stringzillas-cuda 4.6.0
Search, hash, sort, fingerprint, and fuzzy-match strings faster via SWAR, SIMD, and CUDA on Nvidia GPUs
License: Apache-2.0
Requires Python: >=3.8

# StringZilla 🦖

![StringZilla banner](https://github.com/ashvardanian/ashvardanian/blob/master/repositories/StringZilla-v4.jpg?raw=true)

Strings are the first fundamental data type every programming language implements in software rather than hardware, so dedicated CPU instructions are rare - and the few that exist are hardly ideal.
That's why most languages lean on the C standard library (libc) for their string operations, which, despite its name, ships its hottest code in hand-tuned assembly.
It does exploit SIMD, but it isn't perfect.
1️⃣ Even on ubiquitous hardware - over a billion 64-bit ARM CPUs - routines such as `strstr` and `memmem` top out at roughly one-third of available throughput.
2️⃣ SIMD coverage is uneven: fast forward scans don't guarantee speedy reverse searches, hashing and case-mapping is not even part of the standard.
3️⃣ Many higher-level languages can't rely on libc at all because their strings aren't NUL-terminated - or may even contain embedded zeroes.
That's why StringZilla exists: predictable, high performance on every modern platform, OS, and programming language.

[![StringZilla Python installs](https://static.pepy.tech/personalized-badge/stringzilla?period=total&units=abbreviation&left_color=black&right_color=blue&left_text=StringZilla%20Python%20installs)](https://github.com/ashvardanian/stringzilla)
[![StringZilla Rust installs](https://img.shields.io/crates/d/stringzilla?logo=rust&label=Rust%20installs)](https://crates.io/crates/stringzilla)
![StringZilla code size](https://img.shields.io/github/languages/code-size/ashvardanian/stringzilla)

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StringZilla is the GodZilla of string libraries, using [SIMD][faq-simd] and [SWAR][faq-swar] to accelerate binary and UTF-8 string operations on modern CPUs and GPUs.
It delivers up to __10x higher CPU throughput in C, C++, Rust, Python__, and other languages, and can be __100x faster than existing GPU kernels__, covering a broad range of functionality.
It __accelerates exact and fuzzy string matching, hashing, edit distance computations, sorting, provides allocation-free lazily-evaluated smart-iterators, and even random-string generators__.

[faq-simd]: https://en.wikipedia.org/wiki/Single_instruction,_multiple_data
[faq-swar]: https://en.wikipedia.org/wiki/SWAR

- 🐂 __[C](#basic-usage-with-c-99-and-newer):__ Upgrade LibC's `<string.h>` to `<stringzilla/stringzilla.h>`  in C 99
- 🐉 __[C++](#basic-usage-with-c-11-and-newer):__ Upgrade STL's `<string>` to `<stringzilla/stringzilla.hpp>` in C++ 11
- 🧮 __[CUDA](#cuda):__ Process in-bulk with `<stringzillas/stringzillas.cuh>` in CUDA C++ 17
- 🐍 __[Python](#quick-start-python):__ Upgrade your `str` to faster `Str`
- 🦀 __[Rust](#quick-start-rust):__ Use the `StringZilla` traits crate
- 🦫 __[Go](#quick-start-golang):__ Use the `StringZilla` cGo module
- 🍎 __[Swift](#quick-start-swift):__ Use the `String+StringZilla` extension
- 🟨 __[JavaScript](#quick-start-javascript):__ Use the `StringZilla` library
- 🐚 __[Shell][faq-shell]__: Accelerate common CLI tools with `sz-` prefix
- 📚 Researcher? Jump to [Algorithms & Design Decisions](#algorithms--design-decisions)
- 💡 Thinking to contribute? Look for ["good first issues"][first-issues]
- 🤝 And check the [guide](https://github.com/ashvardanian/StringZilla/blob/main/CONTRIBUTING.md) to set up the environment
- Want more bindings or features? Let [me](https://github.com/ashvardanian) know!

[faq-shell]: https://github.com/ashvardanian/StringZilla-CLI
[first-issues]: https://github.com/ashvardanian/StringZilla/issues

__Who is this for?__

- For data-engineers parsing large datasets, like the [CommonCrawl](https://commoncrawl.org/), [RedPajama](https://github.com/togethercomputer/RedPajama-Data), or [LAION](https://laion.ai/blog/laion-5b/).
- For software engineers optimizing strings in their apps and services.
- For bioinformaticians and search engineers looking for edit-distances for [USearch](https://github.com/unum-cloud/usearch).
- For [DBMS][faq-dbms] devs, optimizing `LIKE`, `ORDER BY`, and `GROUP BY` operations.
- For hardware designers, needing a SWAR baseline for string-processing functionality.
- For students studying SIMD/SWAR applications to non-data-parallel operations.

