ashvardanian/StringZilla v3.0.0

StringZilla v3: with bindings for C++, Rust, and Swift, AVX-512 acceleration, Levenshtein distances & Needleman-Wunsch scores, faster sorting and rolling fingerprints

on GitHub

This is the largest StringZilla release to date 🥳
It includes, among other things:

• 🤝 Mostly STL-compatible sz::string and sz::string_view with a superset of C++20 features back-ported to C++11.
• 🦥 Lazily-evaluated ranges, avoiding memory allocations for bulk-search and split operations.
• ⏪ Character-set search and symmetric reverse-order APIs for all search interfaces.
• 🧬 String-similarity measures, like the Levenshtein distance and Needleman-Wunsch scores for bioinformatics.
• 🏎️ AVX-512 backend, faster sorting, and a bunch of other performance improvements.
• 🔀 Runtime-dispatch system to select the fastest SIMD implementation from a precompiled library.
• 🛠️ Improved stability and test coverage, thanks to @kmapb!
• 🍏 First bindings for Swift, thanks to @vmanot!
• 🦀 First bindings for Rust, thanks to @michaelgrigoryan25!

...and of course, a rant about STL, and a new meme. Check out the README.md for a much longer list of features, benchmarks, algorithmic design decisions, and open questions 🤗

Throughput Benchmarks

Supported Functionality

🌳 parts are used in production.
🧐 parts are in beta.
🚧 parts are under active development, and are likely to break in subsequent releases.

Quick Start: Python 🐍

1. Install via pip: pip install stringzilla
2. Import the classes you need: from stringzilla import Str, Strs, File

Basic Usage

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

from stringzilla import Str, File

text_from_str = Str('some-string')
text_from_file = Str(File('some-file.txt'))

The File class memory-maps a file from persistent memory 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 sizeable 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
• String conversion: str(text) -> str
• Substring check: 'substring' in text -> bool
• Hashing: hash(text) -> int

Advanced Operations

• text.contains('substring', start=0, end=9223372036854775807) -> bool
• text.find('substring', start=0, end=9223372036854775807) -> int
• text.count('substring', start=0, end=9223372036854775807, allowoverlap=False) -> int
• text.splitlines(keeplinebreaks=False, separator='\n') -> Strs
• text.split(separator=' ', maxsplit=9223372036854775807, keepseparator=False) -> Strs

Collection-Level Operations

Once split into a Strs object, you can sort, shuffle, and reorganize the slices.

lines: Strs = text.split(separator='\n') # 4 bytes per line overhead for under 4 GB of text
lines.sort() # explodes to 16 bytes per line overhead for any length text
lines.shuffle(seed=42) # reproducing dataset shuffling with a seed

Assuming superior search speed splitting should also work 3x faster than with native Python strings.
Need copies?

sorted_copy: Strs = lines.sorted()
shuffled_copy: Strs = lines.shuffled(seed=42)

Those collections of Strs are designed to keep the memory consumption low.
If all the chunks are located in consecutive memory regions, the memory overhead can be as low as 4 bytes per chunk.
That's designed to handle very large datasets, like RedPajama.
To address all 20 Billion annotated english documents in it, one will need only 160 GB of RAM instead of Terabytes.

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, the call latency should be similar to native classes.

import stringzilla as sz

contains: bool = sz.contains("haystack", "needle", start=0, end=9223372036854775807)
offset: int = sz.find("haystack", "needle", start=0, end=9223372036854775807)
count: int = sz.count("haystack", "needle", start=0, end=9223372036854775807, allowoverlap=False)

Edit Distances

edit_distance: int = sz.edit_distance("needle", "nidl")

Several Python libraries provide edit distance computation.
Most of them are implemented in C, but are rarely as fast as StringZilla.
Computing pairwise distances between words in an English text you may expect following results:

• EditDistance: 28.7s
• JellyFish: 26.8s
• Levenshtein: 8.6s
• StringZilla: 4.2s

Moreover, you can pass custom substitution matrices to compute the Needleman-Wunsch alignment scores.
That task is very common in bioinformatics and computational biology.
It's natively supported in BioPython, and its BLOSUM matrices can be converted to StringZilla's format.
Alternatively, you can construct an arbitrary 256 by 256 cost matrix using NumPy.
Depending on arguments, the result may be equal to the negative Levenshtein distance.

import numpy as np
import stringzilla as sz

costs = np.zeros((256, 256), dtype=np.int8)
costs.fill(-1)
np.fill_diagonal(costs, 0)

assert sz.alignment_score("first", "second", substitution_matrix=costs, gap_score=-1) == -sz.edit_distance(a, b)

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 for of tri-peptides (made of 3 aminoacids)
glutathione = "ECG" # Need to rebuild human tissue?
thyrotropin_releasing_hormone = "QHP" # Or to regulate your metabolism?

assert sz.alignment_score(
    glutathione,
    thyrotropin_releasing_hormone, 
    substitution_matrix=subs_reconstructed, 
    gap_score=1) == aligner.score(glutathione, thyrotropin_releasing_hormone) # Equal to 6

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.

git submodule add https://github.com/ashvardanian/stringzilla.git

Or using a pure CMake approach:

FetchContent_Declare(stringzilla GIT_REPOSITORY https://github.com/ashvardanian/stringzilla.git)
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 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_avx512 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.

