A client’s Elementor website suddenly started breaking — layouts collapsing, 502 errors, and database write failures.
After investigation, I found the root cause:
👉 Database exceeded hosting limit (1GB+)
👉 postmeta table alone was ~957MB
👉 Over 800MB was unused orphan data
🔍 What I did:
• Analyzed database using SQL (no blind cleanup)
• Identified ~94K orphaned metadata entries
• Safely removed unused postmeta records
• Rebuilt the table to eliminate fragmentation
• Optimized database structure
⚡ Results:
• Database reduced from 1239MB → ~300MB
• postmeta reduced from 957MB → 20MB
• Eliminated 502 errors
• Elementor saving issues fixed
• Site performance significantly improved
💡 Key Insight:
Most WordPress issues aren’t frontend problems — they’re database inefficiencies.
🛠️ Stack:
WordPress • Elementor • MySQL • SiteGround
If your site feels slow or unstable, the issue might be hidden in your database 👀
What makes an A/B test readout useful to a product team?
My preferred first page answers four questions:
What changed, and by how much?
How uncertain is the estimate?
Did an important guardrail get worse?
What decision does the evidence support, and what remains unresolved?
A result can be statistically significant and still too small to matter. An inconclusive result can still leave a meaningful gain or loss plausible. The decision needs more than a green badge.
This is a strong framing of experiment readouts: separating signal, uncertainty, guardrails, and the actual decision keeps the team honest. The reminder that significance is not the same as usefulness is especially important.
The single visual and oversized statement make the positioning instantly clear. I’d be curious to see how this bold art direction carries into the actual landing-page interactions and mobile layout.