell out of Everything
4. Scale Messaging
5. Deal With Abuse
6. Profit
the
     more
      you
        know

{ Part the First }
We Failed at This.
Don’t Be Like Us

• Munin
• Nagios
• AWStats & Google Analytics
• Exception Notifier / Exception Logger
• Immediately add reporting to track problems.
Test Everything

•   Start Before You Start

•   No Need To Be Fancy

•   Tests Will Save Your Life

•   Agile Becomes
    Important When Your
    Site Is Down
<!-- served to you through a copper wire by sampaati at 22 Apr
    15:02 in 343 ms (d 102 / r 217). thank you, come again. -->
 <!-- served to you through a copper wire by kolea.twitter.com at
22 Apr 15:02 in 235 ms (d 87 / r 130). thank you, come again. -->
 <!-- served to you through a copper wire by raven.twitter.com at
22 Apr 15:01 in 450 ms (d 96 / r 337). thank you, come again. -->



                  Benchmarks?
                       let your users do it.
 <!-- served to you through a copper wire by kolea.twitter.com at
22 Apr 15:00 in 409 ms (d 88 / r 307). thank you, come again. -->
  <!-- served to you through a copper wire by firebird at 22 Apr
   15:03 in 2094 ms (d 643 / r 1445). thank you, come again. -->
   <!-- served to you through a copper wire by quetzal at 22 Apr
     15:01 in 384 ms (d 70 / r 297). thank you, come again. -->
The Database
  { Part the Second }
“The Next Application I Build is Going
to Be Easily Partitionable” - S. Butterfield
“The Next Application I Build is Going
to Be Easily Partitionable” - S. Butterfield
“The Next Application I Build is Going
to Be Easily Partitionable” - S. Butterfield
Too Late.
Index Everything
class AddIndex < ActiveRecord::Migration
     def self.up
       add_index :users, :email
     end

     def self.down
       remove_index :users, :email
     end
   end


Repeat for any column that appears in a WHERE clause

             Rails won’t do this for you.
Denormalize A Lot
class DenormalizeFriendsIds < ActiveRecord::Migration
  def self.up
    add_column "users", "friends_ids", :text
  end

  def self.down
    remove_column "users", "friends_ids"
  end
end
class Friendship < ActiveRecord::Base
  belongs_to :user
  belongs_to :friend

 after_create :add_to_denormalized_friends
 after_destroy :remove_from_denormalized_friends

  def add_to_denormalized_friends
    user.friends_ids << friend.id
    user.friends_ids.uniq!
    user.save_without_validation
  end

  def remove_from_denormalized_friends
    user.friends_ids.delete(friend.id)
    user.save_without_validation
  end
end
Don’t be Stupid
bob.friends.map(&:email)
     Status.count()
“email like ‘%#{search}%’”
That’s where we are.
                  Seriously.
  If your Rails application is doing anything more
complex than that, you’re doing something wrong*.



        * or you observed the First Rule of Butterfield.
Partitioning Comes Later.
   (we’ll let you know how it goes)
The Cache
 { Part the Third }
MemCache
MemCache
MemCache
!
class Status < ActiveRecord::Base
  class << self
    def count_with_memcache(*args)
      return count_without_memcache unless args.empty?
      count = CACHE.get(“status_count”)
      if count.nil?
        count = count_without_memcache
        CACHE.set(“status_count”, count)
      end
      count
    end
    alias_method_chain :count, :memcache
  end
  after_create :increment_memcache_count
  after_destroy :decrement_memcache_count
  ...
end
class User < ActiveRecord::Base
  def friends_statuses
    ids = CACHE.get(“friends_statuses:#{id}”)
    Status.find(:all, :conditions => [“id IN (?)”, ids])
  end
end

class Status < ActiveRecord::Base
  after_create :update_caches
  def update_caches
    user.friends_ids.each do |friend_id|
      ids = CACHE.get(“friends_statuses:#{friend_id}”)
      ids.pop
      ids.unshift(id)
      CACHE.set(“friends_statuses:#{friend_id}”, ids)
    end
  end
end
The Future


            ve d
          ti r
         co
         Ac
           e
         R
90% API Requests
     Cache Them!
“There are only two hard things in CS:
 cache invalidation and naming things.”

