[212] large scale backend service develpment

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The document discusses developing large scale backend services for a new game using Node.js, Docker, and AWS. It describes problems with scalability and performance, and solutions using Node.js clustering, reverse proxying, and CPU profiling to optimize services. The goals are to build services that are scalable, have high performance, and allow for fast iterations.

Large Scale
Backend Service Development
using
Node.js, Docker, and AWS
Daniel Kang
Sr. Software Engineer / Riot Games
dkang@riotgames.com

I’ll talk about…
• Problems
• Solutions
• Goals
• Node.js, Redis, Docker and AWS
• Results

"How can you reliably schedule tasks in an unreliable, autoscaling cloud environment? This presentation talks about the design of our Fenzo scheduler, built on Apache Mesos, that serves as the core of our stream-processing platform, Mantis, designed for real-time insights. We focus on the following aspects of the scheduler: - Resource granularity - Fault tolerance - Bin packing, task affinity, stream locality - Autoscaling of the cluster and of individual service jobs - Constraints (hard and soft) for individual tasks such as zone balancing, unique, and exclusive instances This talk also includes detailed information on a holistic approach to scheduling in a distributed, autoscaling environment to achieve both speed and advanced scheduling optimizations."

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Congratulations: you've been selected to build an application that will manage reservations for rooms! On the surface, this sounds simple, but you are building a system for managing a high traffic reservation web page, so we know that a lot of people will be accessing the system. Therefore, we need to ensure that the system can handle all of the eager users that will be flooding the website checking to see what availability each room has. Fortunately, PostgreSQL is prepared for this! And even better, we will be using Postgres 14 to make the problem even easier! We will explore the following PostgreSQL features: * Data types and their functionality, such as: * Data/Time types * Ranges / Multirnages Indexes such as: * GiST * Common Table Expressions and Recursion (though multiranges will make things easier!) * Set generating functions and LATERAL queries * Functions and the PL/PGSQL * Triggers * Logical decoding and streaming We will be writing our application primary with SQL, though we will sneak in a little bit of Python and using Kafka to demonstrate the power of logical decoding. At the end of the presentation, we will have a working application, and you will be happy knowing that you provided a wonderful user experience for all users made possible by the innovation of PostgreSQL!

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Anatomy of the libvirt virtualization library http://www.ibm.com/developerworks/library/l-libvirt/ libvirt http://libvirt.org/index.html Scheduling http://docs.openstack.org/icehouse/config-reference/content/section_compute-scheduler.html Openstack Zoning – Region/Availability Zone/Host Aggregate https://kimizhang.wordpress.com/2013/08/26/openstack-zoning-regionavailability-zonehost-aggregate/ Availability Zones and Host Aggregates in OpenStack Compute (Nova) http://blog.russellbryant.net/2013/05/21/availability-zones-and-host-aggregates-in-openstack-compute-nova/ An Introduction to Droplet Metadata https://www.digitalocean.com/community/tutorials/an-introduction-to-droplet-metadata HOW WE USE CLOUDINIT IN OPENSTACK HEAT http://sdake.io/2013/03/03/how-we-use-cloudinit-in-openstack-heat/ How to inject file/meta/ssh key/root password/userdata/config drive to a VM during nova boot https://kimizhang.wordpress.com/2014/03/18/how-to-inject-filemetassh-keyroot-passworduserdataconfig-drive-to-a-vm-during-nova-boot/ Cloud-init https://cloudinit.readthedocs.org/en/latest/

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FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

[212] large scale backend service develpment

1. Large Scale Backend Service Development using Node.js, Docker, and AWS Daniel Kang Sr. Software Engineer / Riot Games dkang@riotgames.com

3. I’ll talk about… • Problems • Solutions • Goals • Node.js, Redis, Docker and AWS • Results

4. Problems

10. 서버

11. 개발

12. 이제

13. 시작해야하는데요 하...하겠습니다 출처:

14. 아이유

15. Real

16. Fantasy

17. 2012

18. 콘서트

19. photo

20. frame

21. set 출처:

22. MBC

23. 무한도전

24. 방송

25. 화면

26. 캡쳐

27. 일단

28. 뭐

29. 대단한건

30. 아니고요

31. 그냥

32. 동시

33. 접속

34. 유저는

35. 한

36. 10만명

37. 쯤?

