Strings are prevalent in real-world data sets. They often occupy a large fraction of the data and are slow to process. In this work, we present Fast Static Symbol Table (FSST), a lightweight compression scheme for strings. On text data, FSST offers decompression and compression speed similar to or better than the best speed-optimized compression methods, such as LZ4, yet offers significantly better compression factors. Moreover, its use of a static symbol table allows random access to individual, compressed strings, enabling lazy decompression and query processing on compressed data. We believe these features will make FSST a valuable piece in the standard compression toolbox.

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Peter Boncz holds appointments as tenured researcher at CWI and professor at VU University Amsterdam. His academic background is in core database architecture, with the MonetDB the systems outcome of his PhD -- MonetDB much later won the 2016 ACM SIGMOD systems award. He has a track record in bridging the gap between academia and commercial application, receiving the Dutch ICT Regie Award 2006 for his role in the CWI spin-off company Data Distilleries. In 2008 he co-founded Vectorwise around the analytical database system by the same name which pioneered vectorized query execution -- later acquired by Actian. He is co-recipient of the 2009 VLDB 10 Years Best Paper Award, and in 2013 received the Humboldt Research Award for his research on database architecture. He also works on graph data management, founding in 2013 the Linked Database Benchmark Council (LDBC), a benchmarking organization for graph database systems