#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
sz_size_t substring_position = sz_find(haystack.start, haystack.length, needle.start, needle.length);
sz_size_t substring_position = sz_find_avx512(haystack.start, haystack.length, needle.start, needle.length);
sz_size_t substring_position = sz_find_neon(haystack.start, haystack.length, needle.start, needle.length);

// Hash strings
sz_u64_t hash = sz_hash(haystack.start, haystack.length);

// Perform collection level operations
sz_sequence_t array = {your_order, your_count, your_get_start, your_get_length, your_handle};
sz_sort(&array, &your_config);

Basic Usage with C++ 11 and Newer

There is a stable C++ 11 interface available in the ashvardanian::stringzilla namespace.
It comes with two STL-like classes: string_view and string.
The first is a non-owning view of a string, and the second is a mutable string with a Small String Optimization.

#include <stringzilla/stringzilla.hpp>

namespace sz = ashvardanian::stringzilla;

sz::string haystack = "some string";
sz::string_view needle = sz::string_view(haystack).substr(0, 4);

auto substring_position = haystack.find(needle); // Or `rfind`
auto hash = std::hash<sz::string_view>(haystack); // Compatible with STL's `std::hash`

haystack.end() - haystack.begin() == haystack.size(); // Or `rbegin`, `rend`
haystack.find_first_of(" \w\t") == 4; // Or `find_last_of`, `find_first_not_of`, `find_last_not_of`
haystack.starts_with(needle) == true; // Or `ends_with`
haystack.remove_prefix(needle.size()); // Why is this operation in-place?!
haystack.contains(needle) == true; // STL has this only from C++ 23 onwards
haystack.compare(needle) == 1; // Or `haystack <=> needle` in C++ 20 and beyond

StringZilla also provides string literals for automatic type resolution, similar to STL:

using sz::literals::operator""_sz;
using std::literals::operator""sv;

auto a = "some string"; // char const *
auto b = "some string"sv; // std::string_view
auto b = "some string"_sz; // sz::string_view

Memory Ownership and Small String Optimization

Most operations in StringZilla don't assume any memory ownership.
But in addition to the read-only search-like operations StringZilla provides a minimalistic C and C++ implementations for a memory owning string "class".
Like other efficient string implementations, it uses the Small String Optimization (SSO) to avoid heap allocations for short strings.

typedef union sz_string_t {
    struct internal {
        sz_ptr_t start;
        sz_u8_t length;
        char chars[SZ_STRING_INTERNAL_SPACE]; /// Ends with a null-terminator.
    } internal;

    struct external {
        sz_ptr_t start;
        sz_size_t length;        
        sz_size_t space; /// The length of the heap-allocated buffer.
        sz_size_t padding;
    } external;

} sz_string_t;

As one can see, a short string can be kept on the stack, if it fits within internal.chars array.
Before 2015 GCC string implementation was just 8 bytes, and could only fit 7 characters.
Different STL implementations today have different thresholds for the Small String Optimization.
Similar to GCC, StringZilla is 32 bytes in size, and similar to Clang it can fit 22 characters on stack.
Our layout might be preferential, if you want to avoid branches.
If you use a different compiler, you may want to check it's SSO buffer size with a simple Gist.

This design has been since ported to many high-level programming languages.
Swift, for example, can store 15 bytes in the String instance itself.
StringZilla implements SSO at the C level, providing the sz_string_t union and a simple API for primary operations.

sz_memory_allocator_t allocator;
sz_string_t string;

// Init and make sure we are on stack
sz_string_init(&string);
sz_string_is_on_stack(&string); // == sz_true_k

// Optionally pre-allocate space on the heap for future insertions.
sz_string_grow(&string, 100, &allocator); // == sz_true_k

// Append, erase, insert into the string.
sz_string_append(&string, "_Hello_", 7, &allocator); // == sz_true_k
sz_string_append(&string, "world", 5, &allocator); // == sz_true_k
sz_string_erase(&string, 0, 1);

// Unpacking & introspection.
sz_ptr_t string_start;
sz_size_t string_length;
sz_size_t string_space;
sz_bool_t string_is_external;
sz_string_unpack(string, &string_start, &string_length, &string_space, &string_is_external);
sz_equal(string_start, "Hello_world", 11); // == sz_true_k

// Reclaim some memory.
sz_string_shrink_to_fit(&string, &allocator); // == sz_true_k
sz_string_free(&string, &allocator);

Unlike the conventional C strings, the sz_string_t is allowed to contain null characters.
To safely print those, pass the string_length to printf as well.

printf("%.*s\n", (int)string_length, string_start);

What's Wrong with the C++ Standard Library?

