A Go tool and library for downloading URLs and files from Common Crawl and Wayback Machine web archives.
GoGetCrawl is a Go-based tool and library designed to extract URLs and files from web archives like Common Crawl and the Wayback Machine. It solves the problem of programmatically accessing historical web data by providing a unified interface for querying, filtering, and downloading archived content.
Developers, researchers, and data engineers who need to programmatically retrieve historical web data for analysis, archiving, or automation purposes.
Developers choose GoGetCrawl for its dual CLI/library design, support for multiple archives, and efficient concurrent operations, making it a practical alternative to manual archive queries or custom scraping solutions.
Extract web archive data using Wayback Machine and Common Crawl
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Unifies querying for both Common Crawl and Wayback Machine archives, simplifying data extraction from multiple sources through a single interface as highlighted in the multi-source support feature.
Allows precise filtering by status code, MIME type, date ranges, and custom patterns, demonstrated in CLI examples with flags like --ext pdf and --from/--to for targeted results.
Uses worker pools for concurrent downloads, improving efficiency with configurable workers via the -w flag, as shown in the download example with 3 workers for PDF files.
Offers both a standalone CLI for quick tasks and a Go package for custom integration, evidenced by installation options and code snippets for library usage.
As a Go-based tool, it requires a Go environment for library use, limiting adoption in polyglot projects and adding setup overhead for non-Go developers.
Only covers Common Crawl and Wayback Machine, missing other web archives like Archive.is or specialized collections, which restricts data source variety.
From the provided code examples, error handling is minimal with simple returns, potentially leading to unhandled failures in production automation scenarios.
Focuses solely on extraction without tools for processing, transforming, or analyzing downloaded data, requiring additional steps for insights beyond raw retrieval.