C STL — C++ Standard Library & Python-style utilities, reimplemented in C

This project reimplements a large slice of the C++ Standard Library (containers, algorithms, smart pointers, …) together with many Python-style conveniences (statistics, random, secrets, json, regex, …) in pure C17. The goal is to give C developers familiar, well-documented building blocks — dynamic arrays, maps, strings, JSON, networking, big integers, and much more — without leaving the C ecosystem.

Highlights

• Zero memory leaks. Every module, test suite, and README example is verified under Valgrind (--leak-check=full) — 0 leaks, 0 errors. Network/socket code is additionally checked for descriptor leaks (--track-fds=yes).

• Cross-platform. Builds and runs on Windows (MSVC and MinGW-w64) and Linux (GCC/Clang), with POSIX/Win32 backends behind one API. Compiles cleanly under -Wall -Wextra.

• Pure C17. No C++ — just portable standard C with thin platform shims.

• 40+ modules. Containers, algorithms, smart pointers, strings, JSON/XML/CSV/INI, networking (TCP/UDP/HTTP), crypto & JWT, arbitrary-precision math, graphics, and more.

• Familiar APIs. Modeled on the C++ STL (vector, map, unique_ptr, …) and the Python standard library (random, statistics, json, regex, turtle, …).

• Heavily tested with true results. Deep per-module test suites, plus runnable examples in every README whose shown output is captured from a real run.

• Fast & predictable. Efficient data structures (geometric vector growth with inlined push_back/at, O(1) average hashmap, O(log n) map), clear ownership rules and deallocators.

• CMake build. Build the whole library, a single module, or one example at a time, with GCC, Clang, or MSVC.

Benchmarks vs the C++ STL

The library aims to be fast where it counts. The table below pits each C container against its C++ <stl> counterpart on the same workload, the same data, and the same compiler family at -O2. Both programs walk an identical deterministic key stream and compute identical checksums, so they do exactly the same work — only the container implementation differs.

Environment: Windows 10, MinGW-w64 GCC / G++ 14.2.0, -O2, single thread, best of three runs (lower is better). Absolute numbers vary by machine; the ratio is what carries over.

C ÷ C++ is the C time divided by the C++ time — below 1.0 means the C library is faster.

• Where C wins — map and hashmap. Both allocate their tree nodes / buckets from a pooled slab allocator, so a million inserts cost a handful of big mallocs instead of a million tiny ones, beating the STL's per-node new.
• About set — near parity. Same pooled-slab, inline-element RB-tree as map. Lookups are actually a hair faster than std::set; inserts are a little slower, so it nets ~1.1×. Unlike map (which stores caller-owned pointers and skips the copy), set has value semantics and deep-copies each element exactly like std::set, so it can't win the copy back — and its node carries the publicly-inspectable key/left/right/parent/color fields (the test suite walks them to check the red-black invariants), which a templated std::set node doesn't expose. A large RB-tree is cache-miss-bound, so that slightly larger node is the whole gap.
• Where C++ wins — vector and priority_queue. Both are tight loops over raw ints. The C vector now inlines push_back/at at the call site just like std::vector and closes most of the gap (~1.3×); the small residual is the type-erased ABI — a runtime itemSize stride and a size-dispatched element copy where C++ has a compile-time sizeof(int), plus the bounds check in vector_at that operator[] skips. The C priority_queue size-dispatches its heap moves the same way (~1.4×); its residual is the comparator — one indirect call through a function pointer per comparison, which std::priority_queue inlines from its template argument. Either way it's a small fixed per-element cost — the price of a uniform C API — not a worse complexity.
• About deque. The C deque stores elements inline in byte-budgeted block buffers (one allocation per block, not one malloc per element) and inlines front / back / at with shift-indexed block math like std::deque, so it lands at ~2.2×; the residual is the out-of-line push_back / pop_front calls that std::deque inlines from templates.
• About list. Each node stores its element inline in the same allocation (one malloc per node, like std::list) instead of a second heap block per element, which roughly halves the allocation/teardown cost and lands it at ~1.2×. The residual is a slightly larger node — the type-erased API keeps a value pointer and aligns the inline element for any type — plus the out-of-line per-node call std::list inlines.
• About forward_list — at parity. It already uses the same single-node- allocation, inline-element design as std::forward_list, so push_front / iterate / teardown match it within measurement noise (≈1.05×). No type-erasure penalty survives here because the per-node malloc dominates and both pay it once per element.
• About queue. The C queue is a contiguous vector used as a ring: enqueue appends and dequeue advances a front index (an O(1) bump, not a buffer shift), with the dead front prefix reclaimed in bulk when the buffer fills — so both ends are amortized O(1), like std::queue. The hot ops are inlined; the ~2× residual is the per-element copy into the contiguous buffer vs std::queue's deque node writes. (Previously dequeue shifted the whole buffer — O(n), an O(n²) drain; that's fixed.)
• About stack. LIFO at the back of a contiguous vector, so push and pop are already O(1). push / top / pop / empty are now inlined in the header (the way std::stack inlines through its container), which cut it from ~3.3× to ~1.7×; the residual is the vector's amortized realloc-copy on growth vs std::stack's default deque adding blocks — in exchange the C stack keeps its elements fully contiguous.