StringZilla is designed to be a drop-in replacement for the C++ Standard Templates Library.
That said, some of the design decisions of STL strings are highly controversial, error-prone, and expensive.
Most notably:

1. Argument order for replace, insert, erase and similar functions is impossible to guess.
2. Bounds-checking exceptions for substr-like functions are only thrown for one side of the range.
3. Returning string copies in substr-like functions results in absurd volume of allocations.
4. Incremental construction via push_back-like functions goes through too many branches.
5. Inconsistency between string and string_view methods, like the lack of remove_prefix and remove_suffix.

Check the following set of asserts validating the std::string specification.
It's not realistic to expect the average developer to remember the 14 overloads of std::string::replace.

using str = std::string;

assert(str("hello world").substr(6) == "world");
assert(str("hello world").substr(6, 100) == "world"); // 106 is beyond the length of the string, but its OK
assert_throws(str("hello world").substr(100), std::out_of_range);   // 100 is beyond the length of the string
assert_throws(str("hello world").substr(20, 5), std::out_of_range); // 20 is beyond the length of the string
assert_throws(str("hello world").substr(-1, 5), std::out_of_range); // -1 casts to unsigned without any warnings...
assert(str("hello world").substr(0, -1) == "hello world");          // -1 casts to unsigned without any warnings...

assert(str("hello").replace(1, 2, "123") == "h123lo");
assert(str("hello").replace(1, 2, str("123"), 1) == "h23lo");
assert(str("hello").replace(1, 2, "123", 1) == "h1lo");
assert(str("hello").replace(1, 2, "123", 1, 1) == "h2lo");
assert(str("hello").replace(1, 2, str("123"), 1, 1) == "h2lo");
assert(str("hello").replace(1, 2, 3, 'a') == "haaalo");
assert(str("hello").replace(1, 2, {'a', 'b'}) == "hablo");

To avoid those issues, StringZilla provides an alternative consistent interface.
It supports signed arguments, and doesn't have more than 3 arguments per function or
The standard API and our alternative can be conditionally disabled with SZ_SAFETY_OVER_COMPATIBILITY=1.
When it's enabled, the subjectively risky overloads from the Standard will be disabled.

using str = sz::string;

str("a:b").front(1) == "a"; // no checks, unlike `substr`
str("a:b").back(-1) == "b"; // accepting negative indices
str("a:b").sub(1, -1) == ":"; // similar to Python's `"a:b"[1:-1]`
str("a:b").sub(-2, -1) == ":"; // similar to Python's `"a:b"[-2:-1]`
str("a:b").sub(-2, 1) == ""; // similar to Python's `"a:b"[-2:1]`
"a:b"_sz[{-2, -1}] == ":"; // works on views and overloads `operator[]`

Assuming StringZilla is a header-only library you can use the full API in some translation units and gradually transition to safer restricted API in others.
Bonus - all the bound checking is branchless, so it has a constant cost and won't hurt your branch predictor.

Beyond the C++ Standard Library - Learning from Python

Python is arguably the most popular programming language for data science.
In part, that's due to the simplicity of its standard interfaces.
StringZilla brings some of that functionality to C++.

• Content checks: isalnum, isalpha, isascii, isdigit, islower, isspace, isupper.
• Trimming character sets: lstrip, rstrip, strip.
• Trimming string matches: remove_prefix, remove_suffix.
• Ranges of search results: splitlines, split, rsplit.
• Number of non-overlapping substring matches: count.
• Partitioning: partition, rpartition.

For example, when parsing documents, it is often useful to split it into substrings.
Most often, after that, you would compute the length of the skipped part, the offset and the length of the remaining part.
This results in a lot of pointer arithmetic and is error-prone.
StringZilla provides a convenient partition function, which returns a tuple of three string views, making the code cleaner.

auto parts = haystack.partition(':'); // Matching a character
auto [before, match, after] = haystack.partition(':'); // Structure unpacking
auto [before, match, after] = haystack.partition(char_set(":;")); // Character-set argument
auto [before, match, after] = haystack.partition(" : "); // String argument
auto [before, match, after] = haystack.rpartition(sz::whitespaces); // Split around the last whitespace

Combining those with the split function, one can easily parse a CSV file or HTTP headers.

for (auto line : haystack.split("\r\n")) {
    auto [key, _, value] = line.partition(':');
    headers[key.strip()] = value.strip();
}

Some other extensions are not present in the Python standard library either.
Let's go through the C++ functionality category by category.

