Building a Product Monitoring System with Python and LuckData Walmart API: A Full Workflow from Search to Alert

Introduction

In today's fast-paced e-commerce environment, the ability to stay updated on new product launches, price changes, and popularity trends is a vital competitive advantage for brands and operators. Traditional manual methods of checking and monitoring product data are no longer efficient or scalable. Automated data scraping and alerting systems have become the new standard, enabling real-time monitoring and decision-making with minimal human effort.

Why Use the Search API as the Entry Point?

Search APIs offer high flexibility and real-time access to dynamic data. Compared to static product listings, search-based endpoints have several advantages:

• Retrieve the most up-to-date product listings for any keyword;

• Pagination and sorting options mimic human behavior and help bypass anti-scraping mechanisms;

• Easily scale to monitor multiple keywords in batch mode;

• Allow targeted tracking of popular, new, or highly rated products, increasing the value of collected data.

The LuckData search API endpoint is structured as follows:

GET https://luckdata.io/api/walmart-API/get_hugc?page=1&keyword=computer

It allows you to search for Walmart products using keywords and pagination, returning structured JSON data with fields like product title, URL, price, rating, brand, review count, and more. This provides a solid foundation for downstream monitoring and analysis.

Three Core Stages of Building the Product Monitoring Tool

Stage 1: Keyword-Based Search and Product Data Scraping

First, we create a Python script to fetch Walmart product data based on a specific keyword. Here's a basic implementation:

import requests
API_KEY = 'your_luckdata_key'
HEADERS = {'X-Luckdata-Api-Key': API_KEY}
def search_products(keyword, page=1):
url = f'https://luckdata.io/api/walmart-API/get_hugc?page={page}&keyword={keyword}'
response = requests.get(url, headers=HEADERS)
if response.status_code == 200:
return response.json()
else:
print(f"Error: {response.status_code}")
return None
result = search_products("laptop")
for item in result.get('data', []):
print(item['title'], item['price'], item['product_url'])

This code retrieves the first page of search results for the keyword "laptop." You can extend it to include other fields such as brand, rating, review_count, and category_path for richer analysis.

Stage 2: Historical Data Comparison and Change Detection

Scraping data alone isn't sufficient—we need to compare current data with historical records to detect new product listings or changes in existing products. For this, we store each day's results in a local SQLite database and use product URLs as unique identifiers.

Here's how to implement basic storage and new item detection:

import sqlite3
import hashlib
def init_db():
conn = sqlite3.connect('walmart_monitor.db')
c = conn.cursor()
c.execute('''
CREATE TABLE IF NOT EXISTS products (
id TEXT PRIMARY KEY,
title TEXT,
price TEXT,
last_seen DATE DEFAULT CURRENT_DATE
)
''')
conn.commit()
conn.close()
def save_and_compare(products):
conn = sqlite3.connect('walmart_monitor.db')
c = conn.cursor()
new_items = []
for product in products:
pid = hashlib.md5(product['product_url'].encode()).hexdigest()
c.execute('SELECT * FROM products WHERE id = ?', (pid,))
exists = c.fetchone()
if not exists:
new_items.append(product)
c.execute('INSERT INTO products (id, title, price) VALUES (?, ?, ?)',
(pid, product['title'], product['price']))
else:
# You may add logic here to track price or rating changes
pass
conn.commit()
conn.close()
return new_items

We use an MD5 hash of the product URL as a unique ID to track each product. Items not found in the database are considered new. For more advanced monitoring, you can add fields and track changes over time.

Stage 3: Change Alerts and Automated Execution

Once changes are detected, the system should notify the relevant stakeholders. Here's a simple implementation using email notifications:

import smtplib
from email.message import EmailMessage
def send_email_alert(new_items, keyword):
if not new_items:
return
msg = EmailMessage()
msg['Subject'] = f'[Walmart] {len(new_items)} New Items Found for Keyword "{keyword}"'
msg['From'] = 'you@example.com'
msg['To'] = 'user@example.com'
content = "\n".join([f"{p['title']} - {p['product_url']}" for p in new_items])
msg.set_content(content)
with smtplib.SMTP('smtp.example.com') as server:
server.login('your_user', 'your_password')
server.send_message(msg)

Besides email, you can also integrate with:

• Slack bots;

• Webhooks for internal systems;

• LINE Notify or Telegram bots;

• Enterprise messaging platforms like WeCom or DingTalk.

This ensures fast, multi-channel delivery of alerts.

Multi-Keyword Monitoring and Scheduled Tasks

In real-world applications, you'll likely want to monitor multiple keywords across different categories or brands. You can use Python's APScheduler to automate this process at regular intervals.

Here's a complete example using a scheduled task:

from apscheduler.schedulers.blocking import BlockingScheduler
keywords = ['laptop', 'headphones', 'tv', 'smartwatch', 'gaming console']
def run_monitor():
for kw in keywords:
data = search_products(kw)
if data:
products = data.get('data', [])
new_items = save_and_compare(products)
send_email_alert(new_items, kw)
scheduler = BlockingScheduler()
scheduler.add_job(run_monitor, 'interval', hours=6)
scheduler.start()

You can also use cron expressions for more flexible scheduling, such as daily or weekly monitoring, depending on your needs.

Use Case Examples

This monitoring system is highly flexible and can serve a wide range of business use cases:

• Brands: Track competitor product launches, naming patterns, and pricing strategies;

• Distributors: Detect new SKUs early and adjust supply chain planning;

• SEO Teams: Analyze trending search terms to guide content and category optimization;

• E-commerce Operators: Evaluate category activity levels and adjust promotional strategies;

• Data Analysts: Monitor pricing fluctuations and generate retail trend insights.

Conclusion

By leveraging LuckData’s Walmart API, we can easily build a robust product monitoring system that covers data scraping, historical change detection, real-time alerting, and automated execution. This tool not only saves valuable time but also empowers more responsive and data-driven decisions in an increasingly competitive e-commerce landscape.

For users looking to get started with minimal setup, LuckData’s free tier and simple API design are more than sufficient to create a working prototype. For high-frequency or large-scale needs, upgrading to a Pro or Ultra plan can provide higher throughput and extended features.

Start with just one keyword and build your own monitoring radar. Let data work for you—and stay ahead of the curve in the retail battlefield.

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