DEV Community

Mohammad Waseem
Mohammad Waseem

Posted on

Streamlining Legacy Databases with Web Scraping: A QA Lead’s Approach

#database #webscraping #legacy #qualityassurance

Addressing Database Clutter in Legacy Systems Through Web Scraping

In the realm of legacy codebases, excessive and redundant data entries often accumulate over time, leading to cluttered production databases that hinder performance and data integrity. As a Lead QA Engineer, one effective strategy to mitigate this challenge involves leveraging web scraping techniques to extract, analyze, and clean up obsolete or duplicated data.

Understanding the Challenge

Legacy systems frequently lack robust data management features, resulting in a buildup of unused or redundant records. Traditional database cleanup methods can be complex, risky, and time-consuming, especially when direct modifications could impact system stability. To circumvent this, using web scraping to extract data for analysis allows safer decision-making about which entries to retain or delete.

Strategy Overview

The core idea revolves around integrating web scraping into the testing and validation pipeline. By programmatically harvesting data from the application's user interface or services, we can identify anomalies, duplicates, or outdated information without directly interfacing with the database.

Implementation Approach

1. Data Extraction via Web Scraping

Suppose we have a legacy web application with extensive list pages displaying database records. Using tools like BeautifulSoup in Python, we can scrape and parse these pages:

import requests
from bs4 import BeautifulSoup

def extract_records(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    records = []
    for row in soup.select('table#records tbody tr'):
        record_id = row.find('td', class_='id').text.strip()
        name = row.find('td', class_='name').text.strip()
        date_modified = row.find('td', class_='modified').text.strip()
        records.append({"id": record_id, "name": name, "modified": date_modified})
    return records

# Example usage
records = extract_records('https://legacyapp.com/records')
Enter fullscreen mode Exit fullscreen mode

This extracts all records visible on the webpage, including attributes like ID, name, and modification date.

2. Data Analysis & Deduplication

Once data is scraped, the next step involves analyzing for duplicate or obsolete records. Python’s pandas library excels here:

import pandas as pd

# Convert list of dicts to DataFrame
df = pd.DataFrame(records)

# Find duplicates based on name
duplicates = df[df.duplicated(subset=['name'], keep=False)]

# Find outdated entries older than a threshold
outdated = df[pd.to_datetime(df['modified']) < pd.Timestamp('2022-01-01')]
Enter fullscreen mode Exit fullscreen mode

Combining these insights helps identify candidates for cleanup.

3. Validation & Safeguarding

Before deletion, cross-validate scraped insights against the database using a read-only or staging environment, or via database snapshots, to ensure accuracy. Implement scripts to flag or export records for manual review.

# Generate deletion list
to_delete = outdated['id'].tolist()
with open('records_for_deletion.txt', 'w') as f:
    for record_id in to_delete:
        f.write(f"DELETE FROM records WHERE id = {record_id};\n")
Enter fullscreen mode Exit fullscreen mode

4. Executing Cleanup

The actual deletion scripts should be run within a controlled environment, with backups and rollback plans in place.

Benefits & Best Practices

  • Safety: Avoids direct schema modifications; provides a non-intrusive view of data health.
  • Auditability: Maintains a record of what data was considered obsolete, aiding compliance.
  • Automation: Integrate scrapers into CI/CD pipelines for continuous monitoring.

Conclusion

Using web scraping as part of a QA and database hygiene strategy empowers teams to manage and declutter legacy systems effectively. It circumvents heavy database changes while providing valuable insights, enabling safer, data-driven decisions. As approach complexity grows, consider combining scraping with AI-powered deduplication tools or integrating directly with APIs when possible.

Ensuring application stability during cleanup is paramount. Always validate scraped data, maintain backups, and proceed incrementally for optimal results.

Keywords: web scraping, legacy systems, database cleanup, QA, automation, data analysis

🛠️ QA Tip

To test this safely without using real user data, I use TempoMail USA.

Top comments (0)