Real-Time Systems

WebSockets, Message Queues, and Live Data

Real-time means users see changes the moment they happen — no refresh, no polling, no stale data. I have built WebSocket systems for live trading, message queue pipelines for reliable event processing, and streaming data platforms for enterprise dashboards. The recurring lesson: the hard problem is not speed — it is what happens when things go wrong at speed.

What This Means for Your Business

The failure mode most teams miss in real-time systems is backpressure. When your WebSocket server pushes updates faster than clients can consume them, messages queue in kernel buffers, memory climbs, and eventually the server crashes — taking all connections with it. You need explicit backpressure handling: detect slow consumers, drop stale messages, and degrade gracefully instead of catastrophically.

If your product needs live updates — price feeds, notifications, collaborative editing, monitoring dashboards — the architecture decisions around reconnection, state reconciliation, and message ordering determine whether it works at scale or collapses under load.

How I Have Used This in Production

  1. Live Trading Data Pipeline

    Bitsten exchange

    Built WebSocket system delivering order book updates, trade executions, and price feeds to thousands of concurrent users. Implemented incremental order book updates — clients receive a snapshot on connect, then diffs thereafter — reducing bandwidth by 10x compared to full-state pushes.

  2. Embeddable Messaging Widget

    Mailchain

    Developed the Near Mailchain Widget enabling dApps to embed wallet-to-wallet messaging. Built on WebSockets for real-time message delivery with encryption test suite ensuring secure delivery across blockchain protocols.

  3. Streaming Data Visualization

    Data Laboratories

    Architected streaming data pipeline feeding D3-based dashboards processing millions of data points in real time. The challenge was rendering performance — D3 DOM manipulation becomes a bottleneck beyond 10K elements, so we switched to Canvas rendering for dense datasets while keeping SVG for interactive overlays.

Technologies

WebSocketsRabbitMQRedisNode.jsNestJSD3.jsKeyDB

The matching engine produces the event stream. The real-time system delivers it. See how I built both sides of that pipeline.

Building Matching Engines — Sub-Millisecond Order Processing

Streaming data needs to render without blocking the UI. See how I solved the D3 performance cliff with Canvas-SVG hybrid rendering.

D3.js and Data Visualization — Making Data Tell Stories

Real-time reads come from Redis and KeyDB — not the primary database. See how I designed the data layer to serve both hot and cold paths.

Database Architecture and Performance — Designing Data Systems That Scale

Users seeing stale data?

Real-time systems fail in ways that are invisible until they are catastrophic — backpressure crashes, message ordering bugs, reconnection race conditions. I have built these systems for live trading and enterprise dashboards. If your product needs to feel alive, let's architect it correctly.

Discuss your real-time architecture