Interview Questions for SignalR

SignalR’s architecture is built around a client-server model, enabling real-time, bidirectional communication between a server and multiple clients. Imagine it like a bustling party line, where everyone connected can instantly hear and respond to each other. At the core is the SignalR server, which manages connections, handles messages, and broadcasts updates. Clients connect to this server, and the communication happens using a persistent connection. SignalR cleverly abstracts the complexities of managing the underlying transport mechanisms, ensuring efficient communication even with unreliable network conditions. The server utilizes hubs (or persistent connections – we’ll differentiate these shortly) as endpoints for clients to interact with server-side code. These hubs facilitate the invoking of methods on the server from the client and vice-versa, enabling a rich, interactive experience. The magic happens through a persistent connection which continuously polls for data or employs long polling, WebSockets, or Server-Sent Events, dynamically choosing the best option based on client browser capabilities and network conditions. This ensures optimal performance and responsiveness.

SignalR is incredibly adaptable, choosing the best transport mechanism available to ensure optimal communication. It prioritizes WebSockets for its speed and efficiency if supported by both the client and server. However, it gracefully falls back to other transports like Server-Sent Events (SSE) for one-way communication (server to client) or long polling for situations where WebSockets aren’t available. Think of it as having a backup plan; if the fastest route is unavailable, SignalR takes an alternative, ensuring communication continues without interruption. The selection process is largely transparent to the developer, providing a seamless experience. The exact transport used can be inspected through the SignalR connection object, although usually this isn’t necessary in day-to-day development.

Hubs and Persistent Connections represent different approaches to real-time communication within SignalR. Hubs provide a higher-level abstraction, offering a more structured and method-based interaction. Imagine Hubs as a formal meeting room where clients and the server exchange messages using pre-defined methods. This makes it easier to develop complex interactions. Persistent Connections, on the other hand, are a lower-level approach, handling raw data streams. Think of them as open communication channels where the client and server exchange data freely, giving you ultimate control but requiring more manual handling. Hubs are typically preferred for their simplicity and ease of use in most applications, while Persistent Connections provide more power and control for very specialized scenarios. For example, if you are handling binary data or need absolute control over how data is sent, a persistent connection might be a better fit.

SignalR cleverly handles connection management, automatically reconnecting clients in case of temporary network issues. It monitors the connection status and attempts to re-establish a connection whenever a disruption occurs. It does this transparently, without requiring the developer to write explicit reconnection logic in most scenarios. This resilience is crucial in real-world applications where network connectivity can be unpredictable. Behind the scenes, SignalR utilizes sophisticated techniques like heartbeats (periodic messages exchanged between client and server to maintain connection) and intelligent reconnection strategies. In addition, it offers events that you can hook into, like OnConnected, OnDisconnected, and OnReconnected, providing insights into connection events for more advanced scenarios.

Hub groups in SignalR allow broadcasting messages to subsets of connected clients. Instead of sending messages to everyone, you can target specific groups of users. Imagine organizing a party into smaller themed rooms. Each room represents a hub group. You can then send messages to only the participants in a specific room, without disturbing others. This is implemented by joining clients to groups using Groups.AddToGroupAsync and sending messages to these groups using Clients.Group. This feature is highly effective for creating chat rooms, collaborative editing tools, and other applications requiring targeted communication. The Groups API allows dynamic management of group membership, facilitating flexible and dynamic communication structures.

Handling disconnections in SignalR is crucial for maintaining a robust application. SignalR provides the OnDisconnectedAsync method within a Hub, which is triggered whenever a client disconnects. Within this method, you can perform cleanup tasks, such as removing the client from groups, updating user status, or logging the disconnection event. By using this method effectively, you can ensure your application remains consistent and clean. In a real-world chat application, you might use this event to notify other users about a disconnected participant. The approach you take will depend on your application’s specific needs and requirements.

Implementing authentication and authorization in SignalR is paramount for security. You typically leverage existing authentication mechanisms of your application framework (like ASP.NET Identity or JWT) and integrate them with SignalR. For example, with ASP.NET Core, you can use the built-in authentication middleware to verify user identities before granting access to SignalR hubs. You can then use attributes or custom authorization logic within your Hub methods to control access based on user roles or claims. This ensures only authorized users can perform specific actions, safeguarding sensitive data and preventing unauthorized access. Consider an application with premium features; authorization would restrict premium-only methods to users with a paid subscription. Remember to thoroughly consider security best practices when implementing these safeguards.

