EuRuKo 2017 - Data driven production apps

A talk geared towards making a legacy Rails app smart and setting up the pillars for future success

Transcript

1. Data-driven production apps Sai Warang @cyprusad !
3. The Problem Statement
4. The Problem Statement inspired by true events
5. Turning back time Platform for merchants to sell online Fund merchants who are showing promise to help them do better! Use data we already have to predict how merchants will do in the future
6. The Setup Rails app that allows merchants to set up an online store, and that can process orders Following standard Rails design patterns to set up your application
7. Model View Controller Web Mobile Controller Model View
8. Model Ruby class that encapsulates behaviour Has a persistence layer backed by a relational database Lastly, has historical data
9. Model class Shop < ApplicationRecord has_many :orders end class Order < ApplicationRecord belongs_to :shop end
10. Controller Wrangle out the behaviour and data that lives in models Serve different consumers with this model data and logic
11. View Templates that describe how to present data Doesn’t know where the data comes from that gets filled into the views
12. Cool, basics covered So what’s the problem?
13. Artificial Intelligence
14. Paradigm shifts Having an app that works well as the user expects is not enough Need to be able to predict sane choices for the user and automate repetitive actions in your apps Recommendations
15. .. specifically, we want to Predict which merchant is risky to fund and which merchant is not risky to fund Predict the amount that a merchant will reasonably be able to pay back Confidently automate underwriting
16. The Dream ™ shop = Shop.new(merchant_id)
 
 funding = BigData::Predict.amount(shop)
17. Does MVC suffice?
18. Engage the genius part of the brain
19. AI and Machine Learning tools
20. AI and Machine Learning tools
21. Wait a minute This is getting complex
22. Wait a minute We can already process data within application
23. What is the simplest thing? Aggregations ± Cron background jobs for more complex calculations
24. ‼
25. Caveats Aggregations ± Cron background jobs for more complex calculations Performance issues in the app as it performs complex calculations leading to CPU resources getting tied up and there will be that one moment when you start dropping traffic because of DB locks
26. What is another simple thing? Reporting views and dashboards
27. ‼
28. More caveats Reporting views and dashboards Can your data be trusted? Does your app have test data in production? (Here’s looking at you past Sai )
29. Data quality Complex analysis Real time predictions Rails app
30. Data quality Complex analysis Real time predictions Rails app
31. Data quality Complex analysis Real time predictions Rails app ⚠
32. Data quality Complex analysis Real time predictions Rails app ⚠
33. The Unknown Web Mobile Controller Model View
34. Data Warehouse
35. Extract Transform Load ETL all your tables into a warehouse Timely snapshots of production database Sent over to an external system Clean your data
36. ActiveRecord::Base.connection.tables.each do |table| klass = table.classify if Time.now > @checkpoint[klass] warehouse_extract(klass.constantize.where(updated_at: @checkpoint[klass]..Time.now)) end end
37. ActiveRecord::Base.connection.tables.each do |table| klass = table.classify if Time.now > @checkpoint[klass] warehouse_extract(klass.constantize.where(updated_at: @checkpoint[klass]..Time.now, test: false, secret: false)) end end
38. Benefits of warehousing No more constraint of production environment Opens up the possibility of using many Machine Learning libraries No stress on production database
39. But.. Is simply pulling data out of live production system enough?
40. Dimensional Modelling
41. Main idea Not entity relationships Doesn’t follow the schema of production database tables Modelled around business domain
42. Principal components Facts (numerical values) Dimensions (data points that define the facts)
43. Star schema Periodic snapshot fact Dimensions Dimensions Dimensions Dimensions Dimensions
44. For example Daily sales over a period of time Inventory Date Product Order Shop
45. Good habits Dimensional modelling of data in your warehouse relies on you modelling your application well
47. Data quality Complex analysis Real time predictions Rails app ⚠ ETL + Dimensional Modelling ✅ ✅
48. Data quality Complex analysis Real time predictions Rails app ⚠ ETL + Dimensional Modelling ✅ ✅
49. Data is the new product
50. HTTP REST APIs Data warehouse tooling that generates insights HTTP REST API to deliver those insights to your app
51. Insight models Easily plug view layer Loose, can be freely modelled (JSON blobs, some fields) JOIN tables in normal flow of application logic Native Rails validations (data integrity) when inputted back into Rails
52. Data quality Complex analysis Real time predictions Rails app ⚠ ETL + Dimensional Modelling ✅ ✅ Web tooling + New models ✅ ✅
53. The New Architecture Web Mobile Controller Model View Dimensionally modelled data ETL Insights
54. That’s a lot of pieces How is this not fragile?
55. The Failure Scenarios
56. In order to succeed, avoid the most common ways to fail
57. Resiliency questionnaire How are we keeping APIs up to date? What happens when your ETL cron goes down? What happens when your dimension schemas become outdated? What is the end user experience?
58. Infrastructure guard rails Dimensional modelling validations Metrics Chat ops that notify when something is not quite right
60. Human verification Web Mobile Controller Model View Dimensionally modelled data ETL Insights
61. Thank you! @cyprusad Come chat with me and my colleagues!