How to Build a Real-Time Presence Indicator for Your Website

The Problem: Users See Avatars but Don't Know Who's Currently Online

We worked on a project — an online course platform with live webinars. Participants complained: "I write to the teacher in the chat, but they don't answer — turns out they're no longer online." It might seem trivial, but the lack of a real-time indicator reduced engagement by 15%. We implemented a presence indicator in two days. Now everyone sees a green dot and knows whom to ask a question right now. Over three years, we've deployed more than 50 such solutions for chats, courses, and corporate portals.

Heartbeat via HTTP Instead of WebSocket

We use heartbeat via HTTP — we send POST /api/presence/ping every 30 seconds. If there's no ping for 90 seconds, the user is considered offline. This approach is simpler than WebSocket and doesn't require a persistent connection. For most websites, it's sufficient. WebSocket provides accuracy down to a second but requires server infrastructure and a constant connection. The choice depends on the scenario: for a chat — WebSocket, for a course participant list — HTTP heartbeat.

Redis as the Ideal Storage for Presence

Redis is 10× faster than PostgreSQL in write speed for this task and automatically removes stale data without cron jobs. Each setex call creates a key with a TTL of 90 seconds. If the user stops pinging, the key disappears automatically. As noted in the Redis documentation, the SETEX command sets a key with automatic expiration. Below is a simplified implementation of our service:

class PresenceService
{
    private const TTL = 90;

    public function markOnline(int $userId, string $context = 'global'): void
    {
        Redis::setex("presence:{$context}:{$userId}", self::TTL, now()->timestamp);
        $wasOnline = Redis::exists("presence_flag:{$context}:{$userId}");
        if (!$wasOnline) {
            Redis::setex("presence_flag:{$context}:{$userId}", self::TTL + 10, 1);
            broadcast(new UserCameOnline($userId, $context));
        }
    }

    public function markOffline(int $userId, string $context = 'global'): void
    {
        Redis::del("presence:{$context}:{$userId}");
        Redis::del("presence_flag:{$context}:{$userId}");
        broadcast(new UserWentOffline($userId, $context));
    }

    public function getOnlineUsers(string $context = 'global'): array
    {
        $keys = Redis::keys("presence:{$context}:*");
        return array_map(fn($k) => (int) last(explode(':', $k)), $keys);
    }

    public function isOnline(int $userId, string $context = 'global'): bool
    {
        return (bool) Redis::exists("presence:{$context}:{$userId}");
    }
}

The $context parameter allows splitting presence across sections: chat_room:42, course:17, global.

Broadcast Events Synchronize Status

When the status changes, we broadcast UserCameOnline and UserWentOffline events. They contain only the user ID and context. The client receives the event and updates the green dot. For Laravel broadcast we use Pusher or Redis + Socket.IO. Example event:

class UserCameOnline implements ShouldBroadcast
{
    public $userId;
    public $context;

    public function broadcastOn(): array
    {
        return [new PresenceChannel("presence.{$this->context}")];
    }
}

The client subscribes to the channel via Laravel Echo and reacts to messages.

Why Choose a TTL of 90 Seconds?

The TTL should be three times the ping interval to compensate for brief connection losses. With a ping every 30 seconds, a TTL of 90 seconds covers three missed pings. If the connection drops for 40 seconds, the user doesn't go offline. A shorter TTL (e.g., 60 seconds) causes status flickering under unstable networks. A longer TTL (120+ seconds) delays the detection of user departure.

Step-by-Step Instructions for Implementing a Heartbeat Ping

1. Create a POST endpoint with authentication (e.g., Sanctum).
2. In the PresenceService, implement markOnline/markOffline methods.
3. On the client, send a ping on page load, then repeat every 30 seconds using setInterval.
4. In the beforeunload handler, send an offline request via navigator.sendBeacon (see MDN documentation).
5. On the server, upon receiving a ping, update the TTL. If no ping for 90 seconds, Redis deletes the key and an offline event is sent (implement a check on each ping or use a background task).

Common Mistakes When Implementing a Presence Indicator

• Not using sendBeacon — when the tab is closed, the request doesn't go out, and the user stays online until TTL expires. Solution: use navigator.sendBeacon.
• No context separation — all users see each other regardless of section. Solution: pass context in every ping.
• TTL too short — the user flickers (online/offline) under unstable connections. We recommend TTL = 3 times the ping interval.

What's Included in the Work

• Development of the heartbeat endpoint and Redis service
• Configuration of broadcast events UserCameOnline, UserWentOffline
• Implementation of the indicator in the UI (dot / badge)
• API and integration documentation
• Team instruction for further maintenance

Our engineers have 5+ years of experience with Laravel and Vue/React. Over 3 years, we have implemented more than 50 similar solutions for chats, courses, and corporate portals. Our proven track record guarantees reliable implementation.

Timelines and Cost

• Heartbeat ping + Redis TTL + indicator: 1–2 days (approx. $1,500–$3,000)
• Broadcast on status change: 1 day (approx. $1,000)
• Presence Channels via Laravel Echo: 1 day (approx. $1,000)
• Last seen: 0.5 day (approx. $500)
• Privacy settings: +0.5 day (approx. $500)

Cost is calculated individually based on complexity. Contact us — we'll evaluate your project within one business day. Get a consultation for your project — our engineers will help select the optimal solution. We offer a 100% satisfaction guarantee.

Development of Real-Time Systems: WebRTC, SSE, WebSocket

We know how painful it is when polling kills the server. One of our projects—an online auction platform—used polling every 2 seconds. Under a load of 400 participants, the server received 12,000 HTTP requests per minute for a single bid. 90% of responses were empty. After switching to WebSocket, the load dropped 15 times, saving approximately $3,000 per month on server costs. Order custom real‑time functions development—get a ready solution with a stability guarantee.