Scaling SignalR applications involves strategies to handle a growing number of concurrent connections and maintain performance. Think of it like managing a large party – you need to organize things effectively to ensure everyone has a good time!

• Using a Load Balancer: Distribute incoming connections across multiple SignalR servers. This prevents any single server from becoming overloaded. Imagine this as having multiple party rooms, each with its own host, so no single room becomes overcrowded.
• Redis or SQL Server Backplane: For real-time communication across multiple servers, use a backplane. This acts as a central hub for messages, allowing servers to communicate with each other and share updates with connected clients. It’s like having a central message board at the party, where announcements can be shared across all rooms.
• Azure SignalR Service: Leverage a fully managed service that handles scaling automatically. This takes away the complexities of managing infrastructure, letting you focus on building your application. Think of this as hiring a professional party planner who handles all the logistics.
• Optimizing Client-Side Logic: Reduce unnecessary updates and messages by implementing efficient client-side handling. This is akin to keeping the party conversations focused and avoiding unnecessary chatter.

The best scaling strategy depends on your specific application needs and infrastructure. For smaller applications, a single server might suffice, but for large-scale deployments, a load balancer and backplane are essential.

Performance in SignalR is crucial for a responsive and enjoyable user experience. Common considerations include:

• Large Messages: Avoid sending excessively large messages, as this can impact performance. Imagine trying to send a huge party favor to everyone – it would take a lot of time and resources!
• Inefficient Client-Side Code: Poorly written client-side code that processes messages inefficiently can bog down the application. It’s like having guests who don’t know how to participate in party games efficiently.
• Connection Management: Efficiently handle connections and disconnections to minimize resource usage. Think of managing guest check-in and check-out smoothly at a party.
• Hub Method Execution Time: Keep hub methods short and efficient to avoid blocking threads. Long-running hub methods are like having a slow-moving line at the party buffet – it causes delays for everyone.
• Scalability Issues (discussed above): Ensure that the application can handle a growing number of concurrent users, especially when many clients are receiving frequent updates. This relates to having enough space and resources for all the party guests.

Regular performance testing and profiling are essential to identify bottlenecks and optimize the application.

Debugging SignalR applications requires a multi-pronged approach.

• Browser Developer Tools: Use the network tab to inspect messages sent between the client and server. This allows you to see the exact content and timing of messages, much like reviewing a party’s detailed guest list and timeline.
• Server-Side Logging: Implement robust logging to track events and identify potential issues. This is like keeping a detailed logbook of all activities during a party, to understand what happened and when.
• SignalR Client Logging: Add logging to your client-side code to track messages received and events handled. This gives insight into the client’s perspective, akin to collecting feedback from each party guest.
• Debugging Tools (Visual Studio, etc.): Use breakpoints and stepping to trace the execution flow within your hub methods. This is like carefully examining each step of a party setup to find errors.
• Network Monitoring Tools: Tools like Fiddler or Wireshark can be used for more in-depth network analysis to identify issues related to network latency or message loss. This is like analyzing traffic patterns around a party venue to optimize guest arrival and departure.

Combining these methods helps you isolate and resolve issues efficiently.

Security is paramount in SignalR applications. Potential vulnerabilities include:

• Cross-Site Scripting (XSS): Sanitize all data received from clients to prevent malicious scripts from being injected into the application. Think of it as carefully checking party invitations to avoid unwanted guests.
• Cross-Site Request Forgery (CSRF): Implement CSRF protection mechanisms to prevent unauthorized actions from being performed on behalf of a user. This is like having a secure invitation system for the party, avoiding unwanted intrusions.
• Authentication and Authorization: Securely authenticate and authorize users to control access to specific hub methods. This is like having a guest list and ensuring only invited individuals are allowed in the party.
• Data Protection: Encrypt sensitive data transmitted between the client and server using HTTPS. This protects data in transit, like keeping confidential party information safe during the communication process.
• Input Validation: Always validate all input received from clients to prevent injection attacks and unexpected behavior. This is like checking IDs at the entrance of a party to ensure only authorized guests enter.

By proactively addressing these concerns, you can build secure and reliable SignalR applications.

Error handling in SignalR is essential for creating a robust application. You should handle exceptions thrown within hub methods, and use the try-catch blocks to catch and log errors.