[faq-dbms]: https://en.wikipedia.org/wiki/Database

## Performance

<table>
  <tr>
    <th align="center" width="25%">C</th>
    <th align="center" width="25%">C++</th>
    <th align="center" width="25%">Python</th>
    <th align="center" width="25%">StringZilla</th>
  </tr>
  <!-- Unicode case-folding -->
  <tr>
    <td colspan="4" align="center">Unicode case-folding, expanding characters like <code>ß</code> → <code>ss</code></td>
  </tr>
  <tr>
    <td align="center">⚪</td>
    <td align="center">⚪</td>
    <td align="center">
      <code>.casefold</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.4</b> GB/s
    </td>
    <td align="center">
      <code>sz.utf8_case_fold</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>1.3</b> GB/s
    </td>
  </tr>
  <!-- Unicode case-insensitive search -->
  <tr>
    <td colspan="4" align="center">Unicode case-insensitive substring search</td>
  </tr>
  <tr>
    <td align="center">⚪</td>
    <td align="center">⚪</td>
    <td align="center">
      <code>icu.StringSearch</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.02</b> GB/s
    </td>
    <td align="center">
      <code>utf8_case_insensitive_find</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>3.0</b> GB/s
    </td>
  </tr>
  <!-- Substrings, normal order -->
  <tr>
    <td colspan="4" align="center">find the first occurrence of a random word from text, ≅ 5 bytes long</td>
  </tr>
  <tr>
    <td align="center">
      <code>strstr</code> <sup>1</sup><br/>
      <span style="color:#ABABAB;">x86:</span> <b>7.4</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>2.0</b> GB/s
    </td>
    <td align="center">
      <code>.find</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>2.9</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>1.6</b> GB/s
    </td>
    <td align="center">
      <code>.find</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>1.1</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>0.6</b> GB/s
    </td>
    <td align="center">
      <code>sz_find</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>10.6</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>7.1</b> GB/s
    </td>
  </tr>
  <!-- Substrings, reverse order -->
  <tr>
    <td colspan="4" align="center">find the last occurrence of a random word from text, ≅ 5 bytes long</td>
  </tr>
  <tr>
    <td align="center">⚪</td>
    <td align="center">
      <code>.rfind</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.5</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>0.4</b> GB/s
    </td>
    <td align="center">
      <code>.rfind</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.9</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>0.5</b> GB/s
    </td>
    <td align="center">
      <code>sz_rfind</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>10.8</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>6.7</b> GB/s
    </td>
  </tr>
  <!-- Characters, normal order -->
  <tr>
    <td colspan="4" align="center">split lines separated by <code>\n</code> or <code>\r</code> <sup>2</sup></td>
  </tr>
  <tr>
    <td align="center">
      <code>strcspn</code> <sup>1</sup><br/>
      <span style="color:#ABABAB;">x86:</span> <b>5.42</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>2.19</b> GB/s
    </td>
    <td align="center">
      <code>.find_first_of</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.59</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>0.46</b> GB/s
    </td>
    <td align="center">
      <code>re.finditer</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.06</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>0.02</b> GB/s
    </td>
    <td align="center">
      <code>sz_find_byteset</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>4.08</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>3.22</b> GB/s
    </td>
  </tr>
  <!-- Characters, reverse order -->
  <tr>
    <td colspan="4" align="center">find the last occurrence of any of 6 whitespaces <sup>2</sup></td>
  </tr>
  <tr>
    <td align="center">⚪</td>
    <td align="center">
      <code>.find_last_of</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.25</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>0.25</b> GB/s
    </td>
    <td align="center">⚪</td>
    <td align="center">
      <code>sz_rfind_byteset</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>0.43</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>0.23</b> GB/s
    </td>
  </tr>
  <!-- Random Generation -->
  <tr>
    <td colspan="4" align="center">Random string from a given alphabet, 20 bytes long <sup>3</sup></td>
  </tr>
  <tr>
    <td align="center">
      <code>rand() % n</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>18.0</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>9.4</b> MB/s
    </td>
    <td align="center">
      <code>uniform_int_distribution</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>47.2</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>20.4</b> MB/s
    </td>
    <td align="center">
      <code>join(random.choices(x))</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>13.3</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>5.9</b> MB/s
    </td>
    <td align="center">
      <code>sz_fill_random</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>56.2</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>25.8</b> MB/s
    </td>
  </tr>
  <!-- Mapping characters with lookup table transforms -->
  <tr>
    <td colspan="4" align="center">Mapping characters with lookup table transforms</td>
  </tr>
  <tr>
    <td align="center">⚪</td>
    <td align="center">
      <code>std::transform</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>3.81</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>2.65</b> GB/s
    </td>
    <td align="center">
      <code>str.translate</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>260.0</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>140.0</b> MB/s
    </td>
    <td align="center">
      <code>sz_lookup</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>21.2</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>8.5</b> GB/s
    </td>
  </tr>
  <!-- Sorting -->
  <tr>
    <td colspan="4" align="center">Get sorted order, ≅ 8 million English words <sup>4</sup></td>
  </tr>
  <tr>
    <td align="center">
      <code>qsort_r</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>3.55</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>5.77</b> s
    </td>
    <td align="center">
      <code>std::sort</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>2.79</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>4.02</b> s
    </td>
    <td align="center">
      <code>numpy.argsort</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>7.58</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>13.00</b> s
    </td>
    <td align="center">
      <code>sz_sequence_argsort</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>1.91</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>2.37</b> s
    </td>
  </tr>
  <!-- Edit Distance -->
  <tr>
    <td colspan="4" align="center">Levenshtein edit distance, text lines ≅ 100 bytes long</td>
  </tr>
  <tr>
    <td align="center">⚪</td>
    <td align="center">⚪</td>
    <td align="center">
      via <code>NLTK</code> <sup>5</sup> and <code>CuDF</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>1,615,306</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>1,349,980</b> &centerdot;
      <span style="color:#ABABAB;">cuda:</span> <b>6,532,411,354</b> CUPS
    </td>
    <td align="center">
      <code>szs_levenshtein_distances_t</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>3,434,427,548</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>1,605,340,403</b> &centerdot;
      <span style="color:#ABABAB;">cuda:</span> <b>93,662,026,653</b> CUPS
    </td>
  </tr>
  <!-- Alignment Score -->
  <tr>
    <td colspan="4" align="center">Needleman-Wunsch alignment scores, proteins ≅ 1 K amino acids long</td>
  </tr>
  <tr>
    <td align="center">⚪</td>
    <td align="center">⚪</td>
    <td align="center">
      via <code>biopython</code> <sup>6</sup><br/>
      <span style="color:#ABABAB;">x86:</span> <b>575,981,513</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>436,350,732</b> CUPS
    </td>
    <td align="center">
      <code>szs_needleman_wunsch_scores_t</code><br/>
      <span style="color:#ABABAB;">x86:</span> <b>452,629,942</b> &centerdot;
      <span style="color:#ABABAB;">arm:</span> <b>520,170,239</b> &centerdot;
      <span style="color:#ABABAB;">cuda:</span> <b>9,017,327,818</b> CUPS
    </td>
  </tr>
</table>

