An Empirical Study of Rabin Fingerprinting Parameters

Summarizing, comparing, and indexing large data files are common operations in information retrieval and data-storage systems. Consequently, Rabin fingerprinting is a common technique for string matching, and to cut a file into variable-length chunks for faster pattern matching of large datasets. Ordered lists of hashes (of the chunks), representing the original files, can require fewer bytes to store and to transmit across a network than the files themselves. Lists of hashes can also be compared like substrings to find files with common chunks of data. Finally, lists of hashes can also be used for data deduplication to save on storage (and transmission) or to synchronize two similar copies of the same file. However, it is important to make appropriate choices for Rabin fingerprinting parameters such as the sliding window size, the degree of the irreducible polynomial, the mask size, and the cut value for the hash. Therefore, we present an empirical, parameter-sweep study of Rabin fingerprinting on a non-trivial workload based on the Linux kernel source code. As a result, we make some best-practice recommendations for using Rabin fingerprinting. For example, we characterize how a cut value of one is less problematic than a natural choice of zero for the cut value.