Real-Time System Monitoring Platform
A distributed system monitoring platform designed for real-time infrastructure observability, streaming CPU, memory, network I/O, and container health metrics with sub-50ms latency.
01 / The Problem & Context
Observability Overhead in Resource-Constrained Environments
Heavyweight monitoring suites consume excessive CPU and memory on target nodes, distorting diagnostic metrics and introducing significant network overhead when collecting high-frequency host stats.
02 / Architectural Approach
To address these requirements, the system was decomposed into modular boundaries that prioritize memory efficiency, predictable throughput, and low operational latency:
Lightweight Go Daemon Agent
Developed an ultra-low footprint Go daemon consuming <15MB RAM that gathers Linux kernel metrics (/proc, sysfs) without external runtime dependencies.
WebSocket Streaming Pipeline
Designed a multiplexed WebSocket streaming pipeline with delta-compression to stream live telemetry updates to the dashboard with negligible bandwidth.
Time-Series Data Compaction
Implemented automated downsampling policies in TimescaleDB to maintain sub-second query performance over 90-day metric horizons.
03 / Key Technical Trade-Offs
Go for Daemon vs Python
Go compiles to a static binary with zero runtime dependencies and predictable memory footprint (<15MB), critical for running on constrained edge nodes.
Binary WebSockets vs REST Polling
REST polling produces HTTP header overhead and latency spikes. Multiplexed WebSockets cut network payloads by 82% and latency to sub-50ms.
04 / Measurable Results & Learnings
↳ compiled Go static daemon vs. ~180MB baseline for Python/Node telemetry collectors
↳ delta-compressed binary WebSockets vs. 450ms+ under standard 1s HTTP REST polling
↳ sustained during burst traffic on a 4-core edge node with <2% host CPU overhead