Modern enterprises collect, store, and analyze increasingly large volumes of data. This relentless explosion in data volumes has driven the need for more effective encoding algorithms for reducing data size. In particular, this paper looks at lossless encodings, which support perfect reconstruction of the original data via decoding. Lossless encodings are widely used in database systems because they are able to not only compress data sizes, thereby reducing the cost of storing data, but also to minimize the I/O overhead of reading data, since compressed data requires less I/O bandwidth to read from persistent storage. In modern cloud-native database systems, where data is stored durably on cloud object stores and a hot set of data is cached on a compute node's local storage, encodings also enable more data to be cached as part of the hot set.
Jialin Ding (Thu,) studied this question.