Reproduce it yourself — a C and a C++ program over the identical key stream:

# C  (link the containers used + algorithm, which queue depends on):
gcc -O2 bench.c vector/vector.c queue/queue.c priority_queue/priority_queue.c \
    hashmap/hashmap.c map/map.c algorithm/algorithm.c -I. -o bench_c && ./bench_c
# C++:
g++ -O2 -std=c++17 bench.cpp -o bench_cpp && ./bench_cpp

A personal note

I undertake this project out of a deep affection for the C programming language. C remains an essential tool for any computer engineer, providing the foundation needed to build efficient and robust software. This effort aims to enrich the language with the conveniences found in higher-level standard libraries.

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Table of contents

• Highlights
• Benchmarks vs the C++ STL
• Modules
• Dependencies
• Installing the dependencies
  • Windows (MSYS2 / MinGW-w64)
  • Debian / Ubuntu
  • Fedora
• Building with CMake
  • Build everything
  • Build / run one piece at a time
  • Choosing a compiler (GCC / Clang / MSVC)
  • Build options
• Examples
• Per-module documentation
• Contributing
• License

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Modules

Every module lives in its own directory with a .c source, a .h header, and a README.md containing a full API reference and runnable examples.

Containers

Algorithms & numerics

Text, data & I/O

Time & date

System & concurrency

Security

Networking & databases

Graphics

Testing

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Dependencies

To build the whole project you need:

Build tools

• CMake ≥ 3.15
• A C17 compiler — GCC, Clang, or MSVC
• A generator — Ninja (recommended) or Make / Visual Studio

Third-party libraries (installed system-wide via your package manager)

The project no longer uses the old compile.py script — the build is CMake-only.

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Installing the dependencies

Windows (MSYS2 / MinGW-w64)

Install MSYS2 (this project assumes it lives at C:\msys64). Open the “MSYS2 MinGW x64” shell and run:

pacman -Syu          # update once (re-open the shell if it asks you to)

pacman -S --needed \
  mingw-w64-x86_64-toolchain \
  mingw-w64-x86_64-clang \
  mingw-w64-x86_64-cmake \
  mingw-w64-x86_64-ninja \
  mingw-w64-x86_64-openssl \
  mingw-w64-x86_64-raylib \
  mingw-w64-x86_64-gmp \
  mingw-w64-x86_64-mpfr \
  mingw-w64-x86_64-postgresql

Always build from the MinGW x64 shell (not the plain MSYS shell) so that C:\msys64\mingw64\bin is on the PATH and CMake picks up the right compiler and libraries.

Debian / Ubuntu

sudo apt update
sudo apt install \
  build-essential clang cmake ninja-build pkg-config \
  libssl-dev libgmp-dev libmpfr-dev libpq-dev libraylib-dev

libraylib-dev is available on Debian 12+ / Ubuntu 22.04+. On older releases, install raylib from source (https://github.com/raysan5/raylib) or omit the plot/turtle modules.

Fedora

sudo dnf install \
  gcc clang cmake ninja-build pkgconf-pkg-config \
  openssl-devel gmp-devel mpfr-devel libpq-devel raylib-devel

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Building with CMake

All commands are run from the project root (c_std/).

Build everything

# Configure (Ninja generator recommended). Omit -G for the platform default.
cmake -S . -B build -G Ninja

# Build the library, the main demo
cmake --build build

# Build and run the main demo (runs from the project root so ./sources is reachable)
cmake --build build --target run

Binaries are written to build/bin/.

Build / run one piece at a time

The project is fully modular — you can compile a single module or a single example without building the rest:

# Compile just one module's objects
cmake --build build --target vector

# Compile every module library (no executables)
cmake --build build --target modules

Choosing a compiler (GCC / Clang / MSVC)

The build is compiler-agnostic and targets the C17 standard.

# GCC (default on MSYS2 / Linux)
cmake -S . -B build -G Ninja -DCMAKE_C_COMPILER=gcc

# Clang
cmake -S . -B build -G Ninja -DCMAKE_C_COMPILER=clang
# On MSYS2, `clang` is usually the CLANG64 toolchain, whose linker does not search
# the MinGW-w64 library directory. If you installed the dependencies into mingw64,
# point clang's linker at them:
#   cmake -S . -B build -G Ninja -DCMAKE_C_COMPILER=clang \
#         -DCMAKE_EXE_LINKER_FLAGS="-LC:/msys64/mingw64/lib"

# MSVC — from an “x64 Native Tools Command Prompt for VS”
cmake -S . -B build -G "Ninja"            # or: -G "Visual Studio 17 2022"
cmake --build build --config Release

On MSVC, install the third-party libraries with vcpkg (vcpkg install openssl raylib gmp mpfr libpq) and pass -DCMAKE_TOOLCHAIN_FILE=<vcpkg>/scripts/buildsystems/vcpkg.cmake when configuring.

