Big Data / SEO

Server Log Analyzer

Visualizing crawl budgets and spider traps with Python & Pandas.

Role

Data Engineer

Timeline

2 Weeks

Stack

Python, Pandas, Matplotlib

                            IP Address | Timestamp | Method | Status | User-Agent
                        
66.249.66.1 | 12/Dec/2023:14:02 | GET /blog/seo | 200 | Googlebot/2.1
66.249.66.1 | 12/Dec/2023:14:03 | GET /old-page | 404 | Googlebot/2.1
66.249.66.1 | 12/Dec/2023:14:05 | GET /api/v1 | 301 | Googlebot/2.1
[Analysis] Crawl Budget Waste Detected on
                            /old-page

The Challenge

Googlebot visits are invisible to standard analytics tools (GA4). Without analyzing server logs, it's impossible to know "how" search engines see your site, often leading to wasted crawl budget on 404s or parameters.

Key Features

✓ Log file parsing (Apache/Nginx)
✓ Googlebot Filtering
✓ Status Code Heatmaps

View on GitHub

The Solution

Using `Python` and `Pandas`, I created a script that ingests massive server log files (.log, .gz). It filters requests to isolate verified Search Engine bots (Google, Bing) and excludes fake user-agents.

The data is then visualized using `Matplotlib` to show crawl frequency over time, revealing potential server downtime issues or "spider traps" where bots get stuck in infinite loops.

Bot Verification

Checks IP ranges via Reverse DNS to ensure the visitor is truly Googlebot, not a scraper.

Error Detection

Highlights 404 errors and 5xx server errors that directly impact SEO rankings.

The Result

5M+

Lines Parsed / Minute

+15%

Crawl Efficiency

This analysis capability allows for deep technical audits that go far beyond what a typical SEO crawler (like Screaming Frog) can see from the "outside".

Next Project

AI SEO Generator →