I built a search engine over 1,600+ cybersecurity articles — here's what I actually learned
The author developed a search engine for over 1,600 cybersecurity articles to improve search quality. By using Meilisearch, they addressed issues with inconsistent metadata and implemented better content management practices. The result was a significant reduction in search latency and improved user experience.
- ▪The previous search method using MySQL was ineffective, returning irrelevant results.
- ▪Meilisearch was chosen for its speed, typo tolerance, and ease of setup.
- ▪Key improvements included enforcing excerpt quality, implementing category filtering, and adding a fallback to MySQL.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3944946) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ayi NEDJIMI Posted on May 21 I built a search engine over 1,600+ cybersecurity articles — here's what I actually learned #ai #meilisearch #webdev #security A year ago I had a problem: 1,600+ cybersecurity articles spread across a Go backend, and a search bar that returned garbage. The standard MySQL LIKE '%keyword%' approach was embarrassing.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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