Building Incremental Crawlers Using Scrapy for Large Websites

Crawling large websites efficiently is a challenging task, especially when we want to incrementally update our stored data. Incremental crawling focuses on visiting and extracting data only from pages that have been updated or added since the last crawl, thus saving bandwidth and computational resources. In this article, we will explore how to build an incremental crawler using Scrapy, a popular open-source web crawling framework for Python.

Why Incremental Crawling?

Incremental crawling offers several advantages, particularly for large websites:

• Reduced Bandwidth: By only downloading new or changed content, incremental crawlers consume less bandwidth.
• Efficiency: Saves computational power and time as processing only involves new data.
• Up-to-date Data: Ensures that the crawled data is kept current without significant delays.

Setting Up Scrapy

Before we dive into the implementation of an incremental crawler, let's ensure that you have Scrapy installed. You can install it using pip in your Python environment:

pip install scrapy

Creating A New Scrapy Project

To start a new Scrapy project, use the following command in the terminal:

scrapy startproject mycrawler

This command creates a directory named mycrawler with the necessary files and folders.

Structuring The Spider

The core of the crawling process in Scrapy is the spider. Here, we will define a spider that crawls pages only if they meet our incremental criteria (e.g., updated recently). Open the spiders directory and create a file named incremental_spider.py:

import scrapy

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

    def parse(self, response):
        # Extract data from the response here
        pass

Handling Incremental Crawling with ETags and Last-Modified

Modern web servers support HTTP headers that help determine if content has changed. The two headers used prominently are ETag and Last-Modified.

ETag Header

An ETag is a unique identifier assigned by the web server for a specific version of a resource. Whenever the resource changes, its ETag value changes too.

Here's how you can modify the spider to check for ETag:

class IncrementalSpider(scrapy.Spider):
    # ... existing code ...

    custom_settings = {
        'HTTPCACHE_ENABLED': True,
        'HTTPCACHE_STORAGE': 'scrapy.extensions.httpcache.FilesystemCacheStorage',
    }

    def parse(self, response):
        # If ETag indicates a change, carry out necessary actions
        etag = response.headers.get('ETag', None)
        self.log(f'ETag: {etag}')

        if self.is_resource_changed(response):
            # Extract and store data
            pass

    def is_resource_changed(self, response):
        # Custom logic to determine if the resource has changed
        # (using saved ETag values locally in persistent storage)
        pass

Last-Modified Header

The Last-Modified header indicates the exact time the resource was last modified. You can also leverage this in a similar manner to determine changes since the last crawl.

Storing and Comparing Data

To implement a truly robust incremental crawling strategy, you'll need to store either the ETag values or the timestamps from the Last-Modified headers. You can persist them in a database or flat file, against which you will compare in subsequent crawls.

Advantages and Challenges

Advantages: Faster crawls and preserved server resources.

Challenges: Complexity of setting up storage mechanisms for change metadata and implementation specifics tailored to individual sites.

By setting up an incremental crawling mechanism using Scrapy as described, you will make your data collection processes more efficient and easy-to-update, especially when working with massive sites where changes are ongoing.