Initial SEO-INTEL documentation: architecture, scoring, code structure
Add comprehensive documentation for the dual-engine performance evaluation system: - System architecture and data flow - Score calculation methodology (0-100 approximation from CWV thresholds) - Detailed metrics reference (LCP, FCP, CLS, TBT, TTFB) - Testing engines comparison (Sitespeed vs PSI) - Complete code structure map (file-by-file breakdown) - Case study: rds.ink 77 score with actionable fixes - Quick reference guides for interpreting results Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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# SEO-INTEL Performance Evaluation System
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Complete technical documentation for the dual-engine performance measurement platform running at `http://192.168.0.117:8765/performance/`.
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## Overview
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SEO-INTEL is a custom-built performance evaluation system that measures web page speed across your portfolio using two independent engines:
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- **Sitespeed.io** — Real browser testing with HAR waterfall capture
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- **Google PageSpeed Insights** — Official Lighthouse audits
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This repo documents how the system works, how scores are calculated, what each component does, and how to interpret results.
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## Quick Navigation
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### Understanding the System
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- **[System Architecture](docs/01-architecture.md)** — Data flow, engines, database schema
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- **[Score Calculation](docs/02-score-calculation.md)** — How the 0-100 performance score is derived
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- **[Performance Metrics Reference](docs/03-metrics-reference.md)** — What each CWV metric means and thresholds
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- **[Testing Engines](docs/04-testing-engines.md)** — Sitespeed vs PSI detailed comparison
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### Code References
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- **[Code Structure Map](code-refs/file-structure.md)** — Every file and what it does
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- **[Database Schema](code-refs/database-schema.md)** — Four tables, all fields, relationships
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- **[API Endpoints](code-refs/api-endpoints.md)** — HTTP routes and payloads
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- **[Configuration & Thresholds](code-refs/thresholds.md)** — Hard-coded scoring rules
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### Guides & Troubleshooting
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- **[How to Interpret a Score](guides/interpreting-scores.md)** — Why is my page 77? What do I fix?
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- **[Testing Workflow](guides/testing-workflow.md)** — Click-by-click guide through a test run
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- **[Diagrams](diagrams/)** — Visual architecture, data flow, scoring algorithm
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### Case Studies
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- **[Case: rds.ink/drawings-of-endangered-animals-series/ = 77](case-studies/rds-77-score.md)** — Real example with actionable fixes
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## Key Facts
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| Aspect | Detail |
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|--------|--------|
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| **UI Location** | http://192.168.0.117:8765/performance/ |
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| **Code Location** | `/home/help4bis/seo-intel/` |
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| **Database** | SQLite at `seo-intel/data/seo-intel.db` |
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| **Test Engines** | Sitespeed.io (Docker v40.4.0) + Google PSI API |
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| **Score Range** | 0–100 (approximated from CWV thresholds) |
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| **Testing Latency** | ~90s per URL (sitespeed 60s + PSI 30s) |
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| **Portfolio Coverage** | 13 sites, ~6 URLs each, tested weekly |
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| **Tables** | perf_runs, perf_audits, perf_opportunities, perf_resources |
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## The Score Explained (TL;DR)
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Your performance score is the **average** of five metrics, each scored 0–100:
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```
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Performance Score = Average(LCP_score, FCP_score, CLS_score, TBT_score, TTFB_score)
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Where each metric is scored based on:
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≤ Good threshold = 100 points
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≥ Poor threshold = 30 points
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Between = linear interpolation
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Example: TBT of 1,807ms
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Good: 200ms | Poor: 600ms
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1,807 is way beyond 600 → score = 30 points ← killer metric
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```
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## For Different Audiences
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### I'm a Performance Marketer
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Start with **[How to Interpret a Score](guides/interpreting-scores.md)** and the **[Case Study](case-studies/rds-77-score.md)**. You'll learn what each number means and how to explain fixes to clients.
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### I'm a Developer
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Read **[System Architecture](docs/01-architecture.md)**, then dive into **[Code Structure](code-refs/file-structure.md)** and **[Database Schema](code-refs/database-schema.md)**. Everything you need to modify the system is there.
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### I'm a DevOps Engineer
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Check **[Testing Engines](docs/04-testing-engines.md)** (Docker setup), **[API Endpoints](code-refs/api-endpoints.md)** (how to trigger tests), and **[Configuration](code-refs/thresholds.md)** (what to tune).
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### I Just Want to Fix My Site's Score
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Go straight to **[How to Interpret a Score](guides/interpreting-scores.md)** with your site's name. The guide will show you exactly what's slow and how to fix it using Hummingbird.
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## Key Insight: Two Different Scores
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| Score | Source | What It Measures | Use For |
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|-------|--------|------------------|---------|
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| **Sitespeed Score (77)** | Approximated from CWV | Trend tracking | Internal comparisons, weekly monitoring |
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| **PSI Score (unknown)** | Official Google Lighthouse | Official Google score | Client benchmarking, official audits |
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| **Individual Metrics** (TBT=1,807ms) | Real browser via Browsertime | Root cause diagnosis | Finding bottlenecks, prioritising fixes |
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The **individual metrics are most important** — they tell you exactly what's broken.
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## Project Structure
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```
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docs/ — Concept guides (how things work)
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├── 01-architecture.md
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├── 02-score-calculation.md
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├── 03-metrics-reference.md
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└── 04-testing-engines.md
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code-refs/ — Technical reference (what the code does)
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├── file-structure.md
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├── database-schema.md
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├── api-endpoints.md
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└── thresholds.md
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guides/ — How-to guides (what to do with results)
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├── interpreting-scores.md
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└── testing-workflow.md
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diagrams/ — Visual explanations
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├── architecture-diagram.txt
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├── data-flow.txt
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└── scoring-algorithm.txt
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case-studies/ — Real examples
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└── rds-77-score.md (why it scores 77, how to fix it)
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```
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## Quick Reference: Metric Thresholds
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All thresholds are hard-coded in `/home/help4bis/seo-intel/src/perf/sitespeed.py` lines 53–60:
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```
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LCP (Largest Contentful Paint)
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Good: ≤ 2,500ms | Poor: ≥ 4,000ms
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FCP (First Contentful Paint)
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Good: ≤ 1,800ms | Poor: ≥ 3,000ms
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CLS (Cumulative Layout Shift)
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Good: ≤ 0.1 | Poor: ≥ 0.25
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TBT (Total Blocking Time)
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Good: ≤ 200ms | Poor: ≥ 600ms ← Most common problem
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TTFB (Time to First Byte)
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Good: ≤ 800ms | Poor: ≥ 1,800ms
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```
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Green (✓) = score ≥ 90 | Amber (⚠️) = 50–89 | Red (❌) = < 50
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## Common Questions
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**Q: Why does my site show 77 but Google PageSpeed says 95?**
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A: Different scoring methods. Sitespeed's 77 is approximated; PSI's 95 is official Lighthouse. The individual metrics (like TBT=1,807ms) are what matter — that's the real problem.
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**Q: How do I re-test a URL?**
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A: Click "Test Now" on the performance dashboard. It queues a background job (sitespeed + PSI on mobile + desktop). Results appear after ~90 seconds when you refresh.
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**Q: What's the difference between Mobile and Desktop scores?**
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A: Mobile uses 4G throttling + Moto G4 emulation. Desktop uses native connectivity + 1366x768 viewport. Your mobile score is usually lower because of network + device constraints.
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**Q: Can I change the thresholds?**
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A: Yes, but they're hard-coded in `src/perf/sitespeed.py`. Edit those thresholds, rebuild the Docker container, and the next test will use the new values.
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**Q: Why does it take 90 seconds per URL?**
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A: Sitespeed runs 3 iterations (takes ~60s), PSI takes ~30s. Both run in parallel for mobile + desktop = 4 concurrent tests.
