Breaking Down The Monolith - NodeConfBP

RisingStack's journey about breaking down a monolith application into microservices.
Presented at https://nodeconf.risingstack.com in 2017 January.

Contact speaker here: https://twitter.com/slashdotpeter

Included topics:

- Business and technology benefits and drawbacks of microservices
- Why we moved
- Service principles: versioning and documenting
- Automation
- Proxy and API Gateway approaches
- Fault tolerance: Caching, CQRS
- Request Signing
- Zero downtime deployment: Kubernetes, Graceful shutdown, Rolling deployment, Self-healing
- Microservices monitoring and debugging challenges
- Distributed Tracing
- Network Delay use-case

What's next?

https://blog.risingstack.com/tag/node-js-at-scale
https://www.martinfowler.com/microservices
https://microserviceweekly.com/
https://trace.risingstack.com

Transcript

1. Breaking Down The Monolith
   

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2. - CTO, Co-founder of RisingStack
   - @slashdotpeter on Twitter
   - https://blog.risingstack.com
   $ whoami
   

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3. Microservices
   Img: http://ryanjbaxter.com
   

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4. Moving to microservices
   

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5. Moving to microservices
   

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6. - Co-located teams can work more efficiently
   - different space / time zone
   - Smaller teams are usually more efficient
   - More focused people
   - Faster onboarding for new developers
   Possible business reasons to move
   

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7. - Improved fault isolation
   - Independent development
   - Independent deployment
   - Allows technology diversity
   Technology benefits
   

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8. - Increasing architectural complexity
   - Increasing operational complexity
   - Monitoring and debugging are much harder
   - Handling eventual Consistency
   Technology drawbacks
   

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9. microservice architecture is
   not a silver bullet
   

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10. Why we moved?
    

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11. - Microservice monitoring tool
    - Node.js focused
    - Built in Node.js
    - We migrated to microservices architecture
    Trace by RisingStack
    

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12. - We wanted to have more focused teams
    - We have very different challenges
    - Fault tolerance is a key in our business
    Why we moved?
    

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13. Before we moved
    

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14. Today
    

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15. How we moved?
    

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16. Services and teams
    

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17. - Separate DB per service
    - Maximize the depth of service call chains
    Service principles
    

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18. - We create backward compatible endpoints
    - We don’t version services (only endpoints)
    - Use feature flippers/toggles (dark launches)
    Service principles
    

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19. - Good and available documentation
    - Update docs and code together
    Good to start here: https://github.com/Yelp/service-principles
    Service principles
    

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20. Automation
    

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21. - Automation is a key in building and operating microservices
    - Easy and fast to deploy
    - Easy and fast to rollback
    - Testing, Service bootstrap, DB migration etc.
    Automation
    

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22. Proxy / API Gateway
    

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23. Proxy / API Gateway
    

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24. - Client(s) specific things:
    - Authentication: Cookie headers, JWT token etc.
    - Protocols: http, WebSocket etc.
    - Response format: JSON, XML etc.
    - Combining resources: from multiple services
    API Gateway
    

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25. API Gateway
    Img: http://bits.citrusbyte.com/microservices
    

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26. Fault tolerance
    

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27. - Services fail separately (in theory)
    - Critical resources should be cached
    - Messaging queues can help (HTTP request is not recoverable)
    Fault tolerance
    

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28. Fault tolerant data collection - CQRS
    read
    write
    

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29. Fault tolerant data collecting
    Queue size increasing during issue
    Queue size decreasing after issue resolved
    

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30. Caching
    

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31. - On service level
    - Automatically via response headers
    - Via our communication package
    - Multi store caching (in memory, Redis):
    https://www.npmjs.com/package/cache-manager
    Caching
    

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32. Security
    

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33. - Trusted sources (services) on public channel
    - Request signing between services
    - Significant CPU overhead
    Request signing
    

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34. Request signing
    

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35. Infrastructure
    

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36. - Started with PaaS
    - We moved to Kubernetes
    - Zero downtime deployment
    Infrastructure
    

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37. Zero downtime deployment
    Deploy
    

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38. - Graceful shutdown
    - Rolling deployment
    - Self healing applications
    - Horizontal autoscaling
    Infrastructure
    

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39. Monitoring and debugging
    

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40. Microservices producing lot’s of data
    - Logs, Errors
    - Metrics per service
    - Transactions
    - Logical connections
    - Side effects
    

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41. microservice monitoring is
    impossible for humans
    

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42. How to find an issue?
    Alerting, Dashboard
    Topology
    Metrics, Errors
    Traces
    Profiler
    Investigate a service
    Locate on code level
    Understand connections
    Identify participating services
    Always know about it
    

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43. Distributed tracing
    

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44. - Transaction ID, Correlation ID
    - Google Dapper white paper
    - Trace by RisingStack
    - Zipkin
    - OpenTracing
    Distributed tracing
    

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45. Case study: Network delay
    

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46. Case study: Network delay
    3.8ms 564.8ms
    95th response time:
    Call depth: 1 service 2 services
    

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47. Case study: Network delay
    network delay
    

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48. network delay is evil
    in microservices
    

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49. - https://blog.risingstack.com/tag/node-js-at-scale
    - https://www.martinfowler.com/microservices
    - https://microserviceweekly.com/
    - https://trace.risingstack.com
    What’s next?
    

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50. Thanks!
    

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