Long polling improves communication efficiency by keeping the request open for an extended period until new data is available. The server holds the request open and waits until it has new information to send back to the client. Once the server has new data, it responds to the client, which can then process the data and initiate a new long polling request.

By maintaining a long-lived connection between the client and the server, long polling reduces the total number of requests. This makes it a good tech stack for building scalable, responsive chat apps, multiplayer games, live notifications, and industrial automation solutions.

How does long polling work?

Long polling is a push-based approach that allows the server to send updates to the client as soon as they are available, eliminating need for the client to check for updates repeatedly. 

Long Polling Process Workflow:

1. The client initiates a request to the server, typically through an HTTP request.

2. Instead of immediately responding, the server holds the request open, keeping the connection active (live).

3. If no new data is available, the server waits until it has something to send back.

4. Once the server has new data or a predefined timeout occurs, it responds to the client with the latest information.

5. Upon receiving the response, the client immediately sends another request to the server to maintain the connection.

6. This cycle of sending requests and receiving responses continues, ensuring real-time updates.

Long Polling vs Short Polling

In traditional HTTP, the client sends a request to the server and waits for a response, known as short polling. This method is inefficient for real-time scenarios due to frequent requests causing network overhead and increased latency. Short polling, a pull-based approach, results in delays as the client repeatedly checks for updates.

What technologies are used to implement long polling?

HTTP long polling is a widely used long polling approach that leverages HTTP to maintain a long-lived client-server connection. The client sends a request, which the server holds open until new data is available or a timeout occurs. Once the server responds, the client immediately sends another request, continuing the cycle. This method is simple to implement and requires no special backend technologies.

Long polling Security

Long polling security focuses on protecting data and connections between clients and servers. Use HTTPS to encrypt communications, ensuring confidentiality and preventing man-in-the-middle attacks. Implement strong authentication and authorization mechanisms to restrict access to legitimate users and limit data exposure based on roles.

To prevent Cross-Site Request Forgery (CSRF), include anti-CSRF tokens in requests. Additionally, validate and sanitize all incoming data to guard against injection attacks.

Implement rate limiting to mitigate denial-of-service (DoS) attacks by controlling the number of requests a client can make in a specific timeframe. Regularly conduct security audits and update the application to address vulnerabilities.

Remember: PubNub offers an additional range of data security features such as message encryption and access management.

Long Polling Error Handling

Long polling error handling involves several key strategies.

First, set a reasonable connection timeout period for the server to wait before closing the request; if no new data is available when the timeout occurs, the server should return an empty response or a status indicating no data. Implement automatic retry mechanisms on the client side, so if a request fails or times out, the client waits briefly before sending a new request to the server.

Ensure the server sends appropriate HTTP status codes (e.g., 500 for server errors, 404 for not found) to allow the client to handle errors accordingly. For repeated errors, use an exponential backoff strategy, progressively increasing the wait time between retries to prevent overwhelming the server.

Regularly check the health of the connection; if it is lost, the client should attempt to reconnect using the long polling process. Additionally, implement fallback mechanisms to switch to regular polling or another communication method if long polling fails repeatedly, ensuring continuous service.

Finally, log errors and monitor long polling interactions to identify patterns and address potential issues effectively.

How is long polling used in real-time applications?

One of the primary advantages of long polling is its efficiency in delivering real-time updates. Minimizing the number of requests sent by clients significantly reduces network latency and improves overall performance. Additionally, it allows servers to push updates to clients immediately, ensuring that messages and notifications are delivered promptly.

Furthermore, long polling facilitates scalability in real-time applications. Reducing the number of open connections enables servers to handle more concurrent clients. This is particularly crucial in chat and messaging applications, where the number of users constantly fluctuates.

Long polling helps save on system resources. With traditional (short) polling, each request requires the server to process and respond, even without updates. This constant processing can strain server resources and negatively impact performance. In contrast, long polling only triggers server processing when new data is available or a timeout occurs, minimizing the strain on system resources allowing for better scalability and reliability.

