Boosting API Performance with C and Python

Modern APIs often receive massive JSON or XML payloads, and processing them efficiently in Python alone can become a bottleneck. To speed things up, we can leverage C for heavy data processing while keeping Python for API interaction and database management.

Real-World Use Case

Imagine an API that receives a large financial transactions JSON payload. Before storing the data, we need to quickly compute sum, average, and standard deviation of transaction values.

Python alone might struggle with performance, so we’ll use C to handle the calculations efficiently.

1?? Writing the C Code for Data Processing

Create a file named processing.c:

#include <stdio.h>
#include <math.h>

void process_data(double *values, int size, double *sum, double *avg, double *std_dev) {
    double temp_sum = 0.0, sum_of_squares = 0.0;

    for (int i = 0; i < size; i++) {
        temp_sum += values[i];
        sum_of_squares += values[i] * values[i];
    }

    *sum = temp_sum;
    *avg = temp_sum / size;
    *std_dev = sqrt((sum_of_squares / size) - (*avg * *avg));
}        

2?? Compiling the C Code

On Linux/Mac:

gcc -shared -o processing.so -fPIC processing.c -lm        

On Windows:

gcc -shared -o processing.dll -fPIC processing.c -lm        

3?? Building the FastAPI Server in Python

Install FastAPI and uvicorn if you haven’t already:

pip install fastapi uvicorn numpy        

Now, create api.py:

import ctypes
import json
import numpy as np
from fastapi import FastAPI, Request

# Load the C library
processing_lib = ctypes.CDLL("./processing.so")

# Define function argument types
processing_lib.process_data.argtypes = [
    ctypes.POINTER(ctypes.c_double),
    ctypes.c_int,
    ctypes.POINTER(ctypes.c_double),
    ctypes.POINTER(ctypes.c_double),
    ctypes.POINTER(ctypes.c_double),
]

# Create the API
app = FastAPI()

@app.post("/process/")
async def process_payload(request: Request):
    # Receive JSON payload
    data = await request.json()
    
    # Extract numerical values
    values = np.array([item["value"] for item in data["transactions"]], dtype=np.float64)
    values_ptr = values.ctypes.data_as(ctypes.POINTER(ctypes.c_double))

    # Create variables to store results
    sum_result = ctypes.c_double()
    avg_result = ctypes.c_double()
    std_dev_result = ctypes.c_double()

    # Call the C function for fast processing
    processing_lib.process_data(values_ptr, len(values), 
                                ctypes.byref(sum_result), ctypes.byref(avg_result), ctypes.byref(std_dev_result))

    return {
        "total_transactions": len(values),
        "sum": sum_result.value,
        "average": avg_result.value,
        "standard_deviation": std_dev_result.value
    }

# Run the API
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)        

4?? Testing the API

Create a sample JSON file (payload.json):

{
    "transactions": [
        {"id": 1, "value": 150.75},
        {"id": 2, "value": 320.10},
        {"id": 3, "value": 245.80},
        {"id": 4, "value": 1000.50},
        {"id": 5, "value": 98.25}
    ]
}        

Send a request using cURL or Postman:

curl -X POST "https://127.0.0.1:8000/process/" -H "Content-Type: application/json" -d @payload.json        

Expected response:

{
    "total_transactions": 5,
    "sum": 1815.40,
    "average": 363.08,
    "standard_deviation": 339.14
}        

Why This Works

By offloading the heavy computation to C, we:

?? Speed up data processing for high-volume APIs

?? Reduce Python’s performance bottleneck

?? Optimize financial, log analysis, and data science applications

?? What other high-performance API use cases do you want to see? Let’s talk!

#Python #CProgramming #FastAPI #HighPerformance #DataProcessing #API #SoftwareEngineering #BackendOptimization #TechInnovation