Building a Real-Time Polling System with Spring Boot and SSE
Live polling has become standard practice across Australian workplaces, from federal election coverage streamed out of Canberra to corporate town halls in Sydney and Melbourne. Spring Boot gives Java developers a clean path for pushing fresh numbers to every connected client, and pairing it with Server-Sent Events keeps the architecture refreshingly simple.
The streaming format shines when updates flow in one direction from server to many browsers. Unlike full-duplex WebSockets, SSE rides on plain HTTP, slips through corporate proxies without fuss, and reconnects automatically after network blips. That resilience matters when your audience checks results from a Brisbane office one minute and a beachside café in Noosa the next.
This walkthrough covers setting up the Maven project, exposing the event stream, broadcasting votes, and wiring a minimal JavaScript listener. By the end you will have a working voting dashboard that any team in Parramatta or Perth can spin up locally and trust in production.
Server-Sent Events Compared With Alternatives
Before writing any code, it helps to see where Server-Sent Events sit alongside the other streaming options that Java backends often reach for. Long polling has been around since the early AJAX days, WebSockets dominate chat applications, and SSE lands somewhere in the middle for fan-out workloads like live polling.
| Feature | Server-Sent Events | WebSockets | Long Polling |
|---|---|---|---|
| Transport | HTTP | Custom (ws/wss) | HTTP |
| Direction | Server to client | Bidirectional | Server to client |
| Auto-reconnect | Built into browser | Manual | Manual |
| Browser support | All modern | Excellent | Universal |
| Spring Boot support | First-party | First-party | DIY |
| Best fit | Live dashboards | Chat, games | Fallback only |
For a polling system where the server holds authority and clients just listen, SSE is the natural fit. It avoids the overhead of a full duplex channel while still delivering sub-second updates.
Bootstrapping the Spring Boot Project
Start by generating a fresh Spring Boot project through the Spring Initializr with the Web dependency. A standard Maven layout works fine, and the embedded Tomcat means no separate container to manage when you deploy to a small instance in ap-southeast-2 or a local VM in Sydney.
If you are newer to the language, brush up on streams, annotations, and configuration properties before tackling the controller code that follows. A solid foundation matters more than picking fancy dependencies, so spend time with a core Java guide first.
The application properties file stays minimal. Set the server port, give the application a descriptive name, and leave the rest at defaults while prototyping.
Building the Polling Service and Broadcaster
The heart of the system is a service that holds the current tally in memory and notifies every connected emitter whenever a new vote arrives. Using Reactor's Sinks.Many gives a thread-safe broadcast channel without pulling in a heavyweight message broker. Each call to tryEmitNext pushes the latest snapshot to every subscriber, which is exactly the fan-out behaviour a polling screen needs.
Keep the data model simple: a poll has an id, a question, and a map of option to count. The service exposes methods for casting votes and querying the current state. For heavier traffic a Redis-backed implementation would slot in cleanly, but the in-memory version is plenty for a single-region deployment serving an internal town hall.
Inject the broadcaster into both the REST controller that receives votes and the SSE controller that streams them out. That shared dependency is the wiring that makes the whole pattern click together.
Implementing the SSE Controller
The controller is short and reads almost like documentation. Annotate it with @RestController, declare a @GetMapping producing MediaType.TEXT_EVENT_STREAM_VALUE, and return a Flux<ServerSentEvent> built from the sink.
Each emitted event wraps the latest poll snapshot as JSON in its data field, and the controller never blocks waiting for votes. Spring holds the stream open, flushes each event as it arrives, and cleans up when the client disconnects. Adding @CrossOrigin keeps the frontend frictionless during development.
When the application restarts or crashes, every browser reconnects automatically because the EventSource API handles the handshake. That automatic recovery is a major win during live events where dropped connections would otherwise leave screens frozen.
Wiring Up the Browser Client
On the frontend, an EventSource pointing at /polls/stream does the heavy lifting. Register an onmessage handler that parses the payload and updates the bar chart, percentage labels, and total count. A tiny vanilla JavaScript file keeps the demo dependency-free, though the same hook drops into a React component just as easily.
Render the initial state from a regular REST call, then let SSE take over for live updates. This separation avoids race conditions on first paint and gives crawlers something sensible to index.
Production Considerations Down Under
When the system goes live, log timestamps in Australia/Sydney rather than UTC so on-call engineers read them without mental conversion. Most Aussie teams standardise on AEST for user-facing copy and keep server logs in UTC, but the mix works only when it is deliberate.
Host close to your users. Sydney and Melbourne regions on the major clouds give sub-50ms latency to most of the eastern seaboard, and the NBN has finally caught up with symmetric speeds that persistent streams need. Run a load test that simulates the largest event you realistically expect, watch the broadcaster's backpressure, and the polling system will just work on election night and any other noisy arvo after.