Real-time Metrics Dashboard

Overview

Create a monitoring dashboard that displays real-time metrics (CPU, memory, request latency, error rates) with live-updating charts. Users configure panels, set alert thresholds, choose time ranges, and arrange widgets in a grid layout.

Observability dashboards are critical infrastructure for every production system. Tools like Grafana, Datadog, and New Relic are used daily by engineering teams to monitor system health, investigate incidents, and track performance trends. Building a simplified version teaches you the core patterns: time-series data storage and query, real-time chart rendering, configurable panel layouts, and alerting logic.

The technical challenges span both backend and frontend. On the backend, time-series data requires specialized storage that handles high write throughput and efficient range queries with downsampling. On the frontend, rendering multiple live-updating charts at 60fps while receiving WebSocket data streams requires careful batching, memoization, and canvas-based rendering for large datasets.

For infrastructure and platform engineering interviews, this project is directly relevant. You can discuss time-series data modeling, downsampling strategies (raw data for recent timeframes, aggregates for historical), WebSocket streaming architecture, and alert state machine design with concrete implementation details.

Interview questions

• How do I handle time-series data efficiently?
• How do I stream real-time data to charts?
• How should the alerting system work?
• How do I make the dashboard configurable?
• How do I optimize chart rendering performance?
• How do I build the time range selection?

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