(ARC309) Getting to Microservices: Cloud Architecture Patterns

(ARC309) Getting to Microservices: Cloud Architecture Patterns

1. 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Adrian Trenaman SVP Engineering, gilt.com @adrian_trenaman October 2015 From Monolithic to Microservices Evolving Architecture Patterns in the Cloud Derek Chiles Sr. Mgr, Solutions Architecture, AWS @derekchiles ARC309
2. 2. Gilt’s Journey
3. 3. Gilt: Luxury designer brands at members-only prices
4. 4. ... we shoot the product in our studios
5. 5. ... we receive, store, pack, and ship ...
6. 6. ... we sell every day at noon EST
7. 7. … stampede!
8. 8. ... this is what noon really looks like.
9. 9. How It All Started…
10. 10. From Rails to Riches ― 2007: A Ruby on Rails monolith Jobs Ruby on Rails memcache Postgres
11. 11. 2011: Java, loosely-typed, monolithic services (1) Large, loosely typed services (2) Teams focused on business lines (3) Monolithic Java application; huge bottleneck for innovation (4) Hidden linkages; buried business logic
12. 12. “How can we arrange our teams around strategic initiatives? How can we make it fast and easy to get to change to production?” Enter: µ-services
13. 13. 2015: LOSA (Lots of Small Apps) & Microservices
14. 14. Driving Forces Behind Gilt’s Emergent Architecture • Team autonomy • Voluntary adoption (tools, techniques, processes) • KPI / goal-driven initiatives • Failing fast and openly • Being open and honest, even when it’s difficult
15. 15. Service growth over time: point of inflexion === Scala.
16. 16. What are all these services doing?
17. 17. Anatomy of a service
18. 18. Anatomy of a gilt service – typical choices gilt-service- framework, , Java, Javascript Log4j, Cloudwatch Cave or
19. 19. Lines of code per service (logarithmic scale)
20. 20. # source files per service (includes build, config, xml, Java, Scala, Ruby...)
21. 21. Service discovery: straightforward ZooKeeper Brocade Traffic Manager (aka Zeus, Stringray, SteelApp,...)
22. 22. From bare metal… PHX IAD
23. 23. … to vapour.
24. 24. Lift-and-shift + elastic teams Existing Data Centre Dual 10-Gb direct connect line, 2-ms latency. “Legacy VPC” MobileCommon Person- alisation Admin Data (1) Deploy to VPC (2) “Department” accounts for elasticity & DevOps
25. 25. Single-tenant deployment: one service per EC2 instance
26. 26. Reproducible, immutable deployments: Docker
27. 27. Service discovery: new services use ELB ZooKeeper Elastic Load Balancing (ELB)
28. 28. # running instances per service: “rule of three” (previously “rule of four”)
29. 29. EC2 instance sizing: lots of small instances
30. 30. Evolution of architecture and tech organization
31. 31. We (heart) μ-services • Lessen dependencies between teams: faster code-to-prod • Lots of initiatives in parallel • Your favourite <tech/language/framework> here • Graceful degradation of service • Disposable code: easy to innovate, easy to fail and move on.
32. 32. We (heart) cloud • Do DevOps in a meaningful way. • Low barrier of entry for new tech (Amazon DynamoDB, Amazon Kinesis,...) • Isolation • Cost visibility • Security tools (IAM) • Well documented • Resilience is easy • Hybrid is easy • Performance is great
33. 33. Common Challenges and Patterns
34. 34. Monolithic Microservices • Simple deployments • Binary failure modes • Inter-module refactoring • Technology monoculture • Vertical scaling • Partial deployments • Graceful degradation • Strong module boundaries • Technology diversity • Horizontal scaling
35. 35. • Organization • Discovery • Data management • Deployment • I/O explosion • Monitoring Common Challenges and Patterns
36. 36. Organization
37. 37. Monolithic Ownership Organized on technology capabilities UI Team DBA Team App Logic Team Web Tier App Tier DB Organizational Structure Application Architecture
38. 38. Microservices Ownership Organized on business responsibilities Login Registration Order Personalization Accounts team Mobile Personalization team Mobile team
39. 39. Microservices Ownership • Requirements • Technology selection • Development • Quality • Deployment • Support
40. 40. How to Be a Good Citizen (Service Consumer) • Design for failure • Expect to be throttled • Retry w/ exponential backoff • Degrade gracefully • Cache when appropriate
41. 41. How to Be a Good Citizen (Service Provider) • Publish your metrics • Protect yourself • Keep your implementation details private • Maintain backwards compatibility
42. 42. Amazon API Gateway • Throttling (global and per-method) • Caching (with TTLs and invalidation) • Monitoring (RPS, latency, error rate) • Versioning • Authentication
43. 43. Discovery
44. 44. Use DNS Convention-based naming <service-name>-<environment>.domain.com shoppingcart-gamma.example.com <service-name>.<environment>.domain.com shoppingcart.gamma.example.com
