Microservices in the Apache Kafka Ecosystem
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In this presentation, Ben Stopford, Engineer, Confluent discusses the world of Microservices in the streaming world.
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Microservices in the Apache Kafka Ecosystem
- 1. Ben Stopford, Engineer, Confluent
- 2. • Event Driven Microservices • The toolset: Kafka, KStreams, Connect • 10 Principals for Streaming Services What we’ll cover
- 6. The Monolith Can we do reuse, encapsulation?
- 7. The Monolith What happens when we grow?
- 8. Companies are inevitably a collection of applications They must work together to some degree
- 9. Inverse Conway Maneuver The 'Inverse Conway Maneuver' recommends evolving your team and organizational structure to promote your desired architecture. Org Structure Software Architecture
- 11. Service based approaches separate state
- 12. But state must inevitably be shared between services
- 13. Use a toolkit that embraces decentralisation
- 15. Some simple patterns of distributed systems
- 17. When do we need Request Response? Looking things up What is in my shopping basket?
- 18. Event Driven Async / Fire and Forget / Brokered
- 19. Businesses are often modeled as a sequence events. Add to cart Buy Make Payment Update Stock Email Conf Package Item Dispatch Fraud
- 20. When do we need Event Driven?
- 21. SOA / Microservices Message Broker Event Based Request/Response
- 23. Request Response Buy Items Order Service Stock Service Ful- filment Service Fraud Det- ection Mmm… New IPad REST etc UI Service
- 24. Event Driven Buy Items Order Service Stock Service Ful- filment Service Fraud Det- ection Message Broker Mmm… New IPad UI Service
- 26. As software engineers we are inevitably affected by the tools we surround ourselves with. Languages, frameworks, even processes all act to shape the software we build.
- 27. GUI UI Service Orders Service Returns Service Fulfilment Service Payment Service Stock Service The tools we choose have a big effect on our architecture GUI UI Service Orders Service Returns Service Fulfilment Service Payment Service Stock Service Event Driven Request Response
- 28. Kafka is well suited to Event Driven Architectures It will lead you down that path
- 29. The Tool Set
- 31. What is a Distributed Log?
- 32. Shard on the way in Producing Services Kafka Consuming Services
- 33. Each shard is a queue Producing Services Kafka Consuming Services
- 35. Reduces to a globally ordered queue
- 38. Build ‘Always On’ Services Rely on Fault Tolerant Broker
- 39. Services can “Rewind & Replay” the log Rewind & Replay
- 40. Compacted Log (retains only latest version) Version 3 Version 2 Version 1 Version 2 Version 1 Version 5 Version 4 Version 3 Version 2 Version 1
- 41. A database engine for data-in-flight
- 42. Max(price) From orders where ccy=‘GBP’ over 1 day window emitting every second Continuously Running Queries
- 43. What is stream processing engine? Data Index Query Engine Query Engine vs Database Finite source Stream Processor Infinite source
- 44. Windowing For unordered or unpredictable streams Sliding Fixed (tumbling)
- 45. Features: similar to database query engine JoinFilter Aggr- egate View Window
- 46. Stateful Stream Processing stream Compacted stream Join Stream Data Stream-Tabular Data Windowed Stream Locally Cached Table (disk resident) KafkaKafka Streams
- 48. Scales Out
- 51. Sometimes you need to physically move data
- 52. Replicate it, so both copies are identical
- 55. So…
- 56. Service Backbone Scalable, Fault Tolerant, Concurrent, Strongly Ordered, Stateful
- 57. Embeddable tool for data manipulation Adapt data streams (transformation, stream maintenance, analytic function)
- 58. Replicate Data Sources Exactly Create Regenerable, Streaming Views
- 59. How do we actually do this? 10 (opinionated) principals for Streaming Services
- 60. 1. Don’t use Kafka for shopping carts! (OK, you can, but use sparingly) Shopping Cart UI Service Broker/durability/broadcast add little to request response
- 61. Do use Kafka for your core business processing. Think “business events”. An order was created, a payment was received, a trade was booked etc. Pub/Sub. GUI UI Service Orders Service Returns Service Fulfilment Service Payment Service Stock Service
- 62. 2. Pick Topics with Business Significance Orders Payments Returns Invoices
- 63. Give your messages meaningful IDs and version them OrdersService1-Order-1234-v2 Should relate to the real world Should be Versioned (if mutable) Include the service name Note the key used for sharding in Kafka may not be this key
