Building event-driven systems on Kubernetes

Event-driven architectures can offer compelling benefits in comparison to traditional REST-based systems. Key use cases include streaming analytics, edge computing, push-based frontends, and systems where traditional fault tolerance solutions are insufficient. This session gives an introduction to eventing and discusses the pros and cons of event-based architectures. Knative Eventing is a framework to enable development of asynchronous event-driven apps on Kubernetes; we will see the system in action including live demos. Additionally, Kubernetes Event Driven Autoscaling (KEDA) solves a separate but related problem: providing "scale to zero" for event driven workloads. We’ll show KEDA in action scaling an event-driven workload with Apache Kafka and discuss the future of eventing on Kubernetes.

Presented at JAX 2020.

https://jax.de/cloud-container-serverless/building-event-driven-systems-on-kubernetes/

Transcript

1. Building event-driven systems on Kubernetes — Ian Partridge
2. Ian Partridge • Senior engineer in IBM Hybrid Cloud • Background • Runtime systems development • Open source software • Cloud & Kubernetes Twitter: @alfa GitHub: @ianpartridge 2
3. Agenda • Event-driven systems • Kubernetes & Apache Kafka • KEDA • Demo • Debezium • Knative • Demo 3
4. © 2020 IBM Corporation Users demand responsive, real-time applications Apps must react to events in real time, as they happen Apps must deliver responsive customer experiences Apps enhanced with real-time intelligence
5. 5 https://www.reactivemanifesto.org Responsive Resilient Elastic Event Driven
6. 6 Responsive Microservices
7. REST Microservices • REpresentational State Transfer became the de facto way of building microservices • Microservices communicate over HTTP(S) • Client/server model • REST APIs use standard verbs: • GET, POST, PUT, DELETE • JSON became the standard data format • REST APIs are documented using OpenAPI • Standard description format 7 $ http swapi.dev/api/planets/1/ HTTP/1.0 200 OK Content-Type: application/json { "climate": "Arid", "diameter": "10465", "gravity": "1 standard", "name": "Tatooine", "orbital_period": "304", "population": "200000", "residents": [ "https://swapi.dev/api/people/1/", "https://swapi.dev/api/people/2/", ... ], "rotation_period": "23", "surface_water": "1", "terrain": "Dessert", "url": "https://swapi.dev/api/planets/1/" }
8. Event-driven Architectures • Instead of command/response, focus on events • An event is a record of fact • Events can be transmitted via messages • Messages can be stored durably • Push vs pull • Messages can be multiplexed 8
9. Responsiveness 9 REST based Microservices Event based Microservices µS µS REST Client must poll to discover data/state changes
10. Responsiveness 10 REST based Microservices Event based Microservices µS µS REST Client must poll to discover data/state changes Data becomes immediately available to Client µS µS
11. Long Running Tasks 11 REST based Microservices Event based Microservices µS REST Client must poll to discover data/state changes against ID HTTP requests timeout at 100 seconds* “Workaround” to return a request ID
12. Long Running Tasks 12 REST based Microservices Event based Microservices µS REST Client must poll to discover data/state changes against ID Update pushed when long running task completes µS HTTP requests timeout at 100 seconds* “Workaround” to return a request ID
13. Multiple Clients 13 REST based Microservices Event based Microservices µS µS µS REST REST BOTH clients must poll for data changes
14. Multiple Clients 14 REST based Microservices Event based Microservices µS µS µS REST REST OR flow must be in reverse: but.. any outage during calls means a notification is lost
15. One produced event is consumed by two clients Multiple Clients 15 REST based Microservices Event based Microservices µS µS µS µS µS µS REST REST OR flow must be in reverse: but.. any outage during calls means a notification is lost
16. Failure of µService causes failure of request Resilience to failure 16 REST based Microservices Event based Microservices µS µS REST µS
17. Failure of µService causes failure of request Resilience to failure 17 REST based Microservices Event based Microservices µS REST µS µS Event backbone stores state enabling requests to continue after µService restart µS µS
18. Resilience to Edge Networks 18 REST based Microservices Event based Microservices µS µS REST Network availability determines request success
19. Requests queued until network availability µS µS Resilience to Edge Networks 19 REST based Microservices Event based Microservices µS µS REST Network availability determines request success
20. Stream Processing 20 REST based Microservices Event based Microservices µS µS REST ML