45. 45. Use a Dynamic Service Registry • Avoids the DNS TTL issue • More than service registry & discovery • Configuration management • Health checks • Plenty of options • ZooKeeper (Apache) • Eureka (Netflix) • Consul (HashiCorp) • SmartStack (Airbnb)
46. 46. Data management
47. 47. Challenge: Centralized Database Monolithic applications typically have a monolithic data store: • Difficult to make schema changes • Technology lock-in • Vertical scaling • Single point of failure user-svc account-svccart-svc DB
48. 48. Centralized Database – Anti-pattern Monolithic applications typically have a monolithic data store: • Difficult to make schema changes • Technology lock-in • Vertical scaling • Single point of failure user-svc account-svccart-svc DB
49. 49. Decentralized Data Stores • Each service chooses its data store technologies • Low impact schema changes • Independent scalability • Data is gated through the service API account- svc cart- svc DynamoDB RDS user- svc ElastiCache RDS
50. 50. Challenge: Transactional Integrity • Use a pessimistic model • Handle it in the client • Add a transaction manager / distributed locking service • Rethink your design • Use an optimistic model • Accept eventual consistency • Retry (if idempotent) • Fix it later • Write it off
51. 51. Challenge: Aggregation • Pull: Make the data available via your service API • Push: To Amazon S3, Amazon CloudWatch, or another service you create • Pub/sub: Via Amazon Kinesis or Amazon SQS
52. 52. Deployment
53. 53. Continuous Delivery & Continuous Deployment Create the right build pipeline for each service • AWS CodeDeploy • AWS Elastic Beanstalk • Jenkins, CircleCI, Travis,… Integration & perf tests Build & unit tests beta Produser-svc Integration & perf tests Build & unit tests beta gamma Prodcart-svc
54. 54. AWS CodePipeline
55. 55. Multiple Services per Container/Instance • Independent monitoring • Independent scaling • Clear ownership • Immutable deployments user-svc cart-svc account-svc Container or instance
56. 56. Multiple Services per Container/Instance – Anti-pattern • Independent monitoring • Independent scaling • Clear ownership • Immutable deployments user-svc cart-svc account-svc Container or instance
57. 57. Single Service per Container/Instance • Independent monitoring • Independent scaling • Clear ownership • Immutable deployments user-svc container or instance account-svc cart-svc container or instance container or instance
58. 58. …Or Just Use AWS Lambda LambdaAPI Gateway RDS DynamoDB
59. 59. I/O explosion
60. 60. Challenge: Request Multiplication checkout- svc cart-svc account-svc user-svc Single request
61. 61. Add Client Caching checkout- svc cart-svc account-svc user-svc Single request cache user & account cache
62. 62. Challenge: Hotspots cart-svc order-svc shipping- svc user-svc single request get (user x, col y) get (user x, col y) get (user x, col y)
63. 63. Use Dependency Injection cart-svc order-svc shipping- svc user-svc single request get (user x, col y) (user x, col y) (user x, col y)
64. 64. Monitoring
65. 65. Challenge: monitoring • Publish externally relevant metrics • Latency • RPS • Error rate • Understand internally relevant metrics • Basic – CloudWatch • OS • Application
66. 66. Challenge: Logging • Pick a common log aggregation solution • Agree on log entry formats • Use naming conventions • Agree on correlation strategy
67. 67. Challenge: Correlating Requests ui-svc catalog- svc checkout- svc shipping- svc payment- svc request
68. 68. Use Correlation IDs 09-02-2015 15:03:24 ui-svc INFO [uuid-123] …… 09-02-2015 15:03:25 catalog-svc INFO [uuid-123] …… 09-02-2015 15:03:26 checkout-svc ERROR [uuid-123] …… 09-02-2015 15:03:27 payment-svc INFO [uuid-123] …… 09-02-2015 15:03:27 shipping-svc INFO [uuid-123] …… ui-svc catalog- svc checkout- svc shipping- svc payment- svc request correlation id: “uuid-123” correlation id: “uuid-123”
69. 69. What did we cover? • Ownership • Discovery • Data management • Deployment • I/O explosion • Monitoring
70. 70. Related Sessions • ARC201 - Microservices Architecture for Digital Platforms with AWS Lambda, Amazon CloudFront and Amazon DynamoDB • CMP302 - Amazon EC2 Container Service: Distributed Applications at Scale • DEV203 - Using Amazon API Gateway with AWS Lambda to Build Secure and Scalable APIs • DVO401 - Deep Dive into Blue/Green Deployments on AWS • SPOT304 - Faster, Cheaper, Safer Products with AWS: Adrian Cockcroft Shares Experiences Helping Customers Move to the Cloud
71. 71. Remember to complete your evaluations!
72. 72. Thank you! Adrian Trenaman SVP Engineering, gilt.com @adrian_trenaman Derek Chiles Sr. Mgr, Solutions Architecture, AWS @derekchiles