Most StringZilla modules ship ready-to-run benchmarks for C, C++, Python, and more.
Grab them from `./scripts`, and see [`CONTRIBUTING.md`](CONTRIBUTING.md) for instructions.
On CPUs that permit misaligned loads, even the 64-bit SWAR baseline outruns both libc and the STL.
For wider head-to-heads against Rust and Python favorites, browse the __[StringWars][stringwars]__ repository.
To inspect collision resistance and distribution shapes for our hashers, see __[HashEvals][hashevals]__.

[stringwars]: https://github.com/ashvardanian/StringWars
[hashevals]: https://github.com/ashvardanian/HashEvals

> Most benchmarks were conducted on a 1 GB English text corpus, with an average word length of 6 characters.
> The code was compiled with GCC 12, using `glibc` v2.35.
> The benchmarks were performed on Arm-based Graviton3 AWS `c7g` instances and `r7iz` Intel Sapphire Rapids.
> Most modern Arm-based 64-bit CPUs will have similar relative speedups.
> Variance within x86 CPUs will be larger.
> For CUDA benchmarks, the Nvidia H100 GPUs were used.
> <sup>1</sup> Unlike other libraries, LibC requires strings to be NULL-terminated.
> <sup>2</sup> Six whitespaces in the ASCII set are: ` \t\n\v\f\r`. Python's and other standard libraries have specialized functions for those.
> <sup>3</sup> All modulo operations were conducted with `uint8_t` to allow compilers more optimization opportunities.
> The C++ STL and StringZilla benchmarks used a 64-bit [Mersenne Twister][faq-mersenne-twister] as the generator.
> For C, C++, and StringZilla, an in-place update of the string was used.
> In Python every string had to be allocated as a new object, which makes it less fair.
> <sup>4</sup> Contrary to the popular opinion, Python's default `sorted` function works faster than the C and C++ standard libraries.
> That holds for large lists or tuples of strings, but fails as soon as you need more complex logic, like sorting dictionaries by a string key, or producing the "sorted order" permutation.
> The latter is very common in database engines and is most similar to `numpy.argsort`.
> The current StringZilla solution can be at least 4x faster without loss of generality.
> <sup>5</sup> Most Python libraries for strings are also implemented in C.
> <sup>6</sup> Unlike the rest of BioPython, the alignment score computation is [implemented in C](https://github.com/biopython/biopython/blob/master/Bio/Align/_pairwisealigner.c).

