How to use Python's asynchronous and scheduled tasks to improve program concurrency and execution efficiency?

Asynchronous tasks and scheduled tasks

For some operations in web applications, they may take a long time to complete, or their execution time cannot be determined. For these operations, if the user only needs to know that the server has received the request and does not need to get the execution result of the request immediately, then we can process them asynchronously. If the use of cache is the first priority for optimizing website performance, then asynchronousizing tasks that take time or whose execution time is uncertain is the second priority for optimizing website performance. Simply put, anything that can be postponed should not be done immediately.

In the previous chapter, we took sending text messages and uploading files to cloud storage as examples. Among these two operations, the former is an operation with uncertain time (because as the caller, we cannot determine the response time of the third-party platform), and the latter is a time-consuming operation (if the file is large or the third-party platform is unstable, it may cause It takes a long time to upload). Obviously, both operations can be asynchronous.

In Python projects, we can use multi-threading or use the third-party library Celery to achieve asynchronous processing.

Use Celery to achieve asynchronousization

Celery is a Python asynchronous task queue/message queue, which can easily complete the processing of asynchronous tasks. Using Celery, tasks can be distributed to multiple task executors, which can be a single process or multiple processes or multiple hosts. Celery also supports task priority, task result saving, task retry and other functions.

Using Celery to implement asynchronousization requires the following steps:

Install Celery

pip install celery

Create a Celery in the project Application

from celery import Celery
app = Celery('tasks', broker='pyamqp://guest@localhost//')

Define tasks

@app.task
def add(x, y):
    return x + y

Call tasks in the project

result = add.delay(4, 4)
print(result.get(timeout=1))

Use multi-threading to achieve asynchronousization

threading## in Python # Modules can be used to create multi-threads. Using multi-threading, time-consuming tasks can be executed in new threads without affecting the execution of the main thread.

Using multi-threading to implement asynchronousization requires the following steps:

Import

threading module

import threading

Define a function as a task

def task():
    print('Hello from task')

Create a new thread and start it

t = threading.Thread(target=task)
t.start()

Timing task

Some tasks need to be executed at a specific time, then we need to use timing Task. There are multiple third-party libraries in Python that can be used to implement scheduled tasks, such as

schedule, APScheduler, etc. Let's take APScheduler as an example to explain how to implement scheduled tasks.

Using

APScheduler requires the following steps to implement scheduled tasks:

Installation

APScheduler

pip install apscheduler

Import

APScheduler Module

from apscheduler.schedulers.blocking import BlockingScheduler

Create a

BlockingScheduler instance and add tasks

def task():
    print('Hello from task')
scheduler = BlockingScheduler()
scheduler.add_job(task, 'interval', seconds=5)
scheduler.start()

The above code will be executed every 5 seconds A

task function.

Comparison between Celery and multi-threading

Although both Celery and multi-threading can implement asynchronous processing, there are some differences, advantages and disadvantages between them.

Advantages and disadvantages of Celery

Advantages:

• Can distribute tasks to multiple task executors, thereby achieving task load balancing and improving Efficiency of task processing.

• Supports functions such as task priority, task result saving, and task retry.

• Supports multiple message transmission protocols, such as AMQP, Redis, RabbitMQ, etc.

• Can be easily integrated into web frameworks such as Django and Flask.

Disadvantages:

• The installation and configuration process may be cumbersome.

• May increase system complexity.

Advantages and disadvantages of multi-threading

Advantages:

• It is relatively simple to implement and does not require the installation of additional libraries.

• Can quickly complete task processing on the local machine.

Disadvantages:

• Tasks cannot be distributed to multiple task executors, so task load balancing cannot be achieved.

• Functions such as task priority, task result saving, and task retry cannot be easily implemented.

• may cause system performance degradation because multi-threading has limited concurrency performance.

Selection of scheduled tasks

In Python, there are multiple third-party libraries that can be used to implement scheduled tasks, such as

schedule, APScheduler etc. These libraries have their own advantages and disadvantages, and we can choose the appropriate library to implement scheduled tasks according to specific needs.

schedule library

• is simple and easy to use. You only need to call the

  schedule function to implement scheduled tasks.

• Cannot achieve load balancing of tasks and concurrent execution of tasks.

APScheduler library

• Supports multiple schedulers, such as BlockingScheduler, BackgroundScheduler, AsyncIOScheduler, etc.

• Supports multiple triggers, such as date, interval, cron, interval_from_last, etc.

• Supports concurrent execution of tasks and load balancing.

• Can be easily integrated into web frameworks such as Django and Flask.

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