41 releases
| 0.5.9 | Jan 2, 2026 |
|---|---|
| 0.5.8 | Aug 23, 2025 |
| 0.5.7 | Mar 17, 2025 |
| 0.5.6 | Nov 18, 2024 |
| 0.5.3 | Jul 26, 2024 |
#201 in Text processing
Used in 2 crates
290KB
2.5K
SLoC
Matcher
A high-performance matcher designed to solve LOGICAL and TEXT VARIATIONS problems in word matching, implemented in Rust.
For detailed implementation, see the Design Document.
Features
- Multiple Matching Methods:
- Simple Word Matching
- Regex-Based Matching
- Similarity-Based Matching
- Text Transformation:
- Fanjian: Simplify traditional Chinese characters to simplified ones.
Example:
蟲艸->虫艹 - Delete: Remove specific characters.
Example:
*Fu&*iii&^%%*&kkkk->Fuiiikkkk - Normalize: Normalize special characters to identifiable characters.
Example:
𝜢𝕰𝕃𝙻𝝧 𝙒ⓞᵣℒ𝒟!->hello world! - PinYin: Convert Chinese characters to Pinyin for fuzzy matching.
Example:
西安->xi an, matches洗按->xi an, but not先->xian - PinYinChar: Convert Chinese characters to Pinyin.
Example:
西安->xian, matches洗按and先->xian
- Fanjian: Simplify traditional Chinese characters to simplified ones.
Example:
- AND OR NOT Word Matching:
- Takes into account the number of repetitions of words.
- Example:
hello&worldmatcheshello worldandworld,hello - Example:
无&法&无&天matches无无法天(because无is repeated twice), but not无法天 - Example:
hello~helloo~hhellomatcheshellobut nothellooandhhello
- Customizable Exemption Lists: Exclude specific words from matching.
- Efficient Handling of Large Word Lists: Optimized for performance.
Usage
Adding to Your Project
To use matcher_rs in your Rust project, run the following command:
cargo add matcher_rs
Explanation of the configuration
Matcher's configuration is defined by theMatchTableMap = HashMap<u32, Vec<MatchTable>>type, the key ofMatchTableMapis calledmatch_id, for eachmatch_id, thetable_idinside is required to be unique.SimpleMatcher's configuration is defined by theSimpleTable = HashMap<ProcessType, HashMap<u32, &str>>type, the valueHashMap<u32, &str>'s key is calledword_id,word_idis required to be globally unique.
MatchTable
table_id: The unique ID of the match table.match_table_type: The type of the match table.word_list: The word list of the match table.exemption_process_type: The type of the exemption simple match.exemption_word_list: The exemption word list of the match table.
For each match table, word matching is performed over the word_list, and exemption word matching is performed over the exemption_word_list. If the exemption word matching result is True, the word matching result will be False.
MatchTableType
Simple: Supports simple multiple patterns matching with text normalization defined byprocess_type.- It can handle combination patterns and repeated times sensitive matching, delimited by
&and~, such ashello&world&hellowill matchhellohelloworldandworldhellohello, but nothelloworlddue to the repeated times ofhello.
- It can handle combination patterns and repeated times sensitive matching, delimited by
Regex: Supports regex patterns matching.SimilarChar: Supports similar character matching using regex.["hello,hallo,hollo,hi", "word,world,wrd,🌍", "!,?,~"]will matchhelloworld!,hollowrd?,hi🌍~··· any combinations of the words split by,in the list.
Acrostic: Supports acrostic matching using regex (currently only supports Chinese and simple English sentences).["h,e,l,l,o", "你,好"]will matchhope, endures, love, lasts, onward.and你的笑容温暖, 好心情常伴。.
Regex: Supports regex matching.["h[aeiou]llo", "w[aeiou]rd"]will matchhello,world,hillo,wurld··· any text that matches the regex in the list.
Similar: Supports similar text matching based on distance and threshold.Levenshtein: Supports similar text matching based on Levenshtein distance.