Implementing long polling presents several challenges. Managing server resources effectively is crucial, as long polling involves keeping connections open for extended periods, which requires significant resources. Technologies like cloud computing, auto-scaling, or containerization can help dynamically allocate resources based on demand.

Handling timeouts and connection failures is another challenge. The server must manage timeouts by closing connections gracefully to free up resources and detect and handle connection failures appropriately. Robust error handling and reconnection mechanisms are essential for maintaining reliability.

PubNub handles all the implementation challenges for you, enabling you to create an application that will scale without having to worry about the underlying infrastructure.

Long polling vs. WebSockets

Long polling and WebSockets are techniques to achieve a real-time connection between a client (such as a web browser) and a server. Although they serve a similar purpose, the two have significant differences.

Similarities between long polling and Web Sockets:

1. Real-time updates: Both long polling and WebSockets enable real-time communication between the server and client, allowing instant updates without continuous refreshing.

2. Reduced server load: Both techniques minimize unnecessary requests by only sending data when it is available, reducing server load and improving scalability.

3. Wide language and framework support: Many popular programming languages and frameworks support both long polling and WebSockets, making them accessible to developers across different ecosystems.

Long polling vs. Server-Sent Events (SSE)

Server-Sent Events (SSE) is networking technology that allows servers to push real-time client updates over a single HTTP connection. It’s part of the HTML5 specification, is supported by all major browsers, and provides a simple and efficient way for server applications to send data to clients.

SSEs establish a long-lived HTTP connection between the server and the client and once the connection is established, the server can send data to the client anytime without requiring the client to make additional requests. Although HTTP long polling requires a periodic reconnection to the server, it does support bidirectional communication, unlike SSE which can only stream data in one direction.

How can you optimize long polling?

Long polling can be resource-intensive and cause scalability issues if not optimized properly. Here are several techniques that can be used to optimize long polling for better performance and scalability.

TLDR: Using a service such as PubNub will avoid having to consider the low level implementations of scaling and optimizing your solution, since we take care of that for you.

Batched responses: Instead of sending a response for each request, batch multiple updates together and send them in a single response. This reduces the number of HTTP requests and helps to minimize the overhead.

Compression: Compressing the data before sending it over the network can significantly reduce the payload size, resulting in faster transmission and lower bandwidth consumption. Techniques like Gzip compression can be used to achieve this.

Caching: Implementing a caching layer can help reduce the load on the database or other data sources. By caching the frequently requested data, subsequent requests can be served from the cache itself, reducing the response time and improving scalability.

Connection pooling: Maintaining a pool of reusable connections instead of creating a new connection for every request can improve the efficiency of the long polling mechanism. This eliminates the overhead of establishing a new connection for each request, resulting in better performance.

Throttling and rate limiting: Implementing throttling mechanisms can prevent excessive requests from overwhelming the server. This ensures fair resource allocation and prevents abuse, improving performance and scalability.

Load balancing: Distributing the incoming requests across multiple servers using load balancing techniques can help distribute the load and prevent any single server from becoming overwhelmed. This improves the overall performance and scalability of the long polling system.

Monitoring and optimization: Regularly monitoring the performance of the long polling software and identifying any bottlenecks or areas of improvement can help optimize the system for better performance and scalability. Techniques like profiling, load testing, and performance tuning can be used to identify and address any performance issues.

Asynchronous (Async) processing: Offloading time-consuming tasks to asynchronous processes or background workers can help free up resources and improve the responsiveness of the long polling system. You can get this via message queues, worker processes, or distributed computing.

Connection timeouts: Implementing appropriate connection timeouts can help prevent idle connections from consuming unnecessary resources. By closing idle connections after a certain period of inactivity, the system can free up resources for other clients and improve scalability.

