Many services / applications now a day are ill equipped with handling a sudden rush of popularity, as is often the case on the internet now a days, to a point where the services either become unavailable or unbearably slow. By taking a chapter from the ant colonies in the wild, where their strength lies in their numbers and the fact that everyone works together towards the same goal, we can apply the same principle to our service by using systems such as - gearman - memcache - daemons - message queues - load balancers and many more, you can achieve greater performance, more redundancy, higher availability and have the ability to scale your services up and down as required easily. During this talk attendees will be lead through the world of distributed systems and scalability, and shown the how, where and what, of how to take the average application and splitting it into smaller more manageable pieces

1. Distribute the
workload
Helgi Þormar Þorbjörnsson
PhpDay, Italy, Verona, 13th May, 2011
Saturday, 14 May 2011

2. Who am I?
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3. Saturday, 14 May 2011

4. VP of Engineering at Orchestra.io
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5. VP of Engineering at Orchestra.io
Developer at PEAR
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6. VP of Engineering at Orchestra.io
Developer at PEAR
From Iceland
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7. VP of Engineering at Orchestra.io
Developer at PEAR
From Iceland
@h on Twitter
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8. Why Distribute?
Saturday, 14 May 2011

9. Why Distribute?
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10. Why Distribute?
Budget
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11. Why Distribute?
Budget
Efficiency
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12. Why Distribute?
Budget
Efficiency
Perception
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13. Budget
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14. Budget
Spend wisely
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15. Budget
Spend wisely
Commodity servers
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16. Budget
Spend wisely
Commodity servers
Low overhead, high yield
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17. Budget
Spend wisely
Commodity servers
Low overhead, high yield
Cloud Computing (EC2)
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18. Efficiency
10 small servers > 1 big
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19. Venue Security
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20. 1000 people can exit quicker
through 10 small doors than 1 big
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21. 1000 people can exit quicker
through 10 small doors than 1 big
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22. 1000 people can exit quicker
through 10 small doors than 1 big
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23. 1000 people can exit quicker
through 10 small doors than 1 big
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24. Saturday, 14 May 2011

25. Saturday, 14 May 2011

26. Saturday, 14 May 2011

27. Saturday, 14 May 2011

28. Perception
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29. Perception
Defer intensive processes
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30. Perception
Defer intensive processes
Give instant feedback
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31. Perception
Defer intensive processes
Give instant feedback
Users keep on browsing
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32. Perception
Defer intensive processes
Give instant feedback
Users keep on browsing
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33. Saturday, 14 May 2011

34. Chapter from Nature
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35. Ant Colonies
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36. Algorithms
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37. Algorithms
Scheduling
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38. Algorithms
Scheduling
Vehicle Routing
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39. Algorithms
Scheduling
Vehicle Routing
Assignment
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40. Algorithms
Scheduling
Vehicle Routing
Assignment
Sets
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41. Algorithms
Scheduling
Vehicle Routing
Assignment
Sets
Other
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42. Algorithms
Scheduling
Vehicle Routing
Assignment
Sets
Other
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43. How do ants fit?
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44. How do ants fit?
Strength in numbers
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45. How do ants fit?
Strength in numbers
Work together
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46. How do ants fit?
Strength in numbers
Work together
Size benefits them
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47. Teamwork
When faced with a problem they will
solve the problem as one.
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48. Saturday, 14 May 2011

49. Saturday, 14 May 2011

50. What if they were
bigger?
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51. Types of Ants
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52. Types of Ants
Military
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53. Types of Ants
Military
Maids
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54. Types of Ants
Military
Maids
Tunnel diggers
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55. Types of Ants
Military
Maids
Tunnel diggers
Food gatherers
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56. How does this map
to my application?
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57. Saturday, 14 May 2011