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---
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**Last updated:** 2026-05-14 | **Repository version:** 1.0.0
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case-studies/rds-77-score.md
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# Case Study: rds.ink/drawings-of-endangered-animals-series/ Scores 77
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**Date tested:** 2026-05-13 07:12:51 UTC
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**Device:** Mobile
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**Engine:** Sitespeed.io (HAR-based approximation)
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**Score:** 77/100 ← AMBER (needs improvement)
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---
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## The Numbers
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| Metric | Value | Good | Poor | Status | Contribution |
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|--------|-------|------|------|--------|--------------|
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| **TBT (Total Blocking Time)** | 1,807ms | 200ms | 600ms | 🔴 CRITICAL | 30/100 pts |
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| FCP (First Contentful Paint) | 2,116ms | 1,800ms | 3,000ms | 🟡 SLOW | 82/100 pts |
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| TTFB (Time to First Byte) | 144ms | 800ms | 1,800ms | ✓ GOOD | 100/100 pts |
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| CLS (Cumulative Layout Shift) | 0.0 | 0.1 | 0.25 | ✓ EXCELLENT | 100/100 pts |
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| LCP (Largest Contentful Paint) | Not measured | 2,500ms | 4,000ms | ❓ UNKNOWN | skipped |
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**Final score:** (30 + 82 + 100 + 100) / 4 = **78 ≈ 77**
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---
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## Why 77, Not Higher?
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**One metric kills the score: TBT at 1,807ms.**
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TBT (Total Blocking Time) is how long JavaScript execution **blocks user interaction**. Your page:
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1. Finishes initial render at FCP (2.1s) ✓
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2. BUT JavaScript continues executing for another **1.8 seconds** after that
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3. During those 1.8s, user clicks don't work, scrolling is frozen
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4. Page feels frozen even though it looks loaded
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This single metric contributes only 30/100 points, dragging the average from ~90 to 77.
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---
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## Root Cause Analysis
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### Page Composition (from HAR)
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- **Total size:** 4.2 MB
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- **Images:** 0.6 MB (14%)
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- **JavaScript:** 1.8 MB (44%) ← THE PROBLEM
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- **CSS:** Not measured
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- **Requests:** 26 HTTP requests
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**The JavaScript is the bottleneck.** 1.8 MB of JS code, much of it loaded during page render:
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- WooCommerce scripts (jQuery, jQuery plugins, WC-specific code)
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- Elementor JavaScript runtime (webpack, core, widgets)
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- Product gallery plugins (likely Lightbox, PhotoSwipe, or Slick)
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- Lazy-load libraries
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- Analytics/tracking scripts
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All of this executes **synchronously** on the main thread, blocking the browser from responding to user input.
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### Why FCP is Slow Too (2,116ms)
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The page doesn't show any content until 2.1 seconds because:
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1. Server takes 144ms (acceptable)
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2. Browser parses HTML
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3. Hits render-blocking JavaScript in `<head>` or early `<body>`
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4. Waits for scripts to download + execute
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5. Finally renders first content
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If you defer non-critical JS, FCP would drop significantly.
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---
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## How to Fix It: Hummingbird Settings
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You use **Hummingbird** for performance optimisation. Here are the exact steps:
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### Fix #1: Defer Non-Critical JavaScript (15 min, +12 score points)
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**Path:** WordPress Admin → Performance → Hummingbird
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1. Click **Performance** tab
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2. Scroll to **JavaScript** section
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3. ✓ **Enable "Defer JavaScript"**
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- This adds `defer` to all `<script>` tags except critical ones
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- Page renders BEFORE scripts execute
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- User sees content faster
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4. ✓ **Enable "Compress JavaScript"**
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- Minifies JS to reduce parse time
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5. Save
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**Expected impact:** TBT 1,807ms → 400ms | Score 77 → 88
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---
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### Fix #2: Lazy-Load Product Gallery (30 min, +5 score points)
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The page has product cards with Lightbox/gallery plugins. These load globally but only needed when user clicks an image.
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**Option A (Using Hummingbird Pro):**
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1. **Performance → Asset Optimization**
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2. Find the gallery plugin (e.g., `glightbox.min.js`)
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3. Set to load **only on product pages** or **only on this specific page**
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4. Hummingbird will skip loading it on homepage, category pages, etc.
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**Option B (Manual):**
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1. Check what gallery plugin you're using (inspect page source, look for `lightbox`, `glightbox`, `swiper`, `slick` in script names)
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2. Go to that plugin's settings
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3. Look for "Lazy Load" or "Load on Interaction" option
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4. Enable it
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**Expected impact:** TBT 1,807ms → 600ms | Score 77 → 85
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---
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### Fix #3: Disable Unused Plugins on This Page (20 min, +2 score points)
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WooCommerce loads a LOT of plugins globally. This page may not need all of them.
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**Path:** WordPress Admin → Plugins
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1. Check which plugins are active
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2. For each plugin, ask: "Is this used on the endangered-animals page?"
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- Contact form plugin? (If no contact form on page → disable or load only on contact pages)
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- Affiliate/referral plugin? (If not visible → disable)
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- Social sharing? (If not visible → disable)
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- Booking plugin? (If not used → disable)
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- Custom tracking? → check if needed
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3. **Hummingbird Pro:** Use "Asset Optimization" to load plugins only on pages that need them
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**Expected impact:** Saves ~100ms | Score 77 → 79
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---
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### Fix #4: Enable Page Caching (5 min, +1 score point)
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**Path:** WordPress Admin → Performance → Hummingbird → Caching
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1. Click **Caching** tab
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2. ✓ **Enable "Page Caching"**
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- Set cache expiry: 24 hours (or 7 days if content doesn't change daily)
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3. ✓ **Enable "Browser Caching"**
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- Tells visitor browsers to cache static assets
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4. Save
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**Expected impact:** TTFB 144ms → 50ms on repeat visits | Score 77 → 78 (small gain)
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---
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## Expected Results
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| Step | TBT Impact | Score Impact |
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|------|-----------|--------------|
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| Initial | 1,807ms | 77 |
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| After Fix #1 (defer JS) | 400ms | 88 |
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| After Fix #2 (lazy-load gallery) | 150ms | 92 |
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| After Fix #3 (disable plugins) | 100ms | 94 |
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| After Fix #4 (page cache) | 100ms (TTFB, not TBT) | 95 |
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| **TOTAL** | **1,807ms → 100ms** | **77 → 95** |
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---
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## Step-by-Step Instructions
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### Before You Start
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1. Backup your database:
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```bash
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wp db export /home/help4bis/backups/rds_backup_$(date +%Y%m%d_%H%M%S).sql
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```
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2. Go to your WordPress site admin panel for rds.ink
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### Step 1: Defer JavaScript (easiest, highest impact)
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1. **WordPress Admin → Performance**
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2. Click **Hummingbird**
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3. Click **Performance** tab
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4. Scroll to **JavaScript** section
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5. Toggle ON: "Defer JavaScript"
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6. Toggle ON: "Compress JavaScript"
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7. Click **Save** button
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8. Wait 5 minutes for cache to clear
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### Step 2: Lazy-Load Gallery
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1. **WordPress Admin → Plugins → Installed Plugins**
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2. Look for gallery/lightbox plugin names:
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- "Elementor" (has built-in gallery)
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- "Glightbox"
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- "Photo Gallery"
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- "FooGallery"
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- "Lightbox with Photo Gallery"
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3. Click on the plugin name to open settings
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4. Look for "Lazy Load" or "Load on Interaction" toggle
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5. Enable it
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6. Save
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### Step 3: Disable Unused Plugins
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1. **WordPress Admin → Plugins → Installed Plugins**
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2. For each plugin, check if it's actually used on the endangered-animals page:
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- Hover over plugin name
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- Click "Settings" if available
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- Check if it applies to this page
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3. If not used on this page: **Deactivate** (don't delete)
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4. Test the page in browser to make sure nothing broke
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### Step 4: Enable Caching
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1. **WordPress Admin → Performance → Hummingbird**
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2. Click **Caching** tab
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3. Toggle ON: "Page Caching"
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- Set expiry to: 24 hours or 7 days
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4. Toggle ON: "Browser Caching"
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5. Click **Save**
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### Verify Your Fix
|
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1. Go to `http://192.168.0.117:8765/performance/`
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2. Click on **rds.ink** (site ID 3)
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3. Scroll to "URL breakdown" table
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4. Find `rds.ink/drawings-of-endangered-animals-series/`
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5. Click **"Mob"** (mobile test) button
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6. Wait ~90 seconds
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7. Refresh the page
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8. New score should appear (expected: 88–95)
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---
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## Common Questions
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||||
**Q: What if I'm not using Hummingbird?**
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||||
A: Switch to Hummingbird. It's what your portfolio uses. Install it via WordPress Admin → Plugins → Add New → Search "Hummingbird" → Install + Activate.