Scalable infrastructure: Ensuring the underlying infrastructure is scalable and can handle the expected load is crucial for optimizing long polling. This may involve using technologies like cloud computing, auto-scaling, or containerization to dynamically allocate resources based on demand.

What Programming Languages are Compatible with Long Polling?

Several software languages are compatible with implementing long polling in real-time chat and live messaging applications. Here are a few examples:

• JS: Long polling is commonly combined with JavaScript, allowing for seamless client-side implementation. JavaScript frameworks like React, Angular, and Vue.js provide libraries and tools that simplify implementing long polling in your application.

• PHP is a popular server-side language often used in web development. It provides features and libraries that enable developers to implement long polling efficiently. The PHP framework Laravel, for example, offers support for long polling through its event broadcasting system.

• Python is another versatile language that can be used for implementing long polling. Python frameworks like Django and Flask provide the tools and libraries for building real-time applications using long polling techniques.

• Ruby is a dynamic, object-oriented programming language well-suited for web development. A popular web framework, Ruby on Rails, supports long polling through various libraries and extensions.

• Java is a widely used language in enterprise development and provides support for long polling. Java frameworks like Spring and Java EE offer libraries and tools for implementing long polling in real-time applications.

• .NET/C# framework, with its programming language C#, is commonly used for building web applications. It provides libraries and frameworks like ASP.NET SignalR that simplify the implementation of long polling techniques.

Many languages support long polling for real-time chat and messaging. When choosing one, consider your application's requirements, including scalability, performance, and ease of implementation. Also, consider the language's community and ecosystem for support, resources, and documentation.

Alternatively, PubNub's supports over 30 SDKs providing out of the box support for real-time applications.

Long Polling Frameworks and Libraries

Various frameworks and libraries facilitate the implementation of long polling in web applications. Here are some notable options:

1. jQuery: The jQuery library simplifies AJAX requests, making it easier to implement long polling. Developers can use jQuery's $.ajax() method to manage the long polling process.

2. Express.js: In Node.js applications, Express.js can be used to handle long polling requests efficiently. The framework allows developers to manage routes and middleware, facilitating the creation of long polling endpoints.

3. Socket.IO: Although primarily designed for WebSocket communication, Socket.IO supports fallback mechanisms like long polling. It abstracts the complexity of managing real-time connections and can seamlessly switch between long polling and WebSockets.

4. Flask: For Python applications, Flask can be used to create long polling endpoints. The simplicity of Flask makes it easy to set up routes that handle long polling logic.

5. Django: a robust Python web framework, allows developers to implement long polling through its views and asynchronous features, particularly with Django Channels for handling real-time communications.

6. Spring Framework: In Java applications, the Spring Framework supports long polling through its REST capabilities, allowing developers to create endpoints that can hold requests until new data is available.

7. ASP.NET: In .NET applications, ASP.NET provides support for long polling through its HTTP handling capabilities, allowing developers to create responsive real-time applications.

8. Laravel: PHP's Laravel framework offers a clean and elegant way to implement long polling through its routing and event broadcasting features.

Other names for long polling

• Comet an umbrella term used to describe various techniques, including long polling, for pushing data from the server to the client over HTTP.

• Reverse Ajax describe techniques like long polling, where the server sends data to the client without the client explicitly requesting it each time.

• HTTP Streaming while distinct from long polling, it is sometimes mentioned alongside it as a server push technique, where the server keeps the HTTP connection open and sends data in chunks.

• Pushlet a variation of long polling used in some early implementations, particularly in Java, where the server "pushes" updates to the client by keeping the connection open.

• AJAX Long Polling Sometimes, long polling is referred to in the context of AJAX (Asynchronous JavaScript and XML) as "AJAX long polling," emphasizing its use in asynchronous web applications.

With over 15 points of presence worldwide supporting 800 million monthly active users and 99.999% reliability, you’ll never have to worry about outages, concurrency limits, or any latency issues caused by traffic spikes. PubNub is perfect for any application that requires real-time data.