This library and NuGet package provides a robust and versatile solution designed to facilitate designing and building distributed system architecture. Whether you’re building microservices, distributed applications, or event-driven systems, this API provides the tools you need to design, construct, and maintain high-performance, concurrent solutions using the actor concurrency model.
The aim is to provide an infrastructure that allows developers to quickly setup a modern and scalable web application based following the famous Actor Model.
The application can be configured freely following your favourite tools and architecture, and depends almost solely on the SignalR library.
In its essence, this repo intends to promote modularity and scalability, helping developers to build parallel and distributed applications.
The Actor Model organizes complex computations in autonomous entities called "actors" which can work concurrently. Each actor manages its own state by executing CRUD (Create, Retrieve, Update, Delete) operations, and is allowed to exchange messages with any other actor asynchronously.
SignalR is a real-time communication library for web applications, enabling bi-directional communication between servers and clients. It allows the exchange of messages and ensures a dynamic and responsible interaction in real-time applications.
The Agency project is the cornerstone of this repo. It provides an easy-to-use API to define agents, managers, and projects.
Aligned with the actor model, this framework empowers developers to swiftly configure numerous concurrent actors, referred to as Agents. Agents are stateful entities, each one owns a unique and private state, and executes CRUD operations based on the message it receives. Communications with other actors is regulated via a contract interface that defines the possible incoming and outgoing messages.
Agents are defined uniquely by their name, state, and contract.
Since messages are handled sequentially and states are not shared, there is no need for a locking mechanism to avoid that the same resource is accessed simultaneously by multiple resources. Conversely, the operations follow a chain of messages that the various actors continuously send to each other based on the application design. Both the request-response and the message stream patterns are supported by this library.
Since v2.1.0, this library enables parallel handling of Read* requests to enhance responsiveness and reduce the Agents workload. This means that, while Update requests are generally processed sequentially, the incoming Read request from another actor is not handled by the same queue. Instead, it is resolved immediately and the current state is sent back as a response. Typically, a resource sending Read requests also uses to subscribe to DataChanged which can be sent when an Update request has been fully processed. In this way, the Agency ensures that every actor consumes new data when they become available.
Typically, in a data-driven application, different Agents are usually deployed to administrate different kind of data, e.g. assumptions, parameters, benchmark, business processes, reference and transactional data.
Agents are not deployed immediately when the application starts, instead, deployment occurs only when strictly necessary, namely when an actor needs them and send a message that a group of Agents is capable to handle. This process is regulated by dedicated Managers.
Managers are agents themselves and handle the creation and destruction of all other agents.
This library allows you to freely configure any number of Agent and contracts in order segregate different concerns as much as possible and truly profit from the decentralized and scalable nature of modern cloud architectures.
A key feature is the possibility to configure applications with more than one Manager, each one being in control of different subsets of agents. As the number of deployed agents can become quite large, this feature ensures scalability by avoiding that the one Manager becomes the limiting factor of the equation.
Together with Agents and Managers, the third configurable actor is the Project.
Projects are versatile light-weight actors offering a vast range of interesting features.
While Agents require their own classes and their lifetime is not under your control, Projects are in full control of the developer. Their instantiation and disposal happens fast, so to make them ideal on the client side, e.g. View Model, or Controller. Moreover, they also run with a well-defined contract that rules the messages that a Project can send, and they give the chance to subscribe to messages, ensuring a user-defined delegate is invoked every time a given message is received. By means of fluent API it is possible to assign any number of Agents to a Project, thus informing the Manager that the communication started from that project can result is a new Agent being spawned. Lastly, Projects can also be specialized to handle logs and register every information logged by the decentralized application for auditability.
While Message hub is the project storing all structures and APIs to deal with message handling, Job is the dedicated state handler.
While the Agency uses the structures defined in the Message Hub project, this can also be used as standalone. It provides the MessageHub class to handle message posting with and without response, a background service Post to process messages in the outbox queue sequentially and safely, the PostingHub defining all the methods invokable on the SignalR server, and a series of static stateless methods that are generally usable to configure the SignalR hub connection with both default and dedicated functionalities.