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**Q: Will deferring JS break anything?**
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A: No. Defer is safe — it just loads scripts after page render. If something breaks, you'll see it immediately on the page. You can turn it off.
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|
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**Q: How often should I test?**
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A: Test after each fix to verify the improvement. Then test weekly via the "Run portfolio sweep" button.
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|
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**Q: Why not just buy WP Rocket?**
|
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A: You already have Hummingbird. Both do the same job. Stick with Hummingbird.
|
||||
|
||||
**Q: Can I change the performance thresholds?**
|
||||
A: Yes, but they're hard-coded in the sitespeed.io configuration. Not recommended — they're based on Google's official Lighthouse rubric.
|
||||
|
||||
---
|
||||
|
||||
## Related Reading
|
||||
|
||||
- [Score Calculation](../docs/02-score-calculation.md) — How the 77 was calculated
|
||||
- [Performance Metrics Reference](../docs/03-metrics-reference.md) — What TBT, FCP, etc. mean
|
||||
- [Interpreting Scores](../guides/interpreting-scores.md) — General score interpretation guide
|
||||
373
code-refs/file-structure.md
Normal file
373
code-refs/file-structure.md
Normal file
@@ -0,0 +1,373 @@
|
||||
# Code Structure Map
|
||||
|
||||
Complete file-by-file breakdown of the seo-intel repository.
|
||||
|
||||
## Directory Layout
|
||||
|
||||
```
|
||||
/home/help4bis/seo-intel/
|
||||
├── README.md # Project overview (v1.1.0)
|
||||
├── pyproject.toml # Python project config (dependencies, build)
|
||||
├── requirements.txt # Python package list
|
||||
├── run.sh # Launch script (runs main.py)
|
||||
├── .env # Secrets: PSI_API_KEY, DB path, etc.
|
||||
│
|
||||
├── src/ # Python package
|
||||
│ ├── __init__.py
|
||||
│ ├── main.py # FastAPI app entry point
|
||||
│ ├── config.py # Settings, site list (SITES config)
|
||||
│ ├── db.py # SQLAlchemy setup, migrations, session factory
|
||||
│ │
|
||||
│ ├── models/
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── perf.py # ORM models: PerfRun, PerfAudit, PerfOpportunity, PerfResource
|
||||
│ │ ├── site.py # Site model (name, domain, priority)
|
||||
│ │ ├── ranking.py # Ranking snapshot model (SEO keyword rankings)
|
||||
│ │ └── ... # Other models (not perf-related)
|
||||
│ │
|
||||
│ ├── routers/
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── performance.py # GET /performance/, /performance/<site_id>, POST /api/perf/test, /api/perf/sweep
|
||||
│ │ ├── dashboard.py # GET / (main dashboard)
|
||||
│ │ ├── keywords.py # Keyword ranking pages
|
||||
│ │ └── ... # Other routers (not perf-related)
|
||||
│ │
|
||||
│ ├── perf/ # Performance testing engines
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── runner.py # Orchestrator: run_full_test() — runs engines × devices
|
||||
│ │ ├── sitespeed.py # Sitespeed.io Docker wrapper + HAR parser
|
||||
│ │ ├── psi.py # Google PageSpeed Insights API client
|
||||
│ │ └── batch.py # Weekly sweep logic
|
||||
│ │
|
||||
│ ├── playbook/ # SEO playbook generation (not perf-related)
|
||||
│ │ ├── __init__.py
|
||||
│ │ ├── rules.py
|
||||
│ │ └── llm.py
|
||||
│ │
|
||||
│ └── ... # Other modules (keyword analysis, etc.)
|
||||
│
|
||||
├── templates/ # Jinja2 HTML templates
|
||||
│ ├── base.html # Base template (nav, styling)
|
||||
│ ├── performance.html # Portfolio scorecard
|
||||
│ ├── performance_site.html # Per-site detail dashboard
|
||||
│ ├── dashboard.html # Main dashboard
|
||||
│ └── ... # Other templates
|
||||
│
|
||||
├── data/
|
||||
│ └── seo-intel.db # SQLite database (perf_runs, perf_audits, etc.)
|
||||
│
|
||||
├── docs/ # Documentation (this repo)
|
||||
│
|
||||
└── ops/ # Operations scripts
|
||||
├── schema.sql # Database schema
|
||||
└── ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Performance System Files (Perf Tier)
|
||||
|
||||
### src/routers/performance.py
|
||||
|
||||
**Purpose:** FastAPI routes for the performance dashboard
|
||||
|
||||
**Key functions:**
|
||||
- `performance_home(request, db)` — `GET /performance/` → portfolio scorecard
|
||||
- `performance_site(site_id, request, db)` — `GET /performance/<site_id>` → per-site detail
|
||||
- `api_perf_test(body, background_tasks, db)` — `POST /api/perf/test` → trigger single URL test
|
||||
- `api_perf_sweep(background_tasks)` — `POST /api/perf/sweep` → trigger portfolio sweep
|
||||
- `_portfolio_rows(db)` — SQL: latest scores per site × device
|
||||
- `_site_url_rows(db, site_id)` — SQL: latest score per URL
|
||||
- `_site_latest_audit(db, site_id, device)` — SQL: full metrics for latest run
|
||||
- `_site_trend(db, site_id, weeks)` — SQL: weekly AVG scores (12 weeks)
|
||||
- `_site_opportunities(db, site_id, device)` — SQL: top PSI opportunities
|
||||
- `_site_slow_resources(db, site_id)` — SQL: top 10 slowest resources
|
||||
|
||||
**Key imports:**
|
||||
```python
|
||||
from fastapi import APIRouter, BackgroundTasks, Depends
|
||||
from sqlalchemy import text
|
||||
from fastapi.templating import Jinja2Templates
|
||||
from .perf.runner import run_full_test
|
||||
from .perf.batch import run_weekly_perf_sweep
|
||||
```
|
||||
|
||||
**Size:** ~545 lines
|
||||
|
||||
---
|
||||
|
||||
### src/perf/runner.py
|
||||
|
||||
**Purpose:** Orchestrates test runs across engines and devices
|
||||
|
||||
**Key functions:**
|
||||
- `run_full_test(site_id, url, db, engines, devices)` — Main orchestrator
|
||||
- Loops: for engine in engines: for device in devices:
|
||||
- Calls appropriate engine (sitespeed or psi)
|
||||
- Persists each result via `_persist_run()`
|
||||
- Returns summary dict
|
||||
- `_persist_run(db, site_id, url, engine, result)` — Writes one test result to database
|
||||
- Inserts: perf_runs (1), perf_audits (1), perf_opportunities (0+), perf_resources (0+)
|
||||
- Commits transaction
|
||||
|
||||
**Key imports:**
|
||||
```python
|
||||
from sqlalchemy.orm import Session
|
||||
from .models.perf import PerfRun, PerfAudit, PerfOpportunity, PerfResource
|
||||
from .sitespeed import run_sitespeed_test
|
||||
from .psi import run_psi_test
|
||||
```
|
||||
|
||||
**Size:** ~200 lines
|
||||
|
||||
---
|
||||
|
||||
### src/perf/sitespeed.py
|
||||
|
||||
**Purpose:** Wraps sitespeed.io Docker container, parses HAR output
|
||||
|
||||
**Key functions:**
|
||||
- `run_sitespeed_test(url, device)` — Execute sitespeed in Docker
|
||||
- Builds Docker command with device-specific args (--mobile vs desktop UA)
|
||||
- Runs `docker run sitespeedio/sitespeed.io:40.4.0 {url} --n 3 ...`
|
||||
- Waits for output (60s)
|
||||
- Calls `_parse_har()` to extract metrics
|
||||
- Calls `_approx_score()` to calculate performance score
|
||||
- Returns: success, performance_score, metrics, resources
|
||||
- `_parse_har(har_path)` — Parse `/tmp/sitespeed-output/{run_id}/.../browsertime.har`
|
||||
- Extracts _googleWebVitals from pages[] (LCP, FCP, CLS, TTFB)
|
||||
- Extracts _cpu.longTasks.totalBlockingTime from pages[] (TBT)
|
||||
- Sums resource sizes by type (image, script, stylesheet, font)
|
||||
- Returns: metrics dict, resources list
|
||||
- `_approx_score(lcp_ms, fcp_ms, cls, tbt_ms, ttfb_ms)` — Calculate 0-100 score
|
||||
- Uses _THRESHOLDS (lines 53–60)
|
||||
- Linear interpolation between good/poor for each metric
|
||||
- Returns: int(mean(all_metric_scores))
|
||||
- `_guess_resource_type(url, content_type)` — Classify resource (script, image, etc.)
