How to implement scheduled task crawler with Scrapy

With the development of the Internet, web crawlers are widely used for data collection and analysis. Scrapy is a commonly used crawler framework that provides powerful functions and efficient processing capabilities. For business scenarios that require regular crawling of data, how does Scrapy implement scheduled task crawlers? This article will introduce Scrapy's method of implementing scheduled task crawlers and provide examples.

1. How to implement Scrapy scheduled tasks

Scrapy is a crawler framework developed based on Python. Scheduled tasks can be implemented through Python's scheduled task tool - APScheduler. APScheduler is a lightweight scheduled task framework that supports multiple task triggers and schedulers. In the Scrapy framework, implementing scheduled tasks through APScheduler is relatively simple and easy to maintain.

2. Specific steps for implementing Scrapy scheduled tasks

1. Install the APScheduler package

Use pip to install the APScheduler package in the Scrapy environment and execute the following command That’s it:

pip install apscheduler

2. Create a scheduled task

Add the following code in the settings.py file of the Scrapy project:

# 配置APScheduler

SCHEDULER = "scrapy_apscheduler.schedulers.Scheduler"

# 启用持久化
SCHEDULER_PERSIST = True

# 任务调度器
SCHEDULER_JOBSTORES = {
    'default': SQLAlchemyJobStore(url='sqlite:///jobs.sqlite')
}

# 调度器时间间隔
SCHEDULER_EXECUTORS = {
    'default': {'type': 'threadpool', 'max_workers': 20},
    'processpool': ProcessPoolExecutor(5)
}

# 任务触发器
SCHEDULER_TRIGGER = 'interval'

# 时间间隔
SCHEDULER_SECONDS = 3600

# 启用定时任务扩展
EXTENSIONS = {
    'scrapy_apscheduler.extension.SchedulerExtension': 500,
}

In the above code, we Configure APScheduler related parameters and set the time interval to 3600 seconds, which means that the crawler task will be executed every hour.

3. Writing crawler tasks

Next, we can start writing specific crawler tasks. We still add the following code to the settings.py file in the Scrapy project:

from scrapy_apscheduler.jobstores import DjangoJobStore

# 配置定时任务
JOBS = [
    {
        'id': 'task1',
        'func': '项目名称.spiders.爬虫名称',
        'args': None,
        'trigger': {
            'type': 'cron',
            ‘hour’: ’14',
            ‘minute’: ’30'
        },
    }
]

# 配置任务存储
SCHEDULER_JOBSTORES = {
    'default': DjangoJobStore(),
}

In the above code, we specify the crawler task with an execution time of 14:30. We need to modify the content in JOBS according to our own needs. The func parameter specifies the crawler task to be executed. For example, in the above example, just replace the crawler name with your own crawler name.

4. Start the Scrapy scheduled task

Finally, introduce the following code into the startup script of the Scrapy project:

from scrapy.cmdline import execute
from scrapy_apscheduler.scheduler import Scheduler

scheduler = Scheduler()
scheduler.start()

execute(['scrapy', 'crawl', '爬虫名称'])

scheduler.shutdown()

In the above code, we introduced The scheduler for Scrapy scheduled tasks, starts the scheduler before executing the crawler task, and closes the scheduler after execution.

3. Case Analysis

The following is an example of crawling weather forecast information and updating it regularly.

import scrapy
import json

class WeatherSpider(scrapy.Spider):
    name = "weather"
    allow_domains = ["tianqi.com"]
    start_urls = ["http://www.tianqi.com/"]
    
    def parse(self, response):
        # 爬取天气预报信息
        weather_info = {"city": "chengdu", "temperature": "25C", "condition": "sun"}
        yield weather_info
        
        # 更新天气预报信息
        with open("weather_file.txt", "w") as f:
            json.dump(weather_info, f)

After writing the crawler file, follow the steps to implement the Scrapy scheduled task and configure the task and scheduled task script in settings.py.

4. Summary

As an efficient and reliable crawler framework, Scrapy is relatively simple to implement scheduled task crawlers. Through the APScheduler tool, we can schedule scheduled tasks and achieve data update and persistence. In actual business scenarios, Scrapy scheduled task crawlers are widely used, providing strong support for enterprise data analysis and application development.

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