How I Replaced Python with C in My Project

For years, I had been using Python as my go-to language for most projects. Its simplicity, vast ecosystem, and rapid development capabilities made it an obvious choice. However, as my latest project grew in complexity and scale, I began hitting performance bottlenecks that Python couldn't overcome. That's when I made the difficult decision to rewrite critical components in C. The Project: A High-Performance Data Processor My project was a data processing pipeline that needed to: Handle millions of data points per second Perform complex mathematical transformations Maintain low latency for real-time applications Run efficiently on resource-constrained hardware While Python with NumPy worked fine initially, as our data volumes grew by 10x, we started seeing: Memory usage spikes CPU bottlenecks Unpredictable garbage collection pauses Difficulty integrating with some hardware accelerators Why C? After benchmarking and profiling, it became clear that for our core processing logic, we needed: Predictable performance Direct memory control Minimal runtime overhead Better hardware integration C offered all these benefits, though at the cost of development velocity and safety nets that Python provides. The Rewrite Process Phase 1: Identifying Hotspots I used Python's cProfile to identify the most time-consuming functions. The top candidates for rewriting were: Matrix transformation algorithms Custom statistical calculations Data serialization/deserialization Low-level device communication Phase 2: Creating C Extensions for Python Instead of a full rewrite, I first tried creating C extensions using Python's C API: #include static PyObject* fast_transform(PyObject* self, PyObject* args) { // Parse Python arguments PyObject* input_list; if (!PyArg_ParseTuple(args, "O", &input_list)) { return NULL; } // Convert Python list to C array Py_ssize_t length = PyList_Size(input_list); double* values = malloc(length * sizeof(double)); for (Py_ssize_t i = 0; i < length; i++) { values[i] = PyFloat_AsDouble(PyList_GetItem(input_list, i)); } // Perform computation for (Py_ssize_t i = 0; i < length; i++) { values[i] = transform_value(values[i]); } // Convert back to Python list PyObject* result = PyList_New(length); for (Py_ssize_t i = 0; i < length; i++) { PyList_SetItem(result, i, PyFloat_FromDouble(values[i])); } free(values); return result; } static PyMethodDef module_methods[] = { {"fast_transform", fast_transform, METH_VARARGS, "Perform fast transformation"}, {NULL, NULL, 0, NULL} }; PyMODINIT_FUNC PyInit_fastmod(void) { return PyModule_Create(&fastmod); } This hybrid approach gave us a 5-8x speedup in the critical functions while keeping most of the system in Python. Phase 3: Full Rewrite of Core Components For components where even the C extension approach wasn't sufficient, we went for a full rewrite: Data Processing Engine: Rewrote the core pipeline in C with careful memory management Network Layer: Implemented a custom protocol handler in C for lower latency Hardware Integration: Created direct hardware communication bypassing Python entirely Challenges Faced 1. Memory Management Going from Python's garbage collection to manual memory management was painful: // Example of careful memory management void process_data(DataPacket* packet) { Buffer* buf = create_buffer(packet->size); if (!buf) { handle_error(); return; } if (transform_data(packet, buf) != SUCCESS) { free_buffer(buf); // Must clean up on all exit paths handle_error(); return; } // ... more processing ... free_buffer(buf); } Solution: Adopted a consistent ownership model and used static analyzers to catch leaks. 2. Error Handling Python's exceptions vs. C's error codes required careful adaptation: typedef enum { ERR_NONE = 0, ERR_INVALID_INPUT, ERR_MEMORY, ERR_IO, // ... } ErrorCode; ErrorCode process_file(const char* filename, Result** out_result) { *out_result = NULL; FILE* fp = fopen(filename, "rb"); if (!fp) return ERR_IO; ErrorCode err = ERR_NONE; Result* result = malloc(sizeof(Result)); if (!result) { err = ERR_MEMORY; goto cleanup; } // ... processing ... *out_result = result; cleanup: if (err != ERR_NONE && result) free(result); if (fp) fclose(fp); return err; } 3. Development Velocity The edit-compile-test cycle was much slower. We mitigated this by: Maintaining thorough test suites Using better tooling (CLion, custom build scripts) Keeping Python wrappers for rapid prototyping Key Lessons Learned Not All Code Needs Rewriting: Only performance-critical paths benefit from C Hybrid Approaches Work: Python for glue code, C for heavy lifting Tooling Matters: Good debuggers (GDB,

#include 

static PyObject* fast_transform(PyObject* self, PyObject* args) {
    // Parse Python arguments
    PyObject* input_list;
    if (!PyArg_ParseTuple(args, "O", &input_list)) {
        return NULL;
    }

    // Convert Python list to C array
    Py_ssize_t length = PyList_Size(input_list);
    double* values = malloc(length * sizeof(double));
    for (Py_ssize_t i = 0; i < length; i++) {
        values[i] = PyFloat_AsDouble(PyList_GetItem(input_list, i));
    }

    // Perform computation
    for (Py_ssize_t i = 0; i < length; i++) {
        values[i] = transform_value(values[i]);
    }

    // Convert back to Python list
    PyObject* result = PyList_New(length);
    for (Py_ssize_t i = 0; i < length; i++) {
        PyList_SetItem(result, i, PyFloat_FromDouble(values[i]));
    }

    free(values);
    return result;
}

static PyMethodDef module_methods[] = {
    {"fast_transform", fast_transform, METH_VARARGS, "Perform fast transformation"},
    {NULL, NULL, 0, NULL}
};

PyMODINIT_FUNC PyInit_fastmod(void) {
    return PyModule_Create(&fastmod);
}

// Example of careful memory management
void process_data(DataPacket* packet) {
    Buffer* buf = create_buffer(packet->size);
    if (!buf) {
        handle_error();
        return;
    }

    if (transform_data(packet, buf) != SUCCESS) {
        free_buffer(buf);  // Must clean up on all exit paths
        handle_error();
        return;
    }

    // ... more processing ...

    free_buffer(buf);
}

typedef enum {
    ERR_NONE = 0,
    ERR_INVALID_INPUT,
    ERR_MEMORY,
    ERR_IO,
    // ...
} ErrorCode;

ErrorCode process_file(const char* filename, Result** out_result) {
    *out_result = NULL;

    FILE* fp = fopen(filename, "rb");
    if (!fp) return ERR_IO;

    ErrorCode err = ERR_NONE;
    Result* result = malloc(sizeof(Result));
    if (!result) {
        err = ERR_MEMORY;
        goto cleanup;
    }

    // ... processing ...

    *out_result = result;

cleanup:
    if (err != ERR_NONE && result) free(result);
    if (fp) fclose(fp);
    return err;
}

Python Frontend] <-IPC-> [C Core Engine] <-Direct-> [Hardware]

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