Modern applications need to show users the latest information instantly. When a friend sends you a message, you expect to see it immediately. When a stock price changes, traders need to know within milliseconds. When someone likes your post, the notification should appear right away.

These experiences feel seamless, almost magical. But behind the scenes, they represent one of the trickiest challenges in distributed systems: getting data from the server to the client the instant something changes.

The traditional HTTP model does not help here. A client makes a request, the server responds, and the connection closes. If something changes on the server a millisecond later, the client has no way of knowing. It would have to ask again. And again. And again.

This is the problem of real-time updates, and there are four fundamentally different ways to solve it. Each has trade-offs that matter deeply depending on your use case. Choose wrong, and you will either waste enormous resources or deliver a frustrating user experience.

Where This Pattern Shows Up

Real-time updates appear across a wide range of systems:

The four main approaches to real-time updates form a spectrum:

Each approach sits at a different point on the simplicity-vs-capability spectrum. The right choice depends on your specific requirements, and understanding the trade-offs deeply will help you make that decision confidently. Let us walk through each one.

Approach 1: Short Polling

Short polling is the simplest approach, and often the first thing that comes to mind. The client asks "anything new?" at regular intervals, and the server responds immediately with whatever it has.

How It Works

The client sets a timer, say every 5 seconds, and sends an HTTP request when it fires. The server checks for new data and responds immediately, either with updates or an empty response. The client processes any data, waits for the next interval, and repeats.

Think of it like checking your mailbox every morning. Most days there is nothing new, but you check anyway because you do not know when something will arrive. The approach is predictable and easy to reason about, but it does not scale gracefully.

The Fundamental Trade-off

Short polling forces you into an uncomfortable choice between latency and resource consumption:

Poll frequently and you get faster updates but hammer your servers. Poll less often and you save resources but users wait longer for new data. There is no middle ground that solves both problems.

The math is unforgiving. With a 5-second polling interval and 1 million connected users, you are looking at 200,000 requests per second, and most of those requests return nothing useful. That is a lot of infrastructure cost for empty responses.

Pros and Cons

Advantages:

Disadvantages:

When Short Polling Makes Sense

Despite its limitations, short polling remains a reasonable choice in specific scenarios:

If updates are infrequent and a delay of a few seconds is acceptable, short polling keeps your architecture simple. But if users expect updates to appear instantly, you need something better.

Approach 2: Long Polling

Long polling takes the basic polling idea and flips it on its head. Instead of responding immediately with "nothing new," the server holds the connection open and waits until there is actually something to send.

This small change makes a significant difference in how the system behaves.

How It Works

The client sends an HTTP request asking for updates. If the server has new data, it responds immediately. If not, it holds the connection open and waits, typically with a timeout of 30 to 60 seconds.

When new data arrives, the server immediately responds to the waiting request. The client processes the data and immediately opens a new request. This creates a continuous loop where updates arrive as soon as they are available.

The key insight is that instead of the client asking "is there anything new?" over and over, it asks once and waits for the answer. The server only responds when it has something meaningful to say.

Visualizing the Difference

The contrast with short polling becomes clear when you look at the request patterns side by side:

Short polling sends 6 requests in 30 seconds, most returning empty. Long polling sends 2 requests, each returning meaningful data.

The Server-Side Challenge

The tricky part with long polling is what happens on the server while it waits. You cannot simply have a thread sitting in a loop checking for updates. That would waste CPU and limit how many connections you can handle.

Production implementations rely on event-driven architectures and pub/sub systems:

When a request comes in, the handler subscribes to a Redis channel and then yields control. No CPU is consumed while waiting. When new data publishes to the channel, all subscribed handlers wake up instantly and respond to their clients.

Pros and Cons

Advantages:

Disadvantages:

When Long Polling Shines

Long polling occupies a useful middle ground. It delivers near real-time updates while staying within the familiar HTTP paradigm.

