To deal with the Akka Cluster failures, it’s time to bring in some powerful features for handling the unavoidable challenges of distributed systems. From node failures to network partitions and split-brain scenarios, clustered applications are under regular attack from a variety of mysterious sources. In these slides, we look at the dynamics behind failure and recovery with Akka Cluster, especially looking at the difference between DIY recovery and automated recovery with Split Brain Resolver.

1. Split Brain Resolver:
Akka Cluster
Sharding
Presented By: Sanjana Aggarwal
Software Consultant
Knoldus Inc.

2. About Knoldus
Knoldus is a technology consulting firm with focus on modernizing the digital systems
at the pace your business demands.
DevOps
Functional. Reactive. Cloud Native

3. 01 Akka Clustering and Sharding - Why?
02 When and How to use Akka Clustering
03 Cluster Singleton
04 Akka Cluster Sharding
0
Our Agenda

4. Need of using Akka Clustering and Sharding
● Dealing with large workloads
● Database is a Bottleneck
● Shared State/Data

5. Akka Clustering
Akka Cluster provides a way to distribute Akka actors across a cluster of
nodes.
With an Akka cluster we can:-
● Distribute large tasks across a cluster of machine.
● Reduce the traffic to an overloaded database.
● Share critical data across nodes without additional infrastructure.

6. Cluster Formation

7. Cluster Management
Akka HTTP Cluster Management provides your application with a set
of HTTP endpoints to manage the cluster.
It can be used to:-
● Remove members from Cluster
● View the status and health of cluster and it’s members
● View information about cluster sharding distribution
AkkaManagement(system).start()

8. Gossip Protocol
Akka Cluster state is communicated using a gossip protocol.
Gossip Convergence
With a stable cluster eventually all nodes will see the same.

10. Member Lifecycle

11. Reasons of a Failure
● Fatal application errors
● Resource exhaustion
● CPU and/or thread exhaustion
● Memory Leaks
● Incorrect JVM sizing

12. Split Brain

13. Lightbend Split Brain Resolver
● Static-quorum
● Keep majority
● Keep oldest
● Keep referee
● Down all

14. Static-quorum

15. Keep majority

16. Keep oldest

17. Keep referee

18. Down all

20. Akka Cluster Sharding
Instead of Distributing database records across a cluster we distribute
Actors.
Each Actor acts as a consistency boundary for the data it manages.
Terminologies:-
● Entities
● Shards
● Shard Region
● Shard Coordinator

22. Rebalancing
Rebalancing occurs whenever the size of the cluster
changes(due to failure, deployment, scaling)
The shard coordinator will initiate the rebalancing process.

24. References
● https://doc.akka.io/docs/akka/current/cluster-sharding.html
● https://blog.knoldus.com/introduction-to-akka-cluster-sharding/
● https://blog.knoldus.com/akka-cluster-in-use-part-9-effectively-resolving-split-brain-problem/

25. Thank You!
https://www.linkedin.co
m/feed/
sanjana.aggarwal@knol
dus.com