The Rocky Road from Monolithic to Microservices Architecture

The explanation of our Microservices architecture and the lessons we learnt from it.

Transcript

1. THE ROCKY ROAD FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE
2. THE ROCKY ROAD FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE
3. SRINIVASAN RANGARAJAN Head of Product Engineering
4. SRINIVASAN RANGARAJAN https://cnu.name Twitter: @cnu Github: @cnu
5. RETAIL AUTOMATION PRODUCT
6. Catalog & User Events Processing Recommendation
7. MONOLITHIC ARCHITECTURE
8. MONOLITHIC ARCHITECTURE Image Processing API Image Searcher File Storage
9. MINIMUM TWO SERVERS BEHIND ELB Load Balancer
10. CLOUD VS BARE METAL
11. EXPENSIVE
12. NOT REALTIME
13. NOT PERSONALIZABLE
14. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine
15. GOTHAM
16. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine
17. JOKER • Convert client’s catalog into one common MAD Format • Normalization of fields and metadata • Can process batch and streaming data • Major cause of chaos in the system
18. GORDON • Routes the product metadata to the right micro services • Is it a new product? or update to an existing product? • Streaming data from AWS SQS
19. WONDER WOMAN • Not a microservice, But a tool used to generate rules for the catalog • Rules are send to the Image Processing microservice • Works on Samples of data and not entire dataset
20. WATCHTOWER • Central Source of Truth for all metadata • Backed by an RDBMS Database (Postgresql) • Input via SQS and REST API • Output via REST API
21. INGESTION Gordon Joker Joker Joker Wonder Woman Watchtower Next Stage
22. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine
23. NIGHTWING • Computer Vision and Deep Learning Models • Convert Image to high dimensional vectors • Tag image with visual attributes • Computer Intensive
24. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine
25. BATMAN • Custom very fast Vector Indexer and Search Engine • Stores everything in memory • Two sub-parts: Indexer and Searcher • Store binary information about image in DynamoDB
26. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine
27. SUPERMAN • User behaviour based recommendation • Multiple products like Collaborative filtering, Cross Product recommendation • Records every user event data and stores in a data warehouse
28. TWO FACE • Individual User level Personalization • Shows a different “face” to each user • Dynamic and realtime
29. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine
30. FLASH • Very fast data structure storage - redis instance • User session level history, Product Availability, etc. • Fast access, but non- expirable
31. GCPD • “Global Cache for Products Digested” • Rough first level of cache for the results
32. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine
33. ROBIN • API Gateway for all our products • Combines data from other micro services like Batman, Two Face, Watchtower, Superman, etc and returns JSON Response
34. API Data Store Ingestion Image Processing Image Searcher User Event Personalization Engine Joker, Gordon, Wonder Woman Nightwing Batman Robin Watchtower, GCPD, Flash Superman, Two Face
35. LESSONS WE LEARNT
36. START WITH A MONOLITH. CHIP OFF PIECES AND BUILD THE MICROSERVICES. Lesson 0
37. –Melvin Conway “… organizations which design systems ... are constrained to produce designs which are copies of the communication structures of these organizations."
38. DEPLOY HETEROGENOUS MICROSERVICES IN A SINGLE SERVER Lesson 1
39. Compute Optimized Server Memory Optimized Server Nightwing Batman Robin Robin Robin Joker Joker Joker Gordon Watch tower Watch tower Joker
40. IMMUTABLE MICROSERVICES Lesson 2
41. Constable Inspector Assistant Commissioner Commissioner
42. ASYNCHRONOUS IS BETTER THAN SYNCHRONOUS Lesson 3
44. NOT ALL MICROSERVICES NEED TO BE SERVERS Lesson 4
45. ADD REQUEST ID OR TRANSACTION ID TO DEBUG EASILY Lesson 5
46. GIVE A CHARACTER TO YOUR MICROSERVICES Lesson 6
48. THANK YOU
49. http://cnu.name/talks/ @cnu