Trait memchr::arch::all::packedpair::HeuristicFrequencyRank

pub trait HeuristicFrequencyRank {
    // Required method
    fn rank(&self, byte: u8) -> u8;
}

This trait allows the user to customize the heuristic used to determine the relative frequency of a given byte in the dataset being searched.

The use of this trait can have a dramatic impact on performance depending on the type of data being searched. The details of why are explained in the docs of crate::memmem::Prefilter. To summarize, the core algorithm uses a prefilter to quickly identify candidate matches that are later verified more slowly. This prefilter is implemented in terms of trying to find rare bytes at specific offsets that will occur less frequently in the dataset. While the concept of a rare byte is similar for most datasets, there are some specific datasets (like binary executables) that have dramatically different byte distributions. For these datasets customizing the byte frequency heuristic can have a massive impact on performance, and might even need to be done at runtime.

The default implementation of HeuristicFrequencyRank reads from the static frequency table defined in src/memmem/byte_frequencies.rs. This is optimal for most inputs, so if you are unsure of the impact of using a custom HeuristicFrequencyRank you should probably just use the default.

Example

use memchr::{
    arch::all::packedpair::HeuristicFrequencyRank,
    memmem::FinderBuilder,
};

/// A byte-frequency table that is good for scanning binary executables.
struct Binary;

impl HeuristicFrequencyRank for Binary {
    fn rank(&self, byte: u8) -> u8 {
        const TABLE: [u8; 256] = [
            255, 128, 61, 43, 50, 41, 27, 28, 57, 15, 21, 13, 24, 17, 17,
            89, 58, 16, 11, 7, 14, 23, 7, 6, 24, 9, 6, 5, 9, 4, 7, 16,
            68, 11, 9, 6, 88, 7, 4, 4, 23, 9, 4, 8, 8, 5, 10, 4, 30, 11,
            9, 24, 11, 5, 5, 5, 19, 11, 6, 17, 9, 9, 6, 8,
            48, 58, 11, 14, 53, 40, 9, 9, 254, 35, 3, 6, 52, 23, 6, 6, 27,
            4, 7, 11, 14, 13, 10, 11, 11, 5, 2, 10, 16, 12, 6, 19,
            19, 20, 5, 14, 16, 31, 19, 7, 14, 20, 4, 4, 19, 8, 18, 20, 24,
            1, 25, 19, 58, 29, 10, 5, 15, 20, 2, 2, 9, 4, 3, 5,
            51, 11, 4, 53, 23, 39, 6, 4, 13, 81, 4, 186, 5, 67, 3, 2, 15,
            0, 0, 1, 3, 2, 0, 0, 5, 0, 0, 0, 2, 0, 0, 0,
            12, 2, 1, 1, 3, 1, 1, 1, 6, 1, 2, 1, 3, 1, 1, 2, 9, 1, 1, 0,
            2, 2, 4, 4, 11, 6, 7, 3, 6, 9, 4, 5,
            46, 18, 8, 18, 17, 3, 8, 20, 16, 10, 3, 7, 175, 4, 6, 7, 13,
            3, 7, 3, 3, 1, 3, 3, 10, 3, 1, 5, 2, 0, 1, 2,
            16, 3, 5, 1, 6, 1, 1, 2, 58, 20, 3, 14, 12, 2, 1, 3, 16, 3, 5,
            8, 3, 1, 8, 6, 17, 6, 5, 3, 8, 6, 13, 175,
        ];
        TABLE[byte as usize]
    }
}
// Create a new finder with the custom heuristic.
let finder = FinderBuilder::new()
    .build_forward_with_ranker(Binary, b"\x00\x00\xdd\xdd");
// Find needle with custom heuristic.
assert!(finder.find(b"\x00\x00\x00\xdd\xdd").is_some());

Required Methods

fn rank(&self, byte: u8) -> u8

Return the heuristic frequency rank of the given byte. A lower rank means the byte is believed to occur less frequently in the haystack.

Some uses of this heuristic may treat arbitrary absolute rank values as significant. For example, an implementation detail in this crate may determine that heuristic prefilters are inappropriate if every byte in the needle has a “high” rank.

Implementations on Foreign Types

impl<'a, R> HeuristicFrequencyRank for &'a Rwhere R: HeuristicFrequencyRank,

This permits passing any implementation of HeuristicFrequencyRank as a borrowed version of itself.

fn rank(&self, byte: u8) -> u8

Implementors