Real-time Updates

Real-time updates have become a fundamental requirement for modern applications. From chat applications and collaborative document editors to live dashboards and multiplayer games, users expect instant feedback and immediate notification of changes. This blog post explores the architectural patterns, protocols, and design decisions that enable real-time communication between clients and servers.

The Challenge of Real-time Communication: Traditional HTTP follows a simple request-response model: clients ask for data, servers respond, and the connection closes. This works perfectly for conventional web browsing where users explicitly request pages and resources. However, it breaks down when we need servers to proactively push updates to clients the moment events occur. Consider a collaborative document editor like Google Docs—when one user types a character, all other users viewing that document need to see the change within milliseconds. Constantly polling the server every few milliseconds would crush most infrastructure and waste enormous bandwidth.

The core challenge is establishing efficient, persistent communication channels that allow servers to push updates to clients while managing concerns like scalability, reliability, and resource utilization. Real-time systems must solve two distinct problems: how to get updates from the server to the client (the first hop), and how to propagate updates from their source to the appropriate server (the second hop).

Understanding the Two Hops: Real-time update delivery involves two critical stages. The first hop concerns client-server connection protocols—the mechanisms that enable efficient communication channels between end users and our servers. The second hop addresses server-side update propagation—how updates flow from their source (a database write, a user action, an external event) to the specific server handling a particular client’s connection. Both hops involve distinct trade-offs and architectural considerations.

Client-Server Connection Protocols: Multiple protocols and patterns exist for the first hop, each with different characteristics suited to specific use cases.

Simple Polling: The baseline approach involves clients regularly requesting updates from the server at fixed intervals. Every few seconds, the client asks “do you have anything new for me?” The server responds with either new data or an empty response, and the connection closes. This doesn’t technically qualify as real-time, but it provides a simple, stateless solution that works with any standard HTTP infrastructure. The main disadvantages are higher latency (updates are delayed by the polling interval) and wasted resources when no updates are available. However, for many applications where sub-second latency isn’t critical, simple polling offers an excellent balance of simplicity and effectiveness.

Long Polling: An evolution of simple polling, long polling has the client make a request to the server, but instead of immediately responding, the server holds the request open until new data becomes available. When data arrives, the server responds and closes the connection, prompting the client to immediately make a new request. If no data arrives within a timeout period (typically 15-30 seconds), the server sends an empty response to allow the client to reconnect. This provides near real-time updates while still building on standard HTTP infrastructure. The approach works well for infrequent updates but can introduce latency issues with high-frequency updates, since each update requires a complete request-response cycle.

Server-Sent Events (SSE): SSE builds on HTTP by using the Transfer-Encoding: chunked header to enable streaming responses. Instead of sending a complete response and closing the connection, the server sends data chunks as they become available while keeping the connection open. Modern browsers provide native support through the EventSource API, which handles connection management and automatic reconnection. SSE works excellently for one-way communication where the server pushes updates to clients but clients don’t need to frequently send data back. It’s particularly popular for streaming AI-generated content, live dashboards, and notification systems. The main limitation is that it only supports server-to-client communication; clients must use separate HTTP requests for sending data to the server.

WebSockets: For true bidirectional, low-latency communication, WebSockets are the standard choice. WebSocket connections begin as HTTP requests but upgrade to a persistent, full-duplex communication channel. Both client and server can send messages at any time without the overhead of HTTP headers and connection setup. This makes WebSockets ideal for chat applications, real-time gaming, and any scenario requiring frequent, bidirectional communication. The trade-off is increased complexity—WebSocket servers must manage persistent connections, handle reconnection logic, and typically require Layer 4 load balancers that maintain connection affinity. Despite this complexity, when you need high-frequency, bidirectional updates, WebSockets are unmatched.

WebRTC: The most complex option, WebRTC enables direct peer-to-peer communication between browsers without server intermediation. Clients connect to a signaling server to discover peers and exchange connection information, then establish direct connections using STUN (Session Traversal Utilities for NAT) or TURN (Traversal Using Relays around NAT) servers to work around firewall and NAT restrictions. WebRTC excels at scenarios like video conferencing, voice calls, and screen sharing where the bandwidth and latency benefits of peer-to-peer communication justify the implementation complexity. Some applications also use WebRTC for collaborative editing to reduce server load, though this requires additional conflict resolution mechanisms.

Choosing the Right Protocol: The decision tree for client-server protocols is relatively straightforward. If you’re not latency-sensitive and can tolerate updates every few seconds, simple polling provides the best balance of simplicity and functionality. If you need lower latency but only server-to-client communication, SSE is an excellent upgrade that eliminates the request-response overhead of long polling. When you need frequent, bidirectional communication, WebSockets become necessary despite their added complexity. Finally, WebRTC should be reserved for audio/video communication or specialized peer-to-peer scenarios where server costs or latency are critical concerns.

Server-Side Update Propagation: The second hop—getting updates from their source to the appropriate server—presents different challenges. Three main patterns address this problem.

Pull-based Polling: With pull-based polling, clients poll their connected servers, which in turn query a database or other data store for updates. The database acts as a decoupling layer between update producers and consumers. For a chat application, servers would query “what messages have been sent to this room with timestamps greater than the last message this client received?” This approach is simple and keeps state confined to the database, but introduces latency and can create significant database load when many clients poll frequently.