38. 그리고

39. 건당

40. 평균

41. 처리속도는

42. 그냥

43. 약

44. 0.1초

45. 이하면

46. 충분해요

47. (뭐

48. 구글도

49. 그

50. 정돈

51. 하잖아요?) (난

52. 할

53. 수

54. 있다.

55. 난

56. 할

57. 수

58. 있다.) 출처:

59. 아이유

60. Real

61. Fantasy

62. 2012

63. 콘서트

64. photo

65. frame

66. set 출처:

67. MBC

68. 무한도전

69. 방송

70. 화면

71. 캡쳐

72. 아참...

73. 그리고

74. 그거

75.

76. 다음

77. 달

78. 말

79. 까지

80.

84. 해

86. http://kpopstreamonline.blogspot.com/2014/09/top-10-richest-k-pop-stars.html 출처:

87. MBC

88. 무한도전

89. 방송

90. 화면

91. 캡쳐

92. 하지만

93. 일단

94. 아이유니까

95. 도전

96. 해

97. 보기로

98. 합니다 하지마

99. 미친놈아! 출처:

100. MBC

101. 무한도전

102. 방송

103. 화면

104. 캡쳐

105. Project: Leaderboards • 목표: 게임 내 특정 조건을 만족하는 플 레이어들에게 실시간으로 보상 아이템을 주고, 플레이어들이 실시간으로 자신의 진행 상황을 확인 할 수 있는 API도 필요 하다.

106. Solutions

107. Goals • Scalability • Performance • Fast Iterations

108.

109. Node.js

110. Multi-Core Systems • Reverse Proxying • Hard to find balanced configs • Tested w/ Nginx and HAProxy • Node.js Native Clustering • Worked pretty well for use cases • Tested on 0.12.x

111.

112.

113. CPU Profiling • V8 profiling • Node options :“--prof” • Performance Bottlenecks • Unnecessary HTTP routing • Unnecessary encryption (crypto) module usages • DEBUG enabled dependencies • NewRelic Node.js agent: negative impact on performance

114. More on Node.js • Node versions: 0.10.x / 0.12.x • ES6 features: generators and block scoping • Linting • Quality of code • Identify potential issues • WebStorm IDE • Code analysis • Interactive debugging features • V8 profiling

115. Redis

116.

117.

118.

119.

120. Docker AWS

121.

122. ECSECS Tasks ECS Services ECS Task Definitions ECS Container Instances ECS Agent EC2 IAM Roles EC2 Instances EC2 Security Groups Elastic Load Balancer CloudWatch Metrics EC2 AMIs VPCs 출처:

123. 나무위키

124. (https://namu.wiki/w/몰라%20뭐야%20그거%20무서워) AWS

125.

126.

127.

128.

129.

130.

131.

132.

133.

134.

135. EC2 as Docker Host • Amazon Linux HVM • Enhanced networking enabled by default • PV not recommended • Larger instances = more performing spec • c4.xlarge (4 cores / 7.5G memory) • T2 class: too much CPU steals • Increase ulimit / nproc • Host: limits.conf, 90-nproc.conf • Docker: --default-ulimit nofile=64000:64000 --default-ulimit nproc=32000:32000

136. Results

137. 11,600,000 reqs/min = c4.xlarge 130 instances x 89,231 reqs/min 1,487 reqs/sec/instance Latency 14 ms

138. 24,000,000 reqs/min = c4.xlarge 130 instances x 184,615 reqs/min 3,077 reqs/sec/instance Latency 80 ms

139. [c4.4xlarge] $0.928 x 100 x 24h x 30d = $66,816 [c4.xlarge] $0.232 x 100 x 24h x 30d = $16,704 saved $50,112

140. Q A