Common Errors and Solutions in Multithreaded Task Queue in Python

1. Introduction

When developing multi-threaded task queues using Python, you often encounter various errors, such as loop import, object access errors, variable scope problems, etc. Based on actual development cases, this article analyzes three typical errors and provides detailed solutions. The scenarios involved include:

Flask + SQLAlchemy Application
Multithreaded task queue (+)
Database record matching processing

2. Question 1: Circular Import

Error analysis

error message:

ImportError: cannot import name 'start_processing' from partially initialized module 'task.national_match_task' (most likely due to a circular import)

reason:

Imported start_processing of national_match_task.py
national_match_task.py has imported the app again
Causes Python to fail to initialize module correctly

Solution

Method 1: Delay import

Import dependencies inside the function, not at the top of the module:

# national_match_task.py
def get_failed_records():
    from app import app  # Delayed import    with app.app_context():
        records = (CustomerOrder).filter(...).all()
    return records

Method 2: Dependency Injection

Let start_processing receive app parameters instead of importing them directly:

# national_match_task.py
def start_processing(app):  # Receive app parameters    # Use the app instead of importing directly
# 
from task.national_match_task import start_processing
start_processing(app)  # Pass in the app instance

Method 3: Use flask.current_app

from flask import current_app as app  # Substitute for direct import

3. Question 2: SQLAlchemy model object is not subscriptable (‘CustomerOrder’ object is not subscriptable)

Error analysis

error message:

TypeError: 'CustomerOrder' object is not subscriptable

reason:

The match_nationwide_numbers() function expects to receive a dictionary, but the SQLAlchemy model object is passed in.
Try accessing the property with item['prefix'], but SQLAlchemy objects should use

Solution

Solution 1: Modify the matching function and directly use the object properties

# match_phone_number.py
def match_nationwide_numbers(item, cookie, logger):
    if not ( and ):  # Use . Access properties        ("Missing necessary prefix or suffix information")
        return {"Match Status": "Failed: Missing prefix or suffix"}
    #Other logic...

Scheme 2: Convert the object to a dictionary before calling

# national_match_task.py
def worker():
    item = ()
    item_dict = {
        'prefix': ,
        'suffix': ,
        'tracking_number': item.tracking_number,
    }
    result = match_nationwide_numbers(item_dict, , logger)

Solution 3: Add a retry mechanism

max_retries = 3
retry_count = getattr(item, '_retry_count', 0)
if retry_count &lt; max_retries:
    item._retry_count = retry_count + 1
    (item)  # Return to queue

4. Question 3: UnboundLocalError: cannot access local variable 'item')

Error analysis

error message:

UnboundLocalError: cannot access local variable 'item' where it is not associated with a value

reason:

The item variable is not initialized outside the try block
When () throws an exception, the item is not assigned, but finally still tries to access it

Solution

Solution 1: Initialize item

def worker():
    item = None  # Initialization    try:
        item = (timeout=1)
        # Processing logic...    except :
        continue
    finally:
        if item is not None:  # Make sure the variable is assigned            queue.task_done()

Scheme 2: Check whether the variable exists

finally:
    if 'item' in locals() and item is not None:
        queue.task_done()

Solution 3: Refactor the code to reduce variable scope obfuscation

def worker():
    while True:
        process_next_item()

def process_next_item():
    item = (timeout=1)
    try:
        # Processing logic...    finally:
        queue.task_done()

5. Summary and best practices

1. Avoid loop import

Use Dependency Injection or Delayed Import
Avoid interdependence between modules

2. Correctly handle SQLAlchemy objects

Use . to access properties instead of []
Convert to dictionary if necessary

3. Safe multi-threaded queue processing

Initialize variables to avoid UnboundLocalError
Add a retry mechanism to prevent infinite loops
Use finally to ensure resource release

6. Complete code example

Fixed national_match_task.py

import threading
import queue
import time
from flask import current_app as app
from models import CustomerOrder

def worker():
    item = None  # Initialization    try:
        item = (timeout=1)
        if item is None:
            return

        (f"Processing records: {item.tracking_number}")
        result = match_nationwide_numbers({
            'prefix': ,
            'suffix': ,
        }, , logger)

        update_record(, result["Match Status"], ("Phone number"))
        
    except :
        return
    except Exception as e:
        (f"Processing failed: {e}")
        if item and getattr(item, '_retry_count', 0) &lt; 3:
            item._retry_count += 1
            (item)
    finally:
        if item is not None:
            queue.task_done()

def start_processing(app):
    for _ in range(5):
        (target=worker, daemon=True).start()

Conclusion

Multithreaded task queues are very practical in Python, but they are also prone to various boundary situations. By rationally designing the code structure, initializing variables, and correctly handling object access methods, errors can be greatly reduced. I hope this article can help you develop Python multi-threaded applications more steadily!

This is the end of this article about common errors and solutions in multithreaded task queues in Python. For more related content on Python multithreaded task queues, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!