Web Crawling › Visualizing Crawl Dynamics

Michael BrenndoerferJanuary 8, 20264 min read
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Chapter 139Web Crawling › Visualizing Crawl Dynamics
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The following visualizations illustrate how content length distribution and domain diversity evolve during a crawl, along with how relevance scoring concentrates focused crawl results.

Out[13]:
Visualization
Histogram of content lengths in bytes on a log x-axis with filter threshold lines marked.
Log-scale histogram of page content lengths from a simulated web crawl. The distribution follows an approximately log-normal shape, with most pages falling between 1,000 and 50,000 bytes. Vertical dashed lines mark the lower threshold (200 bytes, below which pages are almost certainly spam or error responses) and the upper threshold (100 KB, above which pages tend to be boilerplate-heavy). Both thresholds are commonly applied in production data pipelines as the cheapest first-pass quality filter.
Out[14]:
Visualization
Horizontal bar chart showing page counts per domain, with the highest-count domain at top.
Distribution of crawled pages across domains in a simulated focused crawl. The uneven distribution reflects the web''s power-law link structure: a small number of domains receive disproportionate crawler attention when following links, mirroring the real-world concentration of web traffic. In production pipelines, domain capping limits any single domain''s share of the training corpus to prevent the final dataset from being dominated by a handful of sites.
Out[15]:
Visualization
Bar chart showing average content length decreasing at higher link depths.
Average content length by link depth from seed URLs in a simulated crawl. Pages closer to seeds tend to be longer and more substantive, showing the editorial quality of well-linked authoritative pages. The decline at greater depths reflects the increasing prevalence of thin content and spam in the web graph's periphery. Error bars show one standard deviation across simulated pages at each depth level.
Horizontal bar chart comparing change rates across page types on a log scale.
Simulated page change rate by content type on a log scale, showing how frequently pages are updated relative to one another. News articles change orders of magnitude more frequently than academic papers, which justifies very different revisit scheduling for each content type. Freshness-aware crawlers allocate revisit bandwidth proportionally to each category's observed change rate.