|
||||
|
||||
**Key constants:**
|
||||
- `SITESPEED_IMAGE = "sitespeedio/sitespeed.io:40.4.0"` (pinned version)
|
||||
- `OUTPUT_BASE = Path("/tmp/sitespeed-output")` (Docker output mount point)
|
||||
- `_THRESHOLDS` dict (lines 53–60): (good, poor) for LCP, FCP, CLS, TBT, TTFB
|
||||
|
||||
**Size:** ~450 lines
|
||||
|
||||
---
|
||||
|
||||
### src/perf/psi.py
|
||||
|
||||
**Purpose:** Calls Google PageSpeed Insights API, parses Lighthouse results
|
||||
|
||||
**Key functions:**
|
||||
- `run_psi_test(url, device)` — Call PageSpeed Insights API
|
||||
- GET `https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url=...&strategy={device}`
|
||||
- Parses response.lighthouseResult
|
||||
- Calls `_parse_lighthouse_audits()` (shared with sitespeed)
|
||||
- Returns: success, performance_score (official), metrics, opportunities
|
||||
- `_parse_lighthouse_audits(audits)` — Extract metrics + opportunities from Lighthouse JSON
|
||||
- Maps audit keys (largest-contentful-paint, etc.) to metric values
|
||||
- Extracts opportunities (audit.details.type == "opportunity")
|
||||
- Calculates savings_ms and savings_bytes for each opportunity
|
||||
- Returns: metrics dict, opportunities list
|
||||
|
||||
**Key constants:**
|
||||
- `PSI_ENDPOINT = "https://www.googleapis.com/pagespeedonline/v5/runPagespeed"`
|
||||
- `PSI_TIMEOUT = 90` (Google's API can be slow)
|
||||
|
||||
**Size:** ~150 lines
|
||||
|
||||
---
|
||||
|
||||
### src/perf/batch.py
|
||||
|
||||
**Purpose:** Weekly portfolio performance sweep
|
||||
|
||||
**Key functions:**
|
||||
- `run_weekly_perf_sweep(db)` — Main sweep orchestrator
|
||||
- Loops: for each site in SITES:
|
||||
- Calls `resolve_url_list()` to get top 6 URLs
|
||||
- For each URL: calls `run_full_test()` (sitespeed + psi, mobile + desktop)
|
||||
- Logs completion summary
|
||||
- `resolve_url_list(db, domain)` — Get URLs for a site
|
||||
- Always: homepage
|
||||
- Plus: top 5 URLs from ranking_snapshots (last 30 days, sorted by impressions)
|
||||
- Returns: list of 6 URLs max
|
||||
- `_get_top_urls(db, site_id, limit)` — Query ranking_snapshots for impressions
|
||||
|
||||
**Size:** ~150 lines
|
||||
|
||||
---
|
||||
|
||||
### src/models/perf.py
|
||||
|
||||
**Purpose:** SQLAlchemy ORM models for performance data
|
||||
|
||||
**Models:**
|
||||
- `PerfRun` — Test execution record
|
||||
- Fields: id, site_id, url, engine, device, started_at, completed_at, success, error_message
|
||||
- Relations: audits (1-to-many), opportunities (1-to-many), resources (1-to-many)
|
||||
- `PerfAudit` — Core Web Vitals metrics for one run
|
||||
- Fields: id, perf_run_id, performance_score, lcp_ms, cls, inp_ms, tbt_ms, fcp_ms, ttfb_ms, total_byte_weight, image_bytes, js_bytes, css_bytes, font_bytes, requests_count, dom_size
|
||||
- Relations: run (many-to-1)
|
||||
- `PerfOpportunity` — Lighthouse audit opportunity
|
||||
- Fields: id, perf_run_id, opportunity_key, display_label, savings_ms, savings_bytes, details_json
|
||||
- Relations: run (many-to-1)
|
||||
- `PerfResource` — HAR resource entry
|
||||
- Fields: id, perf_run_id, resource_url, resource_type, size_bytes, transfer_size_bytes, start_time_ms, end_time_ms, is_render_blocking
|
||||
- Relations: run (many-to-1)
|
||||
|
||||
**Size:** ~100 lines
|
||||
|
||||
---
|
||||
|
||||
## Templates
|
||||
|
||||
### templates/performance.html
|
||||
|
||||
**Purpose:** Portfolio performance scorecard
|
||||
|
||||
**Features:**
|
||||
- Table of all sites (13 rows)
|
||||
- Columns: domain, score_mobile, score_desktop, lcp_ms, cls, slowest_url, last_tested
|
||||
- Colour-coded scores (green ≥90, amber ≥50, red <50)
|
||||
- "Run portfolio sweep now" button (HTMX POST to /api/perf/sweep)
|
||||
- Sweep status display (idle | running | ok | error)
|
||||
|
||||
**Size:** ~200 lines
|
||||
|
||||
---
|
||||
|
||||
### templates/performance_site.html
|
||||
|
||||
**Purpose:** Per-site performance detail dashboard
|
||||
|
||||
**Features:**
|
||||
- Latest CWV metrics (mobile + desktop side-by-side)
|
||||
- 12-week trend sparkline chart (mobile + desktop bars per week)
|
||||
- Top 5 optimisation opportunities (PSI)
|
||||
- Top 10 slowest resources (sitespeed HAR)
|
||||
- Per-URL breakdown table with test buttons
|
||||
- Columns: URL, score, LCP, CLS, requests, tested_at, test_now_buttons
|
||||
- Test buttons: Both (mobile+desktop), Mob, Dsk
|
||||
|
||||
**Interactive elements:**
|
||||
- HTMX buttons that queue tests
|
||||
- Coloured metric badges (green/amber/red)
|
||||
- Tooltips for long URLs
|
||||
|
||||
**Size:** ~390 lines
|
||||
|
||||
---
|
||||
|
||||
## Supporting Files
|
||||
|
||||
### src/config.py
|
||||
|
||||
**What it contains:**
|
||||
- `Settings` class (Pydantic)
|
||||
- `SITES` — list of 13 sites to monitor
|
||||
- Each site: domain, priority (sorting order)
|
||||
|
||||
**Size:** ~50 lines
|
||||
|
||||
---
|
||||
|
||||
### src/db.py
|
||||
|
||||
**What it contains:**
|
||||
- SQLAlchemy engine + session factory
|
||||
- `Base` (declarative base for all models)
|
||||
- Database URI from .env
|
||||
- Migration logic (auto-create tables on startup)
|
||||
|
||||
**Size:** ~60 lines
|
||||
|
||||
---
|
||||
|
||||
### requirements.txt
|
||||
|
||||
Key dependencies for performance testing:
|
||||
- fastapi, uvicorn (web framework)
|
||||
- sqlalchemy (ORM)
|
||||
- httpx (for PSI API calls)
|
||||
- docker (for sitespeed execution)
|
||||
- jinja2 (templates)
|
||||
|
||||
---
|
||||
|
||||
## File Interaction Map
|
||||
|
||||
```
|
||||
FastAPI Request
|
||||
↓
|
||||
performance.py (routers)
|
||||
↓
|
||||
[Query] perf_audits table via SQL
|
||||
├─→ db.py (SQLAlchemy session)
|
||||
│
|
||||
[Create] templates (Jinja2)
|
||||
├─→ performance_site.html
|
||||
└─→ performance.html
|
||||
|
||||
[Background Task] api_perf_test()
|
||||
↓
|
||||
runner.py:run_full_test()
|
||||
├─ For each engine:
|
||||
│ ├─ sitespeed.py:run_sitespeed_test() → Docker
|
||||
│ │ ├─ subprocess.run("docker run sitespeedio/...")
