How to implement a message queue through Python

What is a message queue and the benefits of using a message queue are not detailed here. This article focuses on how to implement a message queue using python.

To implement a message queue in Python, you can use the built-inqueueModule to create a simple queue, or use a third-party library such asRabbitMQ、RedisorKafkato implement more complex distributed message queues.

How to implement message queues through python

1. Use Python built-in (suitable for stand-alone applications)

Provides thread-safe queue operations, suitable for use in multi-threaded applications.

import queue
import threading
import time

# Create a first-in-first-out (FIFO) queuemsg_queue = ()

# Producer threaddef producer():
    for i in range(5):
        (1)  # Simulate some processing        msg = f"information{i}"
        msg_queue.put(msg)  # Put messages into queue        print(f"Producer puts it in：{msg}")

# Consumer Threaddef consumer():
    while True:
        msg = msg_queue.get()  # Get message from queue        if msg is None:  # Termination conditions            break
        print(f"Consumer processing：{msg}")
        msg_queue.task_done()  # Mark the task completed
# Create producer and consumer threadsproducer_thread = (target=producer)
consumer_thread = (target=consumer)

# Start the threadproducer_thread.start()
consumer_thread.start()

# Wait for the producer thread to completeproducer_thread.join()

# Send a termination signal to the consumer threadmsg_queue.put(None)

# Wait for the consumer thread to completeconsumer_thread.join()

2. Use Redis (suitable for distributed applications)

Redis is an efficient in-memory data store that can be used as a distributed message queue. You can useredis-pyThe library interacts with Redis.

pip install redis

import redis
import time

# Create a Redis connectionr = (host='localhost', port=6379, db=0)

# Producer: Put messages into queuedef producer():
    for i in range(5):
        (1)  # Simulate some processing        msg = f"information{i}"
        ('msg_queue', msg)  # Push messages to queue        print(f"Producer puts it in：{msg}")

# Consumer: Get messages from queuedef consumer():
    while True:
        msg = ('msg_queue')[1].decode('utf-8')  # Get message from queue        print(f"Consumer processing：{msg}")

# Start producers and consumersproducer()
consumer()

3. Use RabbitMQ (for more complex messaging)

RabbitMQIt is a powerful message proxy system that supports multiple message queue modes. If you need a highly reliable and high-performance message queue, you can usepikaLibrary connection RabbitMQ.

pip install pika

import pika

# Connect to RabbitMQconnection = (('localhost'))
channel = ()

# Declare a queuechannel.queue_declare(queue='msg_queue')

# Producer: Send a messagedef producer():
    for i in range(5):
        msg = f"information{i}"
        channel.basic_publish(exchange='',
                              routing_key='msg_queue',
                              body=msg)
        print(f"Send by the producer：{msg}")

# Consumer: Receive and process messagesdef consumer(ch, method, properties, body):
    print(f"Consumer processing：{('utf-8')}")

# Start Consumerchannel.basic_consume(queue='msg_queue', on_message_callback=consumer, auto_ack=True)

# Start producers and consumersproducer()
print('Waiting for the message...')
channel.start_consuming()

Choose the right implementation

If you are a stand-alone application and need a thread-safe queue, use。
If your application is distributed, useRedisorRabbitMQMore appropriate, they provide high availability, message persistence and reliable messaging mechanisms.

How to put http requests in queue to execute

Scenarios where HTTP requests are placed in queues and executed asynchronously are usually used to deal with high concurrency, background tasks, delayed tasks, etc. You can use a message queue system (such as、RedisorRabbitMQ) to put HTTP requests into the queue, consume tasks in the queue and execute corresponding HTTP requests.

Here I will show several different implementation methods for your reference.

1. Use the and requests library

You can encapsulate HTTP requests as tasks and put them into a queue, and then use multiple consumer threads to process requests in the queue asynchronously.

import queue
import threading
import time
import requests

# Create a queuetask_queue = ()

# HTTP request task handling functiondef handle_request():
    while True:
        url = task_queue.get()  # Get tasks from queue        if url is None:  # Termination conditions            break
        try:
            response = (url)
            print(f"ask {url} Response status: {response.status_code}")
        except Exception as e:
            print(f"ask {url} fail: {e}")
        task_queue.task_done()  # Tag task completion
# Producer: Put HTTP requests into queuedef producer():
    urls = [
        "/posts/1",
        "/posts/2",
        "/posts/3"
    ]
    
    for url in urls:
        print(f"Will URL {url} Put into the queue")
        task_queue.put(url)
        (1)  # Simulate the delay generated by the task
# Create multiple consumer threadsconsumer_threads = []
for i in range(3):
    t = (target=handle_request)
    ()
    consumer_threads.append(t)