• Splits and Ranges.
• Concatenating Strings without Allocations.
• Random Generation.
• Edit Distances and Fuzzy Search.

Some of the StringZilla interfaces are not available even Python's native str class.
Here is a sneak peek of the most useful ones.

text.hash(); // -> 64 bit unsigned integer 
text.ssize(); // -> 64 bit signed length to avoid `static_cast<std::ssize_t>(text.size())`
text.contains_only(" \w\t"); // == text.find_first_not_of(char_set(" \w\t")) == npos;
text.contains(sz::whitespaces); // == text.find(char_set(sz::whitespaces)) != npos;

// Simpler slicing than `substr`
text.front(10); // -> sz::string_view
text.back(10); // -> sz::string_view

// Safe variants, which clamp the range into the string bounds
using sz::string::cap;
text.front(10, cap) == text.front(std::min(10, text.size()));
text.back(10, cap) == text.back(std::min(10, text.size()));

// Character set filtering
text.lstrip(sz::whitespaces).rstrip(sz::newlines); // like Python
text.front(sz::whitespaces); // all leading whitespaces
text.back(sz::digits); // all numerical symbols forming the suffix

// Incremental construction
using sz::string::unchecked;
text.push_back('x'); // no surprises here
text.push_back('x', unchecked); // no bounds checking, Rust style
text.try_push_back('x'); // returns `false` if the string is full and the allocation failed

sz::concatenate(text, "@", domain, ".", tld); // No allocations

Splits and Ranges

One of the most common use cases is to split a string into a collection of substrings.
Which would often result in StackOverflow lookups and snippets like the one below.

std::vector<std::string> lines = split(haystack, "\r\n"); // string delimiter
std::vector<std::string> words = split(lines, ' '); // character delimiter

Those allocate memory for each string and the temporary vectors.
Each allocation can be orders of magnitude more expensive, than even serial for-loop over characters.
To avoid those, StringZilla provides lazily-evaluated ranges, compatible with the Range-v3 library.

for (auto line : haystack.split("\r\n"))
    for (auto word : line.split(char_set(" \w\t.,;:!?")))
        std::cout << word << std::endl;

Each of those is available in reverse order as well.
It also allows interleaving matches, if you want both inclusions of xx in xxx.
Debugging pointer offsets is not a pleasant exercise, so keep the following functions in mind.

• haystack.[r]find_all(needle, interleaving)
• haystack.[r]find_all(char_set(""))
• haystack.[r]split(needle)
• haystack.[r]split(char_set(""))

For $N$ matches the split functions will report $N+1$ matches, potentially including empty strings.
Ranges have a few convenience methods as well:

range.size(); // -> std::size_t
range.empty(); // -> bool
range.template to<std::set<std::sting>>(); 
range.template to<std::vector<std::sting_view>>(); 

Concatenating Strings without Allocations

Another common string operation is concatenation.
The STL provides std::string::operator+ and std::string::append, but those are not very efficient, if multiple invocations are performed.

std::string name, domain, tld;
auto email = name + "@" + domain + "." + tld; // 4 allocations

The efficient approach would be to pre-allocate the memory and copy the strings into it.

std::string email;
email.reserve(name.size() + domain.size() + tld.size() + 2);
email.append(name), email.append("@"), email.append(domain), email.append("."), email.append(tld);

That's mouthful and error-prone.
StringZilla provides a more convenient concatenate function, which takes a variadic number of arguments.
It also overrides the operator| to concatenate strings lazily, without any allocations.

auto email = sz::concatenate(name, "@", domain, ".", tld);   // 0 allocations
auto email = name | "@" | domain | "." | tld;                // 0 allocations
sz::string email = name | "@" | domain | "." | tld;          // 1 allocations

Random Generation

Software developers often need to generate random strings for testing purposes.
The STL provides std::generate and std::random_device, that can be used with StringZilla.

sz::string random_string(std::size_t length, char const *alphabet, std::size_t cardinality) {
    sz::string result(length, '\0');
    static std::random_device seed_source; // Too expensive to construct every time
    std::mt19937 generator(seed_source());
    std::uniform_int_distribution<std::size_t> distribution(1, cardinality);
    std::generate(result.begin(), result.end(), [&]() { return alphabet[distribution(generator)]; });
    return result;
}

