[212] large scale backend service develpment

•

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DEVIEW2015 DAY2. large scale backend service develpment

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

Problems

개발자님~❤

서비스

런칭에

필요한

시작해야하는데요
하...하겠습니다
출처:

아이유

Real

Fantasy

2012

콘서트

frame

set
출처:

MBC

무한도전

대단한건

아니고요

유저는

10만명

그리고

처리속도는

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Mais de NAVER D2

[211] 인공지능이 인공지능 챗봇을 만든다

[211] 인공지능이 인공지능 챗봇을 만든다

[211] 인공지능이 인공지능 챗봇을 만든다NAVER D2

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...NAVER D2

[215] Druid로 쉽고 빠르게 데이터 분석하기

[215] Druid로 쉽고 빠르게 데이터 분석하기

[215] Druid로 쉽고 빠르게 데이터 분석하기NAVER D2

[245]Papago Internals: 모델분석과 응용기술 개발

[245]Papago Internals: 모델분석과 응용기술 개발

[245]Papago Internals: 모델분석과 응용기술 개발NAVER D2

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈NAVER D2

[235]Wikipedia-scale Q&A

[235]Wikipedia-scale Q&A

[235]Wikipedia-scale Q&ANAVER D2

[244]로봇이 현실 세계에 대해 학습하도록 만들기

[244]로봇이 현실 세계에 대해 학습하도록 만들기

[244]로봇이 현실 세계에 대해 학습하도록 만들기NAVER D2

[243] Deep Learning to help student’s Deep Learning

[243] Deep Learning to help student’s Deep Learning

[243] Deep Learning to help student’s Deep LearningNAVER D2

[234]Fast & Accurate Data Annotation Pipeline for AI applications

[234]Fast & Accurate Data Annotation Pipeline for AI applications

[234]Fast & Accurate Data Annotation Pipeline for AI applicationsNAVER D2

Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing

Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing

Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load BalancingNAVER D2

[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지

[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지

[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지NAVER D2

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기NAVER D2

[224]네이버 검색과 개인화

[224]네이버 검색과 개인화

[224]네이버 검색과 개인화NAVER D2

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)NAVER D2

[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기

[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기

[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기NAVER D2

[213] Fashion Visual Search

[213] Fashion Visual Search

[213] Fashion Visual SearchNAVER D2

[232] TensorRT를 활용한 딥러닝 Inference 최적화

[232] TensorRT를 활용한 딥러닝 Inference 최적화

[232] TensorRT를 활용한 딥러닝 Inference 최적화NAVER D2

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지NAVER D2

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터NAVER D2

[223]기계독해 QA: 검색인가, NLP인가?

[223]기계독해 QA: 검색인가, NLP인가?

[223]기계독해 QA: 검색인가, NLP인가?NAVER D2

Mais de NAVER D2 (20)

[211] 인공지능이 인공지능 챗봇을 만든다

[211] 인공지능이 인공지능 챗봇을 만든다

[211] 인공지능이 인공지능 챗봇을 만든다

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...

[233] 대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing: Maglev Hashing Scheduler i...

[215] Druid로 쉽고 빠르게 데이터 분석하기

[215] Druid로 쉽고 빠르게 데이터 분석하기

[215] Druid로 쉽고 빠르게 데이터 분석하기

[245]Papago Internals: 모델분석과 응용기술 개발

[245]Papago Internals: 모델분석과 응용기술 개발

[245]Papago Internals: 모델분석과 응용기술 개발

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈

[236] 스트림 저장소 최적화 이야기: 아파치 드루이드로부터 얻은 교훈

[235]Wikipedia-scale Q&A

[235]Wikipedia-scale Q&A

[235]Wikipedia-scale Q&A

[244]로봇이 현실 세계에 대해 학습하도록 만들기

[244]로봇이 현실 세계에 대해 학습하도록 만들기

[244]로봇이 현실 세계에 대해 학습하도록 만들기

[243] Deep Learning to help student’s Deep Learning

[243] Deep Learning to help student’s Deep Learning

[243] Deep Learning to help student’s Deep Learning

[234]Fast & Accurate Data Annotation Pipeline for AI applications

[234]Fast & Accurate Data Annotation Pipeline for AI applications

[234]Fast & Accurate Data Annotation Pipeline for AI applications

Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing

Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing

Old version: [233]대형 컨테이너 클러스터에서의 고가용성 Network Load Balancing

[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지

[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지

[226]NAVER 광고 deep click prediction: 모델링부터 서빙까지

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

[225]NSML: 머신러닝 플랫폼 서비스하기 & 모델 튜닝 자동화하기

[224]네이버 검색과 개인화

[224]네이버 검색과 개인화

[224]네이버 검색과 개인화

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

[216]Search Reliability Engineering (부제: 지진에도 흔들리지 않는 네이버 검색시스템)

[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기

[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기

[214] Ai Serving Platform: 하루 수 억 건의 인퍼런스를 처리하기 위한 고군분투기

[213] Fashion Visual Search

[213] Fashion Visual Search

[213] Fashion Visual Search

[232] TensorRT를 활용한 딥러닝 Inference 최적화

[232] TensorRT를 활용한 딥러닝 Inference 최적화

[232] TensorRT를 활용한 딥러닝 Inference 최적화

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

[242]컴퓨터 비전을 이용한 실내 지도 자동 업데이트 방법: 딥러닝을 통한 POI 변화 탐지

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

[212]C3, 데이터 처리에서 서빙까지 가능한 하둡 클러스터

[223]기계독해 QA: 검색인가, NLP인가?

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[223]기계독해 QA: 검색인가, NLP인가?

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2.

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

5. 개발자님~❤

13. 시작해야하는데요 하...하겠습니다 출처:

21. set 출처:

23. 무한도전

29. 대단한건

30. 아니고요

41. 처리속도는

46. 충분해요

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66. set 출처:

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81. 필요한데...

82. 가능하겠죵~?

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85. 볼래? 출처:

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88. 무한도전

94. 아이유니까

98. 합니다 하지마

99. 미친놈아! 출처:

101. 무한도전

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.

107. Goals • Scalability • Performance • Fast Iterations

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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

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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

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120. Docker AWS

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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

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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

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