ArchiveSpark
An Apache Spark framework for efficient data processing, extraction, and derivation from web archives and archival collections.
What is ArchiveSpark?
ArchiveSpark is an Apache Spark framework specifically designed for processing, extracting, and deriving data from web archives and other archival collections. It solves the problem of efficiently accessing and transforming raw archival data into more usable formats while maintaining data lineage. The framework enables researchers and developers to create derived datasets through filtering and extraction operations.
Data scientists, researchers, and developers working with web archives or archival collections who need to process, analyze, and extract value from large-scale historical data. This includes digital humanities researchers, web archivists, and data engineers working with temporal web data.
Developers choose ArchiveSpark because it provides a specialized, efficient framework for archival data processing built on Apache Spark's distributed computing capabilities. Its unique modular architecture with customizable data specifications allows it to work with diverse archival collections while maintaining data lineage—a critical feature for reproducible research.
Overview
An Apache Spark framework for easy data processing, extraction as well as derivation for web archives and archival collections, developed at Internet Archive.
Use Cases
Best For
- Processing and analyzing large-scale web archive collections
- Extracting specific properties from archived web data for research
- Creating derived datasets from archival collections with preserved lineage
- Performing temporal analysis on historical web data
- Generating hyperlink or knowledge graphs from archived web content
- Transforming raw archival data into accessible formats like JSON
Not Ideal For
- Real-time or streaming data processing applications
- Small-scale data extraction tasks that don't require distributed computing
- Projects needing extensive user interfaces or non-archival data formats
- Teams without prior Apache Spark or distributed systems expertise
Pros & Cons
Pros
Leverages Apache Spark's distributed computing to handle large archival collections efficiently, as highlighted in its philosophy for scalable data access.
Automatically reflects the lineage of derived values back to original sources, ensuring traceability for reproducible research and analysis.
Customizable data specifications allow adaptation beyond web archives to any archival collection, supporting diverse data sources.
Built-in support for formats like WARC/CDX and tools for temporal analysis make it ideal for web archive processing and derived corpus creation.
Cons
Requires deep knowledge of Apache Spark and distributed systems, making it challenging for newcomers without this background.
Based on Sparkling, an internal library from Internet Archive, which may lead to vendor lock-in and limited control over future updates.
Primarily designed for archival data, so it lacks features for general-purpose data processing or real-time applications, as admitted in its specialized use cases.
Frequently Asked Questions
Related Projects
Archives Unleashed ToolkitThe Archives Unleashed Toolkit is an open-source toolkit for analyzing web archives.
Common Crawl Jupyter notebooksVarious Jupyter notebooks about Common Crawl data
Archives Unleashed NotebooksVarious examples of notebooks for working with web archives with the Archives Unleashed Toolkit, and derivatives generated by the Archives Unleashed Toolkit.
Archives Research Compute HubWeb application for distributed compute analysis of Archive-It web archive collections.
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