Python solves the error of multi-threaded asynchronous code running "There is no current event loop"

1. Introduction

In Python development, we often need to handle both highly concurrent network requests and CPU-intensive tasks. At this time, the developer may choose:

• Multithreading (ThreadPoolExecutor) handles blocking IO tasks
• Asynchronous IO (asyncio + aiohttp) optimizes high concurrent network requests

However, when trying to run asynchronous code in a multi-threaded environment, you may encounter an error:

ERROR - There is no current event loop in thread 'Thread-4'.

This article will analyze the causes of the problem and provide 3 solutions, including:

• Pure synchronization solutions (requests)
• Asynchronous + multithreading scheme (aiohttp + asyncio.run_coroutine_threadsafe)
• Multi-process alternative (ProcessPoolExecutor)

Finally, we will also give the equivalent implementation of Java (based on CompletableFuture and HttpClient).

2. Problem background

2.1 Error recurrence

The following code crashes when calling an asynchronous function in multithreading:

from  import ThreadPoolExecutor
import asyncio

async def async_task():
    await (1)
    return "Done"

def run_in_thread():
    # Direct call will report an error: There is no current event loop in thread    result = async_task()  # ❌ Error!    return result

with ThreadPoolExecutor() as executor:
    future = (run_in_thread)
    print(())

2.2 Cause analysis

Asyncio's event loop is thread-local, and each thread needs its own event loop.

The main thread has an event loop by default, but the child thread does not.

Calling await directly in a new thread will result in a RuntimeError.

3. Solution

3.1 Solution 1: Pure synchronous implementation (recommended)

If high-performance asynchronous IO is not required, use the synchronous request library (such as requests):

import requests

def sf_express_order_count_sync(consigneePhone, cookie, createTimeStart, createTimeEnd):
    """Synchronous version: Send HTTP requests using requests"""
    url = '/api/merge/order/count'
    response = (url, headers=headers, json=payload)
    return ()

advantage:

• Simple code, no need to handle event loops
• Compatible with all Python versions

shortcoming:

Low performance (blocking threads per request)

3.2 Solution 2: Asynchronous + Multi-threaded Mix

If you have to use asynchronous IO (such as aiohttp), you need to create an event loop for each thread:

import aiohttp

async def sf_express_order_count_async(consigneePhone, cookie, createTimeStart, createTimeEnd):
    """Async version: Use aiohttp"""
    async with () as session:
        async with (url, headers=headers, json=payload) as resp:
            return await ()

def run_async_in_thread(async_func, *args):
    """Running asynchronous functions in child threads"""
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    try:
        return loop.run_until_complete(async_func(*args))
    finally:
        ()

​​​​​​​# Called in thread poolwith ThreadPoolExecutor() as executor:
    future = (
        run_async_in_thread, 
        sf_express_order_count_async, 
        "13112345678", 
        "cookie123", 
        1630000000
    )
    print(())

advantage:

• High performance (asynchronous IO + multi-threading)
• Suitable for high concurrency scenarios

shortcoming:

• High code complexity
• Need to manually manage event loops

3.3 Solution 3: Use multi-process instead

If asynchronous + multithreading still does not meet the requirements, you can use ProcessPoolExecutor instead:

from  import ProcessPoolExecutor

def check_phones_with_processes(phone_numbers):
    """Using process pools to circumvent GIL and event loop issues"""
    with ProcessPoolExecutor() as executor:
        futures = [(has_orders, phone) for phone in phone_numbers]
        for future in as_completed(futures):
            if ():
                return ()

advantage:

• Bypassing GIL restrictions
• Each process has an independent event loop

shortcoming:

• Process startup overhead
• Complex inter-process communication

4. Java equivalent implementation

In Java, you can use CompletableFuture and HttpClient to implement similar functions:

import ;
import ;
import ;
import ;

public class SfExpressChecker {
    private static final HttpClient httpClient = ();

    public static CompletableFuture<Boolean> hasOrdersAsync(String phone, String cookie) {
        HttpRequest request = ()
                .uri(("/api/merge/order/count"))
                .header("Content-Type", "application/json")
                .header("token", cookie)
                .POST((
                    ("{\"consigneePhone\":\"%s\"}", phone)))
                .build();

        return (request, ())
                .thenApply(response -> {
                    JsonObject json = (()).getAsJsonObject();
                    return ("result").getAsJsonObject().get("total").getAsInt() > 0;
                });
    }

    public static void main(String[] args) {
        CompletableFuture<Boolean> future = hasOrdersAsync("13112345678", "cookie123");
        (hasOrders -> ("Has orders: " + hasOrders));
        (); // Blocking and waiting for results    }
}

Key points:

• Java's HttpClient native supports asynchronous
• CompleteFuture simplifies asynchronous programming
• No need to manually manage event loops

5. Summary

Final suggestions:

• Priority to use synchronization code (unless the performance bottleneck is clear)
• Asynchronous + multi-threading is suitable for high concurrent HTTP requests
• Java's asynchronous solution is more elegant (recommendedCompletableFuture）

By choosing a reasonable plan, it can be avoidedThere is no current event loopError and build high-performance concurrent applications.

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