An Experimental Study of Bitmap Compression vs. Inverted List Compression

Bitmap compression has been studied extensively in the database area and many efficient compression schemes were proposed, e.g., BBC, WAH, EWAH, and Roaring. Inverted list compression is also a well-studied topic in the information retrieval community and many inverted list compression algorithms were developed as well, e.g., VB, PforDelta, GroupVB, Simple8b, and SIMDPforDelta. We observe that they essentially solve the same problem, i.e., how to store a collection of sorted integers with as few as possible bits and support query processing as fast as possible. Due to historical reasons, bitmap compression and inverted list compression were developed as two separated lines of research in the database area and information retrieval area. Thus, a natural question is: Which one is better between bitmap compression and inverted list compression?

To answer the question, we present the first comprehensive experimental study to compare a series of 9 bitmap compression methods and 12 inverted list compression methods. We compare these 21 algorithms on synthetic datasets with different distributions (uniform, zipf, and markov) as well as 8 real-life datasets in terms of the space overhead, decompression time, intersection time, and union time. Based on the results, we provide many lessons and guidelines that can be used for practitioners to decide which technique to adopt in future systems and also for researchers to develop new algorithms.