Mouthful and slow.
StringZilla provides a C native method - sz_generate and a convenient C++ wrapper - sz::generate.
Similar to Python it also defines the commonly used character sets.

auto protein = sz::string::random(300, "ARNDCQEGHILKMFPSTWYV"); // static method
auto dna = sz::basic_string<custom_allocator>::random(3_000_000_000, "ACGT");

dna.randomize("ACGT"); // `noexcept` pre-allocated version
dna.randomize(&std::rand, "ACGT"); // pass any generator, like `std::mt19937`

char uuid[36];
sz::randomize(sz::string_span(uuid, 36), "0123456789abcdef-"); // Overwrite any buffer

Levenshtein Edit Distance and Alignment Scores

sz::edit_distance(first, second[, upper_bound[, allocator]]) -> std::size_t;

std::int8_t costs[256][256]; // Substitution costs matrix
sz::alignment_score(first, second, costs[, gap_score[, allocator]) -> std::ptrdiff_t;

Sorting in C and C++

LibC provides qsort and STL provides std::sort.
Both have their quarks.
The LibC standard has no way to pass a context to the comparison function, that's only possible with platform-specific extensions.
Those have different arguments order on every OS.

// Linux: https://linux.die.net/man/3/qsort_r
void qsort_r(void *elements, size_t count, size_t element_width, 
    int (*compare)(void const *left, void const *right, void *context),
    void *context);
// MacOS and FreeBSD: https://developer.apple.com/library/archive/documentation/System/Conceptual/ManPages_iPhoneOS/man3/qsort_r.3.html
void qsort_r(void *elements, size_t count, size_t element_width, 
    void *context,
    int (*compare)(void *context, void const *left, void const *right));
// Windows conflicts with ISO `qsort_s`: https://learn.microsoft.com/en-us/cpp/c-runtime-library/reference/qsort-s?view=msvc-170
void qsort_s(id *elements, size_t count, size_t element_width, 
    int (*compare)(void *context, void const *left, void const *right),
    void *context);

C++ generic algorithm is not perfect either.
There is no guarantee in the standard that std::sort won't allocate any memory.
If you are running on embedded, in real-time or on 100+ CPU cores per node, you may want to avoid that.
StringZilla doesn't solve the general case, but hopes to improve the performance for strings.
Use sz_sort, or the high-level sz::sorted_order, which can be used sort any collection of elements convertible to sz::string_view.

std::vector<std::string> data({"c", "b", "a"});
std::vector<std::size_t> order = sz::sorted_order(data); //< Simple shortcut

// Or, taking care of memory allocation:
sz::sorted_order(data.begin(), data.end(), order.data(), [](auto const &x) -> sz::string_view { return x; });

Standard C++ Containers with String Keys

The C++ Standard Templates Library provides several associative containers, often used with string keys.

std::map<std::string, int, std::less<std::string>> sorted_words;
std::unordered_map<std::string, int, std::hash<std::string>, std::equal_to<std::string>> words;

The performance of those containers is often limited by the performance of the string keys, especially on reads.
StringZilla can be used to accelerate containers with std::string keys, by overriding the default comparator and hash functions.

std::map<std::string, int, sz::string_view_less> sorted_words;
std::unordered_map<std::string, int, sz::string_view_hash, sz::string_view_equal_to> words;

Alternatively, a better approach would be to use the sz::string class as a key.
The right hash function and comparator would be automatically selected and the performance gains would be more noticeable if the keys are short.

std::map<sz::string, int> sorted_words;
std::unordered_map<sz::string, int> words;

Compilation Settings and Debugging

SZ_DEBUG:

For maximal performance, the C library does not perform any bounds checking in Release builds.
In C++, bounds checking happens only in places where the STL std::string would do it.
If you want to enable more aggressive bounds-checking, define SZ_DEBUG before including the header.
If not explicitly set, it will be inferred from the build type.

SZ_USE_X86_AVX512, SZ_USE_X86_AVX2, SZ_USE_ARM_NEON:

One can explicitly disable certain families of SIMD instructions for compatibility purposes.
Default values are inferred at compile time.

SZ_DYNAMIC_DISPATCH:

By default, StringZilla is a header-only library.
But if you are running on different generations of devices, it makes sense to pre-compile the library for all supported generations at once, and dispatch at runtime.
This flag does just that and is used to produce the stringzilla.so shared library, as well as the Python bindings.

SZ_USE_MISALIGNED_LOADS:

By default, StringZilla avoids misaligned loads.
If supported, it replaces many byte-level operations with word-level ones.
Going from char-like types to uint64_t-like ones can significantly accelerate the serial (SWAR) backend.
So consider enabling it if you are building for some embedded device.

