Implementing Long Polling in Spring Microservices

Introduction
The rapid adoption of microservices in recent years has necessitated a shift in the way services communicate with each other. In certain scenarios, merely relying on the traditional request-response model might not be efficient, especially when you need real-time updates without overburdening the servers with frequent requests. Long polling is a technique that bridges this gap by allowing clients to wait for a response until there’s an update or until a specified timeout occurs.
In this post, we’ll delve into how to implement long polling in Spring microservices. Spring, with its vast ecosystem, provides robust support for building microservices and, by extension, for long polling.
Introduction to Long Polling
In the ever-evolving landscape of web development, ensuring real-time or near-real-time communication between the client and server has always been a topic of interest and innovation. Long polling, one of these strategies, represents a compromise between more primitive request-response models and advanced communication methods, like WebSockets.
Origins and Evolution
The origins of long polling can be traced back to a need for overcoming the limitations of traditional polling. In classic polling, the client sends requests at regular intervals, asking, “Is there any new data?” Most of the time, especially in low-activity systems, the answer is “No.” This leads to a lot of unnecessary network traffic and server load.
The term “Comet programming” was coined to describe web application architectures which allow the server to send data to the client without an explicit request. Long polling is one such Comet technique.
How Does Long Polling Work?
Here’s a step-by-step breakdown:
- Client Request: The client sends a request to the server.
- Server Holding: Instead of sending an immediate response, the server waits until there’s new data available or a timeout is reached. This means the server is essentially holding onto the client’s request for this duration.
- Server Response: Once new data is available or the timeout is reached, the server sends a response back to the client.
- Client Handling: Upon receiving the server’s response, the client immediately processes the data (if available) and sends another request to the server, waiting for the next piece of data.
Comparison with Other Communication Strategies
- Traditional Polling: As discussed, clients ask the server for data at regular intervals. It’s a simple approach but can be highly inefficient.
- WebSockets: A protocol providing full-duplex communication channels over a single, long-lived connection. WebSockets allow both the client and server to send messages at any time, without the overhead of establishing new connections.
- Server-Sent Events (SSE): Allows the server to push information to web clients over an HTTP connection, but it’s a one-way channel (server-to-client).
Among these, long polling stands out as a strategy that doesn’t need the constant overhead of making new connections (like traditional polling) but doesn’t require the setup and maintenance of a dedicated channel (like WebSockets).
Practical Applications of Long Polling
Given its nature, long polling is suitable for applications where:
- Real-time Data Isn’t Crucial: Applications like casual chat platforms, where a slight delay in message delivery is acceptable, can benefit from long polling.
- Infrastructure Limitations: In scenarios where setting up and maintaining WebSockets isn’t feasible, long polling can be a less resource-intensive alternative.
- Transitional Systems: For systems transitioning from a legacy setup, long polling can offer a balance between modern real-time updates and older, established systems’ constraints.
While long polling might not be the go-to solution for all real-time applications, it holds a significant place in the spectrum of client-server communication strategies. Understanding its mechanics, strengths, and weaknesses allows developers to make an informed decision on when and how to use it.
The Need for Long Polling in Microservices
Microservices represent a shift from monolithic architectures, where applications are broken down into smaller, independent services that communicate over the network. This architectural style has numerous benefits, including scalability, resilience, and ease of maintenance. However, it also introduces challenges in terms of communication and data consistency. Here’s where long polling can play a pivotal role.
Decoupled Yet Connected
Microservices are designed to be loosely coupled, meaning each service is independent and can evolve separately. However, services often need to interact, either to request data or be notified of changes. Traditional request-response methods may not be efficient, especially when a service needs data that’s not immediately available. Constant polling between services can lead to:
- High network traffic.
- Excessive load on the service being polled.
- Delays in data retrieval.
Long polling serves as a middle ground. A service can request data and wait without overwhelming the network or other services.
Real-time Synchronization
In a microservices landscape, data consistency becomes a challenge. Suppose Service A updates a piece of data that Service B relies on. If Service B is unaware of this change and continues to work with stale data, it can lead to inconsistencies.
Long polling can help here. Service B can send a long-poll request to Service A, waiting for an update. Once Service A has new data, it can immediately notify Service B, ensuring near-real-time synchronization between the services.
Optimizing Resource Utilization
In a system with dozens (or even hundreds) of microservices, resource optimization is crucial. Establishing and maintaining dedicated communication channels or constantly polling can be resource-intensive, both in terms of network bandwidth and compute power.
Long polling offers an optimization. Since the server holds the client’s request and responds only when there’s new data (or a timeout occurs), the number of total requests made and the associated overhead can be significantly reduced.
Flexibility and Compatibility
While advanced communication mechanisms like WebSockets offer superior real-time capabilities, they may not always be the best fit. For instance, if some microservices are legacy systems or if there are infrastructure constraints, establishing WebSockets might not be feasible.
