Real-Time Systems
WebSockets, Message Queues, and Live Data
Updated
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
Live Trading Data Pipeline
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.
Embeddable Messaging Widget
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.
Streaming Data Visualization
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
Related Expertise
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 ProcessingStreaming 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 StoriesReal-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 ScaleFrequently Asked Questions
What is a real-time system, and why does it matter for product experience?
Real-time means users see changes the moment they happen — no refresh, no polling, no stale data. For products like price feeds, notifications, collaborative editing, and monitoring dashboards, this is the difference between a product that feels alive and one that feels broken. The hard problem is not speed: it is what happens when things go wrong at speed, which is where architecture decisions around reconnection, state reconciliation, and message ordering get tested.
When does a product actually need real-time infrastructure?
A product needs real-time infrastructure when stale data has a cost — live trading, price feeds, notifications, collaborative editing, monitoring dashboards. If users can tolerate refresh-based updates, polling is simpler and cheaper. Once real-time is justified, the architecture must be designed for failure: explicit backpressure handling, slow-consumer detection, and graceful degradation. Without those, a WebSocket layer that works in testing collapses under production load, taking every connection down with it.
What production experience does Oleksii Vasylenko have with real-time systems?
Oleksii Vasylenko has built real-time systems across trading, messaging, and analytics. At Bitsten exchange he built the WebSocket system delivering order book updates, trades, and price feeds to thousands of concurrent users, using snapshot-plus-diff updates that cut bandwidth 10x versus full-state pushes. At Mailchain he developed an embeddable widget for wallet-to-wallet messaging over WebSockets with an encryption test suite. At Data Laboratories he architected a streaming pipeline feeding D3 dashboards processing millions of data points.
What goes wrong when teams build real-time systems?
The failure mode most teams miss is backpressure. When a WebSocket server pushes updates faster than clients can consume them, messages queue in kernel buffers, memory climbs, and the server eventually crashes — taking all connections with it. The fix is explicit: detect slow consumers, drop stale messages, and degrade gracefully instead of catastrophically. Other invisible-until-catastrophic failures include message ordering bugs and reconnection race conditions, all of which must be designed for upfront.
How can I engage Oleksii Vasylenko for a real-time architecture project?
Oleksii works with teams whose products need live data — trading platforms, dashboards, messaging, anything where users seeing stale data is a problem. He has built these systems for live trading and enterprise dashboards, including backpressure handling, snapshot-plus-diff delivery, and streaming pipelines processing millions of data points. Teams facing backpressure crashes, ordering bugs, or reconnection races, or designing real-time infrastructure from scratch, can contact him through ovasylenko.com to discuss architecture.
Further Reading
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