SZ_AVOID_LIBC:

When using the C header-only library one can disable the use of LibC.
This may affect the type resolution system on obscure hardware platforms.

SZ_AVOID_STL:

When using the C++ interface one can disable conversions from std::string to sz::string and back.
If not needed, the <string> and <string_view> headers will be excluded, reducing compilation time.

STRINGZILLA_BUILD_SHARED, STRINGZILLA_BUILD_TEST, STRINGZILLA_BUILD_BENCHMARK, STRINGZILLA_TARGET_ARCH for CMake users:

When compiling the tests and benchmarks, you can explicitly set the target hardware architecture.
It's synonymous to GCC's -march flag and is used to enable/disable the appropriate instruction sets.
You can also disable the shared library build, if you don't need it.

Quick Start: Rust 🦀

StringZilla is available as a Rust crate.
It currently covers only the most basic functionality, but is planned to be extended to cover the full C++ API.

let my_string: String = String::from("Hello, world!");
let my_str = my_string.as_str();
let my_cow_str = Cow::from(&my_string);

// Use the generic function with a String
assert_eq!(my_string.sz_find("world"), Some(7));
assert_eq!(my_string.sz_rfind("world"), Some(7));
assert_eq!(my_string.sz_find_char_from("world"), Some(2));
assert_eq!(my_string.sz_rfind_char_from("world"), Some(11));
assert_eq!(my_string.sz_find_char_not_from("world"), Some(0));
assert_eq!(my_string.sz_rfind_char_not_from("world"), Some(12));

// Same works for &str and Cow<'_, str>
assert_eq!(my_str.sz_find("world"), Some(7));
assert_eq!(my_cow_str.as_ref().sz_find("world"), Some(7));

Quick Start: Swift 🍏

StringZilla is available as a Swift package.
It currently covers only the most basic functionality, but is planned to be extended to cover the full C++ API.

var s = "Hello, world! Welcome to StringZilla. 👋"
s[s.findFirst(substring: "world")!...] // "world! Welcome to StringZilla. 👋")    
s[s.findLast(substring: "o")!...] // "o StringZilla. 👋")
s[s.findFirst(characterFrom: "aeiou")!...] // "ello, world! Welcome to StringZilla. 👋")
s[s.findLast(characterFrom: "aeiou")!...] // "a. 👋")
s[s.findFirst(characterNotFrom: "aeiou")!...] // "Hello, world! Welcome to StringZilla. 👋"
s.editDistance(from: "Hello, world!")! // 29

Algorithms & Design Decisions 📚

StringZilla aims to optimize some of the slowest string operations.
Some popular operations, however, like equality comparisons and relative order checking, almost always complete on some of the very first bytes in either string.
In such operations vectorization is almost useless, unless huge and very similar strings are considered.
StringZilla implements those operations as well, but won't result in substantial speedups.

Exact Substring Search

Substring search algorithms are generally divided into: comparison-based, automaton-based, and bit-parallel.
Different families are effective for different alphabet sizes and needle lengths.
The more operations are needed per-character - the more effective SIMD would be.
The longer the needle - the more effective the skip-tables are.
StringZilla uses different exact substring search algorithms for different needle lengths and backends:

• When no SIMD is available - SWAR (SIMD Within A Register) algorithms are used on 64-bit words.
• Boyer-Moore-Horspool (BMH) algorithm with Raita heuristic variation for longer needles.
• SIMD algorithms are randomized to look at different parts of the needle.

On very short needles, especially 1-4 characters long, brute force with SIMD is the fastest solution.
On mid-length needles, bit-parallel algorithms are effective, as the character masks fit into 32-bit or 64-bit words.
Either way, if the needle is under 64-bytes long, on haystack traversal we will still fetch every CPU cache line.
So the only way to improve performance is to reduce the number of comparisons.
The snippet below shows how StringZilla accomplishes that for needles of length two.

Going beyond that, to long needles, Boyer-Moore (BM) and its variants are often the best choice.
It has two tables: the good-suffix shift and the bad-character shift.
Common choice is to use the simplified BMH algorithm, which only uses the bad-character shift table, reducing the pre-processing time.
We do the same for mid-length needles up to 256 bytes long.
That way the stack-allocated shift table remains small.

In the C++ Standards Library, the std::string::find function uses the BMH algorithm with Raita's heuristic.
Before comparing the entire string, it matches the first, last, and the middle character.
Very practical, but can be slow for repetitive characters.
Both SWAR and SIMD backends of StringZilla have a cheap pre-processing step, where we locate unique characters.
This makes the library a lot more practical when dealing with non-English corpora.