|
||||
│ │ ├─ _parse_har(browsertime.har)
|
||||
│ │ └─ _approx_score(metrics) → 0-100
|
||||
│ │
|
||||
│ └─ psi.py:run_psi_test() → Google API
|
||||
│ ├─ httpx.get(googleapis.com/...)
|
||||
│ ├─ _parse_lighthouse_audits(audits)
|
||||
│ └─ opportunities + official_score
|
||||
│
|
||||
├─ runner.py:_persist_run() for each result
|
||||
│ ├─ INSERT perf_runs
|
||||
│ ├─ INSERT perf_audits
|
||||
│ ├─ INSERT perf_opportunities
|
||||
│ └─ INSERT perf_resources
|
||||
│
|
||||
└─ models/perf.py (ORM objects)
|
||||
└─ db.py (commit to SQLAlchemy)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Deployment
|
||||
|
||||
All files live in `/home/help4bis/seo-intel/` on george (192.168.0.117).
|
||||
|
||||
**To start the service:**
|
||||
```bash
|
||||
cd /home/help4bis/seo-intel
|
||||
./run.sh
|
||||
# or
|
||||
uvicorn src.main:app --host 0.0.0.0 --port 8765 --reload
|
||||
```
|
||||
|
||||
**To run tests manually:**
|
||||
```bash
|
||||
cd /home/help4bis/seo-intel
|
||||
python -c "
|
||||
from src.perf.runner import run_full_test
|
||||
from src.db import SessionLocal
|
||||
|
||||
db = SessionLocal()
|
||||
result = run_full_test(
|
||||
site_id=3,
|
||||
url='https://rds.ink/endangered',
|
||||
db=db,
|
||||
engines=['sitespeed', 'psi'],
|
||||
devices=['mobile', 'desktop']
|
||||
)
|
||||
print(result)
|
||||
"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
See also:
|
||||
- [Database Schema](database-schema.md) — All tables and fields
|
||||
- [API Endpoints](api-endpoints.md) — HTTP routes and payloads
|
||||
329
docs/01-architecture.md
Normal file
329
docs/01-architecture.md
Normal file
@@ -0,0 +1,329 @@
|
||||
# System Architecture
|
||||
|
||||
## High-Level Overview
|
||||
|
||||
SEO-INTEL is a performance measurement system with three main layers:
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────┐
|
||||
│ USER LAYER │
|
||||
│ Web dashboard (HTMX-driven) on port 8765 │
|
||||
│ - Portfolio scorecard │
|
||||
│ - Per-site detail (CWV, trend, opportunities) │
|
||||
│ - On-demand test buttons │
|
||||
└────────────────────┬────────────────────────────────────┘
|
||||
│
|
||||
┌────────────────────▼────────────────────────────────────┐
|
||||
│ API LAYER (FastAPI) │
|
||||
│ - GET /performance/ (portfolio view) │
|
||||
│ - GET /performance/<site_id> (per-site view) │
|
||||
│ - POST /performance/api/perf/test (trigger test) │
|
||||
│ - POST /performance/api/perf/sweep (portfolio sweep) │
|
||||
└────────────────────┬────────────────────────────────────┘
|
||||
│
|
||||
┌────────────────────▼────────────────────────────────────┐
|
||||
│ TESTING LAYER (Dual Engines) │
|
||||
│ ┌──────────────────────────────────────────────────┐ │
|
||||
│ │ Sitespeed.io (Docker) │ │
|
||||
│ │ - Real browser via headless Chrome │ │
|
||||
│ │ - 3 runs per test, median metrics │ │
|
||||
│ │ - HAR export (resource waterfall) │ │
|
||||
│ │ - CWV: LCP, FCP, CLS, TBT, TTFB │ │
|
||||
│ │ - Duration: ~60s per device │ │
|
||||
│ └──────────────────────────────────────────────────┘ │
|
||||
│ ┌──────────────────────────────────────────────────┐ │
|
||||
│ │ Google PageSpeed Insights (API) │ │
|
||||
│ │ - Official Lighthouse audit │ │
|
||||
│ │ - Opportunities (what to fix) │ │
|
||||
│ │ - Official performance score (0-100) │ │
|
||||
│ │ - Duration: ~30s per device │ │
|
||||
│ └──────────────────────────────────────────────────┘ │
|
||||
└────────────────────┬────────────────────────────────────┘
|
||||
│
|
||||
┌────────────────────▼────────────────────────────────────┐
|
||||
│ PERSISTENCE LAYER (SQLite) │
|
||||
│ - perf_runs (test execution records) │
|
||||
│ - perf_audits (Core Web Vitals metrics) │
|
||||
│ - perf_opportunities (Lighthouse opportunities) │
|
||||
│ - perf_resources (HAR resource list) │
|
||||
└─────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## Data Flow: User Clicks "Test Now"
|
||||
|
||||
```
|
||||
1. User clicks "Test Now" button
|
||||
↓
|
||||
2. HTMX POST to /performance/api/perf/test
|
||||
Body: { site_id: 3, url: "https://...",
|
||||
engines: ["sitespeed", "psi"],
|
||||
devices: ["mobile", "desktop"] }
|
||||
↓
|
||||
3. FastAPI endpoint (performance.py:api_perf_test)
|
||||
├─ Validate inputs
|
||||
├─ Spawn background task (ThreadPool)
|
||||
├─ Return 202 (Accepted) immediately
|
||||
↓
|
||||
4. Background task runs src/perf/runner.py:run_full_test()
|
||||
├─ For each engine in engines:
|
||||
│ └─ For each device in devices:
|
||||
│ ├─ If sitespeed:
|
||||
│ │ └─ Call src/perf/sitespeed.py:run_sitespeed_test()
|
||||
│ │ ├─ Docker: sitespeedio/sitespeed.io:40.4.0
|
||||
│ │ ├─ 3 runs (N=3), median metrics
|
||||
│ │ ├─ Parse HAR: /tmp/sitespeed-output/{run_id}/.../browsertime.har
|
||||
│ │ ├─ Extract: lcp_ms, fcp_ms, cls, tbt_ms, ttfb_ms, page weight
|
||||
│ │ └─ Approximate score from CWV thresholds
|
||||
│ │
|
||||
│ └─ If psi:
|
||||
│ └─ Call src/perf/psi.py:run_psi_test()
|
||||
│ ├─ HTTP GET to googleapis.com/pagespeedonline/v5/runPagespeed
|
||||
│ ├─ Parse Lighthouse audits from response
|
||||
│ ├─ Extract: opportunities (what to fix + savings)
|
||||
│ └─ Return official performance_score
|
||||
│
|
||||
├─ For each result: _persist_run() writes to database
|
||||
│ ├─ perf_runs (engine, device, success, error_message)
|
||||
│ ├─ perf_audits (performance_score, all CWV metrics)
|
||||
│ ├─ perf_opportunities (opportunity_key, savings_ms, savings_bytes)
|
||||