ProcessType
None: No transformation.Fanjian: Traditional Chinese to simplified Chinese transformation. Based on FANJIAN.妳好->你好現⾝->现身
Delete: Delete all punctuation, special characters and white spaces. Based on TEXT_DELETE andWHITE_SPACE.hello, world!->helloworld《你∷好》->你好
Normalize: Normalize all English character variations and number variations to basic characters. Based on NORM and NUM_NORM.ℋЀ⒈㈠Õ->he11o⒈Ƨ㊂->123
PinYin: Convert all unicode Chinese characters to pinyin with boundaries. Based on PINYIN.你好->ni hao西安->xi an
PinYinChar: Convert all unicode Chinese characters to pinyin without boundaries. Based on PINYIN.你好->nihao西安->xian
You can combine these transformations as needed. Pre-defined combinations like DeleteNormalize and FanjianDeleteNormalize are provided for convenience.
Avoid combining PinYin and PinYinChar due to that PinYin is a more limited version of PinYinChar, in some cases like xian, can be treat as two words xi and an, or only one word xian.
Basic Example
Here’s a basic example of how to use the Matcher struct for text matching:
use matcher_rs::{text_process, reduce_text_process, ProcessType};
let result = text_process(ProcessType::Delete, "你好，世界！");
let result = reduce_text_process(ProcessType::FanjianDeleteNormalize, "你好，世界！");
use std::collections::HashMap;
use matcher_rs::{Matcher, MatchTableMap, MatchTable, MatchTableType, ProcessType};
let match_table_map: MatchTableMap = HashMap::from_iter(vec![
(1, vec![MatchTable {
table_id: 1,
match_table_type: MatchTableType::Simple { process_type: ProcessType::FanjianDeleteNormalize},
word_list: vec!["example", "test"],
exemption_process_type: ProcessType::None,
exemption_word_list: vec![],
}]),
]);
let matcher = Matcher::new(&match_table_map);
let text = "This is an example text.";
let results = matcher.word_match(text);
use std::collections::HashMap;
use matcher_rs::{ProcessType, SimpleMatcher};
let mut simple_table = HashMap::new();
let mut simple_word_map = HashMap::new();
simple_word_map.insert(1, "你好");
simple_word_map.insert(2, "世界");
simple_table.insert(ProcessType::Fanjian, simple_word_map);
let matcher = SimpleMatcher::new(&simple_table);
let text = "你好，世界！";
let results = matcher.process(text);
For more detailed usage examples, please refer to the test.rs file.
Feature Flags
runtime_build: By enable runtime_build feature, we could build process matcher at runtime, but with build time increasing.serde: By enable serde feature, we could serialize and deserialize matcher and simple_matcher. With serde feature, AhoCorasick's prefilter is disabled, because I don't know how to serialize it correctly, which will lead to performance regression when the patterns size is small (say, less than 100).dfa: By enable dfa feature, we could use dfa to perform simple matching, but with significantly increasing memory consumption.
Default feature is dfa. If you want to make Matcher and SimpleMatcher serializable, you should enable serde feature.
Benchmarks
Bench against pairs (CN_WORD_LIST_100000, CN_HAYSTACK) and (EN_WORD_LIST_100000, EN_HAYSTACK). Word selection is totally random.
