Compression in ScyllaDB, Part One

The blog post delves into the fundamentals of data compression in ScyllaDB, primarily focusing on lossless compression approaches. It begins by explaining the basic concept and necessity of compression, using simple examples to illustrate how data can be compacted without loss. The discussion introduces the pigeonhole principle and Kolmogorov complexity to highlight the theoretical limitations of compression, emphasizing that not all data can be compressed effectively. ScyllaDB utilizes various algorithms like LZ4, Snappy, DEFLATE, and ZStandard, each offering unique benefits in speed and compression ratio. The post explains how these algorithms are applied to ScyllaDB's SSTables, allowing efficient data storage and retrieval without decompressing entire tables. It describes the LZ77 algorithm's role in compression and introduces the concept of entropy encoding, which enhances compression by using prefix codes. The post concludes by encouraging further exploration of compression techniques and previews the second part of the series, which will include benchmarks to compare the efficiency of different algorithms in various scenarios.