๐ฌ๐ผ๐๐ฟ ๐ฆ๐ฐ๐ฟ๐ฎ๐ฝ๐ฒ๐ฟ ๐ฅ๐ฒ๐๐๐ฟ๐ป๐ ๐ฎ๐ฌ๐ฌ ๐ข๐ ๐ฎ๐ป๐ฑ ๐ฆ๐๐ถ๐น๐น ๐๐ถ๐ฒ๐: ๐ณ ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐๐ฝ๐น๐ฎ๐ฐ๐ฒ ๐๐ต๐ฒ๐ฐ๐ธ๐ ๐ ๐จ๐๐ฒ
The hardest scraper bug to find is not a crash. It is a result looking right but remaining wrong. Your request returns 200 OK. Your selector works. Your rows exist. Your dashboard looks good. Your data still lies.
Marketplaces are not simple lists. They are changing machines. Use these checks before you trust your data.
Separate active and sold listings. Active listings show supply. Sold listings show demand. Mixing these makes your analysis unreliable. Use a record type to tell them apart.
Track missing items. Do not delete them from your records. Log a tracking event. You know something changed.
Split market country and seller country. One is the search page. One is the seller location. Merging these creates noise.
Check results against your keywords. Broad queries return unrelated items. Use a second layer to verify products.
Link price to condition. Condition changes the meaning of the price. Do not hide condition in a text string.
Save price history. One scrape shows a snapshot. Multiple scrapes show change. The change is more useful than the snapshot.
Flag similar listings for review. Do not let the scraper make the final call. Create a queue for human review.
Do not trust a scraper because it finds rows. Trust it when it gives context.
Know what exists. Know what sold. Know what changed. Know where it was found. Know where the seller lives. Know if the price changed. Know what needs review.
This is the difference between scraping a page and modeling a marketplace.
Which check do you use for production data?
Source: https://dev.to/datakaz/your-scraper-returns-200-ok-and-still-lies-7-marketplace-checks-i-use-51j7