Kubernetes together with microservice architecture provides perfect support for the new generation of a software solution. Even so, Kubernetes clusters need no be able to communicate between each other or to integrate with external systems. In this session, we will tackle patterns that can be used to provide a high-redundancy and high-available communication channel, that support the powerful backend provided by Kubernetes. Keywords: Kubernetes, AKS, Service Bus, Redis, KubeMQ

2. Dreamer, Technology Enthusiast, Speaker, Trainer, People 1st, Microsoft Azure MVP, Crafter, Community, Sharing

5. K8 Cluster

8. K8 Cluster

10. K8 Cluster

11. K8 Cluster
HTTP(S)
/ MSG

12. K8 Cluster
MSG
/ CALL

13. K8 Cluster
DIRECT

14. K8 Cluster
HTTPS
QUEUE
PUB/SUBS
ASYNCHRONOUSSYNCHRONOUS

17. 6

18. 6
123645

19. 6
123645

20. 6
123645 1234

21. 6

22. 123456
6
Error
7 6Error

23. 123456
6
Error
7 Error
Dead-letter queue
6

24. 6 6

25. 1234566
Error
6 6
6 6

26. 1234566
Error
6 6

28. K8 Cluster
Active
Passive

29. K8 Cluster
Active
Passive

30. Risks
Message
Delay
Message
Lost
Message
Duplicate

31. K8 Cluster
Active
Active

32. K8 Cluster
Active
Active

33. Risks
Duplicate
detection
Extra cost
Additional
computati
on power

35. K8 Cluster
Queue

37. K8 Cluster
Main Queue
Delayed Queue
delay messagerequeue after TTL

38. Duplicate
detection
Session
Dead
lettering
Batching Transactions
Filter and
actions

44. Content needs to be received
with success
Content is not lost
Sometimes acknowledge is
expected
Command like approach
At least one system handles the
content
Clear contract between
producer and consumer
Producer does not care about
the consumer
Acknowledge or inform
Capture activity or changes in
system
Producer does not expect
action from consumer
Data points, data streams
Consume historical messages

46. Command base Reliable async delivery
Transaction handling
Fault tolerance
Content Filtering
Content Routing
Data Stream
Receiver confirmation
At least one delivery
Low latency
Order processing
Telemetry use case
Multiple consumers High volume of data
FIFO like
Data consistencyDuplicate detection
Data ordering
One time delivery
Durability
Dead-letter
Session
Batching
Auto-delete
Auto-forwarding
Token base access
TTL
Scheduled delivery
Message deferral
Retrieve old data

47. To sum up

48. K8 Cluster
PUB / SUBS
EVENT BUS
EVENT BUSEVENT BUS

50. Thank you!
@RaduVunvulea
vunvulearadu.blogspot.com
https://www.linkedin.com/in/raduvunvulea/
vunvulear@outlook.com