Split my monolith - Devoxx
- 1. Split my monolith The workshop Photo : wikimedia
- 3. Split?
- 4. System resilience / Team scalability
- 6. How? We all have seen failed modularisations. Our hypothesis is that it's often about the database https://twitter.com/mathiasverraes/status/711168935798902785?lang=en
- 7. Why is a shared DB bad? - Is it a clear interface? - Where is the code that writes to / read from a column? - Independent deployment? - Can we evolve the DB schema?
- 9. Availability 99.999% uptime ? Ex 99%^20 = 81%
- 11. Train management - Train routing: - Specify itinerary between two stations - Specify train topologies - Schedule train => a train makes the same trip every day - Booking: - Allows our customers to book a train - Traffic info: - Track train on a trip - Detect delay - Invoicing: - Generate invoices and credits notes
- 12. Database schema
- 13. Exercice 1: Identify contexts in data model
- 14. Split database
- 15. Ideal split of Trip table
- 17. Exercise: Split the Booking table
- 18. Which domain depends on which data? T: Traffic info B: Booking R: Train routing I: Invoicing T,B,R B,R T,B,R T,B,R T,R,I T,R,I T,R,I T,I T T T,R,I T,B,I I I B, I B, R, I B, I T, B, R, I T, B, R, I B T, B, R, I T,B T, B, R, I B, I B, I B, I B, R, I T, B, I B, I B, I
- 19. How?
- 20. Bubble context
- 21. Autonomous bubble - synchronous
- 22. Autonomous bubble - state stream
- 23. Autonomous bubble - events
- 24. Autonomous bubble - notif. stream
- 25. Exposing legacy assets as services
- 26. Exercices
- 27. Scenario 1 Today is the day! Everything is flooded! Panic on board! Lots of late trains, we need an immediate solution. Deadline: Yesterday
- 28. Scenario 1 Many technical problems occur regularly in recent days Deadline: Yesterday We want to quickly set up a dashboard indicating in "real time" the number of people impacted. We cannot afford to re-deploy the legacy Expected : - Schema with modules, DB, API, messages, models (use the legend) - Messages frequency / data refresh rate - Make explicit any isolation - Detail data for each post-its
- 29. Scenario 1 - solution
- 30. Scenario 2 The storm is over. The dashboard made it a lot easier to prioritize various incidents. We'll need a solid architecture to build upon. Deadline: Not defined
- 31. Scenario 2 Expected : - Schema with modules, DB, API, messages, models (use the legend) - Messages frequency / data refresh rate - Make explicit any isolation - Detail data for each post-its The dashboard made it a lot easier to prioritize various incidents. We'll need a solid architecture to build upon. How would you do that given enough time? Deadline: Not defined
- 32. Scenario 2 - a solution
- 33. Scenario 2 - data Expected : - Which data is duplicated - Where does it come from - Who is the Writer Reader Many technical problems occur regularly in recent days Try another representation based on data model.
- 34. Scenario 2 - data, new point of view
- 35. Scenario 2 - data - solution
- 36. Scenario 3 We have a new “nonon” offer (nogo train). Improve business opportunity, but success is not guaranteed. Need to test without impact legacy and reuse maximum functionality to reduce costs. Deadline: Month
- 37. Scenario 3 We have a new “nonon” offer (nogo train). How to integrate this offer with the current system? - New commercial offer : other reduction cards, options to buy (wifi, breakfast, cancellation insurance) - Traffic info and seat reservation remain in the legacy - A train is either completely “nonon” or legacy offer Deadline: Month Expected (context and data) : - Schema with modules, DB, API, messages, models (use the legend) - Messages frequency / data refresh rate - Make explicit any isolation - Which data is duplicated - Where does it come from
- 38. Scenario 3 - solution
- 39. Scenario 3 - solution
- 40. Final Notes
- 41. In-process sync event (method call)
- 44. Eventual Consistency is key Most things can be decoupled if we allow a delay in consistency (1s, 1min, …) Ex : The csv exports CALM theorem - State will ALWAYS be consistent if we don't delete information Counter examples: - Set difference - Multiple processes doing increment
- 45. Database change with minimal interruption 1st deploy: Add the new table/database 2nd deploy: fill the new table with values. Check 3rd deploy: New code writing to the new table (but not reading). Possibly a migration script 4th deploy: New code or feature flip, to read from the new table 5th deploy: remove old columns/table and old code. (Possibly days later)
- 46. Questions
- 47. Bibliographie Getting started with DDD when surrounded by legacy systems De Eric Evans Monolith to Microservices De Sam Newman
- 49. Thank you !