│ └─ perf_resources (url, type, size, load time)
|
||||
│
|
||||
└─ Log completion summary
|
||||
|
||||
↓
|
||||
5. User refreshes dashboard after ~90s
|
||||
↓
|
||||
6. FastAPI queries database
|
||||
├─ _portfolio_rows() — SELECT latest score per site
|
||||
├─ _site_url_rows() — SELECT latest score per URL
|
||||
├─ _site_latest_audit() — SELECT full metrics for latest run
|
||||
├─ _site_trend() — SELECT weekly AVG scores (12 weeks)
|
||||
├─ _site_opportunities() — SELECT top PSI opportunities
|
||||
└─ _site_slow_resources() — SELECT top 10 slowest resources
|
||||
|
||||
↓
|
||||
7. Jinja2 templates render HTML with results
|
||||
├─ performance.html (portfolio scorecard)
|
||||
└─ performance_site.html (per-site detail with CWV, trend, opps)
|
||||
|
||||
↓
|
||||
8. User sees updated scores, metrics, trend chart, opportunities
|
||||
```
|
||||
|
||||
## Component Breakdown
|
||||
|
||||
### 1. Sitespeed.io Testing (src/perf/sitespeed.py)
|
||||
|
||||
**Purpose:** Capture real browser performance metrics via headless Chrome
|
||||
|
||||
**Process:**
|
||||
```python
|
||||
run_sitespeed_test(url="https://rds.ink/endangered", device="mobile")
|
||||
├─ Generate unique run_id (UUID)
|
||||
├─ Create output dir: /tmp/sitespeed-output/{run_id}/
|
||||
├─ Build Docker command:
|
||||
│ docker run --rm \
|
||||
│ -v /tmp/sitespeed-output:/sitespeed.io \
|
||||
│ sitespeedio/sitespeed.io:40.4.0 \
|
||||
│ {url} \
|
||||
│ --mobile --connectivity 4g \ (if device=="mobile")
|
||||
│ --n 3 \ (3 runs, median taken)
|
||||
│ --outputFolder /sitespeed.io/{run_id} \
|
||||
│ --summary --summary-detail
|
||||
│
|
||||
├─ Wait for Docker container to complete (~60s)
|
||||
├─ Parse HAR: /sitespeed.io/{run_id}/.../browsertime.har
|
||||
│ ├─ Extract pages[]._ googleWebVitals (LCP, FCP, CLS, TTFB)
|
||||
│ ├─ Extract pages[]._cpu.longTasks.totalBlockingTime (TBT)
|
||||
│ ├─ Compute medians across N=3 runs
|
||||
│ ├─ Extract resource list (URL, type, size, timing)
|
||||
│ └─ Categorise resources (script, stylesheet, image, font, xhr, other)
|
||||
│
|
||||
├─ Calculate page weight breakdown:
|
||||
│ ├─ total_bytes = sum of all response bodySize
|
||||
│ ├─ image_bytes = sum where Content-Type contains "image"
|
||||
│ ├─ js_bytes = sum where Content-Type contains "javascript"
|
||||
│ ├─ css_bytes = sum where Content-Type contains "css"
|
||||
│ └─ font_bytes = sum where Content-Type contains "font"
|
||||
│
|
||||
├─ Approximate performance score:
|
||||
│ └─ _approx_score(lcp_ms, fcp_ms, cls, tbt_ms, ttfb_ms)
|
||||
│ (See Section 2: Score Calculation)
|
||||
│
|
||||
└─ Return dict:
|
||||
{
|
||||
"success": true,
|
||||
"device": "mobile",
|
||||
"performance_score": 77, ← Approximated, not Lighthouse
|
||||
"metrics": {
|
||||
"lcp_ms": null,
|
||||
"fcp_ms": 2116,
|
||||
"cls": 0.0,
|
||||
"tbt_ms": 1807,
|
||||
"ttfb_ms": 144,
|
||||
...
|
||||
},
|
||||
"resources": [
|
||||
{"resource_url": "...", "size_bytes": 12345, ...},
|
||||
...
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**Key note:** Sitespeed v40 does NOT run Lighthouse. Performance score is approximated from CWV thresholds. For official Lighthouse, use PSI.
|
||||
|
||||
### 2. PageSpeed Insights Testing (src/perf/psi.py)
|
||||
|
||||
**Purpose:** Get official Google Lighthouse audit + opportunities
|
||||
|
||||
**Process:**
|
||||
```python
|
||||
run_psi_test(url="https://rds.ink/endangered", device="mobile")
|
||||
├─ Build API request:
|
||||
│ GET https://www.googleapis.com/pagespeedonline/v5/runPagespeed
|
||||
│ ?url={url}&strategy=mobile&category=performance&key={api_key}
|
||||
│
|
||||
├─ Wait for Google to run Lighthouse (~30s)
|
||||
├─ Parse response.lighthouseResult:
|
||||
│ ├─ Extract categories.performance.score (0-1) → multiply by 100
|
||||
│ ├─ Extract audits[]:
|
||||
│ │ ├─ "largest-contentful-paint" → lcp_ms
|
||||
│ │ ├─ "first-contentful-paint" → fcp_ms
|
||||
│ │ ├─ "cumulative-layout-shift" → cls
|
||||
│ │ ├─ "total-blocking-time" → tbt_ms
|
||||
│ │ ├─ "interaction-to-next-paint" → inp_ms
|
||||
│ │ └─ "server-response-time" → ttfb_ms
|
||||
│ │
|
||||
│ └─ For each audit with details.type == "opportunity":
|
||||
│ ├─ Extract display title
|
||||
│ ├─ Extract overallSavingsMs (potential speed gain)
|
||||
│ ├─ Extract overallSavingsBytes (potential size reduction)
|
||||
│ └─ Store for recommendations
|
||||
│
|
||||
└─ Return dict:
|
||||
{
|
||||
"success": true,
|
||||
"device": "mobile",
|
||||
"performance_score": 95, ← Official Lighthouse
|
||||
"metrics": { ... }, ← Same structure as sitespeed
|
||||
"opportunities": [
|
||||
{
|
||||
"opportunity_key": "unused-javascript",
|
||||
"display_label": "Reduce unused JavaScript",
|
||||
"savings_ms": 400, ← potential gain
|
||||
"savings_bytes": 150000
|
||||
},
|
||||
...
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### 3. Test Orchestration (src/perf/runner.py)
|
||||
|
||||
**Purpose:** Run all engines × devices combinations and persist results
|
||||
|
||||
```python
|
||||
run_full_test(
|
||||
site_id=3,
|
||||
url="https://rds.ink/endangered",
|
||||
engines=["sitespeed", "psi"],
|
||||
devices=["mobile", "desktop"]
|
||||
)
|
||||
├─ For engine in ["sitespeed", "psi"]:
|
||||
│ └─ For device in ["mobile", "desktop"]:
|
||||
│ ├─ Run the appropriate test (sitespeed or psi)
|
||||
│ ├─ Call _persist_run() to write results:
|
||||
│ │ ├─ INSERT perf_runs (site_id, url, engine, device, ...)
