Organizations like Starbucks, HPE, and PayPal (see our customers) have selected the Akka toolkit for their enterprise scale distributed applications; and when it comes to squeezing out the best possible performance, the secret is using two particular modules in tandem: Akka Cluster and Akka Streams. In this webinar by Nolan Grace, Senior Solution Architect at Lightbend, we look at these two Akka modules and discuss the features that will push your application architecture to the next tier of performance. For the full blog post, including the video, visit: https://www.lightbend.com/blog/akka-at-enterprise-scale-performance-tuning-distributed-applications

2. Akka at Enterprise Scale
Performance Tuning and Distributed Applications
Nolan Grace
Solution Architect

3. Introduction
3
Nolan Grace
Senior Solution Architect
@theNolanGrace
Nolan.grace@lightbend.com

4. 4

5. 5

6. Reactive Architecture
6
Responsive
Message Driven
Elastic Resilient
Maintainable Extensible
Value
Form
Means

7. What does our architecture need?
1. End to end Monitoring
2. Efficient Resource Usage
3. Scalability
4. In-memory speed
5. Self Healing
7

8. What does our architecture need?
1. End to end Monitoring
2. Efficient Resource Usage
3. Scalability
4. In-memory speed
5. Self Healing
8

9. 9
Akka Cluster
Akka Streams

10. • Backpressured Reactive Stream
• No stages will ever be overwhelmed
• Machine Resources can be distributed to achieve optimal performance
10
Akka Streams
Source Flow Sink

11. 11
Akka Streams Advanced
Source cpuHeavy Sink
Source(List(1, 2, 3))
.map(cpuHeavy)
.to(Sink.ignore)

12. 12
Akka Streams Advanced
Source cpuHeavy
Source(List(1, 2, 3))
.map(cpuHeavy)
.async
.to(Sink.ignore)
Sink
Async

13. 13
Akka Streams Advanced
Source(List(1, 2, 3))
.mapAsync(2)(cpuHeavy)
.to(Sink.ignore)
Future [ T ]
Source cpuHeavy Sink

14. 14

15. 15
Akka Streams
1. End to end Monitoring
2. Efficient Resource Usage
3. Scalability
4. In-memory speed
5. Self Healing

16. • Akka Cluster allows you to expand beyond one node
• Communicate between nodes the same way you would within a node
• Tools for efficient data management and routing in a distributed environment
• Sharding – Split my data up over all my nodes(Strong Consistency)
• Distributed Data – Every node has full copy of data(High Availability)
• Adaptive Load Balancing – Routing work to nodes with the most CPU, Memory, or a mix of both
16
Akka Cluster

17. 17
Akka Cluster

18. 18
Akka Cluster

19. 19

20. 20
Akka Cluster

21. • Tesla Picture
21

22. 22
Akka Cluster

23. 23
Akka Cluster Sharding
User 1 User 2
User 3
• In memory Speed
• Strong Consistency
• Event Sourcing
• Not always Local

24. 24
Akka Distributed Data
Leaderboard
Leaderboard
Leaderboard
• In memory Speed
• Tunable Consistency
• Always Local

25. 25
Akka ClusterAkka Streams
1. End to end Monitoring
2. Efficient Resource Usage
3. Scalability
4. In-memory speed
5. Self Healing
1. End to end Monitoring
2. Efficient Resource Usage
3. Scalability
4. In-memory speed
5. Self Healing

26. 26
Akka ClusterAkka Streams
Source Flow Sink
JVM
Source Flow Sink
JVM

27. 27
Akka ClusterAkka Streams
https://doc.akka.io/docs/akka/current/stream/actor-interop.html

28. 28
Akka Streams to Actor
Source(List(1, 2, 3))
.map(msg => {actor ! msg})
.to(Sink.ignore)
Source Tell Sink

29. 29
Akka Streams to Actor Backpressure
Source Ask Sink
Source(List(1, 2, 3))
.ask[Ack](actor)
.to(Sink.ignore)

30. 30
Akka Streams to Actor Backpressure
Source Ask Sink
Source(List(1, 2, 3))
.mapAsyncUnordered(10)(msg => {
(actor ? msg).mapTo[Ack]
})
.to(Sink.ignore)

31. 31
Actor to Akka Stream
Source Flow Sink
val queue = Source
.queue[Int](bufferSize,
OverflowStrategy.backpressure)
.map(flow)
.toMat(Sink.ignore)
.run()
queue.offer(x).pipeTo(self)

32. 32
Akka ClusterAkka Streams
Http Ingress Backend
Akka Cluster

33. 33
Akka ClusterAkka Streams
Http Ingress Backend
Akka Cluster

34. 34

35. 35
Akka ClusterAkka Streams
Akka Cluster
Frontend
Network
Frontend
CPU
Frontend
Memory
Backend
Network
Backend
CPU
Backend
Memory

36. 36
Akka ClusterAkka Streams
Akka Cluster
Frontend
Network Frontend
CPU
Frontend
Memory
Backend
Network
Backend
CPU
Backend
Memory

37. 37

38. 38
Overflow vs Backpressure
Source
ML
Recommendations
Checkout

39. 39
Overflow vs Backpressure
Source
ML
Recommendations
Checkout
Source

40. 40
Overflow vs Backpressure
Source Flow Sink
val queue = Source
.queue[Int](bufferSize,
OverflowStrategy.dropNew)
.map(flow)
.toMat(Sink.ignore)
.run()
queue.offer(x).map {
case QueueOfferResult.Enqueued =>
Success()
case QueueOfferResult.Dropped =>
Overflow()
}

41. 41
Overflow vs Scaling

42. Reactive Microservices
Core frameworks and runtimes to create responsive, resilient, and scalable distributed systems
42

43. Thank You
Nolan Grace
Senior Solution Architect
Nolan.Grace@lightbend.com