How Akka works
Data and logic together
Clustering from within
Our architectural first principles treat the app, not the database, as the system of record for data. Apps cluster from within to optimize access to logic and data.
Apps are in-mem DBs, recoverable, and movable
Apps have multiple read-write instances
Apps execute across hybrid environments
0 → ∞ → 0
Distributing from within
Akka apps are microservices that embed an Akka runtime that distributes data everywhere it needs to be. Apps scale along four dimensions to ensure elasticity and resilience.
Akka apps shard data and segregate queries while applying dynamic compute separately to each entity and view component type.
Sharded data
App data is partitioned across in-memory, durable nodes. Akka routes user requests to the correct instance.
Data rebalancing
Data shards are re-balanced as the number of runtime nodes changes.
Query elasticity
Data queries are offloaded to separate compute. Services scale their entity and view components separately.
Always recover
Akka apps can auto-recover by persisting state changes. Your app’s state changes are captured as replayable, sequential snapshots and events. The event store is Akka-managed infrastructure that grows and shrinks as needed.
Integration ready
Synchronize
For unreliable environments and digital twins.
Apps synchronize and rationalize their state with a peer instance without concern for data volume, sequence, or network connectivity.
Share
For advertising changes with interested peers.
Apps advertise and broadcast state changes over brokerless, reliable messaging that other apps can subscribe to.
Stream
For continuous real-time data processing.
Apps merge, map, transform, shift, and calculate data in real time with a continuous operating model.
Synthesize
For transforming operational data into analytics.
Apps define projections which modify, push, and propagate data changes into other Akka apps or 3rd party data systems, especially analytics warehouses.