How to Optimize Hamming Distance Calculation on Binary Strings in SQL?

Hamming Distance Calculation on Binary Strings in SQL

Calculating the Hamming distance between two binary strings is a crucial operation in various applications, including error detection and clustering. However, performing this calculation directly on BINARY data in MySQL can be inefficient. This article explores an alternative approach using BIGINT columns to achieve optimal performance.

The Hamming distance between two binary strings is defined as the number of bits that differ at corresponding positions. A common method for calculating this distance is to break down the binary strings into substrings, convert them to integers, and perform the XOR operation on each substring pair. The individual Hamming distances are then summed to obtain the overall distance.

While this approach may seem efficient, it can be computationally intensive when dealing with BINARY columns. To optimize performance, it is recommended to split the BINARY column into multiple BIGINT columns, each containing an 8-byte substring of the original data. This allows you to utilize a custom function, such as the HAMMINGDISTANCE function provided earlier, which directly operates on the BIGINT columns.

The HAMMINGDISTANCE function uses the BIT_COUNT function to efficiently calculate the Hamming distance between the substrings stored in the BIGINT columns. This approach results in significantly improved performance compared to using the BINARY approach.

For example, in MySQL 5.1, testing показало, что использование BIGINT-подхода был более чем в 100 раз быстрее, чем использование BINARY-подхода. Таким образом, для больших таблиц, содержащих много строк и много столбцов BINARY(32), эта оптимизация может привести к существенному сокращению времени обработки.

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