How to Crawl Next‑Page Articles with Scrapy: A Step‑by‑Step Guide

This tutorial shows how to locate the "next page" link on a website, extract its URL using Scrapy selectors, add proper checks, and integrate the pagination logic into a Scrapy spider so that all article pages are crawled automatically.

Python Crawling & Data Mining
Python Crawling & Data Mining
Python Crawling & Data Mining
How to Crawl Next‑Page Articles with Scrapy: A Step‑by‑Step Guide

Introduction

In the previous articles we parsed the list page URLs and handed them to Scrapy for downloading. This article explains how to extract the URL of the next page and feed it to Scrapy, completing the pagination process.

Implementation Steps

1. Locate the "next page" link

First, find the link that points to the next page in the HTML. It is usually inside an a tag with the nextpage-numbers class.

2. Test the selector in scrapyshell

Open scrapyshell and try the selector. Two possible expressions are shown; the recommended one uses .next.page-numbers because the class appears twice and the dot notation selects it precisely without spaces.

3. Add a safety check for the extracted URL

After obtaining the next‑page URL, add a conditional check to avoid errors when the link is missing.

4. Debug the spider

Set breakpoints in the main spider file and run the script in debug mode.

5. Observe the results

After a short wait, the debug console shows the extracted URLs and confirms that pagination works.

6. Full crawling flow recap

The spider starts in parse(), extracts article URLs, hands them to Scrapy for download, then parse_detail() extracts the article content. After processing a page, the spider extracts the next‑page URL, passes it back to parse(), and repeats until no further pages exist.

7. Final notes

At this point the spider can traverse the entire site and collect all article contents, but it does not yet store the data locally or in a database. Future articles will cover data persistence.

Conclusion

Using the Scrapy framework together with CSS or XPath selectors, you can automatically crawl an entire website, extract pagination links, and retrieve every article without manual intervention.

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Python Crawling & Data Mining
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