Building Event Driven Services with Stateful Streams

1. 1 Building Event Driven Services with Stateful Streams Ben Stopford @benstopford

2. 2 Microservice are an evolution

3. 3 Distributed Applications

4. 4 Apply the Single Responsibility Principal

5. 5 Service-Based Application Orders Service Basket Service Payment Service Fulfillment Service Stock Service

6. 6 Release together Orders Service Validation Service Payment Service Fulfillment Service Stock Service

7. 7 Microservices are Independently Deployable

8. 8 Independently Deployable Orders Service Validation Service Payment Service Fulfillment Service Stock Service

9. 9 Service can have different release schedules Services Released Together Services Released Independently Stock Orders Payment Stock Orders Payment

10. 10 Allows us to scale

11. 11 Scale in people terms

12. 12 Microservices •  Single Responsibility •  Fine grained •  Modularized •  Independently deployable •  Run by different teams

13. 13 When it comes to data, microservices promote independence.

14. 14 Stateful services have their own DB Orders Service Validation Service Payment Service Fulfillment Service Stock Service

15. 15 This makes sense

16. 16Database Data on inside Data on outside Interface amplifies data Databases provide a rich point of coupling

17. 17 Service A Service B Service C So microservices avoid shared databases

18. 18 But most business services share the same core stream of facts. Catalog Authorisation

19. 19 Many databases leads to many data islands Orders Service Validation Service Payment Service Fulfillment Service Stock Service

20. 20 Difficult to compose a joined up view

21. 21 Services Evolution 1.0 - SOA 2.0 - Microservices 3.0 - What’s next???

22. 22 How do we get to 3.0? •  Loose Coupling •  Data Enabled •  Event Driven •  Operational Transparency

24. 24 The synchronous world of request response protocols leads to tight, point-to-point couplings.

25. 25 Coupling A measure of the assumptions two services make about one another when they exchange information. Frank Laymann

26. 26 Change to one service => many dependencies to consider CHANGE

27. 27 Leverage Receiver Driven Flow Control

28. 28 UI Orders Service Stock Service Payment Provider Maybe order more stock Request-Response Model

29. 29 UI Orders Service Stock Service Repricing Service Payment Provider Maybe order more stock New services must be called explicitly

30. 30 UI Orders Service Stock Service Payment Provider Maybe order more stock Event Driven Model with Broker

31. 31 UI Orders Service Stock Service Payment Provider Maybe order more stock Flow Control is Receiver Driven: Pluggable Repricing Service

32. 32 Events + Brokers Decouple Architectures

34. 34 Useful Grid Orders Service Payments Service Customers Service We need to join different datasets

35. 35 Useful Grid Orders Service Payments Service Customers Service Need a better way to handle data the data services expose

37. 37 Useful Grid Orders Payments Customers Why Poll when we can Push

38. 38 Useful Grid Orders Service Payments Service Customers Service Streaming leads to being both event driven and data enabled Event Driven Data Enabled

40. 40 Distributed Murder Mysteries

41. 41 Ledger Service Loan Validation Shared Narrative Fraud Service History Service Account Service Account History Login Service Loan Service A Journal of Service Interactions

43. 43 Tenet 1: Events and Asynchronous processes better model the way businesses work

44. 44 Tenet 2: Services need to design for data-on-the- outside

45. 45 Tenet 3: The measure of a ‘good’ architecture is it’s ability to evolve

46. 46 Steps to Streaming Services

47. 47 1. Take Responsibility for the past and evolve

48. 48 Stay Simple. Take Responsibility for the past Browser Webserver

49. 49 Evolve Forwards Browser Webserver Orders Service

50. 50 2. Raise events. Don’t talk to services.