Long polling works well for notification systems, activity feeds, or any application that needs real-time updates but does not require the client to send frequent messages. It is a solid stepping stone before committing to the complexity of WebSockets.

Approach 3: Server-Sent Events (SSE)

Server-Sent Events represent a fundamentally different approach. Instead of the client asking for updates repeatedly, it opens a single long-lived connection and the server pushes data through it whenever something changes.

This is closer to how we intuitively think about real-time: the server notifies you when something happens rather than you asking "has anything changed?" over and over.

How It Works

The client opens a connection using the browser's built-in EventSource API. The server responds with a special content type (text/event-stream) and keeps the connection open. From that point on, the server can push events at any time. The client registers handlers for different event types and processes them as they arrive.

The beauty of SSE is how little code you need on the client side. The browser handles the connection management, reconnection logic, and event parsing. You just listen for events.

The Event Format

SSE uses a dead-simple text-based protocol. Each message is a series of field-value pairs separated by newlines:

The text-based format makes SSE easy to debug. You can see exactly what the server is sending by watching network traffic.

Automatic Reconnection

What makes SSE genuinely powerful is its built-in reconnection handling. When the connection drops, whether from network issues, server restarts, or anything else, the browser automatically attempts to reconnect.

The browser sends the Last-Event-ID header when reconnecting, allowing the server to resume from where it left off. You get reliable delivery with zero client-side reconnection code. This is functionality you would have to build yourself with WebSockets.

Pros and Cons

Advantages:

Disadvantages:

SSE vs Long Polling

At first glance, SSE and long polling seem similar since both involve holding connections open. But there is a crucial difference:

Long polling creates a new HTTP request for each update. SSE streams multiple updates over a single connection. This makes SSE more efficient for high-frequency updates and eliminates the gap between responses where messages could be missed.

When SSE Shines

SSE excels when data flows primarily from server to client:

If the client rarely needs to send data, or when it does a simple HTTP POST suffices, SSE gives you real-time push with less complexity than WebSockets. Many teams default to WebSockets when SSE would serve them better.

Approach 4: WebSockets

WebSockets are the most powerful option. They provide true bidirectional communication where both client and server can send messages at any time over a single persistent connection.

While SSE gives you server-to-client push, WebSockets give you a full two-way channel. This opens up possibilities that are difficult or impossible with the other approaches.

How It Works

The connection begins life as an HTTP request. The client sends an upgrade request asking to switch protocols. If the server agrees, it responds with "101 Switching Protocols" and from that moment on, both sides speak WebSocket instead of HTTP.

Once established, either side can send messages whenever they want. There is no request-response pattern anymore. The client can send while the server is sending. Messages flow in both directions simultaneously. This is true full-duplex communication.

The Protocol Upgrade

The transition from HTTP to WebSocket happens through a handshake:

After the handshake, messages are exchanged as WebSocket frames. Unlike HTTP, which has verbose headers for every request, WebSocket frames have tiny headers of just 2 to 14 bytes. This makes WebSockets dramatically more efficient for high-frequency messaging.

Why WebSockets Are Different

The fundamental difference between WebSockets and everything else is the communication model:

With HTTP, every exchange starts with the client. Even with SSE, the client opens the connection and the server responds. With WebSockets, the server can send a message to the client at any time without any prior request. This enables patterns like typing indicators, presence updates, and real-time collaboration that would be awkward with HTTP.

The Scaling Challenge

WebSockets introduce a scaling complexity that does not exist with stateless HTTP. Each client maintains a persistent connection to a specific server. This creates an interesting problem: what happens when Client A, connected to Server 1, wants to send a message to Client B, who is connected to Server 2?

The standard solution is a pub/sub layer. When Alice sends a message, Server 1 publishes it to a shared message bus like Redis or Kafka. Server 2 subscribes to relevant channels and forwards incoming messages to Bob. Every server acts as both publisher and subscriber, routing messages to whichever clients happen to be connected to it.

