Robust and Scalable Content-and-Structure Indexing

• Published in: The VLDB journal
• Main Authors: Wellenzohn, Kevin, Böhlen, Michael H., Helmer, Sven, Pietri, Antoine, Zacchiroli, Stefano
• Format: Journal Article
• Language: English
• Published: Springer 2022
• Subjects: Computer Science; Software Engineering
• ISSN: 1066-8888

Frequent queries on semi-structured hierarchical data are Content-and-Structure (CAS) queries that filter data items based on their location in the hierarchical structure and their value for some attribute. We propose the Robust and Scalable Content-and-Structure (RSCAS) index to efficiently answer CAS queries on big semi-structured data. To get an index that is robust against queries with varying selectivities we introduce a novel dynamic interleaving that merges the path and value dimensions of composite keys in a balanced manner. We store interleaved keys in our triebased RSCAS index, which efficiently supports a wide range of CAS queries, including queries with wildcards and descendant axes. We implement RSCAS as a log-structured merge (LSM) tree to scale it to data-intensive applications with a high insertion rate. We illustrate RSCAS's robustness and scalability by indexing data from the Software Heritage (SWH) archive, which is the world's largest, publiclyavailable source code archive.