High-Load IoT Platform Architecture

At 14:12, the system processes 200 events per second. At 14:13 — it’s already 20,000. That’s when it becomes clear: the system wasn’t ready.

High-load IoT platforms don’t scale gradually. They scale in spikes — and either survive or fail.

The Core Mistake: Assuming Linear Load

IoT load doesn’t grow smoothly. It comes in bursts:

• simultaneous events from thousands of devices;
• peak traffic spikes;
• synchronized requests.

Your system must be designed for peak load — not average.

Data Streams as the Foundation

IoT is not CRUD. It’s a stream of events.

• ingestion;
• queues;
• processing;
• storage.

If the stream is not controlled, the system loses control.

Asynchronous by Design

Synchronous systems cannot handle high load.

• message queues;
• event-driven architecture;
• service decoupling.

This prevents system collapse under pressure.

Scaling: Horizontal, Not Vertical

Adding more power to a single server is only a temporary fix.

The correct approach:

• horizontal scaling;
• distributed services;
• load balancing.

Real-Time Processing

Latency is also a problem.

• stream processing;
• event-driven reactions;
• low-latency design.

The system must respond instantly.

Fault Tolerance

Failures are inevitable in high-load systems.

• replication;
• retry mechanisms;
• fallback strategies.

The question is not “if it fails” — but “how it survives.”

Technology Stack

• Kafka — data streaming;
• MQTT — device communication;
• Node.js — backend;
• Redis — performance;
• ClickHouse — analytics;
• Kubernetes — scalability.

Business Impact

• stability under load;
• scalability;
• system control;
• risk reduction.

A high-load IoT platform is not about technology. It’s about the system’s ability to handle reality.