An Akka.NET reference architecture demonstrating CQRS with in-memory replication, cluster sharding, and event sourcing for high-throughput, fault-tolerant systems.
Akka.CQRS is an Akka.NET reference architecture that implements the Command-Query Responsibility Segregation (CQRS) pattern using cluster sharding, event sourcing, and in-memory replication. It demonstrates how to build high-throughput, fault-tolerant distributed systems, such as stock trading platforms, where write and read operations are scaled independently.
Akka.NET developers and architects building scalable, resilient distributed systems that require separation of write and read concerns, such as financial trading platforms, real-time analytics, or high-availability microservices.
Developers choose Akka.CQRS because it provides a production-ready blueprint for implementing CQRS with Akka.NET, including best practices for cluster sharding, event sourcing, and in-memory replication, reducing the complexity of building fault-tolerant, horizontally scalable applications.
Akka.NET Reference Architecture - CQRS + Sharding + In-Memory Replication
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Provides a full reference implementation of CQRS using Akka.NET, including best practices for event sourcing, cluster sharding, and read-write separation, as detailed in the README's domain explanation.
Leverages Akka.Cluster.Sharding and split-brain resolvers to ensure high availability and automatic failover during node failures, demonstrated in the failover testing section.
Uses in-memory replication via DistributedPubSub to enable fast, local queries on read-side nodes, improving throughput for high-demand scenarios like stock pricing.
Includes Docker and docker-compose configurations that simplify setting up and running the entire distributed system locally, as highlighted in the running instructions.
Requires significant initial effort to configure Akka.Persistence with MongoDB, set up cluster roles, and manage Docker containers, with a known issue necessitating container deletion on restarts.
The in-memory replication strategy sacrifices strong consistency for better availability and performance, which may not suit applications where data accuracy is critical at all times.
Assumes familiarity with Akka.NET's actor model, clustering, and persistence concepts, making it challenging for teams new to this ecosystem to adopt without extensive training.