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Pascual Vila

Full-Stack Software and AI Engineer

Full-Stack Software and AI Engineer with 6 years of experience building enterprise-grade web applications across React, Angular, Node.js, and Python. Recently completed a Master's in AI Development specializing in LLMs, RAG, and AI agent architectures, and currently builds enterprise systems that integrate AI and Digital Twins to optimize industrial and logistics processes.

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Common Pitfalls & Errors

The Error //

Adding a high-cardinality label (raw user ID, full URL, IP address) to a metric

// Wrong: unbounded cardinality, one series per user
requestCounter.inc({ userId: req.user.id });

// Correct: bounded set of possible values
requestCounter.inc({ method: req.method, status: res.statusCode });

The Solution //

Each unique combination of label values creates a separate time series in the metrics backend — a label with millions of possible values (like a user ID) can create millions of time series, overwhelming storage and dramatically slowing down queries. Keep labels limited to a small, bounded set of known values.

The Error //

Relying only on average latency to judge whether a service is performing well

// Misleading: average looks fine, hides slow outliers
const avgLatency = totalDuration / requestCount;

// Correct: reveals the real distribution
const p99 = histogram.percentile(99);

The Solution //

An average can look perfectly healthy while a meaningful fraction of requests are experiencing very poor latency — a small number of very slow outliers gets diluted by many fast requests. Use a histogram and look at percentiles (p95, p99) to see the actual distribution, not just the mean.