- 64. 3. Decouple publishers from subscribers GUI UI Service Orders Service Returns Service Fulfilment Service Payment Service Stock Service
- 65. Add Request/Response only where needed GUI UI Service Orders Service Returns Service Fulfilment Service Payment Service Stock Service REST
- 66. 4. Use the log to regenerate state Avoid journaling incoming events
- 67. Event Source side effects • Use offsets to tie these back to the stream • Store in: • Kafka • Kstreams state store • Other DB
- 68. 5. Apply the Single Writer Principal • Change at source (by calling that service) • Let the change propagate back • Keep local copies read only. GUI UI Service Orders Service Returns Service Fulfilment Service Payment Service Stock Service (1) Change Orders at source (2) Let the change propagate through
- 69. 6. Leverage keeping datasets inside the broker customer country supplier ex- rates
- 70. Leverage keeping only the latest version (table view) Version 3 Version 2 Version 1 Version 2 Version 1 Version 5 Version 4 Version 3 Version 2 Version 1
- 71. Join & Process on the fly stream Compacted stream Join Orders Customers KafkaKafka Streams
- 72. 7. Prefer stream processing over maintaining historic views UI Service Orders Service Returns Service Fulfilment Service Payment Service Stock Service Orders Historic “copies” diverge
- 73. Join & Process on the fly stream Compacted stream Join Orders Customers KafkaKafka Streams
- 74. 8. Sometimes you need historic views. => Replicate & Keep Read Only
- 76. Iterate
- 78. 9. Use Schemas (especially if data is retained)
- 79. Schemaless data doesn’t age well
- 80. Messages
- 81. Confluent Schema Registry can help Orders Service Schema Registry Email Service Avro
- 82. 10. Consider “Stream Management” Services Stream Management Kafka • Retaining data => Admin tasks • Similar to the role of a DBA • Data Migration • Repartitioning • Latest/versioned • Environment Management • CQRS
- 83. KStreams is a good toolset for this
- 85. So… 1. Don’t use Kafka for shopping carts! 2. Pick Topics with Business Significance 3. Decouple publishers from subscribers 4. Use the log to regenerate state 5. Apply the Single Writer Principal 6. Leverage keeping datasets inside the broker 7. Prefer stream processing over maintaining historic views 8. Sometimes you need historic views. => Replicate Read Only 9. Use Schemas 10. Consider “Stream Management” Services
- 86. Microservices push us away from shared, mutable state
- 87. But state needs to be communicated
- 88. In an increasingly data-heavy world we need tools to do this efficiently
- 89. …and in real time.
- 90. We need a data-centric toolset to do this All Your Data Immutable Streams Event Driven Services Stream Management Services Legacy CDC View Replication Polyglotic Persistence tables streams Streaming Services
- 91. Keep it simple, Keep it moving
Notes de l'éditeur
- Today we’re going to talk about two fields which sit somewhat apart. Stream Processing & Business Systems Services encourage us to share responsibilities. To rely on others. To collaborate and evolve a business’s view of the world over time. A world that is inherently decentralised. Inherently spread across many interconnected systems. Spreading many disparate subsets of our business’s state. Yet our approach rarely reflects this. We tend to think in centralised ways, we accumulate data. We protect it. We control it. We hoard it. But it does not need to be this way.
- Today we’re going to talk about two fields which sit somewhat apart. Stream Processing & Business Systems Services encourage us to share responsibilities. To rely on others. To collaborate and evolve a business’s view of the world over time. A world that is inherently decentralised. Inherently spread across many interconnected systems. Spreading many disparate subsets of our business’s state. Yet our approach rarely reflects this. We tend to think in centralised ways, we accumulate data. We protect it. We control it. We hoard it. But it does not need to be this way.
- My name is Ben Stopford. I work as an engineer on Apache Kafka. In a previous life I did the most centralised thing you could possibly do. I built a single, central database for a large financial institution. This talk is about exploring the exact opposite approach to this problem. It’s about thinking of the world in terms of the evolution and provisioning of state across many systems. It’s about embracing decentralisation. It’s about thinking in streams.