# Start the producer threadproducer_thread = (target=producer)
producer_thread.start()

# Wait for the producer thread to completeproducer_thread.join()

# Send a termination signal to the consumer threadfor _ in range(3):
    task_queue.put(None)

# Wait for the consumer thread to completefor t in consumer_threads:
    ()

2. Use the Redis and requests libraries

Redis can be used as a distributed message queue, suitable for queuing HTTP requests into queues and executing asynchronously in distributed systems. You can use Redis's list data structure (lpush、brpop) to achieve it.

import redis
import requests
import time

# Create a Redis connectionr = (host='localhost', port=6379, db=0)

# Producer: Put HTTP requests into queuedef producer():
    urls = [
        "/posts/1",
        "/posts/2",
        "/posts/3"
    ]
    
    for url in urls:
        print(f"Will URL {url} Put in Redis queue")
        ('task_queue', url)
        (1)  # Simulate the delay generated by the task
# Consumer: Get request from the queue and executedef consumer():
    while True:
        url = ('task_queue')[1].decode('utf-8')  # Get tasks from queue        try:
            response = (url)
            print(f"ask {url} Response status: {response.status_code}")
        except Exception as e:
            print(f"ask {url} fail: {e}")

# Start producers and consumersproducer_thread = (target=producer)
consumer_thread = (target=consumer)

producer_thread.start()
consumer_thread.start()

# Wait for the producer thread to completeproducer_thread.join()

# Since the Redis queue will keep blocking and waiting for tasks, you can add exit logic as needed

3. Use the RabbitMQ and requests libraries

RabbitMQ provides a powerful message queueing mechanism suitable for large-scale messaging. You can create a task queue, put HTTP requests into the queue, and process requests in the queue through the consumer.

import pika
import requests
import time

# Connect to RabbitMQconnection = (('localhost'))
channel = ()

# declare queuechannel.queue_declare(queue='http_requests')

# Producer: Put HTTP requests into queuedef producer():
    urls = [
        "/posts/1",
        "/posts/2",
        "/posts/3"
    ]
    
    for url in urls:
        print(f"Will URL {url} Put in RabbitMQ queue")
        channel.basic_publish(exchange='',
                              routing_key='http_requests',
                              body=url)
        (1)  # Simulate the delay generated by the task
# Consumer: Process HTTP requestsdef consumer(ch, method, properties, body):
    url = ('utf-8')
    try:
        response = (url)
        print(f"ask {url} Response status: {response.status_code}")
    except Exception as e:
        print(f"ask {url} fail: {e}")

# Start Consumerchannel.basic_consume(queue='http_requests', on_message_callback=consumer, auto_ack=True)

# Start Producerproducer_thread = (target=producer)
producer_thread.start()

# Start the consumer and wait for the messageprint('Waiting for the consumer to process the HTTP request...')
producer_thread.join()
channel.start_consuming()

4. Use Celery asynchronous task queue

Celeryis a powerful asynchronous task queue suitable for distributed task execution. passCelery, you can encapsulate HTTP requests into tasks and put them into queues for asynchronous execution.

First, you need to installCeleryandrequests：

pip install celery requests

Then inConfiguration Celery:

from celery import Celery
import requests

app = Celery('http_requests', broker='redis://localhost:6379/0')

@
def fetch_url(url):
    try:
        response = (url)
        print(f"ask {url} Response status: {response.status_code}")
    except Exception as e:
        print(f"ask {url} fail: {e}")

Then submit the task in the main program:

from celery import Celery
from  import fetch_url

# Add tasks to queuefetch_url.apply_async(args=["/posts/1"])
fetch_url.apply_async(args=["/posts/2"])
fetch_url.apply_async(args=["/posts/3"])

Start Celery Worker:

celery -A celery worker --loglevel=info

Summarize

: Suitable for both stand-alone and multithreaded environments, HTTP requests can be executed asynchronously via queues.
Redis: Suitable for distributed environments, putting HTTP requests into Redis queues, and multiple consumers execute asynchronously.
RabbitMQ: A distributed environment suitable for highly concurrent tasks and messaging, using queues to manage HTTP requests.
Celery: Suitable for large-scale asynchronous task queues, you can use Redis or other message middleware as a proxy.

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