Long polling provides flexibility. It operates over standard HTTP, making it compatible with almost any system, regardless of its age or the technologies it uses. This compatibility ensures that even older microservices can participate in real-time communication without substantial overhauls.
Easing Transition Phases
Organizations often transition from monolithic to microservices architectures in phases. During this transition, there’s a mix of old and new, with parts of the system still relying on monolithic structures. Long polling can serve as an interim real-time communication strategy, easing the transition and ensuring that both monolithic components and new microservices can communicate efficiently.
The adoption of long polling in microservices is not just about real-time communication. It’s about ensuring efficient, reliable, and scalable interactions in a distributed system. While it may not be the ultimate solution for all scenarios, it offers a balanced approach that can cater to diverse needs in a microservices environment.
Setting Up a Spring Boot Microservice
Initial Setup with Spring Initializr
- Go to Spring Initializr.
- Project Type: Choose
Maven Project
. - Language: Opt for
Java
. - Spring Boot Version: Select the latest stable release.
- Group: Enter your organization, e.g.,
com.example
. - Artifact: Name your project, e.g.,
my-microservice
. - Dependencies: Add
Spring Web
.
Once completed, click Generate
and download the .zip
project file.
Project Import in IDE
IntelliJ IDEA:
- Choose
Open
> select the extracted project directory.
Eclipse:
- Go to
File
>Import
>Existing Maven Projects
> select project directory.
VS Code:
- Open the project directory. Ensure Java and Spring Boot extensions are installed.
Project Structure
src/main/java
: Application source code.src/main/resources
: Application properties and resources.pom.xml
: Maven configurations, dependencies, and plugins.
Run Your Application
- Navigate to
src/main/java/com.example.my-microservice
. - Locate
MyMicroserviceApplication.java
. - Execute this file as a Java application.
Your Spring Boot application will start on port 8080
.
Test Your Application
To confirm the setup:
- Use a browser or
curl
tool. - Access
http://localhost:8
080. - A
404 Not Found
error indicates your microservice is up and running, as no endpoints have been defined yet.
The process simplifies the initialization, development, and testing of a Spring Boot microservice, making it an ideal choice for rapid microservice deployment.
Implementing Long Polling in Spring
Setting Up Dependencies
Ensure your pom.xml
has the Spring Web
dependency:
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
Creating the Controller
First, set up a controller to handle the long polling request.
@RestController
@RequestMapping("/api")
public class LongPollingController {
private final Queue<DeferredResult<ResponseEntity<String>>> responseBodyQueue = new ConcurrentLinkedQueue<>();
@GetMapping("/long-polling")
public DeferredResult<ResponseEntity<String>> longPolling() {
DeferredResult<ResponseEntity<String>> output = new DeferredResult<>(5_000L);
output.onTimeout(() -> {
responseBodyQueue.remove(output);
output.setResult(ResponseEntity.status(HttpStatus.REQUEST_TIMEOUT).build());
});
responseBodyQueue.add(output);
return output;
}
@PostMapping("/notify")
public ResponseEntity<String> notifyClients(@RequestBody String data) {
for (DeferredResult<ResponseEntity<String>> result : responseBodyQueue) {
result.setResult(ResponseEntity.ok(data));
responseBodyQueue.remove(result);
}
return ResponseEntity.ok().build();
}
}
In this example:
- The
/long-polling
endpoint keeps the client's connection open until data is available or a timeout occurs. - The
/notify
endpoint allows you to send data to all waiting clients.
Client-Side Implementation
Clients can use various methods, such as Fetch API in JavaScript, to initiate a long-polling request.
async function initiateLongPolling() {
try {
const response = await fetch("/api/long-polling");
if (response.ok) {
const data = await response.text();
console.log("Received:", data);
// Process data and restart long polling
initiateLongPolling();
}
} catch (error) {
console.error("Error during long polling:", error);
}
}
This function keeps requesting new data from the server and logs it once received.
Considerations for Production
- Scalability: Ensure your infrastructure can handle long-lived connections from multiple clients.
- Timeout Handling: The client needs strategies to handle timeouts, either by retrying or notifying the user.
- Service Layer Integration: In a real-world application, the controller would often communicate with a service layer for data retrieval or persistence.
Long polling in Spring is straightforward thanks to the DeferredResult
class, allowing asynchronous request handling. When combined with the right client-side mechanisms, this enables an effective near-real-time communication model.
Conclusion
Long polling offers an efficient way to achieve near-real-time communication in microservices without constantly bombarding services with requests. With Spring Boot, implementing long polling is straightforward, making it a valuable tool in a microservices developer’s toolkit. While it has its merits, it’s essential to understand when to use it and when to look towards alternatives like WebSockets for more interactive and dynamic applications.