51. 51 Events => Decouple Events == Facts

52. 52 Raise events. Don’t talk to services Browser Webserver Orders Service

53. 53 Order Requested Order Received Browser Webserver Orders Service Raise events. Don’t talk to services

54. 54 Order Requested Order Validated Order Received Browser Webserver Orders Service Raise events. Don’t talk to services

55. 55 3. Use Kafka as a Service Backbone

56. 56 KAFKA Order Requested Order Validated Order Received Browser Webserver Orders Service Use Kafka as a Backbone for Events

57. 57 KAFKA - Highly available - Linearly scalable - Load balance consumers - HA consumers

58. 58 4. Use CDC to evolve away from legacy

59. 59 KAFKA Order Requested Order Validated Order Received Browser Webserver Orders Service Evolve away from Legacy

60. 60KAFKA Order Requested Order Validated Order Received Browser Webserver Orders Service Use the Database as a ‘Seam’ Connect Products

61. 61 5. Make use of Schemas

62. 62KAFKA Order Requested Order Validated Order Received Browser Webserver Orders Service Use Schemas to validate Backwards Compatibility Connect Products Schema Registry

63. 63 6. Use the Single Writer Principal

64. 64KAFKA Order Requested Order Validated Order Received Browser Webserver Orders Service Apply the single writer principal Connect Products Schema Registry Order Completed

65. 65 Single Writer Principal -  Creates local consistency points in the absence of Global Consistency -  Makes schema upgrades easier to manage.

66. 66 7. Embrace Multi-tenancy

67. 67KAFKA Order Requested Order Validated Order Received Browser Webserver Orders Service Apply Bandwidth Limits to services Connect Products Schema Registry Order Completed 100Mb/s

68. 68 8. Store Datasets in the Log

69. 69 Messaging that Remembers Orders Customers Payments Stock

70. 70 Rewind and Replay Rewind Replay Orders Service

71. 71 KAFKA Order Requested Order Validated Order Received Browser Webserver Orders Service Utilize Whole Datasets Connect Products Schema Registry Order Completed Repricing

72. 72 Compacted Topic: Delete superceded messages that share the same key K1 K1 K1 K2 K2 K2 K1 V1 V1 V2 V3 V2 V4 V3 Compacted Topics

73. 73 Orders Customers Payments Stock Single, Shared Source of Truth Data-on-the-Outside

74. 74 But how do you query a log?

75. 75 9. Use Materialized Views to Query the Log

76. 76 Materialized View Create via a query (select * from Orders Where Region == ‘USA’) Base Table Materialized Views in a DB

77. 77 Insert data Materialized View Create via a query (select * from Orders Where Region == ‘USA’) Query is rerun on data as it arrives Base Table Materialized Views in a DB

78. 78 In practice often more complex select Order.region, count(order.quantity) from Orders, Product, Customer where Product.group = ‘Houshold’ and Customer.type = external group by Order.region Orders Product Customer View Read Optimized

79. 79 Same problem but with services select Order.region, count(order.quantity) from Orders, Product, Customer where Product.group = ‘Houshold’ and Customer.type = external group by Order.region My Service View Read Optimized Orders Service Product Service Customer Service

80. 80 Use Materialized Views, but turned inside out!

81. 81 Or Embed Views with the Streams API Insert data Kafka Streams Business Logic KAFKA Orders Service Query in DSL

82. 82 On startup: Rewind and Replay Rewind Replay Orders Service

83. 83 Data Services: Distributed Materialized View Kafka Queryable State API Service KStreams View

84. 84 Create Views with Connect DB of choice Kafka Connect APIOrders Service Context Boundary Kafka

85. 85 10. Move Data to Code

86. 86 High Throughput Data Movement Service Service Service Service instance 1 Service instance 2 Service instance 3 Service instance 4 Kafka Cluster (many machines)

87. 87

88. 88 Connect Order Requested Order Validated Order Completed Order Received Products Browser Webserver Schema Registry Orders Service Stock Stock Materialize Stock ‘View’ Inside Service KAFKA

89. 89 If messaging Remembers: Views don’t have to!

