Actor model programming in Orleans framework

I’ve spent some time recently playing around with Orleans Framework. It’s an alternative to Akka.NET offering similar actor-based architecture.

What is actor model?

Actors are called Grains in Orleans. Actor or Grain is a class representing some business entity. It can be instance of a Player, Trader, Customer or Account depending on domain topic.  In practice Grains are implemented like normal C# classes. A constraint that we have is that all methods have to be asynchronous to keep all the communication between grains non-blocking.

Local state

Grains have local state. It means that they live in memory between requests to system. It gives big performance benefits compared to creating entity instance based on data from database for each request. State can be persisted to database to avoid loosing data on system restarts. As programmer you can invoke saving to storage any time when state has changed.

Horizontal scalability

Orleans can run in cluster using many servers. Framework can place grains in all nodes across the cluster, so that each grain is located only in a single node. There can be some exceptions from that rule: when node crashes framework may be not sure when exactly grain has finished its processing. This problem is in general called a split-brain in computing. But this is an edge-case which falls into error handling strategies, overall assumption is that each grain is activated only once.

Grains are exchanging massages between each other. That messages use super-fast .NET binary serialization. Messages can go over network if 2 grains are on separate nodes. So it is important to make grains not too chatty if you care about performance, and you probably care if you are interested in frameworks like Orleans 🙂

Possibility to run Orleans in a cluster gives beautiful linear scalability.

What problems is actor-model good for?

Actor model is suitable when you have a lot of objects communicating with each other. Example use cases:

• Real-time trading systems
• Multiplayer games
• IoT applications connected to many devices

Grain activations should be distributed randomly and decentralized. Actor-model is not suitable for batch processing or centralized design where some entities have to process most of the requests (so called hot-spots).

Event sourcing

Actors are  a good fit to match with event sourcing pattern. Grain supports that pattern by JournaledGrains. But here comes a disappointment. Available storage mechanisms for event log persistence are poor. The only built in storage provider saves event log for given grain as collection serialized into single state object, so the whole event log needs to be read before recreating grain state. Other built in storage saves only state snapshot without saving event log. Good thing is that there is flexible extensibility point  allowing to write your own provider by implementing just 2 methods for reading and writing events. There is also a community contribution available which integrates Orleans with Event Store but this database is not my favorite. Probably I’m complaining too much and should instead contribute by implementing events log storage based on Cassandra or CosmosDB, it does not look like a hard task, but the next topic is much harder – distributed transactions.

Distributed transactions

Creators of Orleans framework did a great job to formally describe frameworks semantics. You can have a look at how they implemented distributed transactions: https://www.microsoft.com/en-us/research/publication/transactions-distributed-actors-cloud-2/

The algorithm is very interesting but from practical point if view, what I miss is lack of support for transactional communication between JournaledGrains. Again, support for event sourcing pattern seems to have been not a top priority in Orleans so far.

I you would like to jump deeper into other theoretical aspects of actor-based architecture, you may be interested in other Microsoft Research materials:
https://www.microsoft.com/en-us/research/project/orleans-virtual-actors/

Message delivery

Orleans can give you one of the guarantees:

• message will be delivered at most once
• message will be delivered at least once

There is not guarantee to deliver the message exactly once. We are in distributed system and this problem is not easy to solve without sacrificing performance. This is something to be aware of. It’s up to you how to introduce fault tolerance.

Orleans and microservices

You can think of Orleans as of microservices framework. The services are really micro. Each grain is a service. You probably cannot go more micro with microservices  than in actor-based architecture. If you are building a microservices-based system, please have a look at Orleans docs and ask yourself an honest question: have you thought about all that problems that Orleans addresses and solves when building your microservices solution? We often make shortcuts through mud and bush because we do not even know that there is a better way. Please have a look at this presentation to illustrate some examples:

Summary

I’m very grateful for all contributors who put Orleans into existence because it provides decent ground for building well-defined actor based architecture. Even if this model is not suitable for your needs, Orleans is very educational. Making a deep dive into its architecture and implementation can broaden architectural horizons a lot.

But on the other hand in my opinion you have to be prepared to make quite a lot of custom extensions / contributions on a framework level to build production-class system. There is an interesting initiative called Microdot framework which adds to Orleans many must-have features when building real system. But even with Microdot, this ecosystem looks more like an academical research rather than a shiny framework ready to ship to production out-of-the box. For everyone looking for something more mature with bigger support I would recommend to look at Azure Service Fabric.

But forgetting about production and enterprise readiness, programming model in Orleans is sweet. APIs are well designed and framework offers many extensions points to play with. Worth trying before signing-up for a cloud solution.