             – Phil Karlton, via Tim Bray
Messaging
{ Part the Fourth }
You Already Knew All
That Other Stuff, Right?
Producer             Consumer
           Message
Producer             Consumer
           Queue
Producer             Consumer
DRb
• The Good:
 • Stupid Easy
 • Reasonably Fast
• The Bad:
 • Kinda Flaky
 • Zero Redundancy
 • Tightly Coupled
ejabberd


            Jabber Client
                (drb)




           Incoming         Outgoing
Presence
           Messages         Messages


              MySQL
Server
     DRb.start_service ‘druby://localhost:10000’, myobject




                         Client
myobject = DRbObject.new_with_uri(‘druby://localhost:10000’)
Rinda

• Shared Queue (TupleSpace)
• Built with DRb
• RingyDingy makes it stupid easy
• See Eric Hodel’s documentation
• O(N) for take(). Sigh.
Timestamp: 12/22/06 01:53:14 (4 months ago)
      Author: lattice
      Message: Fugly. Seriously. Fugly.




        SELECT * FROM messages WHERE
substring(truncate(id,0),-2,1) = #{@fugly_dist_idx}
It Scales.
(except it stopped on Tuesday)
Options

• ActiveMQ (Java)
• RabbitMQ (erlang)
• MySQL + Lightweight Locking
• Something Else?
erlang?


What are you doing?
 Stabbing my eyes out with a fork.
Starling

• Ruby, will be ported to something faster
• 4000 transactional msgs/s
• First pass written in 4 hours
• Speaks MemCache (set, get)
Use Messages to
Invalidate Cache
   (it’s really not that hard)
Abuse
{ Part the Fifth }
The Italians
9000 friends in 24 hours
        (doesn’t scale)
http://flickr.com/photos/heather/464504545/
http://flickr.com/photos/curiouskiwi/165229284/
http://flickr.com/photo_zoom.gne?id=42914103&size=l
http://flickr.com/photos/madstillz/354596905/
http://flickr.com/photos/laughingsquid/382242677/
http://flickr.com/photos/bng/46678227/

More Related Content

What's hot

Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, Preset
Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, PresetStreaming Data Analytics with ksqlDB and Superset | Robert Stolz, Preset
Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, PresetHostedbyConfluent
 
Introduction to Kafka Streams
Introduction to Kafka StreamsIntroduction to Kafka Streams
Introduction to Kafka StreamsGuozhang Wang
 
Kicking ass with redis
Kicking ass with redisKicking ass with redis
Kicking ass with redisDvir Volk
 
SQLアンチパターン 幻の第26章「とりあえず削除フラグ」
SQLアンチパターン 幻の第26章「とりあえず削除フラグ」SQLアンチパターン 幻の第26章「とりあえず削除フラグ」
SQLアンチパターン 幻の第26章「とりあえず削除フラグ」Takuto Wada
 
How to build massive service for advance
How to build massive service for advanceHow to build massive service for advance
How to build massive service for advanceDaeMyung Kang
 
[236] 카카오의데이터파이프라인 윤도영
[236] 카카오의데이터파이프라인 윤도영[236] 카카오의데이터파이프라인 윤도영
[236] 카카오의데이터파이프라인 윤도영NAVER D2
 
Blazing Performance with Flame Graphs
Blazing Performance with Flame GraphsBlazing Performance with Flame Graphs
Blazing Performance with Flame GraphsBrendan Gregg
 
クラウド環境下におけるAPIリトライ設計
クラウド環境下におけるAPIリトライ設計クラウド環境下におけるAPIリトライ設計
クラウド環境下におけるAPIリトライ設計Kouji YAMADA
 
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021StreamNative
 
MySQL 5.7にやられないためにおぼえておいてほしいこと
MySQL 5.7にやられないためにおぼえておいてほしいことMySQL 5.7にやられないためにおぼえておいてほしいこと
MySQL 5.7にやられないためにおぼえておいてほしいことyoku0825
 
