Workflow:PrefectHQ Prefect Web Scraping Pipeline
| Knowledge Sources | |
|---|---|
| Domains | Data_Engineering, Web_Scraping |
| Last Updated | 2026-02-09 22:00 GMT |
Overview
End-to-end process for scraping article content from web pages using Prefect tasks with automatic retries, separating network I/O from HTML parsing for independent retry and caching.
Description
This workflow demonstrates how to enhance standard Python web scraping code with Prefect decorators to add production-ready features. It separates the pipeline into two distinct concerns: fetching raw HTML from URLs (network-bound, retryable) and parsing the HTML to extract structured article text (CPU-bound, deterministic). By isolating these concerns into separate tasks, each can be retried, cached, and observed independently. The flow iterates over a list of URLs and logs the extracted content.
Key outputs:
- Extracted article text from target web pages
- Structured logs of every fetch and parse operation in the Prefect UI
Scope:
- From a list of URLs to extracted text content
- Handles transient network failures with automatic retries
Usage
Execute this workflow when you need to reliably scrape content from web pages and want automatic retry handling for flaky network connections. It is suitable for content extraction pipelines, monitoring website changes, or building datasets from web sources.
Execution Steps
Step 1: Define Target URLs
Specify the list of web page URLs to scrape. The URLs are passed as a parameter to the flow, enabling reuse across different scraping targets.
Key considerations:
- URLs should point to pages with identifiable article or main content sections
- The list can be dynamically generated from a sitemap or database
Step 2: Fetch HTML Content
Download the raw HTML from each URL using an HTTP GET request. This task is configured with 3 retries and a 2-second delay between attempts to handle transient network failures. A 10-second timeout prevents hanging on unresponsive servers.
Key considerations:
- Network I/O is isolated in its own task for independent retry logic
- Non-2xx responses raise exceptions to trigger the retry mechanism
- Each URL fetch is a separate task run for granular observability
Step 3: Parse and Extract Article Text
Parse the downloaded HTML using BeautifulSoup to extract meaningful article content. The parser identifies the main content container (article or main tag), removes code blocks, and extracts text from headings, paragraphs, and list elements. The output is formatted with section separators for readability.
Key considerations:
- Code blocks are removed to focus on prose content
- Falls back gracefully when no article container is found
- Parsing is deterministic and does not require retries
Step 4: Output Results
Log the extracted text content for each URL. The flow uses log_prints to surface all print statements as structured Prefect logs, enabling review in the UI.
Key considerations:
- Empty content is flagged with a clear message
- Results can be redirected to storage (file, database) by modifying this step