21. Stream Processing 21 REST based Microservices Event based Microservices µS µS REST ML µService has to be repeatedly called with new data and manage/store history
22. µService has to be repeatedly called with new data and manage/store history Stream Processing 22 REST based Microservices Event based Microservices µS µS REST µS µS µService receives continuous stream of data and has access to history ML ML
23. 23 Event Driven Ecosystem
24. 24 Knative ▪ MySQL ▪ MongoDB ▪ PostgreSQL ▪ Oracle ▪ SQL Server ▪ Db2 ▪ Cassandra
26. •Provision, manage & scale containerised applications •Manage infrastructure resources needed by applications •Volumes •Networks •Secrets •And much more.. •What's in a name? •Kubernetes (k8s/Kube): "Helmsman" in ancient Greek Container Orchestrator
27. DB API Server Client/User Watchers Controllers Node Networks Volumes Secrets ... Request Monitor • At its core, Kubernetes is: • A distributed database (etcd) • "Watchers" & "controllers" that react to changes in etcd. The controllers are what make it Kubernetes. • DB holds the user's desired state • Watchers & controllers attempt to make reality match the desired state • "API Server" is an HTTP/REST front-end to the DB Architecture
28. 28 Distributed streaming platform Publish and subscribe to streams of records Store records in durable way Process streams of records as they occur Apache Kafka
29. © 2020 IBM Corporation Resilient
30. © 2020 IBM Corporation Resilient
31. © 2020 IBM Corporation Resilient
32. © 2020 IBM Corporation Resilient KAFKA CLIENTS
33. © 2020 IBM Corporation Resilient Topic A Partition 1 Broker 1
34. © 2020 IBM Corporation Resilient Topic A Partition 1 Broker 1 Topic A Partition 1 Offline Leader KAFKA CLIENTS
35. © 2020 IBM Corporation Scalability in Kafka
36. 36 Kubernetes Event-driven Autoscaling (KEDA) Serverless for Event Driven Apps Scales on “lag” or “queue depth” Scales to zero when idle KEDA
37. 37 • 2 roles: • Activate/deactivate Deployment for scale to zero • Expose event metrics (like queue length) to the HPA • Lightweight (3 CRDs) • KedaController • ScaledObject • TriggerAuthentication • Deploy via operator, Helm or YAML • Lots of scalars • Kafka, AWS SQS, Azure Event Hub, GCP Pub/Sub, Liiklus, NATS, Prometheus, RabbitMQ, Redis Lists Kubernetes Event-driven Autoscaling (KEDA)
38. 38 • Joint Microsoft / Red Hat project • v1.0 for KubeCon 2019 ( https://youtu.be/ZK2SS_GXF-g ) • Donated to CNCF for incubation • Headed for OpenShift: • “Our goal is to continue making KEDA a first class citizen in OpenShift, easy to consume and install, so you won’t need to be an expert and can focus on your application, freeing developers from infrastructure concerns. With the work being done to build a Kubernetes Operator for KEDA using the Operator SDK, and the built-in support for Operators in OpenShift 4 we are making the installation and management of KEDA in OpenShift easier.” • https://www.redhat.com/en/blog/celebrating-keda-10-providing-event-driven-scale-capability-any- container-workload Kubernetes Event-driven Autoscaling (KEDA)
39. 39 Coffeeshop scenario Coffeeshop UI Kafka event bus HTTP Barista Kafka Baristas REST over HTTP * Kafka pub/sub occurs using MicroProfile Reactive Messaging Kafka pub/sub Kafka pub/sub https://github.com/ianpartridge/coffeeshop-demo
40. 40 Demo…
41. 41 Change Data Capture (CDC) Create events on data change Event enable existing data Modernization of monolithic data stores Debezium
42. : Change Data Capture (CDC) 42
44. Knative • Knative Serving • Run serverless containers on Kubernetes with ease, Knative takes care of the details of networking, autoscaling (even to zero), and revision tracking. • Knative Eventing • Universal subscription, delivery, and management of events. Build modern apps by attaching compute to a data stream with declarative event connectivity and developer- friendly object model. 44
45. Knative Eventing • Knative Eventing services are loosely coupled • Events can be transient or durable • Integrates with Knative Services and k8s Services • Key terminology • Broker - mediates events, forwarding and persisting as appropriate • Trigger - filter on events • Service - receive events 45
46. 46 Demo…
47. Blockchain Scenario 47 event-source Broker Trigger event-display
48. Blockchain Scenario 48 event-source Broker Trigger event-display Trigger classified- display Trigger classifier
49. 49 • Event-driven architectures can deliver the responsive, resilient and elastic characteristics modern applications require • An emerging cloud-native stack of Kubernetes, Kafka, Knative and KEDA has the capabilities needed for building internet-scale event-driven applications • Thank you! Summary