Storing the retrieved data in a database like MongoDB allows for efficient querying and trend analysis, such as tracking popular job titles or locations.

When dealing with large datasets, I paginate through results using the nextPageToken parameter provided by the API. This ensures I capture all available job postings.

• I use Java’s ScheduledExecutorService to automate scrapes, capturing travel deals at different times and comparing day-to-day price changes.

• Exporting reviews to JSON or CSV allows easy access to review data, which is helpful for tracking trends across different product types.

• I limit scraping to only essential data fields like closing prices or volume, reducing the number of requests and minimizing risk of detection.

• Some sites include a “date added” field, which I can scrape and filter to identify the latest products only.

Scheduling scrapes at different times captures Xiaomi’s price fluctuations and sales patterns, particularly around product launches.

Parsing JSON data from network requests provides structured product information, bypassing HTML parsing for faster data retrieval.

Parsing product ratings and user-generated content helps understand trends in consumer preferences on Xiaohongshu.