The matcher_rs library includes benchmarks to measure the performance of the matcher. You can find the benchmarks in the bench.rs file. To run the benchmarks, use the following command:
cargo bench
Current default simple match type: ProcessType(None)
Current default simple word map size: 1000
Current default combined times: 2
Timer precision: 41 ns
bench fastest │ slowest │ median │ mean │ samples │ iters
├─ build_cn │ │ │ │ │
│ ├─ build_cn_by_combined_times │ │ │ │ │
│ │ ├─ 1 2.421 ms │ 3.108 ms │ 2.433 ms │ 2.468 ms │ 100 │ 100
│ │ ├─ 2 4.98 ms │ 5.647 ms │ 5.047 ms │ 5.073 ms │ 100 │ 100
│ │ ├─ 3 7.651 ms │ 10.03 ms │ 7.802 ms │ 7.947 ms │ 100 │ 100
│ │ ├─ 4 10.23 ms │ 12.06 ms │ 10.5 ms │ 10.61 ms │ 100 │ 100
│ │ ╰─ 5 12.93 ms │ 14.1 ms │ 13.15 ms │ 13.24 ms │ 100 │ 100
│ ├─ build_cn_by_multiple_process_type 25.3 ms │ 59.86 ms │ 26 ms │ 26.53 ms │ 100 │ 100
│ ├─ build_cn_by_process_type │ │ │ │ │
│ │ ├─ "delete" 5.053 ms │ 5.439 ms │ 5.176 ms │ 5.191 ms │ 100 │ 100
│ │ ├─ "delete_normalize" 4.962 ms │ 5.768 ms │ 5.069 ms │ 5.1 ms │ 100 │ 100
│ │ ├─ "fanjian" 5.109 ms │ 8.929 ms │ 5.19 ms │ 5.366 ms │ 100 │ 100
│ │ ├─ "fanjian_delete_normalize" 4.987 ms │ 8.449 ms │ 5.26 ms │ 5.424 ms │ 100 │ 100
│ │ ├─ "none" 5.03 ms │ 14.95 ms │ 5.159 ms │ 5.353 ms │ 100 │ 100
│ │ ├─ "normalize" 5.039 ms │ 5.872 ms │ 5.214 ms │ 5.247 ms │ 100 │ 100
│ │ ├─ "pinyin" 6.722 ms │ 14.46 ms │ 7.347 ms │ 7.344 ms │ 100 │ 100
│ │ ╰─ "pinyinchar" 6.603 ms │ 9.37 ms │ 7.147 ms │ 7.197 ms │ 100 │ 100
│ ╰─ build_cn_by_simple_word_map_size │ │ │ │ │
│ ├─ 100 471.7 µs │ 681.7 µs │ 501.9 µs │ 512.3 µs │ 100 │ 100
│ ├─ 1000 5.186 ms │ 5.858 ms │ 5.292 ms │ 5.321 ms │ 100 │ 100
│ ├─ 10000 47.09 ms │ 51.62 ms │ 47.4 ms │ 47.77 ms │ 100 │ 100
│ ╰─ 50000 180.3 ms │ 194.4 ms │ 185.7 ms │ 186.1 ms │ 27 │ 27
├─ build_en │ │ │ │ │
│ ├─ build_en_by_combined_times │ │ │ │ │
│ │ ├─ 1 5.629 ms │ 6.387 ms │ 5.733 ms │ 5.759 ms │ 100 │ 100
│ │ ├─ 2 13.33 ms │ 17.14 ms │ 13.51 ms │ 13.55 ms │ 100 │ 100
│ │ ├─ 3 19.83 ms │ 23.14 ms │ 20.85 ms │ 20.85 ms │ 100 │ 100
│ │ ├─ 4 27.55 ms │ 30.19 ms │ 27.73 ms │ 27.8 ms │ 100 │ 100
│ │ ╰─ 5 35.21 ms │ 37.18 ms │ 35.55 ms │ 35.6 ms │ 100 │ 100
│ ├─ build_en_by_multiple_process_type 15.21 ms │ 16.72 ms │ 15.8 ms │ 15.79 ms │ 100 │ 100
│ ├─ build_en_by_process_type │ │ │ │ │
