Demands and expectations for .NET developers have never been higher. We're expected increase customer value against higher and higher expectations; deliver our services and content across a greater variety of devices; retain, analyze, and use ever-growing volumes of data faster; and to do all of this while being available 24/7. That's a tall order, but it's one that is being done successfully by .NET companies all over the world - right now. In this webinar, lead by Petabridge CEO Aaron Stannard, we're going to cover how the obvious ways of scaling software in the past are doomed to fail and how .NET shops are developing and deploying their own distributed systems to tackle these problems efficiently and effectively using Akka.NET and Akka.Cluster.

1. Introduction to Akka.NET and
Akka.Cluster
Aaron Stannard,
Co-founder Akka.NET
@Aaronontheweb

2. High Expectations for Backend
Developers

3. And the obvious ways to scale
are wrong.

4. Enterprise CRUD App

5. Scenario 1: Surge in User
Activity

6. Obvious Solution: Sharding

7. Brittle

8. Scenario 2: Real-time User
Interactivity

9. Obvious Solution: Read-after-
Write

10. Reality

11. Why is Distributed Programming
Hard?

12. Distributed Systems 101

13. Enter the Actor Model

14. Origins

15. Actor Model Core Concepts

16. Akka.NET: Actor Model for .NET

17. Akka.NET Modules
• Akka – core actor library
• Akka.Remote – cross-node actor deployment /
communication
• Akka.Cluster – elastic actor networks (HA)
• Akka.Persistence – event-sourcing, durable actor state &
recovery
• Akka.Streams – streaming workflows
• Akka.Cluster.Tools – cluster singleton, distributed pub
sub
• Akka.Cluster.Sharding – durable state partitioning
• Akka.DData – eventually consistent data replication

18. What's an Actor?

19. Statefulness & Concurrency
with Actors

20. Actors have internal state

21. Actor state is always private,
modified when handling
messages, only shared via
immutable messages

22. Actors live in a family tree

23. Actors process one message at
a time

24. Actors on the Network

25. Event and Message Driven
Programming

26. Properties of Messages

27. Messaging Patterns

28. Messaging Protocols

29. Communicate with Actors via
Reference

30. Actor References Have
Transparent Locations

31. Akka.Remote & Akka.Cluster:
Location Doesn't Matter

32. What Akka.Cluster Does

33. What is a "Cluster?"

34. Network View of a Cluster

35. When do we use clustering?

36. Clustering Examples

37. CAP Theorem

38. CAP Terminologies

39. Clustering is for AP

40. How an Akka.NET Cluster
Forms

41. Initial State

42. Seed Node: Initial, well-known
nodes contacted by nodes
attempting to join the cluster

43. Joining the Cluster

44. Leader Election

45. Ring Formation

46. Gossip: How Nodes Discover
Each Other

47. Leaders Act on Gossip
Convergence

48. Node Member States

49. Node Leaves

50. Node Exits

51. Running Multiple Akka.NET
Apps in a Cluster

52. Cluster Roles

53. Cluster Role HOCON
Declaration

54. Clustered Routers
How (most) actors on one node
send messages to each other.

55. Clustered Group Routers

56. Cluster Routers

57. Cluster Pool Routers

58. Cluster Group Router HOCON

59. Clusters & State

60. Fastest Response Time?

61. Stateful Apps Serve Results
from Memory

62. State Locality

64. State Makes Protocols Work

65. CAP & Protocols

66. State has to be Moved

67. Akka.Cluster Makes This Easy

68. How to Move State

69. Recovering State on the Fly

70. Petabridge