Consider providing informative error messages to clients to enhance the user experience. For example, a user-friendly message indicating a temporary service outage is better than a cryptic error code. Think of this as having a contingency plan for unexpected issues during the party, such as a backup generator in case of power failure.

Implementing a centralized error logging mechanism, such as logging to a file or database, aids in tracking and diagnosing issues. This is like keeping a detailed record of party incidents, which can be reviewed later for improving future events.

public async Task MyHubMethod(string message) {     try     {         // Your code here     }     catch (Exception ex)     {         // Log the exception         await Clients.All.SendAsync("Error", "An error occurred: " + ex.Message);     } } 

SignalR gracefully handles reconnections using a keep-alive mechanism. If a connection is lost due to network issues, SignalR attempts to automatically reconnect the client. This is like having a backup communication system at a party in case the primary communication channel fails.

The client can configure reconnection attempts, and the server will recognize the reconnection and resume communication without any additional intervention. The reconnection process is transparent to the client. Imagine the party continuing smoothly even if the main speaker system momentarily goes down. The host can continue communicating via alternate methods.

You can customize reconnection behavior by setting options such as reconnectAttempts and reconnectInterval.

SignalR provides flexibility in sending messages:

• Server-to-Client: This is the most common scenario where the server pushes updates to connected clients. The server initiates this communication. Think of the DJ at a party announcing a song change – the server (DJ) sends the message to all clients (partygoers).
• Client-to-Server: Clients can invoke methods on the server by calling methods defined in the hub. Imagine a guest requesting a drink from the bartender – the client (guest) sends a request to the server (bartender).
• Client-to-Client (with Server as intermediary): SignalR doesn’t directly support peer-to-peer communication between clients. Instead, the server acts as an intermediary. Clients send messages to the server, which then relays the message to the intended recipient client. Think of sending a message to a friend at a party via the host; the host relays the message.

Clients.All.SendAsync("Message", "Hello from server!"); // Server to All Clients
await Clients.Caller.SendAsync("Message", "Hello from server to you!"); // Server to Specific Client

SignalR is a powerful library for building real-time applications, offering significant advantages over traditional polling methods or other technologies like WebSockets alone. Let’s break down its strengths and weaknesses.

Advantages:

• Simplicity: SignalR abstracts away the complexities of managing WebSockets, Server-Sent Events, and long polling, providing a consistent, easy-to-use API across various transport methods. This significantly reduces development time and effort.
• Cross-platform support: It works seamlessly with various clients (JavaScript, .NET, Java, etc.), allowing you to build applications accessible across different devices and platforms.
• Scalability (with proper design): While scaling can be challenging with any real-time system, SignalR, when combined with appropriate strategies (like using Azure SignalR Service), can handle a large number of concurrent connections.
• Built-in features: SignalR offers features like groups, broadcasting, and streaming, simplifying the development of complex real-time scenarios.

Disadvantages:

• Dependency on server infrastructure: SignalR requires a server-side component, which introduces potential points of failure and increased infrastructure costs.
• Potential for complexity in large-scale applications: Managing a highly concurrent SignalR application can be challenging, demanding careful architectural design and efficient resource management.
• Debugging challenges (occasionally): Debugging real-time interactions can be more complex compared to typical request-response models.

In summary, SignalR offers a compelling balance of simplicity and power. The advantages often outweigh the disadvantages, especially for projects where real-time functionality is paramount and development speed is crucial. However, for extremely high-volume, low-latency applications, a more specialized solution might be warranted.

Designing a real-time chat application with SignalR involves several key components. Let’s outline the architecture.

1. Server-Side Hub: This is the core of your application. It’s a SignalR Hub class that defines methods for sending and receiving messages.

public class ChatHub : Hub
{
    public async Task SendMessage(string user, string message)
    {
        await Clients.All.SendAsync("ReceiveMessage", user, message);
    }
}

2. Client-Side JavaScript: Your JavaScript code will connect to the hub and handle message sending and receiving.

const connection = new signalR.HubConnectionBuilder()
    .withUrl("/chatHub")
    .build();

connection.on("ReceiveMessage", (user, message) => {
    // Update UI with received message
});

// Send a message
connection.invoke("SendMessage", "User1", "Hello!");

3. User Authentication and Authorization: Implement a robust authentication mechanism (e.g., using ASP.NET Identity) to ensure secure communication and identify users. You can use this to control message visibility and access.