Design patterns for microservice architecture
Design patterns for microservice architectureDesign patterns for microservice architecture
Design patterns for microservice architectureThe Software House
 
10+ Deploys Per Day: Dev and Ops Cooperation at Flickr
10+ Deploys Per Day: Dev and Ops Cooperation at Flickr10+ Deploys Per Day: Dev and Ops Cooperation at Flickr
10+ Deploys Per Day: Dev and Ops Cooperation at FlickrJohn Allspaw
 
基幹業務もHadoopで!! -ローソンにおける店舗発注業務への Hadoop + Hive導入と その取り組みについて-
基幹業務もHadoopで!! -ローソンにおける店舗発注業務へのHadoop + Hive導入と その取り組みについて-基幹業務もHadoopで!! -ローソンにおける店舗発注業務へのHadoop + Hive導入と その取り組みについて-
基幹業務もHadoopで!! -ローソンにおける店舗発注業務への Hadoop + Hive導入と その取り組みについて-Keigo Suda
 
Apache Flink in the Cloud-Native Era
Apache Flink in the Cloud-Native EraApache Flink in the Cloud-Native Era
Apache Flink in the Cloud-Native EraFlink Forward
 
로그 기깔나게 잘 디자인하는 법
로그 기깔나게 잘 디자인하는 법로그 기깔나게 잘 디자인하는 법
로그 기깔나게 잘 디자인하는 법Jeongsang Baek
 
MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"
MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"
MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"Kentaro Yoshida
 
Thousands of Threads and Blocking I/O
Thousands of Threads and Blocking I/OThousands of Threads and Blocking I/O
Thousands of Threads and Blocking I/OGeorge Cao
 
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
 Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng ShiDatabricks
 

What's hot (20)

Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, Preset
Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, PresetStreaming Data Analytics with ksqlDB and Superset | Robert Stolz, Preset
Streaming Data Analytics with ksqlDB and Superset | Robert Stolz, Preset
 
Introduction to Kafka Streams
Introduction to Kafka StreamsIntroduction to Kafka Streams
Introduction to Kafka Streams
 
Kicking ass with redis
Kicking ass with redisKicking ass with redis
Kicking ass with redis
 
SQLアンチパターン 幻の第26章「とりあえず削除フラグ」
SQLアンチパターン 幻の第26章「とりあえず削除フラグ」SQLアンチパターン 幻の第26章「とりあえず削除フラグ」
SQLアンチパターン 幻の第26章「とりあえず削除フラグ」
 
How to build massive service for advance
How to build massive service for advanceHow to build massive service for advance
How to build massive service for advance
 
[236] 카카오의데이터파이프라인 윤도영
[236] 카카오의데이터파이프라인 윤도영[236] 카카오의데이터파이프라인 윤도영
[236] 카카오의데이터파이프라인 윤도영
 
Blazing Performance with Flame Graphs
Blazing Performance with Flame GraphsBlazing Performance with Flame Graphs
Blazing Performance with Flame Graphs
 
クラウド環境下におけるAPIリトライ設計
クラウド環境下におけるAPIリトライ設計クラウド環境下におけるAPIリトライ設計
クラウド環境下におけるAPIリトライ設計
 
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021
Trino: A Ludicrously Fast Query Engine - Pulsar Summit NA 2021
 
Hive tuning
Hive tuningHive tuning
Hive tuning
 
MySQL 5.7にやられないためにおぼえておいてほしいこと
MySQL 5.7にやられないためにおぼえておいてほしいことMySQL 5.7にやられないためにおぼえておいてほしいこと
MySQL 5.7にやられないためにおぼえておいてほしいこと
 
Design patterns for microservice architecture
Design patterns for microservice architectureDesign patterns for microservice architecture
Design patterns for microservice architecture
 
10+ Deploys Per Day: Dev and Ops Cooperation at Flickr
10+ Deploys Per Day: Dev and Ops Cooperation at Flickr10+ Deploys Per Day: Dev and Ops Cooperation at Flickr
10+ Deploys Per Day: Dev and Ops Cooperation at Flickr
 