│ │ ├─ "delete" 12.63 ms │ 26.19 ms │ 13.2 ms │ 13.32 ms │ 100 │ 100
│ │ ├─ "delete_normalize" 11.76 ms │ 12.68 ms │ 11.94 ms │ 11.95 ms │ 100 │ 100
│ │ ├─ "none" 12.21 ms │ 13.52 ms │ 12.67 ms │ 12.71 ms │ 100 │ 100
│ │ ╰─ "normalize" 11.45 ms │ 12.09 ms │ 11.59 ms │ 11.61 ms │ 100 │ 100
│ ╰─ build_en_by_simple_word_map_size │ │ │ │ │
│ ├─ 100 820 µs │ 1.184 ms │ 830.6 µs │ 851.1 µs │ 100 │ 100
│ ├─ 1000 13 ms │ 14.52 ms │ 13.65 ms │ 13.62 ms │ 100 │ 100
│ ├─ 10000 151.4 ms │ 169.1 ms │ 157.5 ms │ 157.6 ms │ 32 │ 32
│ ╰─ 50000 640.3 ms │ 677.1 ms │ 655 ms │ 655.3 ms │ 8 │ 8
├─ search_cn │ │ │ │ │
│ ├─ search_cn_baseline │ │ │ │ │
│ │ ├─ 100 2.904 ms │ 7.824 ms │ 2.927 ms │ 2.986 ms │ 100 │ 100
│ │ ├─ 1000 3.046 ms │ 3.81 ms │ 3.066 ms │ 3.095 ms │ 100 │ 100
│ │ ├─ 10000 7.651 ms │ 8.541 ms │ 7.77 ms │ 7.854 ms │ 100 │ 100
│ │ ╰─ 50000 26.67 ms │ 47.51 ms │ 28.74 ms │ 30.15 ms │ 100 │ 100
│ ├─ search_cn_by_combined_times │ │ │ │ │
│ │ ├─ 1 3.967 ms │ 4.308 ms │ 4.031 ms │ 4.039 ms │ 100 │ 100
│ │ ├─ 2 5.201 ms │ 5.742 ms │ 5.246 ms │ 5.264 ms │ 100 │ 100
│ │ ├─ 3 6.405 ms │ 7.174 ms │ 6.442 ms │ 6.47 ms │ 100 │ 100
│ │ ├─ 4 7.012 ms │ 7.671 ms │ 7.039 ms │ 7.067 ms │ 100 │ 100
│ │ ╰─ 5 8.471 ms │ 9.027 ms │ 8.606 ms │ 8.621 ms │ 100 │ 100
│ ├─ search_cn_by_multiple_process_type 61.42 ms │ 92.44 ms │ 64.06 ms │ 65.2 ms │ 100 │ 100
│ ├─ search_cn_by_process_type │ │ │ │ │
│ │ ├─ "delete" 14.44 ms │ 15.15 ms │ 14.59 ms │ 14.59 ms │ 100 │ 100
│ │ ├─ "delete_normalize" 20.58 ms │ 21.86 ms │ 21.19 ms │ 21.08 ms │ 100 │ 100
│ │ ├─ "fanjian" 6.902 ms │ 7.653 ms │ 7.232 ms │ 7.179 ms │ 100 │ 100
│ │ ├─ "fanjian_delete_normalize" 21.72 ms │ 23.12 ms │ 21.98 ms │ 22.11 ms │ 100 │ 100
│ │ ├─ "none" 5.013 ms │ 5.628 ms │ 5.053 ms │ 5.073 ms │ 100 │ 100
│ │ ├─ "normalize" 15.25 ms │ 16.69 ms │ 15.44 ms │ 15.62 ms │ 100 │ 100
│ │ ├─ "pinyin" 41.1 ms │ 45.53 ms │ 43.78 ms │ 43.21 ms │ 100 │ 100
│ │ ╰─ "pinyinchar" 42.93 ms │ 48.92 ms │ 45.06 ms │ 44.83 ms │ 100 │ 100
│ ╰─ search_cn_by_simple_word_map_size │ │ │ │ │
│ ├─ 100 3.205 ms │ 3.498 ms │ 3.242 ms │ 3.268 ms │ 100 │ 100
│ ├─ 1000 5.057 ms │ 5.674 ms │ 5.273 ms │ 5.277 ms │ 100 │ 100
│ ├─ 10000 16.31 ms │ 19.4 ms │ 17.24 ms │ 17.12 ms │ 100 │ 100
│ ╰─ 50000 53.87 ms │ 93.62 ms │ 58.71 ms │ 62.27 ms │ 81 │ 81
├─ search_en │ │ │ │ │
│ ├─ search_en_baseline │ │ │ │ │