4. Database (Optional): For persistent chat history, you might store messages in a database. You’d need to update the database on message send and potentially use SignalR to notify clients of new messages from the database.

5. Error Handling and Robustness: Include comprehensive error handling for disconnections and network issues to provide a smooth user experience. Consider using techniques to handle reconnections gracefully.

This design emphasizes simplicity while allowing for scalability and extensibility. For example, you could add features like private messaging by using SignalR Groups.

Building a real-time dashboard with SignalR involves regularly pushing updates to connected clients. Think of it like a live stock ticker—data changes constantly, and users need to see it immediately.

1. Data Source: Your data source could be a database, an API, or any other system providing the information you want to display. For example, let’s say we’re monitoring server performance.

2. Server-Side Hub: The hub will periodically retrieve data from the source and broadcast it to connected clients.

public class DashboardHub : Hub
{
    public async Task UpdateDashboardData(DashboardData data)
    {
        await Clients.All.SendAsync("ReceiveDashboardData", data);
    }
}

Where DashboardData is a class representing your data (e.g., CPU usage, memory usage).

3. Client-Side Logic: The client-side code connects to the hub and updates the UI based on received data.

connection.on("ReceiveDashboardData", (data) => {
    // Update dashboard elements with data.CPU, data.Memory etc.
});

4. Data Polling Frequency: Balance between real-time updates and server load. Consider using a timer or other scheduling mechanism. A good approach might be to adjust the update frequency dynamically, slowing it down during periods of inactivity and speeding up when data changes more frequently.

5. Data Serialization: Use an efficient serialization method (e.g., JSON) to minimize overhead. Consider the size of the updates; sending only changes rather than the entire data set every time is far more efficient.

Remember to implement proper error handling to manage potential network interruptions and gracefully handle data processing failures.

Handling large-scale concurrency with SignalR requires a multi-faceted approach. SignalR itself is not inherently limited but can become inefficient if not properly scaled.

1. Azure SignalR Service: This is often the best approach for substantial scaling. It handles connection management, scaling, and other complex tasks, freeing you to focus on application logic. The service manages the connections, allowing you to scale horizontally more easily.

2. Connection Management: Efficiently manage connections. This involves proper connection pooling, handling disconnections gracefully, and limiting the number of active connections if necessary. Azure SignalR Service helps significantly with this.

3. Back-pressure Handling: Implement mechanisms to handle situations where the server cannot keep up with the rate of messages. This could involve queueing messages, dropping less important data, or sending acknowledgement messages to clients to control the rate at which they send data.

4. Data Optimization: Optimize data transmission to reduce bandwidth usage. Only send necessary information and use efficient serialization formats like JSON. Consider techniques like delta updates, which only transmit the changes rather than the complete data set.

5. Load Balancing: Distribute the load across multiple server instances. This can be handled automatically by Azure SignalR Service or by setting up a load balancer in front of your application servers.

6. Database Optimization: If your application interacts with a database, ensure that database access is efficient to prevent bottlenecks. Use caching and optimized queries.

Remember to monitor your application’s performance and scale your infrastructure proactively based on observed load and usage.

Testing SignalR applications requires a combination of techniques to validate both the server-side and client-side components.

1. Unit Tests: Test individual methods in your SignalR Hub classes using unit testing frameworks (e.g., xUnit, NUnit for .NET; Jest, Mocha for JavaScript). Focus on verifying message handling and data manipulation logic independently of the real-time aspects.

2. Integration Tests: Use integration tests to verify the interactions between your SignalR Hub and the client. You might use test clients to simulate client connections and send/receive messages. This helps verify the entire communication pipeline.

3. End-to-End Tests: Perform end-to-end tests using tools that simulate real user interactions. These tests will validate the complete application flow, including UI updates and data handling. This is important to ensure all the pieces work correctly together.

4. Load Tests: Simulate a high volume of concurrent users to evaluate the application’s scalability and performance under stress. Tools like k6 or JMeter are useful for this purpose.

5. Mocking: For unit and integration tests, use mocking to isolate components and avoid dependencies on external services like databases or other APIs.

Remember that testing real-time applications requires careful consideration of timing and concurrency. You’ll likely need to use asynchronous testing techniques and strategies to handle delays and concurrent operations.

SignalR’s features significantly enhance its capabilities for building sophisticated real-time applications. Let’s explore some key features:

1. Streaming: Allows you to send a stream of data from the server to the client without blocking the connection. This is ideal for scenarios like live video feeds or real-time data updates. Imagine streaming live stock prices to a financial dashboard. The server continues to push data as it becomes available.