基幹業務もHadoopで!! -ローソンにおける店舗発注業務への Hadoop + Hive導入と その取り組みについて-
基幹業務もHadoopで!! -ローソンにおける店舗発注業務へのHadoop + Hive導入と その取り組みについて-基幹業務もHadoopで!! -ローソンにおける店舗発注業務へのHadoop + Hive導入と その取り組みについて-
基幹業務もHadoopで!! -ローソンにおける店舗発注業務への Hadoop + Hive導入と その取り組みについて-
 
Apache Flink in the Cloud-Native Era
Apache Flink in the Cloud-Native EraApache Flink in the Cloud-Native Era
Apache Flink in the Cloud-Native Era
 
로그 기깔나게 잘 디자인하는 법
로그 기깔나게 잘 디자인하는 법로그 기깔나게 잘 디자인하는 법
로그 기깔나게 잘 디자인하는 법
 
MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"
MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"
MySQLと組み合わせて始める全文検索プロダクト"elasticsearch"
 
Edge architecture ieee international conference on cloud engineering
Edge architecture   ieee international conference on cloud engineeringEdge architecture   ieee international conference on cloud engineering
Edge architecture ieee international conference on cloud engineering
 
Thousands of Threads and Blocking I/O
Thousands of Threads and Blocking I/OThousands of Threads and Blocking I/O
Thousands of Threads and Blocking I/O
 
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
 Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
Apache Kylin: Speed Up Cubing with Apache Spark with Luke Han and Shaofeng Shi
 

Similar to Scaling Twitter

Hiveminder - Everything but the Secret Sauce
Hiveminder - Everything but the Secret SauceHiveminder - Everything but the Secret Sauce
Hiveminder - Everything but the Secret SauceJesse Vincent
 
Beijing Perl Workshop 2008 Hiveminder Secret Sauce
Beijing Perl Workshop 2008 Hiveminder Secret SauceBeijing Perl Workshop 2008 Hiveminder Secret Sauce
Beijing Perl Workshop 2008 Hiveminder Secret SauceJesse Vincent
 
Microblogging via XMPP
Microblogging via XMPPMicroblogging via XMPP
Microblogging via XMPPStoyan Zhekov
 
Aprendendo solid com exemplos
Aprendendo solid com exemplosAprendendo solid com exemplos
Aprendendo solid com exemplosvinibaggio
 
Socket applications
Socket applicationsSocket applications
Socket applicationsJoão Moura
 
Dynomite at Erlang Factory
Dynomite at Erlang FactoryDynomite at Erlang Factory
Dynomite at Erlang Factorymoonpolysoft
 
Performance Optimization of Rails Applications
Performance Optimization of Rails ApplicationsPerformance Optimization of Rails Applications
Performance Optimization of Rails ApplicationsSerge Smetana
 
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...MongoDB
 
WebPerformance: Why and How? – Stefan Wintermeyer
WebPerformance: Why and How? – Stefan WintermeyerWebPerformance: Why and How? – Stefan Wintermeyer
WebPerformance: Why and How? – Stefan WintermeyerElixir Club
 
NPW2009 - my.opera.com scalability v2.0
NPW2009 - my.opera.com scalability v2.0NPW2009 - my.opera.com scalability v2.0
NPW2009 - my.opera.com scalability v2.0Cosimo Streppone
 
Fisl - Deployment
Fisl - DeploymentFisl - Deployment
Fisl - DeploymentFabio Akita
 
SD, a P2P bug tracking system
SD, a P2P bug tracking systemSD, a P2P bug tracking system
SD, a P2P bug tracking systemJesse Vincent
 
RubyEnRails2007 - Dr Nic Williams - Keynote
RubyEnRails2007 - Dr Nic Williams - KeynoteRubyEnRails2007 - Dr Nic Williams - Keynote
RubyEnRails2007 - Dr Nic Williams - KeynoteDr Nic Williams
 
MongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & AnalyticsMongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & AnalyticsServer Density
 
JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...
JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...
JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...PROIDEA
 
Web 2.0 Performance and Reliability: How to Run Large Web Apps
Web 2.0 Performance and Reliability: How to Run Large Web AppsWeb 2.0 Performance and Reliability: How to Run Large Web Apps
Web 2.0 Performance and Reliability: How to Run Large Web Appsadunne
 
How to avoid hanging yourself with Rails
How to avoid hanging yourself with RailsHow to avoid hanging yourself with Rails
How to avoid hanging yourself with RailsRowan Hick
 
Monkeybars in the Manor
Monkeybars in the ManorMonkeybars in the Manor
Monkeybars in the Manormartinbtt
 

Similar to Scaling Twitter (20)

Hiveminder - Everything but the Secret Sauce
Hiveminder - Everything but the Secret SauceHiveminder - Everything but the Secret Sauce
Hiveminder - Everything but the Secret Sauce
 
Beijing Perl Workshop 2008 Hiveminder Secret Sauce
Beijing Perl Workshop 2008 Hiveminder Secret SauceBeijing Perl Workshop 2008 Hiveminder Secret Sauce
Beijing Perl Workshop 2008 Hiveminder Secret Sauce
 
Microblogging via XMPP
Microblogging via XMPPMicroblogging via XMPP
Microblogging via XMPP
 
Aprendendo solid com exemplos
Aprendendo solid com exemplosAprendendo solid com exemplos
Aprendendo solid com exemplos
 
Socket applications
Socket applicationsSocket applications
Socket applications
 
From crash to testcase
From crash to testcaseFrom crash to testcase
From crash to testcase
 
Dynomite at Erlang Factory
Dynomite at Erlang FactoryDynomite at Erlang Factory
Dynomite at Erlang Factory
 
Performance Optimization of Rails Applications
Performance Optimization of Rails ApplicationsPerformance Optimization of Rails Applications
Performance Optimization of Rails Applications
 
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
Ensuring High Availability for Real-time Analytics featuring Boxed Ice / Serv...
 
WebPerformance: Why and How? – Stefan Wintermeyer
WebPerformance: Why and How? – Stefan WintermeyerWebPerformance: Why and How? – Stefan Wintermeyer
WebPerformance: Why and How? – Stefan Wintermeyer
 
NPW2009 - my.opera.com scalability v2.0
NPW2009 - my.opera.com scalability v2.0NPW2009 - my.opera.com scalability v2.0
NPW2009 - my.opera.com scalability v2.0
 
Fisl - Deployment
Fisl - DeploymentFisl - Deployment
Fisl - Deployment
 
SD, a P2P bug tracking system
SD, a P2P bug tracking systemSD, a P2P bug tracking system
SD, a P2P bug tracking system
 
RubyEnRails2007 - Dr Nic Williams - Keynote
RubyEnRails2007 - Dr Nic Williams - KeynoteRubyEnRails2007 - Dr Nic Williams - Keynote
RubyEnRails2007 - Dr Nic Williams - Keynote
 
Sinatra for REST services
Sinatra for REST servicesSinatra for REST services
Sinatra for REST services
 
MongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & AnalyticsMongoDB: Optimising for Performance, Scale & Analytics
MongoDB: Optimising for Performance, Scale & Analytics
 
JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...
JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...
JDD2015: Sharding with Akka Cluster: From Theory to Production - Krzysztof Ot...
 
Web 2.0 Performance and Reliability: How to Run Large Web Apps
Web 2.0 Performance and Reliability: How to Run Large Web AppsWeb 2.0 Performance and Reliability: How to Run Large Web Apps
Web 2.0 Performance and Reliability: How to Run Large Web Apps
 
How to avoid hanging yourself with Rails
How to avoid hanging yourself with RailsHow to avoid hanging yourself with Rails
How to avoid hanging yourself with Rails
 
Monkeybars in the Manor
Monkeybars in the ManorMonkeybars in the Manor
Monkeybars in the Manor
 

More from Blaine

Social Privacy for HTTP over Webfinger
Social Privacy for HTTP over WebfingerSocial Privacy for HTTP over Webfinger
Social Privacy for HTTP over WebfingerBlaine
 
