Compressing Relations and Indexes

We propose a new compression algorithm that is tailored to database applications. It can be applied to a collection of records, and is especially e ective for records with many low to medium cardinality elds and numeric elds. In addition, this new technique supports very fast decompression. Promising application domains include decision support systems (DSS), since \fact tables", which are by far the largest tables in these applications, contain many low and medium cardinality elds and typically no text elds. Further, our decompression rates are faster than typical disk throughputs for sequential scans in contrast, gzip is slower. This is important in DSS applications, which often scan large ranges of records. An important distinguishing characteristic of our algorithm, in contrast to compression algorithms proposed earlier, is that we can decompress individual tuples (even individual elds), rather than a full page (or an entire relation) at a time. Also, all the information need...

• Type: Conference
• Year: 1998
• Where: ICDE
• Authors: Jonathan Goldstein, Raghu Ramakrishnan, Uri Shaft
• Keywords: Bu Er, Compression Algorithm, Database, ICDE 1998, Medium Cardinality Elds