Build options

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Examples

The examples/ directory contains a small, self-contained program for (almost) every module — examples/<module>_example.c — taken from that module's README.md. Each one is compiled by CMake into its own executable named <module>_example, so you can build and run them individually (see Build / run one piece at a time).

A few examples are compile-only because they need external resources at run time: network (sockets), database (a running PostgreSQL server), serial_port (hardware), and plot / turtle (open a graphical window).

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Quick examples

A taste of a few modules. Each snippet is a complete program; the per-module README.md files have many more (with expected output).

vector — a growable typed array

#include "vector/vector.h"
#include "fmt/fmt.h"

int main(void) {
    Vector* v = vector_create(sizeof(int));
    for (int i = 1; i <= 5; ++i) {
        vector_push_back(v, &i);
    }

    int sum = 0;
    for (size_t i = 0; i < vector_size(v); ++i) {
        sum += *(int*)vector_at(v, i);
    }

    fmt_printf("size=%zu sum=%d\n", vector_size(v), sum);
    vector_deallocate(v);
    return 0;
}

size=5 sum=15

json — parse and pretty-print

#include "json/json.h"
#include "fmt/fmt.h"

int main(void) {
    JsonElement* root = json_parse("{\"lib\": \"c_std\", \"version\": 1.0, \"tags\": [\"c\", \"stl\"]}");

    json_print(root);
    json_deallocate(root);

    return 0;
}

{
    "lib": "c_std",
    "tags": [
      "c",
      "stl"
  ],
    "version": 1
}

csv — build a row and export

#include "csv/csv.h"
#include "fmt/fmt.h"
#include <stdlib.h>

int main(void) {
    CsvFile* csv = csv_file_create(',');
    CsvRow*  row = csv_row_create();

    csv_row_append_cell(row, "Alice");
    csv_row_append_cell(row, "30");
    csv_file_append_row(csv, row);

    char* out = csv_file_export_to_string(csv);
    fmt_printf("%s", out);

    free(out);
    csv_file_destroy(csv);
    return 0;
}

Alice,30

config — load an INI file and read values

#include "config/config.h"
#include "fmt/fmt.h"
#include <stdio.h>

int main(void) {
    /* write a small INI file, then load and read it */
    FILE* f = fopen("app.ini", "w");
    fputs("[server]\nhost = localhost\nport = 8080\n", f);
    fclose(f);

    ConfigFile* cfg = config_create("app.ini");
    fmt_printf("host=%s port=%d\n",
               config_get_value(cfg, "server", "host"),
               config_get_int(cfg, "server", "port", 0));

    config_deallocate(cfg);
    remove("app.ini");

    return 0;
}

host=localhost port=8080

random — deterministic with a fixed seed

#include "random/random.h"
#include "fmt/fmt.h"

int main(void) {
    random_seed(42);
    fmt_printf("dice rolls: %d %d %d\n", random_randint(1, 6), random_randint(1, 6), random_randint(1, 6));
    
    return 0;
}

dice rolls: 4 5 1

crypto — SHA-256 of a string

#include "crypto/crypto.h"
#include "fmt/fmt.h"
#include <stdlib.h>

int main(void) {
    size_t   n;
    uint8_t* h   = crypto_hash_string("hello", CRYPTO_SHA256, &n);
    char*    hex = crypto_hash_to_hex(h, n);

    fmt_printf("sha256(\"hello\") = %s\n", hex);

    free(hex);
    free(h);

    return 0;
}

sha256("hello") = 2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824

concurrent — spawn a thread and join it

#include "concurrent/concurrent.h"
#include "fmt/fmt.h"

static int worker(void* arg) { *(int*)arg += 41; return 0; }

int main(void) {
    int value = 1;
    Thread t;

    thread_create(&t, worker, &value);
    thread_join(t, NULL);
    fmt_printf("value=%d\n", value);

    return 0;
}

value=42

thread_pool — run 100 tasks on 4 workers

#include "concurrent/thread_pool.h"
#include "concurrent/concurrent.h"
#include "fmt/fmt.h"

static Mutex m;
static int counter = 0;

static int task(void* arg) { 
    (void)arg; 

    mutex_lock(&m); 
    counter++; 
    mutex_unlock(&m); 

