How do you handle data consistency and partition tolerance in an Akka Cluster using the CAP theorem?
In an Akka Cluster, data consistency and partition tolerance are handled using the CAP theorem by prioritizing consistency (C) and partition tolerance (P), while sacrificing availability (A). To achieve this, Akka employs a few strategies:
1. Sharding : Distributes entities across cluster nodes, ensuring only one instance of each entity is active at any time.
2. Persistence : Stores events and snapshots to recover actor state in case of node failures or rebalancing.
3. Distributed Data module : Implements Conflict-Free Replicated Data Types (CRDTs) for converging replicas’ states without coordination.
For example, consider a shopping cart application with multiple instances running on different nodes. Using sharding, each cart’s state is managed by a single entity, avoiding conflicts. Persistent actors store events like “item added” or “item removed,” allowing recovery after failure. Finally, CRDTs ensure that concurrent updates from different nodes eventually converge to a consistent state.