Together, the class, the service, the server hub, and the configuration methods, give a generalized mechanism to deal with a wide range of scenarios. The developer can freely decide when to rely on the request-response pattern and when to simply broadcast streams of events (messages) to all the connected clients. This flexibility was from the beginning enforced on the design of this library to broaden the range of applications and facilitate its adoption.
The project named "Job" provides an abstraction layer to manage a state undergoing several updates in a step-by-step approach. Job is thread-safe and guarantees that each step is executed following the simple first-in-first-out (FIFO) rule. Thanks to its clean and easy-to-use API, it helps the developer to quickly set up logging and visualize progress for the given operation to be started.
This is a typical configuration of the Program:
using Enterprise.Agency;
using Enterprise.MessageHub;
.
.
.
builder.Services.AddSignalR();
builder.Services.AddAgencyServices(baseUrl, o => o
.WithAgentTypes([typeof(Agent<Model, DataHub, IDataContract>)])
.WithHireAgentsPeriod(TimeSpan.FromMinutes(30))
.WithOnBoardingWaitingTime(TimeSpan.FromSeconds(1))
.WithOffBoardingWaitingTime(TimeSpan.FromSeconds(1)));
Here below is how to configure one project on the WPF client side:
var project = Project<IApp>.Create(baseUrl)
.Register(agent => agent.DataChangedEvent, DataUpdate)
.Register(agent => agent.ShowProgress, ShowProgress)
.Register(agent => agent.Display, Logger)
.AddAgent<Model, DataHub, IDataContract>().Run();
and here the configuration of a different project with logging capabilities on a different client
var project = Project<IAgencyContract>.Create(NavManager.BaseUri)
.ReceiveLogs((sender, senderId, message) =>
{
var formattedMessage = $"{sender}: {message}";
messages.Add(formattedMessage);
InvokeAsync(StateHasChanged);
})
.Run();
From the Program configuration we learn there is going to be one agent (actor) deployed when needed. This is a simple data agent owning some 'Model' and performing some operation, e.g. import, update, deletion. Moreover, from the API we learn that the first project will be using this agent, that is, the client defining this project will eventually need to access the data Model.
Within this framework, the team responsible of architecting the data agent would need to set up both the contract and the hub. While the former defines all possible incoming or outgoing requests that this agent can handle, the latter provides the corresponding methods that will be called automatically when each given request is processed.
An example of the IDataContract interface is the following:
public interface IDataContract : IAgencyContract
{
/* in */
Task<MyData> ReadRequest();
Task ImportRequest(string fileName);
/* out */
Task DataChangedEvent();
Task ShowProgress(double progress);
Task Display(string message);
}
whereas the corresponding DataHub reads
public class DataHub : MessageHub<IDataContract>
{
public async Task<Model> CreateRequest(Model model)
{
await model.Database.ReadAllAsync();
return model;
}
public async Task<List<string>> ReadRequest(Model model)
{
return model.GetPrintable();
}
public async Task ImportRequest(string fileName, Model model)
{
if (fileName is null || fileName is "")
{
Post(agent => agent.Display, $"Cannot process null or empty fileName");
return;
}
var dirInfo = new DirectoryInfo(".");
var files = dirInfo.GetFiles("*.*", SearchOption.AllDirectories);
var file = files.FirstOrDefault(x => x.FullName.Contains(fileName));
Post(agent => agent.ShowProgress, 1d/3);
model.Update(DataOperators.Import(file));
Post(agent => agent.Display, $"File {fileName} imported");
Post(agent => agent.ShowProgress, 2d/3);
model.Process();
Post(agent => agent.Display, $"Data has been processed");
Post(agent => agent.ShowProgress, 3d/3);
Post(agent => agent.DataChangedEvent);
}
}
Note the usage of outgoing messages 'ShowProgress', 'Display', and 'DataChangedEvent' to signal the state of the process and any eventual change on the data. Clients can subscribe to these messages providing the corresponding handler.
From the client side the kick off of an Import Request is achievable with the following simple API:
project.Post(agent => agent.ImportRequest, fileName);