All those, still, have $O(hn)$ worst case complexity.
To guarantee $O(h)$ worst case time complexity, the Apostolico-Giancarlo (AG) algorithm adds an additional skip-table.
Preprocessing phase is $O(n+sigma)$ in time and space.
On traversal, performs from $(h/n)$ to $(3h/2)$ comparisons.
It however, isn't practical on modern CPUs.
A simpler idea, the Galil-rule might be a more relevant optimizations, if many matches must be found.

Other algorithms previously considered and deprecated:

• Apostolico-Giancarlo algorithm for longer needles. Control-flow is too complex for efficient vectorization.
• Shift-Or-based Bitap algorithm for short needles. Slower than SWAR.
• Horspool-style bad-character check in SIMD backends. Effective only for very long needles, and very uneven character distributions between the needle and the haystack. Faster "character-in-set" check needed to generalize.

§ Reading materials.
Exact String Matching Algorithms in Java.
SIMD-friendly algorithms for substring searching.

Levenshtein Edit Distance

Levenshtein distance is the best known edit-distance for strings, that checks, how many insertions, deletions, and substitutions are needed to transform one string to another.
It's extensively used in approximate string-matching, spell-checking, and bioinformatics.

The computational cost of the Levenshtein distance is $O(n * m)$, where $n$ and $m$ are the lengths of the string arguments.
To compute that, the naive approach requires $O(n * m)$ space to store the "Levenshtein matrix", the bottom-right corner of which will contain the Levenshtein distance.
The algorithm producing the matrix has been simultaneously studied/discovered by the Soviet mathematicians Vladimir Levenshtein in 1965, Taras Vintsyuk in 1968, and American computer scientists - Robert Wagner, David Sankoff, Michael J. Fischer in the following years.
Several optimizations are known:

1. Space Optimization: The matrix can be computed in $O(min(n,m))$ space, by only storing the last two rows of the matrix.
2. Divide and Conquer: Hirschberg's algorithm can be applied to decompose the computation into subtasks.
3. Automata: Levenshtein automata can be effective, if one of the strings doesn't change, and is a subject to many comparisons.
4. Shift-Or: Bit-parallel algorithms transpose the matrix into a bit-matrix, and perform bitwise operations on it.

The last approach is quite powerful and performant, and is used by the great RapidFuzz library.
It's less known, than the others, derived from the Baeza-Yates-Gonnet algorithm, extended to bounded edit-distance search by Manber and Wu in 1990s, and further extended by Gene Myers in 1999 and Heikki Hyyro between 2002 and 2004.

StringZilla introduces a different approach, extensively used in Unum's internal combinatorial optimization libraries.
The approach doesn't change the number of trivial operations, but performs them in a different order, removing the data dependency, that occurs when computing the insertion costs.
This results in much better vectorization for intra-core parallelism and potentially multi-core evaluation of a single request.

Next design goals:

• Generalize fast traversals to rectangular matrices.
• Port x86 AVX-512 solution to Arm NEON.

§ Reading materials.
Faster Levenshtein Distances with a SIMD-friendly Traversal Order.

Needleman-Wunsch Alignment Score for Bioinformatics

The field of bioinformatics studies various representations of biological structures.
The "primary" representations are generally strings over sparse alphabets:

• DNA sequences, where the alphabet is {A, C, G, T}, ranging from ~100 characters for short reads to 3 billion for the human genome.
• RNA sequences, where the alphabet is {A, C, G, U}, ranging from ~50 characters for tRNA to thousands for mRNA.
• Proteins, where the alphabet is made of 22 amino acids, ranging from 2 characters for dipeptide to 35,000 for Titin, the longest protein.

The shorter the representation, the more often researchers may want to use custom substitution matrices.
Meaning that the cost of a substitution between two characters may not be the same for all pairs.

StringZilla adapts the fairly efficient two-row Wagner-Fisher algorithm as a baseline serial implementation of the Needleman-Wunsch score.
It supports arbitrary alphabets up to 256 characters, and can be used with either BLOSUM, PAM, or other substitution matrices.
It also uses SIMD for hardware acceleration of the substitution lookups.
This however, does not yet break the data-dependency for insertion costs, where 80% of the time is wasted.
With that solved, the SIMD implementation will become 5x faster than the serial one.

Random Generation

Generating random strings from different alphabets is a very common operation.
StringZilla accepts an arbitrary Pseudorandom Number Generator to produce noise, and an array of characters to sample from.
Sampling is optimized to avoid integer division, a costly operation on modern CPUs.
For that a 768-byte long lookup table is used to perform 2 lookups, 1 multiplication, 2 shifts, and 2 accumulations.

Sorting

For lexicographic sorting of strings, StringZilla uses a "hybrid-hybrid" approach with $O(n * log(n))$ and.