Social Software for Robots
Social Software for RobotsSocial Software for Robots
Social Software for RobotsBlaine
 
Building the Real Time Web
Building the Real Time WebBuilding the Real Time Web
Building the Real Time WebBlaine
 
You & Me & Everyone We Know
You & Me & Everyone We KnowYou & Me & Everyone We Know
You & Me & Everyone We KnowBlaine
 
Social Software for Robots
Social Software for RobotsSocial Software for Robots
Social Software for RobotsBlaine
 

More from Blaine (6)

Social Privacy for HTTP over Webfinger
Social Privacy for HTTP over WebfingerSocial Privacy for HTTP over Webfinger
Social Privacy for HTTP over Webfinger
 
Social Software for Robots
Social Software for RobotsSocial Software for Robots
Social Software for Robots
 
OAuth
OAuthOAuth
OAuth
 
Building the Real Time Web
Building the Real Time WebBuilding the Real Time Web
Building the Real Time Web
 
You & Me & Everyone We Know
You & Me & Everyone We KnowYou & Me & Everyone We Know
You & Me & Everyone We Know
 
Social Software for Robots
Social Software for RobotsSocial Software for Robots
Social Software for Robots
 

Recently uploaded

Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenHervé Boutemy
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsMark Billinghurst
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 

Recently uploaded (20)

Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
DevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache MavenDevoxxFR 2024 Reproducible Builds with Apache Maven
DevoxxFR 2024 Reproducible Builds with Apache Maven
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Human Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR SystemsHuman Factors of XR: Using Human Factors to Design XR Systems
Human Factors of XR: Using Human Factors to Design XR Systems
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 