[faq-mersenne-twister]: https://en.wikipedia.org/wiki/Mersenne_Twister

## Functionality

StringZilla is compatible with most modern CPUs, and provides a broad range of functionality.
It's split into 2 layers:

1. StringZilla: single-header C library and C++ wrapper for high-performance string operations.
2. StringZillas: parallel CPU/GPU backends used for large-batch operations and accelerators.

Having a second C++/CUDA layer greatly simplifies the implementation of similarity scoring and fingerprinting functions, which would otherwise require too much error-prone boilerplate code in pure C.
Both layers are designed to be extremely portable:

- [x] across both little-endian and big-endian architectures.
- [x] across 32-bit and 64-bit hardware architectures.
- [x] across operating systems and compilers.
- [x] across ASCII and UTF-8 encoded inputs.

Not all features are available across all bindings.
Consider contributing if you need a feature that's not yet implemented.

|                                | Maturity |   C   |  C++  | Python | Rust  |  JS   | Swift |  Go   |
| :----------------------------- | :------: | :---: | :---: | :----: | :---: | :---: | :---: | :---: |
| Substring Search               |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ✅   |   ✅   |   ✅   |
| Character Set Search           |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ✅   |   ✅   |   ✅   |
| Sorting & Sequence Operations  |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ⚪   |   ⚪   |   ⚪   |
| Lazy Ranges, Compressed Arrays |    🌳     |   ❌   |   ✅   |   ✅    |   ✅   |   ❌   |   ⚪   |   ⚪   |
| One-Shot & Streaming Hashes    |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ✅   |   ✅   |   ✅   |
| Cryptographic Hashes           |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ✅   |   ✅   |   ✅   |
| Small String Class             |    🧐     |   ✅   |   ✅   |   ❌    |   ⚪   |   ❌   |   ❌   |   ❌   |
| Random String Generation       |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ⚪   |   ⚪   |   ⚪   |
|                                |          |       |       |        |       |       |       |       |
| Unicode Case Folding           |    🧐     |   ✅   |   ✅   |   ✅    |   ⚪   |   ⚪   |   ⚪   |   ⚪   |
| Case-Insensitive UTF-8 Search  |    🚧     |   ✅   |   ✅   |   ✅    |   ⚪   |   ⚪   |   ⚪   |   ⚪   |
| TR29 Word Boundary Detection   |    🚧     |   ✅   |   ✅   |   ⚪    |   ⚪   |   ⚪   |   ⚪   |   ⚪   |
|                                |          |       |       |        |       |       |       |       |
| Parallel Similarity Scoring    |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ⚪   |   ⚪   |   ⚪   |
| Parallel Rolling Fingerprints  |    🌳     |   ✅   |   ✅   |   ✅    |   ✅   |   ⚪   |   ⚪   |   ⚪   |

> 🌳 parts are used in production.
> 🧐 parts are in beta.
> 🚧 parts are under active development, and are likely to break in subsequent releases.
> ✅ are implemented.
> ⚪ are considered.
> ❌ are not intended.

## Quick Start: Python

Python bindings are available on PyPI for Python 3.8+, and can be installed with `pip`.

```bash
pip install stringzilla         # for serial algorithms
pip install stringzillas-cpus   # for parallel multi-CPU backends
pip install stringzillas-cuda   # for parallel Nvidia GPU backend
```

You can immediately check the installed version and the used hardware capabilities with following commands:

```bash
python -c "import stringzilla; print(stringzilla.__version__)"
python -c "import stringzillas; print(stringzillas.__version__)"
python -c "import stringzilla; print(stringzilla.__capabilities__)"     # for serial algorithms
python -c "import stringzillas; print(stringzillas.__capabilities__)"   # for parallel algorithms
```

### Basic Usage

If you've ever used the Python `str`, `bytes`, `bytearray`, or `memoryview` classes, you'll know what to expect.
StringZilla's `Str` class is a hybrid of the above, providing a `str`-like interface to byte arrays.

```python
from stringzilla import Str, File

text_from_str = Str('some-string') # no copies, just a view
text_from_bytes = Str(b'some-array') # no copies, just a view
text_from_file = Str(File('some-file.txt')) # memory-mapped file

import numpy as np
alphabet_array = np.arange(ord("a"), ord("z"), dtype=np.uint8)
text_from_array = Str(memoryview(alphabet_array))
```

The `File` class memory-maps a file from persistent storage without loading its copy into RAM.
The contents of that file would remain immutable, and the mapping can be shared by multiple Python processes simultaneously.
A standard dataset pre-processing use case would be to map a sizable textual dataset like Common Crawl into memory, spawn child processes, and split the job between them.

