A dataset and npm module quantifying the performance impact of third-party scripts across the web, categorized by entity.
Third Party Web is a data collection and analysis project that identifies and measures the performance impact of third-party scripts (like ads, analytics, social widgets) on website loading times. It processes data from HTTP Archive to attribute JavaScript execution time to specific companies and services, providing a ranked view of which third parties are most responsible for slow page loads.
Web developers, performance engineers, and site owners who need to understand and optimize the performance cost of external scripts on their websites.
It offers a unique, data-backed perspective on third-party performance at scale, helping developers choose faster alternatives and hold vendors accountable, unlike generic performance tools that only measure individual sites.
Data on third party entities and their impact on the web.
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Aggregates JavaScript execution time from HTTP Archive Lighthouse data to attribute performance impact to specific third-party entities, providing a factual basis for analysis across millions of sites.
Organizes third parties into categories like Advertising and Analytics, ranking them by average impact and usage, enabling informed comparisons when selecting vendors.
Includes an npm module that allows developers to programmatically identify and classify third-party domains, facilitating integration into custom tools and audit workflows.
Clearly documents data sources, updates, and methodological changes, such as the 2019 shift from per-script to per-page attribution, ensuring credibility and helping users interpret the data correctly.
Relies on monthly HTTP Archive crawls, so the data is not real-time and may not reflect the latest changes in third-party scripts or website usage patterns.
Focuses exclusively on JavaScript execution time, ignoring other critical performance factors like network latency, rendering delays, or bandwidth consumption that contribute to page load times.
Requires careful analysis due to nuances like per-page vs. per-script metrics and methodology shifts, which can lead to misinterpretation without technical expertise, as noted in the README updates.