FastAPI ThreadPool Executor

Use Python's concurrent features to execute these tasks concurrently. One common approach is to use the concurrent.futures module, which provides a high-level interface for asynchronously executing callables.

Here's a step-by-step guide to restructuring your FastAPI application to handle this: Install FastAPI and other dependencies: Make sure you have FastAPI and uvicorn installed. You can install these using pip if you haven't already.

pip install fastapi uvicorn  

1. Create the FastAPI application: Define your FastAPI application and the function that will run the solves in parallel.

2. Use concurrent.futures.ThreadPoolExecutor: This allows you to run multiple solve operations concurrently using threads.

Here is an example implementation:

from fastapi import FastAPI, HTTPException  
from concurrent.futures import ThreadPoolExecutor, as_completed  
import time  
import random  
  
app = FastAPI()  
  
# Dummy solver function  
def solve(param):  
    # Simulate a time-consuming operation  
    time.sleep(random.uniform(1, 5))  
    return f"Result of solving with param {param}"  
  
# Function to run multiple solves concurrently  
def run_parallel_solves(params):  
    results = []  
    with ThreadPoolExecutor(max_workers=30) as executor:  
        futures = {executor.submit(solve, param): param for param in params}  
        for future in as_completed(futures):  
            param = futures[future]  
            try:  
                result = future.result()  
                results.append(result)  
            except Exception as exc:  
                results.append(f"Param {param} generated an exception: {exc}")  
    return results  
  
@app.post("/run-solves/")  
async def run_solves(params: list):  
    try:  
        results = run_parallel_solves(params)  
        return {"results": results}  
    except Exception as e:  
        raise HTTPException(status_code=500, detail=str(e))  
  
# To run the FastAPI app, use the following command in the terminal:  
# uvicorn script_name:app --host 0.0.0.0 --port 8000 --reload  

Key Points:

ThreadPoolExecutor: The ThreadPoolExecutor is used to manage a pool of threads. The max_workers parameter specifies the maximum number of threads that can be used to execute the given callables. The submit method schedules the callable to be executed and returns a Future object. Handling Results: The as_completed function yields futures as they complete, allowing you to process results as they are ready. Error handling is done within the loop to catch any exceptions raised during the execution of the tasks. Asynchronous Endpoint: The FastAPI endpoint is defined as asynchronous (async def) to allow for non-blocking request handling. Running the Application: To run your FastAPI application, use the following command:

 uvicorn main:app --host 0.0.0.0 --port 8000 --reload  

Replace main with the name of your Python file without the .py extension. Considerations:

• Thread Safety: Ensure that your solve function is thread-safe since it will be executed in multiple threads.

• Timeouts: Depending on the expected execution time, you might want to handle timeouts for your futures.

• Resource Management: Consider the resources on your server (CPU, memory) to determine an appropriate number of worker threads.

By following these steps, you can handle multiple solve operations in parallel, reducing the overall time taken for a request that requires multiple solves.