How to batch download or upload files using Scrapy?

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Release: 2023-06-23 09:37:39
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Scrapy is a powerful Python crawler framework that can greatly simplify the crawler development and deployment process. In practical applications, we often need to use Scrapy to download or upload files in batches, such as pictures, audio or video resources. This article will introduce how to use Scrapy to implement these functions.

  1. Batch download files

Scrapy provides multiple ways to download files in batches. The simplest way is to use the built-in ImagePipeline or FilesPipeline. These two pipelines can automatically extract images or file links from HTML pages and download them to the local disk.

To use these two pipelines, we need to configure them in the settings.py file of the Scrapy project. For example, if we want to download images, we can configure it as follows:

ITEM_PIPELINES = {'scrapy.pipelines.images.ImagesPipeline': 1}
IMAGES_STORE = '/path/to/download/folder'
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Where, ITEM_PIPELINES is the list of pipes, and we add ImagesPipeline to the first position. IMAGES_STORE is the saving path of the downloaded file.

Next, we need to define the file types to be downloaded in the crawler's items.py file. For pictures, it can be defined like this:

import scrapy

class MyItem(scrapy.Item):
    name = scrapy.Field()
    image_urls = scrapy.Field()
    images = scrapy.Field()
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Among them, name is the name of the picture, image_urls is a list of picture links, and images is the location of the downloaded picture.

In the spider.py file, we need to add an image link to the item and put the item into the pipeline queue. For example:

import scrapy
from myproject.items import MyItem

class MySpider(scrapy.Spider):
    name = 'myspider'
    start_urls = ['http://example.com']

    def parse(self, response):
        item = MyItem()
        item['name'] = 'example'
        item['image_urls'] = ['http://example.com/image.jpg']
        yield item
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In this way, when we run Scrapy, the image will be automatically downloaded from the example website and saved to the specified folder. If you want to download other types of files, such as PDF or videos, you can use FilesPipeline. The method is similar to ImagePipeline. You only need to replace ImagePipeline with FilesPipeline in the settings.py file and define the file type to be downloaded in the items.py file. , and add the corresponding links and items in the spider.py file.

  1. Batch upload files

In addition to downloading files, Scrapy can also help us upload files in batches. Suppose we need to upload files from the local disk to a remote server, we can use the FormRequest provided by Scrapy.

In the spider.py file, we can use FormRequest to construct a POST request to send the local file to the server as binary data. For example:

import scrapy

class MySpider(scrapy.Spider):
    name = 'myspider'
    start_urls = ['http://example.com']

    def parse(self, response):
        with open('/path/to/local/file', 'rb') as f:
            data = f.read()
        
        yield scrapy.FormRequest('http://example.com/upload',
                                 formdata={'file': data},
                                 callback=self.parse_result)
    
    def parse_result(self, response):
        # 处理上传结果
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In the above example, we open a local file, read the file content, and send it to the server as binary data. After receiving the request, the server saves the file in the specified directory and returns the upload result. We can process the upload results in the parse_result function, such as printing the upload results, saving the upload results, etc.

Summary

Scrapy provides multiple ways to download or upload files in batches. For the most common file types, such as pictures and documents, you can use the built-in ImagePipeline or FilesPipeline to automatically download to your local disk. For other types of files, you can use FormRequest to construct a POST request and send the local file to the server as binary data. Using Scrapy to download or upload files in batches can greatly simplify your workload and improve efficiency.

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