This architecture works, but it adds operational complexity. You need to manage the message bus, handle its failure modes, and design your channel structure thoughtfully.

Connection State Management

Another challenge with WebSockets is managing connection state. With HTTP, each request is independent. With WebSockets, you need to track which users are connected to which servers.

You need to handle what happens when a server crashes, when clients reconnect to different servers, and when the connection registry gets out of sync with reality. These are solvable problems, but they require careful design.

Pros and Cons

Advantages:

Disadvantages:

When WebSockets Are Worth the Complexity

WebSockets make sense when you genuinely need bidirectional, high-frequency communication. Before reaching for them, ask yourself: does the client really need to send messages frequently, or could SSE with occasional HTTP POSTs work?

Chat applications, multiplayer games, collaborative editing, and live trading platforms genuinely benefit from WebSockets. For notification systems, live feeds, or progress updates, SSE is usually simpler and sufficient.

Comparing All Four Approaches

Now that we have examined each approach in detail, let us put them side by side to see how they compare across different dimensions.

Choosing the Right Approach

The decision tree for selecting an approach is surprisingly straightforward once you understand the trade-offs:

The key questions to ask:

Recommendations by Use Case

Implementation Best Practices

Regardless of which approach you choose, certain problems appear in every real-time system. Handling them well separates robust production systems from fragile prototypes.

Reconnection with Exponential Backoff

Connections will drop. Mobile users switch between WiFi and cellular. Servers restart for deployments. Network equipment fails. Your system must handle disconnection gracefully.

The standard approach is exponential backoff with jitter:

Start with a short delay (1 second), double it on each failure, and cap at a reasonable maximum (30 seconds). Add random jitter to prevent thundering herd problems when many clients reconnect simultaneously after a server restart. Reset the delay counter after a successful connection.

Heartbeats for Connection Health

A quiet connection and a dead connection look identical from the network perspective. Without active probing, you cannot tell if the other side is still there.

Send periodic pings, every 30 seconds is a reasonable default, and expect a response within a timeout window. If no response arrives, assume the connection is dead and initiate reconnection. This catches scenarios like server crashes, network partitions, and zombie connections that TCP keepalives might miss.

Message Ordering and Deduplication

In distributed systems, messages can arrive out of order or be delivered multiple times. This is especially common after reconnections when the client might request a replay of recent messages.

Assign sequence numbers or unique IDs to messages. Track the last processed ID on the client. Buffer out-of-order messages until gaps are filled. Detect and discard duplicates. For SSE, the built-in event ID mechanism handles much of this automatically.

Backpressure and Flow Control

Sometimes clients cannot keep up with the rate of incoming messages. A mobile device on a slow connection, a browser tab in the background, or a client doing expensive processing can all fall behind.

Monitor send buffer sizes. When a client falls behind, you have choices: drop non-critical messages, aggregate multiple updates into one, or disconnect the client entirely. The right choice depends on your use case. A stock ticker might drop intermediate price updates and send only the latest. A chat application might need to deliver every message and should disconnect clients that fall too far behind.

Compression for High-Volume Connections

For connections that carry significant data volume, per-message compression can reduce bandwidth substantially. WebSocket supports the permessage-deflate extension, which compresses each message individually. Most WebSocket libraries support this with a configuration flag.

The trade-off is CPU usage for compression and decompression. For text-heavy payloads like JSON, compression typically reduces size by 60-80%. For already-compressed data like images, it provides little benefit. Enable it selectively based on your payload characteristics.

Key Takeaways

Real-time updates have become table stakes for modern applications. Users expect immediate feedback, whether they are chatting with friends, tracking a delivery, or monitoring a build pipeline.

The four approaches we covered form a progression from simple to sophisticated:

Key principles to remember:

The best approach is the one that solves your problem with the least complexity. Start with simpler solutions and evolve only when you have evidence that you need more capability.

References

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