### Basic Operations

- Length: `len(text) -> int`
- Indexing: `text[42] -> str`
- Slicing: `text[42:46] -> Str`
- Substring check: `'substring' in text -> bool`
- Hashing: `hash(text) -> int`
- String conversion: `str(text) -> str`

### Advanced Operations

```py
import sys

x: bool = text.contains('substring', start=0, end=sys.maxsize)
x: int = text.find('substring', start=0, end=sys.maxsize)
x: int = text.count('substring', start=0, end=sys.maxsize, allowoverlap=False)
x: str = text.decode(encoding='utf-8', errors='strict')
x: Strs = text.split(separator=' ', maxsplit=sys.maxsize, keepseparator=False)
x: Strs = text.rsplit(separator=' ', maxsplit=sys.maxsize, keepseparator=False)
x: Strs = text.splitlines(keeplinebreaks=False, maxsplit=sys.maxsize)
```

It's important to note that the last function's behavior is slightly different from Python's `str.splitlines`.
The [native version][faq-splitlines] matches `\n`, `\r`, `\v` or `\x0b`, `\f` or `\x0c`, `\x1c`, `\x1d`, `\x1e`, `\x85`, `\r\n`, `\u2028`, `\u2029`, including 3x two-byte-long runes.
The StringZilla version matches only `\n`, `\v`, `\f`, `\r`, `\x1c`, `\x1d`, `\x1e`, `\x85`, avoiding two-byte-long runes.

[faq-splitlines]: https://docs.python.org/3/library/stdtypes.html#str.splitlines

### Character Set Operations

Python strings don't natively support character set operations.
This forces people to use regular expressions, which are slow and hard to read.
To avoid the need for `re.finditer`, StringZilla provides the following interfaces:

```py
x: int = text.find_first_of('chars', start=0, end=sys.maxsize)
x: int = text.find_last_of('chars', start=0, end=sys.maxsize)
x: int = text.find_first_not_of('chars', start=0, end=sys.maxsize)
x: int = text.find_last_not_of('chars', start=0, end=sys.maxsize)
x: Strs = text.split_byteset(separator='chars', maxsplit=sys.maxsize, keepseparator=False)
x: Strs = text.rsplit_byteset(separator='chars', maxsplit=sys.maxsize, keepseparator=False)
```

StringZilla also provides string trimming functions and random string generation:

```py
x: str = text.lstrip('chars')  # Strip leading characters
x: str = text.rstrip('chars')  # Strip trailing characters
x: str = text.strip('chars')   # Strip both ends
x: bytes = sz.random(length=100, seed=42, alphabet='ACGT')  # Random string generation
sz.fill_random(buffer, seed=42, alphabet=None)  # Fill mutable buffer with random bytes
```

You can also transform the string using Look-Up Tables (LUTs), mapping it to a different character set.
This would result in a copy - `str` for `str` inputs and `bytes` for other types.

```py
x: str = text.translate('chars', {}, start=0, end=sys.maxsize, inplace=False)
x: bytes = text.translate(b'chars', {}, start=0, end=sys.maxsize, inplace=False)
```

For efficiency reasons, pass the LUT as a string or bytes object, not as a dictionary.
This can be useful in high-throughput applications dealing with binary data, including bioinformatics and image processing.
Here is an example:

```py
import stringzilla as sz
look_up_table = bytes(range(256)) # Identity LUT
image = open("/image/path.jpeg", "rb").read()
sz.translate(image, look_up_table, inplace=True)
```

### Hash

Single-shot and incremental hashing are both supported:

```py
import stringzilla as sz

# One-shot - stable 64-bit output across all platforms!
one = sz.hash(b"Hello, world!", seed=42)

# Incremental updates return itself; digest does not consume state
hasher = sz.Hasher(seed=42)
hasher.update(b"Hello, ").update(b"world!")
streamed = hasher.digest() # or `hexdigest()` for a string
assert one == streamed
```

### SHA-256 Checksums

SHA-256 cryptographic checksums are also available for single-shot and incremental hashing:

```py
import stringzilla as sz

# One-shot SHA-256
digest_bytes = sz.sha256(b"Hello, world!")
assert len(digest_bytes) == 32

# Incremental SHA-256
hasher = sz.Sha256()
hasher.update(b"Hello, ").update(b"world!")
digest_bytes = hasher.digest()
digest_hex = hasher.hexdigest()  # 64 character lowercase hex string

# HMAC-SHA256 for message authentication
mac = sz.hmac_sha256(key=b"secret", message=b"Hello, world!")
```

StringZilla integrates seamlessly with memory-mapped files for efficient large file processing.
The traditional approach with `hashlib`:

```python
import hashlib

with open("xlsum.csv", "rb") as streamed_file:
    hasher = hashlib.sha256()
    while chunk := streamed_file.read(4096):
        hasher.update(chunk)
    checksum = hasher.hexdigest()
```

Can be simplified with StringZilla:

```python
from stringzilla import Sha256, File

mapped_file = File("xlsum.csv")
checksum = Sha256().update(mapped_file).hexdigest()
```

Both output the same digest: `7278165ce01a4ac1e8806c97f32feae908036ca3d910f5177d2cf375e20aeae1`.
OpenSSL (powering `hashlib`) has faster Assembly kernels, but StringZilla avoids file I/O overhead with memory mapping and skips Python's abstraction layers:

- OpenSSL-backed `hashlib.sha256`: 12.6s
- StringZilla end-to-end: 4.0s — __3× faster!__

### Unicode Case-Folding and Case-Insensitive Search

StringZilla implements both Unicode Case Folding and Case-Insensitive UTF-8 Search.
Unlike most libraries only capable of lower-casing ASCII-represented English alphabet, StringZilla covers over 1M+ codepoints.
The case-folding API expects the output buffer to be at least 3× larger than the input, to accommodate for the worst-case character expansions scenarios.

```python
import stringzilla as sz

sz.utf8_case_fold('HELLO')      # b'hello'
sz.utf8_case_fold('Straße')     # b'strasse' — ß (1 char) expands to "ss" (2 chars)
sz.utf8_case_fold('eﬃcient')    # b'efficient' — ﬃ ligature (1 char) expands to "ffi" (3 chars)
```

The case-insensitive search returns the byte offset of the match, handling expansions correctly.

```python
import stringzilla as sz

sz.utf8_case_insensitive_find('Der große Hund', 'GROSSE')   # 4 — finds "große" at codepoint 4
sz.utf8_case_insensitive_find('Straße', 'STRASSE')          # 0 — ß matches "SS"
sz.utf8_case_insensitive_find('eﬃcient', 'EFFICIENT')       # 0 — ﬃ ligature matches "FFI"

# Iterator for finding ALL matches
haystack = 'Straße STRASSE strasse'
for match in sz.utf8_case_insensitive_find_iter(haystack, 'strasse'):
    print(match, match.offset_within(haystack))  # Yields: 'Straße', 'STRASSE', 'strasse'

# With overlapping matches
list(sz.utf8_case_insensitive_find_iter('aaaa', 'aa'))  # ['aa', 'aa'] — 2 non-overlapping
list(sz.utf8_case_insensitive_find_iter('aaaa', 'aa', include_overlapping=True))  # 3 matches
```

### Collection-Level Operations

Once split into a `Strs` object, you can sort, shuffle, and reorganize the slices with minimal memory footprint.
If all the chunks are located in consecutive memory regions, the memory overhead can be as low as 4 bytes per chunk.

```python
lines: Strs = text.split(separator='\n') # 4 bytes per line overhead for under 4 GB of text
batch: Strs = lines.sample(seed=42) # 10x faster than `random.choices`
lines_shuffled: Strs = lines.shuffled(seed=42) # or shuffle all lines and shard with slices
lines_sorted: Strs = lines.sorted() # returns a new Strs in sorted order
order: tuple = lines.argsort() # similar to `numpy.argsort`
```

Working on [RedPajama][redpajama], addressing 20 billion annotated English documents, one will need only 160 GB of RAM instead of terabytes.
Once loaded, the data will be memory-mapped, and can be reused between multiple Python processes without copies.
And of course, you can use slices to navigate the dataset and shard it between multiple workers.

```python
lines[::3] # every third line
lines[1::1] # every odd line
lines[:-100:-1] # last 100 lines in reverse order
```

[redpajama]: https://github.com/togethercomputer/RedPajama-Data

### Iterators and Memory Efficiency

Python's operations like `split()` and `readlines()` immediately materialize a `list` of copied parts.
This can be very memory-inefficient for large datasets.
StringZilla saves a lot of memory by viewing existing memory regions as substrings, but even more memory can be saved by using lazily evaluated iterators.

```py
x: SplitIterator[Str] = text.split_iter(separator=' ', keepseparator=False)
x: SplitIterator[Str] = text.rsplit_iter(separator=' ', keepseparator=False)
x: SplitIterator[Str] = text.split_byteset_iter(separator='chars', keepseparator=False)
x: SplitIterator[Str] = text.rsplit_byteset_iter(separator='chars', keepseparator=False)
```

StringZilla can easily be 10x more memory efficient than native Python classes for tokenization.
With lazy operations, it practically becomes free.

```py
import stringzilla as sz
%load_ext memory_profiler

text = open("enwik9.txt", "r").read() # 1 GB, mean word length 7.73 bytes
%memit text.split() # increment: 8670.12 MiB (152 ms)
%memit sz.split(text) # increment: 530.75 MiB (25 ms)
%memit sum(1 for _ in sz.split_iter(text)) # increment: 0.00 MiB
```

### Low-Level Python API

Aside from calling the methods on the `Str` and `Strs` classes, you can also call the global functions directly on `str` and `bytes` instances.
Assuming StringZilla CPython bindings are implemented [without any intermediate tools like SWIG or PyBind](https://ashvardanian.com/posts/pybind11-cpython-tutorial/), the call latency should be similar to native classes.

```py
import stringzilla as sz

contains: bool = sz.contains("haystack", "needle", start=0, end=sys.maxsize)
offset: int = sz.find("haystack", "needle", start=0, end=sys.maxsize)
count: int = sz.count("haystack", "needle", start=0, end=sys.maxsize, allowoverlap=False)
```