1. Radix sort for first bytes exported into a continuous buffer for locality.
2. IntroSort on partially ordered chunks to balance efficiency and worst-case performance.
   1. IntroSort begins with a QuickSort.
   2. If the recursion depth exceeds a certain threshold, it switches to a HeapSort.

Next design goals:

• Generalize to arrays with over 4 billion entries.
• Algorithmic improvements may yield another 3x performance gain.
• SIMD-acceleration for the Radix slice.

Hashing

Choosing the right hashing algorithm for your application can be crucial from both performance and security standpoint.
In StringZilla a 64-bit rolling hash function is reused for both string hashes and substring hashes, Rabin-style fingerprints.
Rolling hashes take the same amount of time to compute hashes with different window sizes, and are fast to update.
Those are not however perfect hashes, and collisions are frequent.
StringZilla attempts to use SIMD, but the performance is not yet satisfactory.
On Intel Sapphire Rapids, the following numbers can be expected for N-way parallel variants.

• 4-way AVX2 throughput with 64-bit integer multiplication (no native support): 0.28 GB/s.
• 4-way AVX2 throughput with 32-bit integer multiplication: 0.54 GB/s.
• 4-way AVX-512DQ throughput with 64-bit integer multiplication: 0.46 GB/s.
• 4-way AVX-512 throughput with 32-bit integer multiplication: 0.58 GB/s.
• 8-way AVX-512 throughput with 32-bit integer multiplication: 0.11 GB/s.

Next design goals:

• Try gear-hash and other rolling approaches.

Why not CRC32?

Cyclic Redundancy Check 32 is one of the most commonly used hash functions in Computer Science.
It has in-hardware support on both x86 and Arm, for both 8-bit, 16-bit, 32-bit, and 64-bit words.
The 0x1EDC6F41 polynomial is used in iSCSI, Btrfs, ext4, and the 0x04C11DB7 in SATA, Ethernet, Zlib, PNG.
In case of Arm more than one polynomial is supported.
It is, however, somewhat limiting for Big Data usecases, which often have to deal with more than 4 Billion strings, making collisions unavoidable.
Moreover, the existing SIMD approaches are tricky, combining general purpose computations with specialized instructions, to utilize more silicon in every cycle.

§ Reading materials.
Comprehensive derivation of approaches
Faster computation for 4 KB buffers on x86
Comparing different lookup tables
Great open-source implementations.
By Peter Cawley
By Stephan Brumme

Other Modern Alternatives

MurmurHash from 2008 by Austin Appleby is one of the best known non-cryptographic hashes.
It has a very short implementation and is capable of producing 32-bit and 128-bit hashes.
The CityHash from 2011 by Google and the xxHash improve on that, better leveraging the super-scalar nature of modern CPUs and producing 64-bit and 128-bit hashes.

Neither of those functions are cryptographic, unlike MD5, SHA, and BLAKE algorithms.
Most of cryptographic hashes are based on the Merkle-Damgård construction, and aren't resistant to the length-extension attacks.
Current state of the Art, might be the BLAKE3 algorithm.
It's resistant to a broad range of attacks, can process 2 bytes per CPU cycle, and comes with a very optimized official implementation for C and Rust.
It has the same 128-bit security level as the BLAKE2, and achieves its performance gains by reducing the number of mixing rounds, and processing data in 1 KiB chunks, which is great for longer strings, but may result in poor performance on short ones.

All mentioned libraries have undergone extensive testing and are considered production-ready.
They can definitely accelerate your application, but so may the downstream mixer.
For instance, when a hash-table is constructed, the hashes are further shrunk to address table buckets.
If the mixer looses entropy, the performance gains from the hash function may be lost.
An example would be power-of-two modulo, which is a common mixer, but is known to be weak.
One alternative would be the fastrange by Daniel Lemire.
Another one is the Fibonacci hash trick using the Golden Ratio, also used in StringZilla.

Unicode, UTF-8, and Wide Characters

StringZilla does not yet implement any Unicode-specific algorithms.
The content is addressed at byte-level, and the string is assumed to be encoded in UTF-8 or extended ASCII.
Refer to simdutf for fast conversions and icu for character metadata.

This may introduce frictions, when binding to some programming languages.
Namely, Java, JavaScript, Python 2, C#, and Objective-C use wide characters (wchar) - two byte long codes.
This leads to all kinds of offset-counting issues when facing four-byte long Unicode characters.

Contributing 👾

Please check out the contributing guide for more details on how to setup the development environment and contribute to this project.
If you like this project, you may also enjoy USearch, UCall, UForm, and SimSIMD. 🤗

License 📜

Feel free to use the project under Apache 2.0 or the Three-clause BSD license at your preference.