2. Groups: Enables you to organize clients into groups and send messages to specific groups rather than all connected clients. This is crucial for features like chat rooms or targeted notifications. For instance, in a multiplayer game, you’d group players in a game room to communicate just within that specific game session.

3. Broadcasting: Allows you to send messages to all connected clients at once. This is useful for system-wide announcements or updates. For example, pushing a critical software update notification to all connected users.

4. Hub Pipelines: Allow you to intercept and modify messages before they are handled by the Hub methods. This is useful for logging, security checks, or implementing custom middleware.

5. Client-side methods invocation: The server can invoke methods on the client, enabling direct control over client behavior. This is useful for tasks like updating the UI or triggering actions.

My experience with these features has been highly positive. They significantly simplify the development of complex real-time interactions. For instance, using Groups dramatically reduces unnecessary data transfer and improves efficiency in group chat applications.

Integrating SignalR with other technologies is often essential for building complete applications. Let’s explore a couple of common scenarios:

1. Integration with Azure: The Azure SignalR Service is a highly recommended approach for production deployments. It handles scaling, connection management, and other complex infrastructure aspects. You can integrate it with your application by configuring the connection string appropriately.

2. Integration with Databases: To store and retrieve data related to your application, use databases like SQL Server, Cosmos DB, or other relevant databases. Your SignalR Hub can interact with the database to retrieve data updates and push these updates to clients. For example, in a real-time chat, the messages could be stored in a database and used to display past chat messages when a user joins a room.

3. Integration with other Services: SignalR can interact with external APIs or microservices using HTTP clients. You can retrieve data from other services, process it, and push it to connected clients via SignalR.

Example (Database Integration):

You could have a background service (e.g., a background task in ASP.NET) that polls the database for new data and then uses your SignalR hub to broadcast this data to connected clients. This ensures that clients are notified of new data without requiring them to poll the database.

Choosing the right integration strategy depends on the specific needs of your application. Careful planning and design are important to ensure efficient and reliable integration.

SignalR’s message buffering mechanism is crucial for maintaining responsiveness and efficiency, especially in scenarios with intermittent connectivity or high message volume. When a client is temporarily unable to receive messages – perhaps due to network latency or a brief disconnection – SignalR buffers these messages on the server. Once the connection is re-established, these buffered messages are efficiently sent to the client. This prevents message loss and ensures a smooth, continuous user experience.

Think of it like a postman holding your mail while you’re away. The messages are kept safe until you’re back to receive them. The buffering is implemented intelligently; SignalR doesn’t indefinitely hold onto an unbounded number of messages. It employs strategies to manage buffer size and prevent resource exhaustion. If the buffer becomes full, depending on the configuration, it may drop older messages to prioritize newer ones or disconnect the client.

The specific buffering strategies and thresholds can be customized, offering you granular control over how your application handles transient network interruptions.

SignalR offers several ways to configure its behavior, impacting performance, scalability, and feature availability. You can configure SignalR using various methods, primarily through code and configuration files. Let’s explore the key approaches:

• Startup Configuration (Program.cs): This is the most common method, using the AddSignalR() method in your application’s startup code. Here, you can specify options like enabling detailed logging, setting the maximum buffer size for messages, and configuring transport options (WebSockets, Server-Sent Events, Long Polling).
• appsettings.json (or equivalent): Configuration settings can be stored in your application’s configuration file. This allows for external management of configurations without modifying the code. This approach is favored for separating configuration concerns.
• Attribute-Based Configuration: You can use attributes directly within your Hub classes to define specific behaviors. For example, you might use attributes to set up routing or specify how connection IDs are generated.
• Middleware Configuration: You can use middleware components to add additional layers of control over the SignalR pipeline. This is useful for advanced scenarios, like implementing custom authentication or authorization.

The choice of configuration method depends on the complexity of your application and your preferred approach to managing application settings. For most cases, a combination of Startup configuration and appsettings.json provides a well-balanced approach.

Optimizing SignalR for low-latency communication requires a multi-faceted approach focusing on both server-side and client-side improvements. Latency is the delay between sending a message and receiving a response. The goal is to minimize this delay.