### Similarity Scores

StringZilla exposes high-performance, batch-oriented similarity via the `stringzillas` module. 
Use `DeviceScope` to pick hardware and optionally limit capabilities per engine.

```py
import stringzilla as sz
import stringzillas as szs

cpu_scope = szs.DeviceScope(cpu_cores=4)    # force CPU-only
gpu_scope = szs.DeviceScope(gpu_device=0)   # pick GPU 0 if available

strings_a = sz.Strs(["kitten", "flaw"])
strings_b = sz.Strs(["sitting", "lawn"])

strings_a = szs.to_device(strings_a) # optional ahead of time transfer
strings_b = szs.to_device(strings_b) # optional ahead of time transfer

engine = szs.LevenshteinDistances(
    match=0, mismatch=2,        # costs don't have to be 1
    open=3, extend=1,           # may be different in Bio
    capabilities=("serial",)    # avoid SIMD 🤭
)
distances = engine(strings_a, strings_b, device=cpu_scope)
assert int(distances[0]) == 3 and int(distances[1]) == 2
```

Note, that this computes byte-level distances.
For UTF-8 codepoints, use a different engine class:

```py
strings_a = sz.Strs(["café", "αβγδ"])
strings_b = sz.Strs(["cafe", "αγδ"])
engine = szs.LevenshteinDistancesUTF8(capabilities=("serial",))
distances = engine(strings_a, strings_b, device=cpu_scope)
assert int(distances[0]) == 1 and int(distances[1]) == 1
```

For alignment scoring provide a 256×256 substitution matrix using NumPy:

```py
import numpy as np
import stringzilla as sz
import stringzillas as szs

substitution_matrix = np.zeros((256, 256), dtype=np.int8)
substitution_matrix.fill(-1)                # mismatch score
np.fill_diagonal(substitution_matrix, 0)    # match score

engine = szs.NeedlemanWunsch(substitution_matrix=substitution_matrix, open=1, extend=1)
scores = engine(strings_a, strings_b, device=cpu_scope)
```

Several Python libraries provide edit distance computation.
Most are implemented in C but may be slower than StringZilla on large inputs.
For proteins ~10k chars, 100 pairs:

- [JellyFish](https://github.com/jamesturk/jellyfish): 62.3s
- [EditDistance](https://github.com/roy-ht/editdistance): 32.9s
- StringZilla: __0.8s__

Using the same proteins for Needleman-Wunsch alignment scores:

- [BioPython](https://github.com/biopython/biopython): 25.8s
- StringZilla: __7.8s__

<details>
  <summary><b>§ Example converting from BioPython to StringZilla.</b></summary>

```py
import numpy as np
from Bio import Align
from Bio.Align import substitution_matrices

aligner = Align.PairwiseAligner()
aligner.substitution_matrix = substitution_matrices.load("BLOSUM62")
aligner.open_gap_score = 1
aligner.extend_gap_score = 1

# Convert the matrix to NumPy
subs_packed = np.array(aligner.substitution_matrix).astype(np.int8)
subs_reconstructed = np.zeros((256, 256), dtype=np.int8)

# Initialize all banned characters to a the largest possible penalty
subs_reconstructed.fill(127)
for packed_row, packed_row_aminoacid in enumerate(aligner.substitution_matrix.alphabet):
    for packed_column, packed_column_aminoacid in enumerate(aligner.substitution_matrix.alphabet):
        reconstructed_row = ord(packed_row_aminoacid)
        reconstructed_column = ord(packed_column_aminoacid)
        subs_reconstructed[reconstructed_row, reconstructed_column] = subs_packed[packed_row, packed_column]

# Let's pick two examples of tripeptides (made of 3 amino acids)
glutathione = "ECG" # Need to rebuild human tissue?
thyrotropin_releasing_hormone = "QHP" # Or to regulate your metabolism?

import stringzillas as szs
engine = szs.NeedlemanWunsch(substitution_matrix=subs_reconstructed, open=1, extend=1)
score = int(engine(sz.Strs([glutathione]), sz.Strs([thyrotropin_releasing_hormone]))[0])
assert score == aligner.score(glutathione, thyrotropin_releasing_hormone) # Equal to 6
```

