How Elixir helped us scale our Video User Profile Service for the Olympics
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The document discusses how a video user profile service originally built in Ruby on Rails was scaled using Elixir. It initially increased resources and used containers, improving performance by 32% and 12% respectively. It was found one endpoint handled 80% of traffic. Rewriting this in Elixir improved performance by 95% and reduced containers by 35%. The results showed Elixir was able to significantly improve scalability and metrics for the growing user profile API.
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How Elixir helped us scale our Video User Profile Service for the Olympics
- 1. Using Elixir to scale Video User Profile Service Emerson Macedo @emerleite
- 3. CONTEXT User Profile API was developed in 2012. It’s a well written Ruby on Rails application , responsible for track logged user actions
- 4. CONTEXT User Profile API was developed in 2012. It’s a well written Ruby on Rails application , responsible for track logged user actions
- 5. CONTEXT Our Video Player POST Watched Percentage to our Endpoint so we can provide Keep Watching Percentage to our Logged Users. It does it every 10 seconds
- 6. CONTEXT Our Video Player POST Watched Percentage to our Endpoint so we can provide Keep Watching Percentage to our Logged Users. It does it every 10 secondsx x
- 7. CONTEXT Globo is making Binge Watching experiments combined with Online First for new TV Shows TV Shows
- 8. CONTEXT Prepare all Video Applications for the Olympics
- 10. THE PROBLEM 2013 10k The throughput increased between 2013 and 2016
- 11. 2013 2016 10k 60k THE PROBLEM The throughput increased between 2013 and 2016
- 12. 2013 2016 10k 60k THE PROBLEM The throughput increased between 2013 and 2016 ~600%
- 13. 2013 2016 10k 60kTHE PROBLEM It was hard to predict the new Appication Throughput 2016 / 2 ?
- 14. INFRASTRUCTURE 2 bare metals, each one with 24 CPUs and 64GB of RAM
- 15. THE PROBLEM The average response time was good, but percentiles were hurting the application Avg 70ms
- 16. 70ms THE PROBLEM The average response time was good, but percentiles were hurting the application 99 2.5s 95 1s Avg
- 17. FIRST CHANGE We increased the computational resources from 2 to 4 bare metals
- 18. FIRST CHANGE We increased the computational resources from 2 to 4 bare metals
- 19. FIRST CHANGE We increased the computational resources from 2 to 4 bare metals Each one with 24 CPUs and 64GB of RAM
- 20. 70ms 99 2.5s 95 1s Avg FIRST CHANGE This change improved metrics by ~32%
- 21. FIRST CHANGE This change improved metrics by ~32% 9995 1.7s 0.7s Avg 47ms
- 22. FIRST CHANGE This change improved metrics by ~32% 9995 1.7s 0.7s Avg 47ms ~32% better
- 23. SECOND CHANGE We decided to try a deployment with Tsuru containers with auto scaling, each one with 1-4 vCPU and 2GB of RAM
- 24. SECOND CHANGE We decided to try a deployment with Tsuru containers with auto scaling, each one with 1-4 vCPU and 2GB of RAM 93 containers
- 25. SECOND CHANGE 9995 1.7s 0.7s Avg 47ms Migrate to containers improved metrics by ~12%
- 26. SECOND CHANGE 9995 1.5s 0.5s Avg 41ms Migrate to containers improved metrics by ~12%
- 27. SECOND CHANGE 9995 1.5s 0.5s Avg 41ms Migrate to containers improved metrics by ~12% ~12% better
- 28. CONTEXT Our Video Player POST Watched Percentage to our Endpoint so we can provide Keep Watching Percentage to our Logged Users. It does it every 10 seconds
- 29. CONTEXT Our Video Player POST Watched Percentage to our Endpoint so we can provide Keep Watching Percentage to our Logged Users. It does it every 10 secondsx x
- 31. ARCHITECTURAL OVERVIEW User Profile API is a classical Ruby on Rails application, which also uses Resque for background jobs
- 32. ARCHITECTURAL OVERVIEW User Profile API is a classical Ruby on Rails application, which also uses Resque for background jobs
- 33. ARCHITECTURAL OVERVIEW User Profile API is a classical Ruby on Rails application, which also uses Resque for background jobs BLOCK BLOCK BLOCK
- 34. ARCHITECTURAL OVERVIEW User Profile API is a classical Ruby on Rails application, which also uses Resque for background jobs
- 35. CONTAINER DISTRIBUTION Our containers was distributed for the Rails App, Resque Workers and Resque Scheduler 93 containers