Scaling Twitter

  • 2. Rails Scales. (but not out of the box)
  • 3. First, Some Facts • 600 requests per second. Growing fast. • 180 Rails Instances (Mongrel). Growing fast. • 1 Database Server (MySQL) + 1 Slave. • 30-odd Processes for Misc. Jobs • 8 Sun X4100s • Many users, many updates.
  • 4.
  • 5.
  • 6.
  • 7. Joy Pain Oct Nov Dec Jan Feb March Apr
  • 8. IM IN UR RAILZ MAKIN EM GO FAST
  • 9. It’s Easy, Really. 1. Realize Your Site is Slow 2. Optimize the Database 3. Cache the Hell out of Everything 4. Scale Messaging 5. Deal With Abuse
  • 10. It’s Easy, Really. 1. Realize Your Site is Slow 2. Optimize the Database 3. Cache the Hell out of Everything 4. Scale Messaging 5. Deal With Abuse 6. Profit
  • 11. the more you know { Part the First }
  • 12. We Failed at This.
  • 13. Don’t Be Like Us • Munin • Nagios • AWStats & Google Analytics • Exception Notifier / Exception Logger • Immediately add reporting to track problems.
  • 14. Test Everything • Start Before You Start • No Need To Be Fancy • Tests Will Save Your Life • Agile Becomes Important When Your Site Is Down
  • 15. <!-- served to you through a copper wire by sampaati at 22 Apr 15:02 in 343 ms (d 102 / r 217). thank you, come again. --> <!-- served to you through a copper wire by kolea.twitter.com at 22 Apr 15:02 in 235 ms (d 87 / r 130). thank you, come again. --> <!-- served to you through a copper wire by raven.twitter.com at 22 Apr 15:01 in 450 ms (d 96 / r 337). thank you, come again. --> Benchmarks? let your users do it. <!-- served to you through a copper wire by kolea.twitter.com at 22 Apr 15:00 in 409 ms (d 88 / r 307). thank you, come again. --> <!-- served to you through a copper wire by firebird at 22 Apr 15:03 in 2094 ms (d 643 / r 1445). thank you, come again. --> <!-- served to you through a copper wire by quetzal at 22 Apr 15:01 in 384 ms (d 70 / r 297). thank you, come again. -->
  • 16. The Database { Part the Second }
  • 17. “The Next Application I Build is Going to Be Easily Partitionable” - S. Butterfield
  • 18. “The Next Application I Build is Going to Be Easily Partitionable” - S. Butterfield
  • 19. “The Next Application I Build is Going to Be Easily Partitionable” - S. Butterfield
  • 22. class AddIndex < ActiveRecord::Migration def self.up add_index :users, :email end def self.down remove_index :users, :email end end Repeat for any column that appears in a WHERE clause Rails won’t do this for you.
  • 24. class DenormalizeFriendsIds < ActiveRecord::Migration def self.up add_column "users", "friends_ids", :text end def self.down remove_column "users", "friends_ids" end end
  • 25. class Friendship < ActiveRecord::Base belongs_to :user belongs_to :friend after_create :add_to_denormalized_friends after_destroy :remove_from_denormalized_friends def add_to_denormalized_friends user.friends_ids << friend.id user.friends_ids.uniq! user.save_without_validation end def remove_from_denormalized_friends user.friends_ids.delete(friend.id) user.save_without_validation end end
  • 27. bob.friends.map(&:email) Status.count() “email like ‘%#{search}%’”
  • 28. That’s where we are. Seriously. If your Rails application is doing anything more complex than that, you’re doing something wrong*. * or you observed the First Rule of Butterfield.
  • 29. Partitioning Comes Later. (we’ll let you know how it goes)
  • 30. The Cache { Part the Third }
  • 34. !
  • 35. class Status < ActiveRecord::Base class << self def count_with_memcache(*args) return count_without_memcache unless args.empty? count = CACHE.get(“status_count”) if count.nil? count = count_without_memcache CACHE.set(“status_count”, count) end count end alias_method_chain :count, :memcache end after_create :increment_memcache_count after_destroy :decrement_memcache_count ... end
  • 36. class User < ActiveRecord::Base def friends_statuses ids = CACHE.get(“friends_statuses:#{id}”) Status.find(:all, :conditions => [“id IN (?)”, ids]) end end class Status < ActiveRecord::Base after_create :update_caches def update_caches user.friends_ids.each do |friend_id| ids = CACHE.get(“friends_statuses:#{friend_id}”) ids.pop ids.unshift(id) CACHE.set(“friends_statuses:#{friend_id}”, ids) end end end
  • 37. The Future ve d ti r co Ac e R
  • 38. 90% API Requests Cache Them!
  • 39. “There are only two hard things in CS: cache invalidation and naming things.” – Phil Karlton, via Tim Bray
  • 41. You Already Knew All That Other Stuff, Right?
  • 42. Producer Consumer Message Producer Consumer Queue Producer Consumer
  • 43. DRb • The Good: • Stupid Easy • Reasonably Fast • The Bad: • Kinda Flaky • Zero Redundancy • Tightly Coupled
  • 44. ejabberd Jabber Client (drb) Incoming Outgoing Presence Messages Messages MySQL
  • 45. Server DRb.start_service ‘druby://localhost:10000’, myobject Client myobject = DRbObject.new_with_uri(‘druby://localhost:10000’)
  • 46. Rinda • Shared Queue (TupleSpace) • Built with DRb • RingyDingy makes it stupid easy • See Eric Hodel’s documentation • O(N) for take(). Sigh.
  • 47. Timestamp: 12/22/06 01:53:14 (4 months ago) Author: lattice Message: Fugly. Seriously. Fugly. SELECT * FROM messages WHERE substring(truncate(id,0),-2,1) = #{@fugly_dist_idx}
  • 48. It Scales. (except it stopped on Tuesday)
  • 49. Options • ActiveMQ (Java) • RabbitMQ (erlang) • MySQL + Lightweight Locking • Something Else?
  • 50. erlang? What are you doing? Stabbing my eyes out with a fork.
  • 51. Starling • Ruby, will be ported to something faster • 4000 transactional msgs/s • First pass written in 4 hours • Speaks MemCache (set, get)
  • 52. Use Messages to Invalidate Cache (it’s really not that hard)
  • 55. 9000 friends in 24 hours (doesn’t scale)