Battle Tested Event-Driven Patterns for your Microservices Architecture - Riskified

1. natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil Battle-tested event-driven patterns for your microservices architecture Natan Silnitsky Backend Infra Developer, Wix.com

2. >180M registered users (website builders) from 190 countries 5% of all Internet websites run on Wix 1,500 Microservices 1,500M Kafka messages produced / Day @NSilnitsky At Wix * Event driven uses cases

3. Agenda Event-driven microservices Event-driven patterns: • Consume and Project • Event-driven from end to end • 0-latency KV Store • Events in Transactions

4. @NSilnitsky Microservices The Monolith

5. @NSilnitsky * Separately Monolith to Distributed Microservices System Microservices

6. @NSilnitsky Microservices Decoupled Microservices Mutable Contention Choice

7. @NSilnitsky Microservices Data Encapsulation * flexibility, schema

8. What about microservices communication?

9. @NSilnitsky Request-reply communication Microservices Communication Checkout Service User Service Inventory Service Payments Service

10. @NSilnitsky Request-reply communication Microservices Communication Request-Reply Model HTTP RPC HTTP RPC HTTP RPC Checkout Service Payments Service Inventory Service User Service

11. @NSilnitsky Request-reply communication * 1 by 1- slow Microservices Communication Request-Reply Model Checkout Service

12. @NSilnitsky Request-reply communication Microservices Communication Request-Reply Model Checkout Service Inventory Service

13. Broker @NSilnitsky * Reliability Event-driven communications Introduce a message broker in between services Message Consumer Message Producer

14. @NSilnitsky * Not ineteract directly Order created Event-driven communications Introduce a message broker in between services Event-driven model Message Consumer Flow control is moved from the sender Broker Message Producer

15. @NSilnitsky Order created Event-driven communications Introduce a message broker in between services Event-driven model Message Consumer Flow control is moved from the sender to the receiver. Broker Message Producer * more robust

16. Kafka Broker Topic Partition Partition Partition @NSilnitsky * Linear scaling Introduce a message broker in between services Distributed log broker Event-driven communications Message Consumer Message Producer

17. @NSilnitsky Append-only log Introduce a message broker in between services Distributed log broker Event-driven communications Kafka Broker Topic Partition Message Consumer Message Producer 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5

18. Order Created Topic 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 @NSilnitsky * Data retained, Immutable Introduce a message broker in between services Distributed log broker Event-driven communications Message Consumer Message Producer Kafka Broker

19. @NSilnitsky * eventually consumed Distributed log broker eventually consume Event-driven communications Order Created Topic 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 Message Consumer Message Producer Kafka Broker

20. Checkout Service Event-driven communications Microservices communication & data Event driven model User Service Inventory Service Cart Service Payments Service @NSilnitsky

21. Checkout Service Cart Service Inventory Service Payments Service User Service Event-driven communications Microservices communication & data Event driven model Kafka Broker Orders Partition Partition Partition Users Partition Partition Partition Cart Partition Partition Partition Inventory Partition Partition Partition

22. Agenda Event-driven microservices Event-driven patterns: • Consume and Project • Event-driven from end to end • 0-latency KV Store • Events in Transactions * used daily. blog

23. Consume and Project 01

24. MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project For Popular Services

25. MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Site installed Apps? Site version? Site owner?

26. MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Request overload Site installed Apps? Site version? Site owner? (~1M RPM requests) Read + Writes Large MetaSite Object

27. Request overload MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Site installed Apps? Site version? Site owner? Read + Writes Large MetaSite Object (~1M RPM requests)

28. Kafka Broker Produce to Kafka Producer MetaSite Site Updated! Consume and Project Entire MetaSite Context

29. Kafka Broker Filter Site installed Apps Updated! Installed Apps ContextProduce to Kafka Consume and Project MetaSite Consumer Reverse lookup writer Consume and Project Producer

30. Kafka Broker Installed Apps ContextProduce to Kafka Consume and Project MetaSite Reverse lookup writer Reverse lookup reader RPC RPC RPC Split Read from Write Consume and Project Producer Consumer

31. Consume and Project — Produce instead of serve - massive load reduction — Project - client-query-optimized “Materialized View” — Read/write split - only scale read-only DB. more resilient

32. Event-driven from end to end 02

33. Kafka Broker Consumer Browser Producer Contacts Importer Service Contacts Jobs Service Web Sockets Service @NSilnitsky Event-driven from end to end Use case: Long-running async business process

34. Subscribe for notiﬁcations ConsumerProducer Browser @NSilnitsky Event-driven from end to end Use case: Long-running async business process Kafka Broker Web Sockets Service

35. Import CSVs ConsumerProducer Browser @NSilnitsky Event-driven from end to end Use case: Long-running async business process Kafka Broker Web Sockets Service

36. Web Sockets Service Consumer * Distributed Producer Browser done! Event-driven from end to end Use case: Long-running async business process Kafka Broker

37. Web Sockets Service Consumer * Distributed Producer Browser Cross DC! Event-driven from end to end Use case: Long-running async business process Kafka Broker done!