- 38. THIRD CHANGE We saw that just ONE endpoint was responsible for 80%, of application throughput
- 39. THIRD CHANGE We saw that just ONE endpoint was responsible for 80%, of application throughput
- 40. THIRD CHANGE x x We saw that just ONE endpoint was responsible for 80%, of application throughput
- 41. CONTEXT Our Video Player POST Watched Percentage to our Endpoint so we can provide Keep Watching Percentage to our Logged Users. It does it every 10 seconds
- 42. CONTEXT Our Video Player POST Watched Percentage to our Endpoint so we can provide Keep Watching Percentage to our Logged Users. It does it every 10 secondsx x
- 43. THIRD CHANGE We saw that just ONE endpoint was responsible for 80%, of application throughput
- 44. THIRD CHANGE We saw that just ONE endpoint was responsible for 80%, of application throughput Pareto
- 45. THIRD CHANGE We saw that just ONE endpoint was responsible for 80%, of application throughput Pareto 20% of effort will solve 80% of the problem
- 46. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version
- 47. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version
- 48. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version
- 49. WHY ELIXIR Elixir is being the cutting-edge for the Ruby community
- 50. WHY ELIXIR Elixir is being the cutting-edge for the Ruby community
- 51. WHY ELIXIR Elixir is being the cutting-edge for the Ruby community x
- 52. WHY ELIXIR Elixir is being the cutting-edge for the Ruby community
- 53. WHY ELIXIR Elixir is being the cutting-edge for the Ruby community x
- 54. WHY ELIXIR Elixir is being the cutting-edge for the Ruby community
- 55. WHY ELIXIR Elixir is being the cutting-edge for the Ruby community x
- 56. WHY ELIXIR Elixir is the language Java programmers were looking when they choose Ruby
- 57. WHY ELIXIR Elixir is the language Java programmers were looking when they choose Ruby x x x
- 58. WHY ELIXIR Elixir generates Erlang byte code and runs on BEAM VM which has 30 years of development
- 59. WHY ELIXIR Elixir generates Erlang byte code and runs on BEAM VM which has 30 years of development x
- 60. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version
- 61. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version
- 62. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version
- 63. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version SAME APPLICATION
- 64. THIRD CHANGE We rewrote the Ruby POST endpoint from scratch to an Elixir version SAME APPLICATION ERL PROCESS ERL PROCESS
- 65. THIRD CHANGE The result was a starting point to change the app to CQRS Architecture
- 66. THIRD CHANGE The result was a starting point to change the app to CQRS Architecture COMMAND QUERY
- 67. THIRD CHANGE 9995 1.5s 0.5s Avg 41ms Migrate to Elixir improved metrics by ~95%
- 68. THIRD CHANGE 9995 30ms 15ms Avg 4ms Migrate to Elixir improved metrics by ~95%
- 69. THIRD CHANGE 9995 30ms 15ms Avg 4ms Migrate to Elixir improved metrics by ~95% ~95% better
- 71. THIRD CHANGE Migrate to Elixir reduced containers by ~35% 3 containers30 containers ElixirRuby
- 72. THIRD CHANGE Migrate to Elixir reduced containers by ~35% 33 containers
- 73. TOOLS We chosse phoenix framework to create our Elixir API and we’re using many other community libs
- 74. TOOLS We chosse phoenix framework to create our Elixir API and we’re using many other community libs Ecto HTTPoison Exrm CacheX GenRetry FakeServer Corsica
- 75. PROBLEMS MongoDB Driver did not has Replica Sets support. We had to implement it
- 76. PROBLEMS MongoDB Driver did not has Replica Sets support. We had to implement it http//github.com/emerleite/mongox http//github.com/emerleite/mongox_ecto
- 77. PROBLEMS Elixir did not has NewRelic support. We need to create an ad-hoc implementation using Exometer
- 78. PROBLEMS Elixir did not has NewRelic support. We need to create an ad-hoc implementation using Exometer https://github.com/Feuerlabs/exometer_core
- 79. 2013 2016 10k 60kFINAL RESULT After the Olympics and with Binge Watching, the throughput increased ~50%
- 80. 2013 2016 10k 60kFINAL RESULT After the Olympics and with Binge Watching, the throughput increased ~50% 2016 / 2 90k
- 81. 70ms 99 2.5s 95 1s Avg FINAL RESULT Migrate to Elixir improved metrics by ~95%
- 82. FINAL RESULT 9995 30ms 15ms Avg 4ms Migrate to Elixir improved metrics by ~95%
- 83. FINAL RESULT 9995 30ms 15ms Avg 4ms Migrate to Elixir improved metrics by ~95% ~95% better