• Prioritize WebSockets: WebSockets provide the lowest latency compared to other transports. Ensure WebSockets are prioritized by configuring your SignalR server and client to use it as the preferred transport if possible. Check browser and server-side compatibility.
• Efficient Message Serialization: SignalR utilizes JSON by default for message serialization. Minimizing the size of JSON payloads directly impacts the time it takes to send and receive them. Use efficient data structures and avoid unnecessary data transfer.
• Reduce Server-Side Processing: Minimize the amount of processing done on the server for each message. This includes database access, complex calculations, or I/O operations within hub methods. Keep your Hub methods focused and efficient.
• Client-Side Optimization: Use efficient data handling on the client-side to minimize processing time after receiving a message. Batch processing of client-side updates can also reduce the overall number of network calls.
• Proper Scaling: Scaling your SignalR backend (using techniques like load balancing and scaling out) can distribute client connections and reduce server overload, thus improving responsiveness.
• Network Infrastructure: Ensure your network infrastructure is optimized for low latency. This involves factors beyond your direct control but often significantly impacts performance.

Remember, it’s often a combination of these optimization strategies that yields the greatest improvements. Profiling your application to identify bottlenecks is crucial.

JSON (JavaScript Object Notation) plays a central role in SignalR communication as the default format for serializing and deserializing data exchanged between the server and clients. SignalR uses JSON to package the data being sent, making it easily parsed by both the server and client applications. This interoperability is vital as both often run on different platforms and programming languages.

When a client sends a message to a SignalR Hub, that message’s data is usually packaged as a JSON object. Conversely, when a Hub sends data to a client, that data is typically serialized into JSON format for transmission. The simplicity and widespread adoption of JSON make it an ideal choice for this purpose. Its human-readable format also assists in debugging and monitoring.

While JSON is the default, SignalR allows for customization, though it’s less common. Custom serialization formats can be implemented for specific requirements, but in the majority of applications, the benefits of JSON’s versatility and broad support are sufficient.

Troubleshooting SignalR issues often requires a systematic approach combining logging, monitoring, and careful analysis of network behavior. I have extensively worked with SignalR applications in various environments, so I’ve tackled many diverse challenges.

My common troubleshooting steps include:

• Detailed Logging: Enabling detailed logging (both on the server and client if feasible) helps pinpoint the location of errors. SignalR provides logging mechanisms to track connections, messages, and errors.
• Network Monitoring: Examining network traffic using tools like Fiddler or Chrome DevTools’ Network tab can expose network issues like dropped packets or slow response times.
• Client-Side Debugging: Debugging the client-side code, often using the browser’s developer tools, can reveal errors in how the client handles messages.
• Server-Side Debugging: Utilizing a debugger or logging statements within your Hub methods on the server allows you to follow the execution flow and identify areas where problems arise.
• Connection Management: Investigating connection status and error messages can help identify whether disconnections are due to network problems or server-side errors.
• Resource Monitoring: Monitoring server resources (CPU, memory, network bandwidth) helps identify performance bottlenecks affecting SignalR.

One particular challenging case involved a SignalR application experiencing intermittent disconnections under heavy load. Through meticulous logging and resource monitoring, we discovered a memory leak on the server. Resolving that leak, through careful code review and optimization, completely resolved the disconnections.

Handling unexpected SignalR client disconnections is essential for maintaining application reliability. The approach depends on the nature of your application and the type of data being shared.

Here’s a multi-pronged strategy:

• Detect Disconnections: SignalR provides events to detect when a client disconnects. Your server-side code should handle these events, such as the OnDisconnectedAsync method in your Hub.
• Clean Up Resources: When a client disconnects, release any server-side resources associated with that client. This might include removing the client from groups, clearing timers, or closing database connections.
• Handle Unsent Messages: Depending on your application’s requirements, you might consider storing unsent messages or implementing a mechanism to resend them when the client reconnects. However, be mindful of buffer sizes and the potential for data duplication.
• Notify Other Clients (Optional): If appropriate for your application’s logic, you might notify other connected clients about the disconnection of a particular client. For example, in a collaborative editing scenario, you might update others to know that a user has left.
• Implement Reconnection Logic: The client-side code should attempt to reconnect automatically after a disconnection. Implement exponential backoff strategies to avoid overwhelming the server with frequent reconnection attempts. The client may also need to check if the server is available before reconnecting.

For instance, in a real-time chat application, you might use OnDisconnectedAsync to remove the user from the chat room and inform other users that the user has left. If the user reconnects, you could automatically re-add them to the room.