38. Event-Driven from End to End — No polling - notify the user on any status change — Scalable - spin up many import job workers — Replication - cross-DC requests are easy to set up

39. 0-latency KV Store 03

40. Kafka Broker Conﬁguration Topic 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 45 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 @NSilnitsky Producer Key - value Consumer 0-latency KV Store Compacted Kafka Topics

41. Kafka Broker Consumer KVStoreReader In-Memory Map Compacted Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 Key Value K1 V1 K2 V1 * Startup 0-latency KV Store @NSilnitsky Compacted Kafka Topics

42. 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 @NSilnitsky 0-latency KV Store Kafka Broker Consumer KVStoreReader In-Memory Map Key Value K1 V1 -> V2 K2 V1 K3 V1 Compacted Kafka Topics * Finished Compacted Topic

43. ConsumerConsumer BookingsBusiness Manager KVStoreReader Time Zones Key Value Albania UTC+1 Algeria UTC+1 Andorra UTC+1 Time Zones KVStoreReader Countries Key Value Albania .al Algeria .dz Andorra .ad Countries@NSilnitsky 0-latency KV Store Kafka Broker

44. Producer Kafka Broker Consumer BookingsBusiness Manager KVStoreReader Countries Countries *New* South Sudan KVStoreWriter Countries @NSilnitsky 0-latency KV Store * Writer producer

45. Kafka Broker Producer Bookings KVStoreWriter Time Zones Time Zones Consumer KVStoreReader Time Zones Key Value Albania UTC+1 ... South Sudan UTC+3 *New* Time Zone for South Sudan *New* Country South Sudan @NSilnitsky 0-latency KV Store

46. Kafka Broker Producer Bookings KVStoreWriter Time Zones Time Zones Consumer KVStoreReader Time Zones Key Value Albania UTC+2 Algeria UTC+1 Andorra UTC+1 *New* Time Zone for Albania Consumer Events KVStoreReader Time Zones DST in-eﬀect for Albania @NSilnitsky 0-latency KV Store

47. 0-latency KV Store — Loading the compacted topic to memory – a 0-latency kv store for dynamic conﬁguration — Compacted topic is still a topic – other services can consume its update events — Small Datasets - for large datasets, the topic should be streamed to a disk-based DB + In-memory Cache

48. Events in Transactions 04 * idempotent

49. Payment Completed Payments Kafka Broker Producer Events in Transactions Classic Ecommerce Flow

50. Payments Checkout Kafka Broker Producer Consumer Producer Order (Items) Completed Events in Transactions Classic Ecommerce Flow Payment Completed

51. Payments Checkout Inventory Kafka Broker Producer Consumer Producer Consumer Delivery Consumer Consumer Invoices Events in Transactions Classic Ecommerce Flow

52. Producer Consumer Producer ConsumerConsumer Consumer Completed Orders Events in Transactions Kafka Broker Classic Ecommerce Flow

53. Producer Events in Transactions Kafka Broker Enable.idempotence = true Attaches oﬀset to message Classic Ecommerce Flow

54. Idempotent Producer Events in Transactions Kafka Broker Classic Ecommerce Flow

55. Events in Transactions Kafka Broker Producer0 1 2 3 4 5 0 1 duplicate!1 Payments Idempotent Producer

56. Transaction Scope Consumer Producer ConsumerConsumer Consumer Producer Events in Transactions Kafka Broker

57. Events in Transactions — Events transactions are quite complex – More boilerplate, performance tuning via transaction size. — DB updates/3rd party calls are not covered – You can dedup by using Kafka record oﬀset as version — Don’t turn on transactions by default - Use in critical ﬂows like payments processing

58. Wix developers have employed these event-driven patterns to make their microservices more decoupled, resilient and scalable.

59. Thank You natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil

60. Resources 6 Event Driven Architecture Patterns - Part 1 & Part 2 - by Natan Silnitsky The Data Dichotomy: Rethinking the Way We Treat Data and Services - by Ben Stopford Shared Database - by Chris Richardson Exactly once delivery - DevOpsDay TLV 2019 - By Natan Silnitsky @NSilnitsky

61. Slides & More slideshare.net/NatanSilnitsky medium.com/@natansil twitter.com/NSilnitsky natansil.com

62. Q&A natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil

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