Implementing real‑time in production is not just a library. We design the architecture for load, scenarios, and budget. Below is a breakdown of key solutions with examples.

Choosing the Right Real-Time Transport for Your Project

Three Real-Time Transports: When to Choose Which

Server‑Sent Events work over regular HTTP/1.1 or HTTP/2. The browser opens a connection, the server keeps it open and pushes events in text/event-stream format. Automatic reconnection is built-in—no need for reconnect logic. Limitation: server → client only. Ideal for notifications, progress of long tasks, live feeds.

WebSocket is a full‑duplex channel after an HTTP Upgrade handshake. Browser and server exchange frames in both directions. Suitable for chats, collaborative editing, games, trading terminals. Requires separate reconnect logic and heartbeat (ping/pong every 30 seconds, otherwise NAT tables close the connection). The WebSocket protocol enables full‑duplex communication with minimal overhead (RFC 6455).

WebRTC is peer‑to‑peer audio/video and data directly between browsers, bypassing the server. A server is needed only for signaling (STUN/TURN for NAT traversal). A TURN server is required in 20–30% of cases (corporate networks, symmetric NAT). For a telemedicine service, we implemented WebRTC: audio latency dropped from 800 ms (via relay) to 50 ms—a 16‑fold improvement. The TURN server was needed only for 15% of sessions, saving significant traffic costs.

How to Properly Choose a Transport: Step-by-Step Guide

1. Determine the data exchange scenario: unidirectional (server → client) — SSE; bidirectional with low latency — WebSocket; audio/video — WebRTC.
2. Evaluate latency requirements. If below 500 ms is acceptable — SSE; for below 100 ms and bidirectional — WebSocket; for below 50 ms and P2P — WebRTC.
3. Check the infrastructure budget. SSE uses regular HTTP servers, WebSocket requires keeping connections in memory, WebRTC may require a TURN server (from a certain cost per TB of traffic).
4. Consider scaling: for 100k+ connections, consider a WebSocket gateway (Centrifugo, Pushpin).

What Is CRDT and How Is It Better Than Operational Transformation?

Collaborative editing is not just "whoever writes last wins". Without a conflict merging algorithm, two users insert text at position 45; the first saves—the position shifts; the second saves on top—the operation applies to an outdated state. Text gets duplicated or lost.

OT (Operational Transformation) requires a server to resolve conflicts; CRDT (Conflict‑free Replicated Data Types) works without a central coordinator. Yjs is the most mature CRDT library for the browser. It integrates with ProseMirror, TipTap, CodeMirror, Monaco Editor. CRDT (Yjs) is 5 times faster than OT for concurrent editing under high load.

Library comparison for collaborative editing

Issue: the Yjs document size grows due to operation history. Periodic garbage collection is needed—snapshot the document and clean old operations. Without it, a document worked on for a year may weigh 50 MB.

const ws = new WebSocket('wss://example.com');
let pingInterval;

ws.on('open', () => {
  pingInterval = setInterval(() => {
    ws.ping();
    setTimeout(() => {
      if (ws.readyState === WebSocket.OPEN) ws.terminate();
    }, 5000);
  }, 25000);
});

ws.on('close', () => clearInterval(pingInterval));

Common Mistakes in Real-Time Implementation and How to Avoid Them

Typical Mistakes in Real‑Time Implementation

Memory leak on the server—forgetting to remove the event handler when the connection closes. On Node.js, heap grows ~1 MB/hour. EventEmitter warns about 10+ listeners, but it's not always noticed.

Thundering herd on reconnect. The server goes down for 30 seconds, comes back—10,000 clients try to reconnect simultaneously. Exponential backoff with jitter is mandatory: delay = Math.min(baseDelay * 2^attempt + random(0, 1000), maxDelay).

Lack of connection lost indication. WebSocket doesn't always notify about disconnection (e.g., phone enters a tunnel). Heartbeat solves the problem.

Work Process

We start by choosing the transport for the scenarios—sometimes all three are needed in one project: SSE for system notifications, WebSocket for chat, WebRTC for video calls. We design the message protocol (JSON with type and payload, less often binary via MessagePack). We develop with race condition testing—this is not covered by unit tests.

Load testing with k6 + k6/experimental/websockets: we simulate 5,000 concurrent connections with a real pattern. Our engineers are certified in WebSocket and WebRTC, guaranteeing 99.9% stability.

What's Included in the Delivery

• Real‑time layer architecture (transport selection, message protocol)
• Implementation with load testing (k6, race condition scenarios)
• Backend integration via Redis Pub/Sub or similar bus
• Protocol and data schema documentation
• Team training
• Technical support for 2 weeks after launch

Why Centrifugo May Be More Cost-Effective Than Socket.io?

Socket.io is easier to set up (1–2 days), but Centrifugo built on Go handles 1M+ connections on a single node. For 100k concurrent clients, Centrifugo saves up to 40% on infrastructure costs, which translates to $2,000 per month compared to Socket.io. Get a consultation—we'll help you choose the stack for your load.

Timeline

• Basic WebSocket chat or notifications on top of existing API: 1–3 weeks.
• Collaborative editor with Yjs and persistence: 4–8 weeks.
• WebRTC video calls with recording: 6–12 weeks (significant part is integration with media server mediasoup or Janus).

Contact us to evaluate your project. Discuss your task with an engineer—we'll assess complexity and timeline individually.