58. Colony = Application
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59. Colony = Application
Ants = Components
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60. Colony = Application
Ants = Components
Ants do many different types of work to keep
their colony running
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61. Architect for
Distribution
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62. Characteristics
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63. Characteristics
Decoupling
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64. Characteristics
Decoupling
Elasticity
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65. Characteristics
Decoupling
Elasticity
High Availability
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66. Characteristics
Decoupling
Elasticity
High Availability
Concurrency
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67. Decoupling
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68. Application
DB API
Cache FE
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69. Application
DB API
Cache FE
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70. DB Application API
Cache FE
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71. DB Application API
Cache FE
Cache
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72. API
DB Application API
Cache FE
Cache
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73. API
DB Application API
API
Cache FE
Cache
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74. Elasticity
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75. Cloud Computing
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76. Load Balancing
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77. My Favourite
HA Proxy
Nginx
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78. Monitoring
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79. Automation
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80. High Availability
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81. Benefits
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82. Benefits
Easy management
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83. Benefits
Easy management
Ability to stop / start servers quickly
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84. Benefits
Easy management
Ability to stop / start servers quickly
Responsibilities are separate
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85. Benefits
Easy management
Ability to stop / start servers quickly
Responsibilities are separate
Quickly move to a new cluster
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86. Benefits
Easy management
Ability to stop / start servers quickly
Responsibilities are separate
Quickly move to a new cluster
Reduced risk
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87. What to avoid
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88. What to avoid
Local:
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89. What to avoid
Local:
Sessions
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90. What to avoid
Local:
Sessions
Files
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91. What to avoid
Local:
Sessions
Files
Memory
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92. What to avoid
Local:
Sessions
Files
Memory
Uploads
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93. What to avoid
Local:
Sessions
Files
Memory
Uploads
Writing to /tmp
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94. Solutions
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95. Solutions
Store sessions in DB / Memcache
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96. Solutions
Store sessions in DB / Memcache
Use S3 to store uploaded files
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97. Solutions
Store sessions in DB / Memcache
Use S3 to store uploaded files
Use networked Memcache
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98. Solutions
Store sessions in DB / Memcache
Use S3 to store uploaded files
Use networked Memcache
Serve up static files from a CDN
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99. Solutions
Store sessions in DB / Memcache
Use S3 to store uploaded files
Use networked Memcache
Serve up static files from a CDN
Or... A multi domain solution
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100. Internal APIs
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101. Application
Internal Storage API
Mongo MySQL FS
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102. SOA
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103. Service Oriented
Architecture
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104. Sort of :-)
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105. Eventually
Consistent
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106. CAP Therom
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107. Consistency
Availability
Partition Tolerance
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108. Queue Systems
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109. Good for
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110. Good for
Image Processing
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111. Good for
Image Processing
Distributed Logs
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112. Good for
Image Processing
Distributed Logs
Data Mining
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113. Good for
Image Processing
Distributed Logs
Data Mining
Mass Emails
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114. Good for
Image Processing
Distributed Logs
Data Mining
Mass Emails
Intensive transformation
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115. Good for
Image Processing
Distributed Logs
Data Mining
Mass Emails
Intensive transformation
Search
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116. Common Tools
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117. Common Tools
Gearman
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118. Common Tools
Gearman
Hadoop
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119. Common Tools
Gearman
Hadoop
Zero MQ (0MQ)
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120. Common Tools
Gearman
Hadoop
Zero MQ (0MQ)
RabbitMQ
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121. Common Tools
Gearman
Hadoop
Zero MQ (0MQ)
RabbitMQ
And many others!
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122. Gearman
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123. Your Client Code
Gearman Client API
(C, PHP, Perl, MySQL UDF, ...)
Your App Gearman Job Server Gearman
gearmand
Gearman Worker API
(C, PHP, Perl, Python, ...)
Your Worker Code
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124. Story time
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125. Story time
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126. Questions?
helgi@orchestra.io
Twitter: @h
Joind.in: http://joind.in/3027
Saturday, 14 May 2011