</details>

### Rolling Fingerprints

MinHashing is a common technique for Information Retrieval, producing compact representations of large documents.
For $D$ hash-functions and a text of length $L$, in the worst case it involves computing $O(D \cdot L)$ hashes.

```py
import numpy as np
import stringzilla as sz
import stringzillas as szs

texts = sz.Strs([
    "quick brown fox jumps over the lazy dog",
    "quick brown fox jumped over a very lazy dog",
])

cpu = szs.DeviceScope(cpu_cores=4)
ndim = 1024
window_widths = np.array([4, 6, 8, 10], dtype=np.uint64)
engine = szs.Fingerprints(
    ndim=ndim,
    window_widths=window_widths,    # optional
    alphabet_size=256,              # default for byte strings
    capabilities=("serial",),       # defaults to all, can also pass a `DeviceScope`
)

hashes, counts = engine(texts, device=cpu)
assert hashes.shape == (len(texts), ndim)
assert counts.shape == (len(texts), ndim)
assert hashes.dtype == np.uint32 and counts.dtype == np.uint32
```

### Serialization

#### Filesystem

Similar to how `File` can be used to read a large file, other interfaces can be used to dump strings to disk faster.
The `Str` class has `write_to` to write the string to a file, and `offset_within` to obtain integer offsets of substring view in larger string for navigation.

```py
web_archive = Str("<html>...</html><html>...</html>")
_, end_tag, next_doc = web_archive.partition("</html>") # or use `find`
next_doc_offset = next_doc.offset_within(web_archive)
web_archive.write_to("next_doc.html") # no GIL, no copies, just a view
```

#### PyArrow

A `Str` is easy to cast to [PyArrow](https://arrow.apache.org/docs/python/arrays.html#string-and-binary-types) buffers.

```py
from pyarrow import foreign_buffer
from stringzilla import Strs

strs = Strs(["alpha", "beta", "gamma"])
arrow = foreign_buffer(strs.address, strs.nbytes, strs)
```

And only slightly harder to convert in reverse direction:

```py
arr = pa.Array.from_buffers(
    pa.large_string() if strs.offsets_are_large else pa.string(),
    len(strs),
    [None,
     pa.foreign_buffer(strs.offsets_address, strs.offsets_nbytes, strs),
     pa.foreign_buffer(strs.tape_address, strs.tape_nbytes, strs)],
)
```

That means you can convert `Str` to `pyarrow.Buffer` and `Strs` to `pyarrow.Array` without extra copies.
For more details on the tape-like layouts, refer to the [StringTape](https://github.com/ashvardanian/StringTape) repository.

## Quick Start: C/C++

The C library is header-only, so you can just copy the `stringzilla.h` header into your project.
Same applies to C++, where you would copy the `stringzilla.hpp` header.
Alternatively, add it as a submodule, and include it in your build system.

```sh
git submodule add https://github.com/ashvardanian/StringZilla.git external/stringzilla
git submodule update --init --recursive
```

Or using a pure CMake approach:

```cmake
FetchContent_Declare(
    stringzilla
    GIT_REPOSITORY https://github.com/ashvardanian/StringZilla.git
    GIT_TAG main  # or specify a version tag
)
FetchContent_MakeAvailable(stringzilla)
```

Last, but not the least, you can also install it as a library, and link against it.
This approach is worse for inlining, but brings [dynamic runtime dispatch](#dynamic-dispatch) for the most advanced CPU features.

### Basic Usage with C 99 and Newer

There is a stable C 99 interface, where all function names are prefixed with `sz_`.
Most interfaces are well documented, and come with self-explanatory names and examples.
In some cases, hardware specific overloads are available, like `sz_find_skylake` or `sz_find_neon`.
Both are companions of the `sz_find`, first for x86 CPUs with AVX-512 support, and second for Arm NEON-capable CPUs.

```c
#include <stringzilla/stringzilla.h>

// Initialize your haystack and needle
sz_string_view_t haystack = {your_text, your_text_length};
sz_string_view_t needle = {your_subtext, your_subtext_length};

// Perform string-level operations auto-picking the backend or dispatching manually
sz_cptr_t ptr = sz_find(haystack.start, haystack.length, needle.start, needle.length);
sz_size_t substring_position = ptr ? (sz_size_t)(ptr - haystack.start) : SZ_SIZE_MAX; // SZ_SIZE_MAX if not found

// Backend-specific variants return pointers as well
sz_cptr_t ptr = sz_find_skylake(haystack.start, haystack.length, needle.start, needle.length);
sz_cptr_t ptr = sz_find_haswell(haystack.start, haystack.length, needle.start, needle.length);
sz_cptr_t ptr = sz_find_westmere(haystack.start, haystack.length, needle.start, needle.length);
sz_cptr_t ptr = sz_find_neon(haystack.start, haystack.length, needle.start, needle.length);

// Hash strings at once
sz_u64_t hash = sz_hash(haystack.start, haystack.length, 42);    // 42 is the seed
sz_u64_t checksum = sz_bytesum(haystack.start, haystack.length); // or accumulate byte values

// Hash strings incrementally with "init", "update", and "digest":
sz_hash_state_t state;
sz_hash_state_init(&state, 42);
sz_hash_state_update(&state, haystack.start, 1);                        // first char
sz