    return 0; 
}

int main(void) {
    mutex_init(&m, MUTEX_PLAIN);
    ThreadPool* pool = thread_pool_create(4);


    for (int i = 0; i < 100; ++i) {
        thread_pool_add_task(pool, task, NULL);
    }
    thread_pool_wait(pool);
    fmt_printf("ran %d tasks\n", counter);

    thread_pool_destroy(pool);
    mutex_destroy(&m);

    return 0;
}

ran 100 tasks

udp — open a socket and bind to an ephemeral port

#include "network/udp.h"
#include "fmt/fmt.h"

int main(void) {
    udp_init();
    UdpSocket sock;

    udp_socket_create(&sock);
    udp_bind(sock, NULL, 0);                 /* any address, kernel-chosen port */

    char host[INET6_ADDRSTRLEN];
    unsigned short port = 0;

    udp_get_local_address(sock, host, sizeof(host), &port);
    fmt_printf("udp socket bound to an ephemeral port: %s\n", port != 0 ? "yes" : "no");

    udp_close(sock);
    udp_cleanup();

    return 0;
}

udp socket bound to an ephemeral port: yes

tcp — create a listening server socket

#include "network/tcp.h"
#include "fmt/fmt.h"

int main(void) {
    tcp_init();
    TcpSocket server;

    tcp_socket_create(&server);
    tcp_set_reuse_addr(server, true);
    tcp_bind(server, "127.0.0.1", 0);        /* ephemeral port */
    tcp_listen(server, 1);

    char host[INET6_ADDRSTRLEN];
    unsigned short port = 0;

    tcp_get_sock_name(server, host, sizeof(host), &port);
    fmt_printf("tcp server on %s, port assigned: %s\n", host, port != 0 ? "yes" : "no");

    tcp_close(server);
    tcp_cleanup();

    return 0;
}

tcp server on 127.0.0.1, port assigned: yes

http — build and serialize a response (no network)

#include "network/http.h"
#include "fmt/fmt.h"
#include <stdlib.h>

int main(void) {
    HttpResponse res = {0};

    http_set_status(&res, 200, "OK");
    http_set_body(&res, "Hello, world!");

    char* raw = http_serialize_response(&res);
    fmt_printf("%s", raw);

    free(raw);
    http_free_response(&res);

    return 0;
}

HTTP/1.1 200 OK
Content-Type: text/plain

Hello, world!

plot — render a line chart to a PNG

#include "plot/plot.h"

int main(void) {
    Plot* p = plot_create("y = x * x", "x", "y");

    float ys[10];
    for (int i = 0; i < 10; ++i) ys[i] = (float)(i * i);

    PlotColor blue = {34, 102, 204, 255};
    plot_add_line(p, ys, 10, "squares", blue);

    plot_export_image(p, "plot_example.png");   /* writes a PNG to disk */
    plot_destroy(p);
    
    return 0;
}

turtle — turtle graphics, saved as a PNG

turtle is built on raylib; here we draw four nested squares and save the canvas with the built-in turtle_save_image (a wrapper over raylib's TakeScreenshot, so you don't need to include raylib.h). Turtle coordinates are screen pixels (origin top-left).

#include "turtle/turtle.h"

int main(void) {
    Turtle* t = turtle_create();

    turtle_init_window(420, 420, "c_std turtle");
    turtle_set_fps(60);
    turtle_set_speed(t, 100);                    /* fast (0 would never finish) */
    turtle_set_background_color(t, 255, 255, 255, 255);
    turtle_pen_size(t, 4);

    unsigned char colors[4][3] = {{220,50,50}, {40,160,60}, {40,90,210}, {200,140,30}};
    for (int s = 0; s < 4; ++s) {
        float side = 80.0f + s * 60.0f;

        turtle_pen_up(t);
        turtle_set_position(t, 210.0f - side / 2.0f, 210.0f + side / 2.0f);  /* center it */
        turtle_set_heading(t, 0);
        turtle_pen_down(t);
        turtle_set_pen_color_rgb(t, colors[s][0], colors[s][1], colors[s][2], 255);

        for (int i = 0; i < 4; ++i) {            /* one square */
            turtle_forward(t, side);
            turtle_right(t, 90);
        }
    }
    turtle_draw(t);

    turtle_save_image("turtle_example.png");     /* built-in screenshot helper */
    turtle_deallocate(t);
    turtle_close_window();

    return 0;
}

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Per-module documentation

Each module ships its own README.md with a complete API reference, function descriptions, and runnable examples that include their expected output. Start with the module directory you are interested in — e.g. vector/README.md, json/README.md, or algorithm/README.md.

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Contributing

Contributions are welcome — extending libraries, improving performance, or fixing bugs. Fork the repository, make your changes, and submit a pull request. New modules just need their directory added to the C_STD_MODULES list in CMakeLists.txt.

License

This project is open-source and available under the ISC License.