│ │ ├─ 100 353.9 µs │ 471.7 µs │ 376.6 µs │ 381.7 µs │ 100 │ 100
│ │ ├─ 1000 369 µs │ 452.2 µs │ 389.1 µs │ 393.8 µs │ 100 │ 100
│ │ ├─ 10000 1.027 ms │ 1.06 ms │ 1.034 ms │ 1.035 ms │ 100 │ 100
│ │ ╰─ 50000 1.004 ms │ 1.055 ms │ 1.016 ms │ 1.018 ms │ 100 │ 100
│ ├─ search_en_by_combined_times │ │ │ │ │
│ │ ├─ 1 1.788 ms │ 4.898 ms │ 1.915 ms │ 1.94 ms │ 100 │ 100
│ │ ├─ 2 2.477 ms │ 2.747 ms │ 2.489 ms │ 2.494 ms │ 100 │ 100
│ │ ├─ 3 2.792 ms │ 3.142 ms │ 2.805 ms │ 2.813 ms │ 100 │ 100
│ │ ├─ 4 2.691 ms │ 3.115 ms │ 2.711 ms │ 2.717 ms │ 100 │ 100
│ │ ╰─ 5 2.786 ms │ 3.342 ms │ 2.803 ms │ 2.824 ms │ 100 │ 100
│ ├─ search_en_by_multiple_process_type 10.12 ms │ 11.85 ms │ 10.76 ms │ 10.56 ms │ 100 │ 100
│ ├─ search_en_by_process_type │ │ │ │ │
│ │ ├─ "delete" 7.104 ms │ 13.92 ms │ 7.145 ms │ 7.235 ms │ 100 │ 100
│ │ ├─ "delete_normalize" 8.588 ms │ 9.469 ms │ 8.71 ms │ 8.848 ms │ 100 │ 100
│ │ ├─ "none" 2.436 ms │ 2.711 ms │ 2.456 ms │ 2.466 ms │ 100 │ 100
│ │ ╰─ "normalize" 4.047 ms │ 4.338 ms │ 4.07 ms │ 4.076 ms │ 100 │ 100
│ ╰─ search_en_by_simple_word_map_size │ │ │ │ │
│ ├─ 100 1.355 ms │ 3.969 ms │ 1.429 ms │ 1.483 ms │ 100 │ 100
│ ├─ 1000 2.064 ms │ 2.279 ms │ 2.077 ms │ 2.084 ms │ 100 │ 100
│ ├─ 10000 3.381 ms │ 4.793 ms │ 3.396 ms │ 3.415 ms │ 100 │ 100
│ ╰─ 50000 4.561 ms │ 6.879 ms │ 4.659 ms │ 4.824 ms │ 100 │ 100
╰─ single_line │ │ │ │ │
├─ search_cn_single_line │ │ │ │ │
│ ├─ 100 252.2 ns │ 426.8 ns │ 262.7 ns │ 271.7 ns │ 100 │ 1600
│ ├─ 1000 309.6 ns │ 338.2 ns │ 317.4 ns │ 317.5 ns │ 100 │ 1600
│ ├─ 10000 540.7 ns │ 10.04 µs │ 624.7 ns │ 725.5 ns │ 100 │ 100
│ ╰─ 50000 1.29 µs │ 43.45 µs │ 1.374 µs │ 1.848 µs │ 100 │ 100
╰─ search_en_single_line │ │ │ │ │
├─ 100 56.04 ns │ 58.64 ns │ 57.01 ns │ 56.92 ns │ 100 │ 12800
├─ 1000 56.69 ns │ 68.4 ns │ 57.99 ns │ 58.17 ns │ 100 │ 12800
├─ 10000 374.7 ns │ 5.291 µs │ 457.7 ns │ 512.6 ns │ 100 │ 100
╰─ 50000 457.7 ns │ 16.99 µs │ 540.7 ns │ 701.9 ns │ 100 │ 100
Contributing
Contributions to matcher_rs are welcome! If you find a bug or have a feature request, please open an issue on the GitHub repository. If you would like to contribute code, please fork the repository and submit a pull request.
License
matcher_rs is licensed under the MIT OR Apache-2.0 license.
More Information
For more details, visit the GitHub repository.
Dependencies
~7–9.5MB
~170K SLoC