Push via Consistent Hashing: When using persistent connections (WebSockets, SSE, long polling), clients maintain connections to specific servers, and those servers must receive updates intended for their connected clients. Consistent hashing solves the routing problem by deterministically mapping clients to servers. When an update needs to be delivered, the system hashes the client identifier to determine which server holds their connection, then routes the update to that server. This approach works well when connections carry significant state that would be expensive to transfer between servers. The main complexity involves handling scale-up and scale-down events—adding or removing servers requires carefully migrating affected clients to maintain the consistent hash invariants.

Push via Pub/Sub: The pub/sub pattern introduces a message broker (like Redis or Kafka) that decouples update sources from connection servers. When clients connect, their servers subscribe to relevant topics on behalf of those clients. When updates occur, they’re published to appropriate topics, and the message broker broadcasts them to all subscribed servers. Those servers then forward updates to the relevant client connections. This approach minimizes state proliferation—connection servers are lightweight and focus solely on maintaining client connections and forwarding messages. The pub/sub service becomes the primary stateful component, but these systems are designed to handle massive message throughput efficiently.

Practical Considerations: Real-world implementations must address several operational challenges. Connection failures and reconnection logic are critical—networks are unreliable, especially for mobile clients. Systems need heartbeat mechanisms to detect zombie connections and sequence numbers or message queues to allow clients to catch up on missed updates when reconnecting. The celebrity problem, where a single user’s update must fan out to millions of followers, requires hierarchical distribution and caching strategies to avoid overwhelming individual components. Message ordering across distributed servers often requires logical timestamps or, for strict ordering requirements, funneling related messages through single partitions.

Load Balancing Considerations: The choice between Layer 4 and Layer 7 load balancers significantly impacts real-time systems. Layer 4 load balancers operate at the TCP/UDP level and maintain persistent connections between clients and backend servers, making them ideal for WebSocket deployments. Layer 7 load balancers operate at the HTTP level, terminating incoming connections and creating new ones to backend servers, which provides more routing flexibility but complicates WebSocket support (though many modern L7 load balancers do support WebSocket upgrades).

When Real-time Updates Matter: Real-time updates appear in numerous application categories. Chat applications require instant message delivery to all participants, with WebSockets handling bidirectional communication and pub/sub distributing messages across servers. Collaborative document editors need character-level change propagation, often combining WebSockets with operational transforms or CRDTs (Conflict-free Replicated Data Types) for conflict resolution. Live dashboards display constantly changing business metrics, where SSE often suffices for one-way data flow. Gaming and interactive applications demand the lowest possible latency, sometimes using WebRTC for peer-to-peer communication to minimize server round trips.

Common Pitfalls: Many developers over-engineer real-time solutions. If polling every 2-5 seconds meets your requirements, embrace that simplicity rather than jumping to complex WebSocket architectures. The added complexity of persistent connections, reconnection logic, and stateful server management should be justified by genuine low-latency requirements. Similarly, reaching for consistent hashing when pub/sub would suffice adds unnecessary complexity to your system.

Infrastructure and Deployment Concerns: Deploying real-time systems introduces operational challenges beyond typical web applications. WebSocket servers with thousands of persistent connections face unique scaling considerations—you can’t simply restart servers without disconnecting all clients. Graceful shutdown procedures that drain connections over time, combined with client reconnection logic, are essential. Connection affinity through load balancers becomes critical, as you can’t freely route requests to any available server. Monitoring becomes more complex when requests remain open for minutes or hours rather than milliseconds.

Scaling Strategies: Scaling real-time systems requires different approaches than stateless HTTP services. Connection servers often scale horizontally by adding more servers behind load balancers, but each server must handle the state of all its connected clients. Using lightweight connection servers that primarily forward messages (the pub/sub pattern) allows easier scaling than servers that maintain substantial per-connection state (the consistent hashing pattern). Pub/sub systems themselves scale through partitioning topics across multiple broker instances.

Security Considerations: Real-time systems introduce security considerations beyond typical web applications. Long-lived connections require careful authentication and authorization—initial handshake authentication via tokens or cookies, with periodic re-validation for long-running connections. WebSocket connections don’t automatically include cookies on every message, so authentication state must be maintained server-side. Rate limiting becomes more nuanced when a single connection sends many messages. DDoS protection must account for connection exhaustion attacks where attackers open many connections without sending substantial data.

The Future of Real-time Updates: As applications become more interactive and users expect more immediate feedback, real-time update patterns continue to evolve. HTTP/2 and HTTP/3 introduce improvements like multiplexing that reduce connection overhead. Edge computing and CDN-based WebSocket termination bring real-time capabilities closer to users, reducing latency. Serverless architectures are beginning to support WebSocket connections through specialized services, though with limitations compared to traditional server deployments.

Real-time updates represent one of the most complex patterns in modern system design, requiring careful consideration of protocols, infrastructure, scaling, and operational concerns. Success comes from matching the right tools to your specific requirements. Start simple with polling when possible, upgrade to SSE for one-way updates, embrace WebSockets when bidirectional low-latency communication is essential, and reserve WebRTC for truly specialized peer-to-peer scenarios. On the server side, prefer pub/sub for its simplicity and scalability unless connection state management specifically requires consistent hashing. Most importantly, resist the temptation to over-engineer—many successful applications use simpler patterns than you might expect, focusing engineering effort on the unique challenges of their domain rather than building complex real-time infrastructure that ultimately provides marginal benefits.

Understanding these patterns empowers you to build responsive, scalable applications that meet modern user expectations for immediate feedback and live collaboration. Whether you’re building a simple notification system or a complex multiplayer game, the principles of real-time update delivery remain the same: choose the right protocol for your latency requirements, implement robust reconnection logic, design for scale from the beginning, and always start with the simplest solution that meets your needs.