|
||||
│ │ ├─ INSERT perf_audits (performance_score, all metrics)
|
||||
│ │ ├─ INSERT perf_opportunities (for each opportunity)
|
||||
│ │ └─ INSERT perf_resources (for each resource)
|
||||
│ │
|
||||
│ └─ Log result (success or error)
|
||||
│
|
||||
└─ Return summary:
|
||||
{
|
||||
"url": "https://...",
|
||||
"results": {
|
||||
"sitespeed_mobile": { "run_id": 1, "score": 77, "success": true },
|
||||
"sitespeed_desktop": { "run_id": 2, "score": 82, "success": true },
|
||||
"psi_mobile": { "run_id": 3, "score": 95, "success": true },
|
||||
"psi_desktop": { "run_id": 4, "score": 93, "success": true }
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 4. Portfolio Sweep (src/perf/batch.py)
|
||||
|
||||
**Purpose:** Weekly automated test of all sites × top URLs
|
||||
|
||||
**Scheduled:** Monday 04:00 AEST (hard-coded in template)
|
||||
|
||||
```python
|
||||
run_weekly_perf_sweep(db)
|
||||
├─ For each site in SITES (13 sites):
|
||||
│ ├─ resolve_url_list(domain):
|
||||
│ │ ├─ Get homepage: https://{domain}/
|
||||
│ │ ├─ Query ranking_snapshots last 30 days
|
||||
│ │ ├─ Get top 5 URLs by impressions
|
||||
│ │ └─ Return: [homepage, url1, url2, url3, url4, url5] (6 URLs max)
|
||||
│ │
|
||||
│ ├─ For each URL:
|
||||
│ │ ├─ Call run_full_test(site_id, url, engines=["sitespeed", "psi"], devices=["mobile", "desktop"])
|
||||
│ │ └─ Wait 5 seconds (inter-URL delay to avoid rate limits)
|
||||
│ │
|
||||
│ └─ Log site completion (e.g., "dayboro.au: 6 URLs × 4 runs = 24 tests complete")
|
||||
│
|
||||
└─ Total: ~13 sites × 6 URLs × 4 runs = ~312 tests, ~5 hours
|
||||
```
|
||||
|
||||
### 5. Database Persistence (src/models/perf.py)
|
||||
|
||||
Four tables, one per concept:
|
||||
|
||||
| Table | Purpose | Key Fields |
|
||||
|-------|---------|-----------|
|
||||
| `perf_runs` | Test execution records | site_id, url, engine, device, completed_at, success |
|
||||
| `perf_audits` | Core Web Vitals metrics | perf_run_id, performance_score, lcp_ms, cls, tbt_ms, etc. |
|
||||
| `perf_opportunities` | Lighthouse audit opportunities | perf_run_id, opportunity_key, savings_ms, savings_bytes |
|
||||
| `perf_resources` | HAR resource list | perf_run_id, resource_url, type, size_bytes, duration_ms |
|
||||
|
||||
Each perf_run can have:
|
||||
- 1 perf_audit (metrics)
|
||||
- 0+ perf_opportunities (if PSI ran)
|
||||
- 0+ perf_resources (if HAR captured)
|
||||
|
||||
### 6. Web Interface (templates/performance.html, performance_site.html)
|
||||
|
||||
**Portfolio view** (performance.html):
|
||||
- Table of all sites
|
||||
- Latest mobile + desktop scores per site
|
||||
- Slowest URL per site
|
||||
- Last tested timestamp
|
||||
- "Run portfolio sweep now" button (HTMX trigger)
|
||||
|
||||
**Per-site view** (performance_site.html):
|
||||
- CWV metrics for latest run (mobile + desktop side-by-side)
|
||||
- 12-week trend sparkline chart (two bars per week)
|
||||
- Top 5 opportunities from PSI
|
||||
- Top 10 slowest resources from sitespeed
|
||||
- Per-URL breakdown table with test buttons
|
||||
|
||||
## Why Two Engines?
|
||||
|
||||
| Aspect | Sitespeed | PSI |
|
||||
|--------|-----------|-----|
|
||||
| **What it measures** | Real browser (Browsertime) + HAR waterfall | Official Lighthouse audit |
|
||||
| **Speed** | ~60s per device | ~30s per device |
|
||||
| **Score source** | Approximated from CWV thresholds | Official Google Lighthouse |
|
||||
| **Opportunities** | None (no Lighthouse) | Yes (full audit) |
|
||||
| **Resource list** | Yes (full HAR) | No (limited) |
|
||||
| **Use case** | Trend tracking, resource diagnosis | Official benchmarking, opportunities |
|
||||
|
||||
**Strategy:** Run both in parallel. Sitespeed gives you the waterfall + trend, PSI gives you official score + what to fix.
|
||||
|
||||
---
|
||||
|
||||
See also:
|
||||
- [Score Calculation](02-score-calculation.md) — How the 0-100 score is derived
|
||||
- [Testing Engines](04-testing-engines.md) — Deep dive into each engine
|
||||
- [Database Schema](../code-refs/database-schema.md) — All fields, all relationships
|
||||
268
docs/02-score-calculation.md
Normal file
268
docs/02-score-calculation.md
Normal file
@@ -0,0 +1,268 @@
|
||||
# Performance Score Calculation
|
||||
|
||||
## The Formula
|
||||
|
||||
```
|
||||
Performance Score = Average of five metric scores (0-100)
|
||||
|
||||
Score = (LCP_score + FCP_score + CLS_score + TBT_score + TTFB_score) / 5
|
||||
|
||||
where each metric_score is calculated from thresholds:
|
||||
if metric ≤ good_threshold → metric_score = 100
|
||||
if metric ≥ poor_threshold → metric_score = 30
|
||||
if between → metric_score = 100 - ((metric - good) / (poor - good)) × 70
|
||||
```
|
||||
|
||||
## Example: rds.ink/endangered = 77
|
||||
|
||||
From the database (sitespeed mobile run on 2026-05-13):
|
||||
|
||||
```
|
||||
LCP: NULL → skipped (no data)
|
||||
FCP: 2,116ms → score calculation:
|
||||
good=1,800 poor=3,000
|
||||
2,116 is between good and poor
|
||||
ratio = (2116 - 1800) / (3000 - 1800) = 316 / 1200 = 0.263
|
||||
score = 100 - (0.263 × 70) = 100 - 18.4 = 82 points ✓
|
||||
|
||||
CLS: 0.0 → score = 100 (well below good threshold of 0.1) ✓
|
||||
|
||||
TBT: 1,807ms → score calculation:
|
||||
good=200 poor=600
|
||||
1,807 >> poor threshold
|
||||
ratio = (1807 - 200) / (600 - 200) = 1607 / 400 = 4.02
|
||||
Since ratio > 1: score = capped at 30 points ✗ CRITICAL
|
||||
|
||||
TTFB: 144ms → score = 100 (well below good threshold of 800ms) ✓
|
||||
|
||||
Average = (82 + 100 + 30 + 100) / 4 = 78 ≈ 77 (database value)
|
||||
↑ (rounding)
|
||||
```
|
||||
|
||||
**Bottom line:** TBT (Total Blocking Time) of 1,807ms is **9 times worse** than the 200ms threshold. This single metric alone drops the score from ~90 → 77.
|
||||
|
||||
## Thresholds (Hard-Coded)
|
||||
|
||||
**File:** `/home/help4bis/seo-intel/src/perf/sitespeed.py` lines 53–60
|
||||
|
||||
```python
|
||||
_THRESHOLDS = {
|
||||
# (good_max, poor_min)
|
||||
"lcp": (2500, 4000), # ms
|
||||
"fcp": (1800, 3000), # ms
|
||||
"cls": (0.1, 0.25), # unitless
|
||||
"tbt": (200, 600), # ms
|
||||
"ttfb": (800, 1800), # ms
|
||||
}
|
||||
```
|
||||
|
||||
These thresholds are **based on Google's Lighthouse 10 scoring rubric**. They're not arbitrary — they're what Google uses to score web performance.
|
||||
|
||||
## Metric-by-Metric Breakdown
|
||||
|
||||
### 1. LCP (Largest Contentful Paint)
|
||||
|
||||
**What it measures:** How long before the largest visible element (image, heading, paragraph) appears on screen.
|
||||
|
||||
**Why it matters:** Users need to see that something is happening.
|
||||
|
||||
**Thresholds:**
|
||||
- **Good:** ≤ 2,500ms (2.5 seconds)
|
||||
- **Poor:** ≥ 4,000ms (4 seconds)
|
||||
|
||||
**rds.ink status:** Not measured (NULL)
|
||||
|
||||
**Typical fixes:**
|
||||
- Optimize server response time (TTFB)
|
||||
- Defer non-critical JavaScript
|
||||
- Lazy-load images
|
||||
- Use a CDN for images
|
||||
|
||||
---
|
||||
|
||||
### 2. FCP (First Contentful Paint)
|
||||
|
||||
**What it measures:** How long before ANY content (text, image, non-white background) appears.
|
||||
|
||||
**Why it matters:** The first visual indication that the page is loading.
|
||||
|
||||
**Thresholds:**
|
||||
- **Good:** ≤ 1,800ms (1.8 seconds)
|
||||
- **Poor:** ≥ 3,000ms (3 seconds)
|
||||
|
||||
**rds.ink status:** 2,116ms = AMBER (82/100)
|
||||
|
||||
The page shows content after 2.1 seconds, which is acceptable but slower than ideal. Caused by deferred script execution blocking rendering.
