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Ethereum Event Loop Error: How to Fix “Event Loop Closed”

As a developer using Python for Binance Futures transactions, you’ve probably encountered the dreaded “Event Loop Closed” error. This issue can cause the program to suddenly stop, leaving incomplete or inconsistent data. In this article, we’ll explore the causes of this error and provide step-by-step solutions to fix the problem.

Understanding the Event Loop

In Python’s asyncio library, which is used to create parallel programs, an event loop is responsible for managing task execution. When a task completes, the event loop checks whether there are any other tasks waiting for resources. If there are no such tasks, the event loop is considered “closed” and the program stops.

Causes of Event Loop Closed Error

The Event Loop Closed error typically occurs when one or more tasks are blocked (waiting) on ​​I/O operations (input/output, network, etc.) without returning control to the event loop. This can happen for a number of reasons, including:

• I/O bound operations: When a task is performing an I/O operation, such as reading from a file or sending data over a network, it may be blocked indefinitely.

• Waiting for tasks to complete: When two or more tasks are waiting for each other to complete, the event loop may become blocked, resulting in an Event Loop Closed error.

Solutions to fix the error

To fix the Event Loop Closed error, you need to make sure that the program exits cleanly and is not blocked indefinitely. Here are some solutions you can try:

1. Use a blocking I/O library

Instead of Python’s asynchronous I/O libraries (such as “asyncio”, “aiohttp”), use blocking I/O libraries such as “ctypes”, “socket” or “select”. These libraries provide direct access to system resources and can help prevent deadlocks.

import ctypes

...
try:

Perform the I/O operation here
ctypes.c_int(1) 
Simulates some I/O operations
except for this exception:
print(f"Error: {e}")

2. Use a parallel execution framework

Python’s asyncio library provides an event loop that can run in parallel on multiple threads or processes. However, when working with external libraries or parallel tasks, it is important to ensure proper synchronization.

To avoid blocking events in the main thread, use a threading library or a concurrent.futures context manager. Here is an example of using “threading”:

Threading
definitely perform_task():

Perform some I/O operations here
ctypes.c_int(1) 
Simulate some I/O operations
lock = threading.lock()
def main():
task = threading.thread(target=executetask)
task.start()
try:

Do something else while the task is running
happen
finally:
task.join()
main()

3. Use a row-based solution

The queue-based solution allows tasks to run in parallel without blocking each other.

Import queue
Threading
Class TaskQueue:
def __init__(self):
self.queue = queue.queue()
def submit_task(self, task):
self.queue.put((task, none))
def perform_task(task, lock):
try:

Perform some I/O operations here
ctypes.c_int(1) 
Simulate some I/O operations
Finally:
with lock:
print ("Task done")

Create a task queue and submit tasks
queue = TaskQueue()
tasks = []
in range i (10):
task = thread.thread(target=task_done, arguments=(i,))
task.start()
task.append((task, "task {}".format(i)))

Run the main thread while executing tasks in parallel
def main():
for task, message in tasks:
task.join()
main()

4.