Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
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The document discusses the evolution of Netflix from monolithic applications to microservices, highlighting the challenges and best practices associated with this transition. It emphasizes the need for proper design to manage service dependencies and potentially chaotic interactions in a microservices architecture. Various Netflix technologies and open source tools are mentioned that support this transition, including service registries, load balancers, and fault injection testing.
Pros and Cons of a MicroServices Architecture talk at AWS ReInvent
- 3. • Netflix – background and evolution
- 7. • Netflix – background and evolution • Monolithic Apps
- 10. Load Balancer Monolithic App Account Component Catalog Component Recommendation Component Customer Service Component Database
- 12. Time
- 16. User Accounts Shopping Cart Product Catalog Customer Service
- 18. Product Catalog Customer Service
- 22. Tipping Point & & Organizational Growth Disverse Functionality Bottleneck in Monolithic stack
- 24. • Netflix – background and evolution • Monolithic Apps • Microservices
- 28. Attribution: Adrian Cockroft, Martin Fowler …
- 29. • Write programs that do one thing and do it well. • Write programs to work together. tr 'A-Z' 'a-z' < doc.txt | tr -cs 'a-z' 'n' | sort | uniq | comm -23 - /usr/share/dict/words Program to print misspelt words in doc.txt
- 30. Comparing Monolithic to MicroServices
- 32. Load Balancer Monolithic App Account Component Catalog Component Recommendation Component Customer Service Component Database
- 33. Load Balancer Account Service Catalog Service Recommendation Service Customer Service Service Catalog DB API Gateway Customer DB
- 34. • Netflix – background and evolution • Monolithic Apps • Microservices – Why?
- 37. Your App/Service Service X Service Y Service Z Service L Service M
- 38. • Netflix – background and evolution • Monolithic Apps • Microservices – Challenges and Solutions
- 40. Vs
- 41. Heterogeneity Can lead to chaos if not designed right …
- 44. non-JVM
- 46. Service Discovery What’s your address?
- 47. Account Service Catalog Service Recommendation Service Customer Service Service X Service Y Service Z Service Registry Service (e.g. Netflix Eureka)
- 49. 2 Billion ~20 Billion Fan 1 Request 1 Request Monolithic App MicroServices
- 51. Your App/Service Service X Service Y Service Z Cache Cluster Cache Cluster Tip: Config your TTL based on flexibility with data staleness!
- 52. Your App/Service Service X Service Y Service Z Fn {A, B, C} Cache Cluster Cache Cluster
- 54. A/B Test Service App Service X Service Y Service Z User Account Service
- 55. A/B Test Service App Service X Service Y Service Z User Account Service reduces dependency load
- 57. Data Serialization Overhead Data transformation getMovies() getMovie() getMovieMetadata( ) C l i e n t D C l i e n t C C l i e n t B Service A Service B Service C Service D X X Avro JSON Xml Overall Response Time
- 60. A single “bad” service can still bring your service down
- 63. Your App/Service Service X Service Y Service Z Service L Service M
- 64. Your App/Service Service X Service Y Service Z Service L Service M Your App/SYeoruvirc e App/Service
- 65. Your App/Service Service X Service Y Service Z Service L Service M Your App/SYeoruvirc e App/Service Service X Service Y Service Z Service L Service M
- 68. 1.
- 69. Account Service 1 Recommendation Service 1 Customer Service Service 1 API Gateway Account Service N Recommendation Service N Customer Service Service N Customer Service Load Balancer Reco Service Load Balancer Account Service Load Balancer ELB
- 71. Account Service 1 Recommendation Service 1 Customer Service Service 1 API Gateway Account Service N Recommendation Service N Customer Service Service N Account Service LB Recommendation Service LB Customer Service Service LB ELB
- 75. Your Service XY Farm
- 76. How many dependencies does my service have? What is the Call Volume on my Service? Are any Dependency Services running Hot? What are the Top N Slowest “Business Transactions”? What are the sample HTTP Requests/Responses that had a 500 Error Code in the last 30 minutes?
- 77. You Your Service Dependency Graph
- 84. • Netflix – background and evolution • Monolithic Apps • Microservices – Challenges and Solutions – Best Practices
- 89. three
- 92. Fault Injection Testing (FIT) Device ELB Service B Service C Internet Zuul Edge Device or Account Override Service A Request-level simulations Refer Re:Invent Talk “Embracing Failure: Fault Injection and Service Reliability” – PFC305
- 96. • Netflix – background and evolution • Monolithic Apps • Microservices – Challenges and Solutions – Best Practices • InterProcess Communication
- 98. Netflix IPC Stack (1.0) A p a c h e H T T P C l i e n t Server (Karyon) HTTP Admin Console Eureka (Service Registry) Apache Tomcat Client H y s t r i x E V C a c h e Ribbon Load Balancing Metrics (Servo) Eureka Integration Bootstrapping (Governator) Metrics (Servo) Eureka Integration Registration Fetch Registry A Blocking Architecture
- 99. Netflix IPC Stack (2.0) Client (Ribbon 2.0) Server (Karyon) Admin Console Eureka (Service Registry) Ribbon Transport Load Balancing Metrics (Servo) Eureka Integration Bootstrapping (Governator) Metrics (Servo) HTTP Eureka Integration Registration Fetch Registry Ribbon Hystrix EVCache R x N e t t y RxNetty UDP TCP WebSockets SSE A Completely Reactive Architecture
- 102. interface @Http(method = HttpMethod.GET, uriTemplate="/users/{userId}/recommendations") @Hystrix(fallbackHandler = RecommendationFallbackHandler.class) @EvCache(name = "movie-rec", appName = "movieService", cacheKeyTemplate = "{userId}") recommendationsByUserId @Var "userId" MovieService movieService = Ribbon.from(MovieService.class); Observable<Recommendations> result = movieService.recommendationsByUserId(“user1”) .toObservable();
- 103. NetflixOSS
- 105. NetflixOSS • Eureka • Karyon • Ribbon • Hystrix • Archaius • Servo • EVCache • Curator/Exhibitor
- 106. Talk Time Title PFC-305 Wednesday, 1:15pm Embracing Failure: Fault Injection and Service Reliability BDT-403 Wednesday, 2:15pm Next Generation Big Data Platform at Netflix PFC-306 Wednesday, 3:30pm Performance Tuning EC2 DEV-309 Wednesday, 3:30pm From Asgard to Zuul, How Netflix’s proven Open Source Tools can accelerate and scale your services ARC-317 Wednesday, 4:30pm Maintaining a Resilient Front-Door at Massive Scale PFC-304 Wednesday, 4:30pm Effective Inter-process Communications in the Cloud: The Pros and Cons of Micro Services Architectures ENT-209 Wednesday, 4:30pm Cloud Migration, Dev-Ops and Distributed Systems APP-310 Friday, 9:00am Scheduling using Apache Mesos in the Cloud
- 107. • Netflix – background and evolution • Monolithic Apps • Microservices – Challenges and Solutions – Best Practices • InterProcess Communication • Takeaways
- 110. http://techblog.netflix.com http://netflix.github.io http://martinfowler.com/articles/microservices.html http://microservices.io/
- 111. Please give us your feedback on this presentation Netflix Booth
Editor's Notes
- #92: Startup dependencies hurt us on scale up event Degrading without knowing is bad