Amazon Books Bestseller Charts Scraper

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Scrape Amazon book bestseller charts including Best Sellers, Hot New Releases, Movers and Shakers, and Most Wished For. Collects rank, title, author, ASIN, format, price, star rating, and review count across book sub-categories with timestamped snapshots for trend analysis.

What it does

For each chart type and category node you specify, the actor:

1. Fetches the chart page for the selected category
2. Extracts all 50 entries (pages 1 and 2) with rank, title, author, ASIN, price, rating, and format
3. Saves each entry as a dataset record with a captured_at timestamp

This makes it ideal for building rank-over-time datasets — schedule it hourly to track how books move up and down the charts.

Input

Example input

{
"chartTypes":["best_sellers","hot_new_releases"],
"categoryNodes":["3","6"],
"maxItems":200
}

Output

Each record contains:

Sample output record

{
"chart_type":"best_sellers",
"category":"Amazon Best Sellers",
"category_node":"3",
"rank":5,
"title":"Unreasonable Hospitality",
"author":"Will Guidara",
"asin":"0593418573",
"format":"Hardcover",
"price":16.00,
"star_rating":4.8,
"review_count":6638,
"cover_url":"https://images-na.ssl-images-amazon.com/...",
"product_url":"https://www.amazon.com/dp/0593418573",
"captured_at":"2026-05-23T04:19:54.245Z"
}

Common category node IDs

Use cases

• KDP analytics: Track how your own book climbs (or falls) across categories
• Publisher research: Monitor competitor titles and pricing
• Trend datasets: Build hourly rank-history time series for ML/analytics
• Retail intelligence: Price monitoring and format mix analysis

Scheduling

This actor is designed for recurring use. Schedule it hourly via Apify's scheduler to capture rank snapshots over time and build a rank-over-time dataset.

Notes

• Amazon's bestseller charts update hourly
• The actor fetches both pages per category (top 100 = pages 1–2 of 50 each)
• Residential proxy rotation is used to bypass Amazon's bot detection