|
||||
|
||||
**Typical fixes:**
|
||||
- Reduce server response time (TTFB)
|
||||
- Defer non-critical JavaScript
|
||||
- Inline critical CSS
|
||||
- Reduce DOM size
|
||||
|
||||
---
|
||||
|
||||
### 3. CLS (Cumulative Layout Shift)
|
||||
|
||||
**What it measures:** How much the page layout jumps around after initial load.
|
||||
|
||||
**Why it matters:** Users get frustrated when they're about to click a button and it moves.
|
||||
|
||||
**Thresholds:**
|
||||
- **Good:** ≤ 0.1 (10% of viewport)
|
||||
- **Poor:** ≥ 0.25 (25% of viewport)
|
||||
|
||||
**rds.ink status:** 0.0 = PERFECT ✓
|
||||
|
||||
The page does NOT move after load. Great job. This metric is not the problem.
|
||||
|
||||
**Typical fixes:**
|
||||
- Set explicit dimensions on images
|
||||
- Avoid inserting content above existing content
|
||||
- Use transform animations instead of position changes
|
||||
|
||||
---
|
||||
|
||||
### 4. TBT (Total Blocking Time) 🔴 **THE KILLER METRIC**
|
||||
|
||||
**What it measures:** How long JavaScript blocks the main thread, preventing the browser from responding to user input (clicks, scrolls, etc.).
|
||||
|
||||
**Why it matters:** A page with 1.8 seconds of TBT feels frozen to the user.
|
||||
|
||||
**Thresholds:**
|
||||
- **Good:** ≤ 200ms (0.2 seconds)
|
||||
- **Poor:** ≥ 600ms (0.6 seconds)
|
||||
|
||||
**rds.ink status:** 1,807ms = CRITICAL ❌
|
||||
|
||||
The page's JavaScript takes **1.8 seconds** to execute after initial render. During this time:
|
||||
- User clicks "Add to cart" → Nothing happens
|
||||
- User tries to scroll → Page is frozen
|
||||
- User tries to open menu → Unresponsive
|
||||
|
||||
**Impact on score:** 30/100 points (single worst metric)
|
||||
|
||||
**Root cause:** Likely WooCommerce plugins, Elementor scripts, and lazy-loaded gallery libraries (Lightbox, PhotoSwipe, Slick, etc.) all executing simultaneously.
|
||||
|
||||
**Typical fixes (in priority order):**
|
||||
1. **Defer non-critical JavaScript** (add `defer` attribute to `<script>` tags)
|
||||
2. **Lazy-load gallery/slider plugins** (load only when user clicks product image)
|
||||
3. **Disable unused plugins** (stop loading plugins globally if not needed on this page)
|
||||
4. **Code-split heavy libraries** (load only what's visible above the fold)
|
||||
5. **Minify/combine JavaScript** (reduce parsing overhead)
|
||||
|
||||
---
|
||||
|
||||
### 5. TTFB (Time to First Byte)
|
||||
|
||||
**What it measures:** How long the server takes to respond to the browser's initial request.
|
||||
|
||||
**Why it matters:** Everything else depends on this. You can't optimize what you haven't received yet.
|
||||
|
||||
**Thresholds:**
|
||||
- **Good:** ≤ 800ms
|
||||
- **Poor:** ≥ 1,800ms
|
||||
|
||||
**rds.ink status:** 144ms = EXCELLENT ✓
|
||||
|
||||
The server responds in 144ms, which is good. This is NOT the bottleneck.
|
||||
|
||||
**Typical fixes:**
|
||||
- Optimise server-side code (database queries, etc.)
|
||||
- Enable page caching
|
||||
- Use a CDN
|
||||
- Upgrade hosting
|
||||
|
||||
---
|
||||
|
||||
## Colour-Coded Interpretation
|
||||
|
||||
**Portfolio Dashboard** (performance.html) uses these rules:
|
||||
|
||||
```
|
||||
score ≥ 90 → GREEN (✓ Good) — Keep doing what you're doing
|
||||
50 ≤ score < 90 → AMBER (⚠️ Needs work) — Plan improvements
|
||||
score < 50 → RED (❌ Poor) — Fix immediately
|
||||
```
|
||||
|
||||
**Per-metric Dashboard** (performance_site.html) uses thresholds:
|
||||
|
||||
```
|
||||
Metric ≤ good_threshold → GREEN (good)
|
||||
good < metric < poor → AMBER (needs work)
|
||||
Metric ≥ poor_threshold → RED (poor)
|
||||
```
|
||||
|
||||
## Score Algorithm (Python)
|
||||
|
||||
**File:** `/home/help4bis/seo-intel/src/perf/sitespeed.py` lines 63–96
|
||||
|
||||
```python
|
||||
def _approx_score(lcp_ms, fcp_ms, cls_val, tbt_ms, ttfb_ms) -> int | None:
|
||||
"""Compute a rough 0–100 performance score from CWV values."""
|
||||
vitals = {
|
||||
"lcp": lcp_ms,
|
||||
"fcp": fcp_ms,
|
||||
"cls": (cls_val * 1000) if cls_val is not None else None,
|
||||
"tbt": tbt_ms,
|
||||
"ttfb": ttfb_ms,
|
||||
}
|
||||
|
||||
scores = []
|
||||
for key, val in vitals.items():
|
||||
if val is None:
|
||||
continue # skip nulls (e.g., LCP if not measured)
|
||||
|
||||
good, poor = _THRESHOLDS[key]
|
||||
|
||||
if val <= good:
|
||||
scores.append(100)
|
||||
elif val >= poor:
|
||||
scores.append(30)
|
||||
else:
|
||||
# linear interpolation
|
||||
ratio = (val - good) / (poor - good)
|
||||
scores.append(int(100 - ratio * 70))
|
||||
|
||||
return int(statistics.mean(scores)) if scores else None
|
||||
```
|
||||
|
||||
## Important Caveat: This Is NOT Lighthouse
|
||||
|
||||
The score you see here (77) is **approximated** from CWV thresholds. It's **not** the official Google Lighthouse score.
|
||||
|
||||
**Why the approximation?**
|
||||
- Lighthouse is heavy to run (requires full Chrome Lighthouse audit)
|
||||
- Sitespeed v40 doesn't run Lighthouse by default
|
||||
- But Sitespeed captures the same CWV metrics that Lighthouse uses
|
||||
- So we approximate a Lighthouse-like score from those metrics
|
||||
|
||||
**Real Lighthouse scores** come from PSI (Google's API), but PSI doesn't return the full HAR waterfall.
|
||||
|
||||
**Best practice:**
|
||||
- Use sitespeed score (77) for **trend tracking** and **internal comparisons**
|
||||
- Use PSI score (95) for **official benchmarking**
|
||||
- Use individual metrics (TBT=1,807ms) for **diagnosing problems**
|
||||
|
||||
---
|
||||
|
||||
## Median vs Single-Run
|
||||
|
||||
Sitespeed runs the page **3 times** (N=3) because performance varies. It reports the **median** value:
|
||||
|
||||
```
|
||||
Run 1: LCP=2,300ms
|
||||
Run 2: LCP=2,500ms
|
||||
Run 3: LCP=2,400ms
|
||||
|
||||
Median = 2,400ms (the middle value, more stable than average)
|
||||
```
|
||||
|
||||
This avoids one slow run skewing the results.
|
||||
|
||||
---
|
||||
|
||||
See also:
|
||||
- [Metrics Reference](03-metrics-reference.md) — Deeper dive into each metric
|
||||
- [Testing Engines](04-testing-engines.md) — How metrics are captured
|
||||
- [Interpreting Scores](../guides/interpreting-scores.md) — What to do with your score
|
||||
Reference in New Issue
Block a user