90. 90 11. Take only the data you need today

91. 91 Connect Order Requested Order Validated Order Completed Order Received Products Browser Webserver Schema Registry Orders Service Stock Stock Take only the data you need! KAFKA

92. 92 Take only the data you need {“Stock Inventory”: { “Id”: “Foo1234”, “Vendor”: “Foo Industries”, “Description”: “This is a …”, “Delivery Category”: “ND”, “Stock Status”: [ “Items in Stock”: 53, “Items on Order”: 0 ]…etc…}

93. 93 Data Movement Be realistic: •  Network is no longer the bottleneck •  Indexing is: •  In memory indexes help •  Keep datasets focussed

94. 94 12. Use the log instead as a ‘database’ (for data-on-the-inside)

95. 95 Connect Order Requested Order Validated Order Completed Order Received Products Browser Webserver Schema Registry Orders Service Reserved Stocks Stock Stock Reserved Stocks Save Internal State to the Log (Event Sourcing) KAFKA

96. 96 Append state as events Service Append KAFKA

97. 97 Rewind and Replay on startup Rewind Replay Service KAFKA

98. 98 Connect Order Requested Order Validated Order Completed Order Received Products Browser Webserver Schema Registry Orders Service Reserved Stocks Stock Stock Reserved Stocks Order Service Loads Reserved Stocks on Startup KAFKA

99. 99 14. Use Transactions to tie Communication & State together

100. 100 OrderRequested (IPad) 2a. Order Validated 2c. Offset Commit 2b. IPad Reserved Internal State: Stock = 17 Reservations = 2 Tie Events & State with Transactions

101. 101 Connect TRANSACTION Order Requested Order Validated Order Completed Order Received Products Browser Webserver Schema Registry Orders Service Reserved Stocks Stock Stock Reserved Stocks Stay Simple. Take Responsibility for the past KAFKA

102. 102 15. Compose Services as a Streaming Pipeline

103. 103 (1) Stateless Processor e.g. - Filter orders by region -  Convert to local domain model -  Simple rule

104. 104 (2) Stateless, Data Enabled Processor Similar to star schema •  Facts are stream •  Dimensions are GKTables e.g. Enrich Orders with Customer Info & Account details Stream GKTable GKTable Stream-Table Join

105. 105 (3) Gates e.g. rules engines or event correlation When Order & Payment then … Stream 1 Window Window Stream-Stream Join Stream 2

106. 106 (4) Stateful Processor e.g. - Average order amount by region - Posting Engine (reverse replace of previous position) State store backed up to Kafka

107. 107 (5) Stream-Aside Pattern public static void main(…){ … } JVM

108. 108 (7) Sidecar Pattern

109. 109 Combine them: 1.  Stateless 2.  Data enriched 3.  Gates 4.  Stateful processors 5.  Stream-aside 6.  Sidecar

110. 110 Group services into bounded contexts

111. 111 Kafka Kafka Independently deployable subsystem

112. 112 Richer Example

113. 113 All order logic, stateless service Orders-by-Customer view (KStreams + Queryable State API) UI Service Stream Maintenance & Monitoring Highly available, stateful service UI uses Orders-by- Customer View directly via Queryable State History Service pushes data to a local Elastic Search Instance Orders Service Derived View UI joins data Tables & Streams Fulfillment Service Analytics OrdersProduct Custo- mers KTables KTables KTables Schemas

114. 114 So…

115. 115 Good architectures have little to do with this:

116. 116 It’s about how systems evolves over time

117. 117 Request driven isn’t enough •  High coupling •  Hard to handle async flows •  Hard to move and join datasets.

118. 118 Services need to be data enabled

119. 119 The decoupling effects of the broker make the architecture pluggable and data enabled Orders Customers Payments Stock

120. 120 ...with a single source of truth Orders Customers Payments Stock

121. 121 ...and many service specific views Orders Customers Payments Stock

122. 122 … which react to events as they occur Orders Customers Payments Stock

123. 123 Microservices enabled by a streaming platform •  Loose coupling •  Data Enabled •  Event Driven •  Operational transparency

124. 124 How do we get to 3.0? •  Loose coupling •  Embrace data •  Event Driven •  Operational transparency

125. 125 Thank You @benstopford https://www.confluent.io/blog/tag/microservices/

126. 126 Tips and Tricks •  Take only the data you need today •  Limit the scope of request response interfaces •  Build small services. Build big ones too. •  Don’t fear the network. Fear the index. •  The Sync-Async divide •  Pure event driven with event streams and views. •  Map request response to event driven (Leverage CQRS) •  Too fine-grained services. •  Too much data •  KStreams rebalancing •  Need for automation

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