Rakuten Data API: Extract Structured JSON in 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR: Use AlterLab's Extract API to get structured JSON from Rakuten by defining a schema for fields like title, price, and SKU. Send a POST request with your API key, target URL, and schema to receive validated, typed data—no HTML parsing needed. Get started with AlterLab in under 5 minutes.

Why use Rakuten data?

Rakuten hosts one of Japan's largest e-commerce ecosystems with millions of product listings. Engineering teams extract this public data for:

• Training price prediction models using historical SKU-level data
• Building competitive intelligence dashboards tracking category-level availability
• Enriching product catalogs with standardized attributes for AI-driven recommendations Unlike social media or financial data, Rakuten's public product pages offer clean, structured e-commerce signals ideal for ML feature engineering and market analysis.

What data can you extract?

From publicly accessible Rakuten product pages, you can reliably extract:

• title: Product name as displayed (string)
• price: Current sale price (string to preserve formatting)
• currency: JPY, USD, etc. (ISO 4217 string)
• sku: Unique product identifier (string)
• availability: In-stock status or pre-order flags (string)
• rating: Aggregate review score (string, e.g., "4.2") AlterLab returns these as typed JSON matching your schema—no regex or DOM traversal required. For example, a smartphone listing yields:

{
  "title": "iPhone 15 Pro 256GB Black Titanium",
  "price": "198,000",
  "currency": "JPY",
  "sku": "4549995293327",
  "availability": "In Stock",
  "rating": "4.6"
}

The extraction approach

Direct HTTP requests to Rakuten return HTML wrapped in anti-bot challenges (JavaScript rendering, fingerprinting, rate limits). Parsing this with BeautifulSoup or Puppeteer creates fragile pipelines that break when:

• Rakuten updates its frontend framework
• Anti-bot mechanisms rotate challenge types
• Dynamic content loads via AJAX after initial HTML AlterLab's data API abstracts this complexity. It handles headless browsing, proxy rotation, and challenge solving internally, returning only the structured data you requested. This shifts maintenance burden from your team to our infrastructure—critical for production pipelines where downtime corrupts downstream analytics.

Quick start with AlterLab Extract API

Install the Python SDK and make your first extraction call:

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "Product title from Rakuten listing"
    },
    "price": {
      "type": "string",
      "description": "Current price as displayed"
    },
    "currency": {
      "type": "string",
      "description": "ISO 4217 currency code (e.g., JPY)"
    },
    "sku": {
      "type": "string",
      "description": "Unique product identifier"
    },
    "availability": {
      "type": "string",
      "description": "Stock status (In Stock, Out of Stock, etc.)"
    },
    "rating": {
      "type": "string",
      "description": "Average customer rating (0-5 scale)"
    }
  }
}

result = client.extract(
    url="https://item.rakuten.co.jp/example/store/123456789/",
    schema=schema,
)
print(result.data)

See full Extract API documentation for parameter details and error handling.

Equivalent cURL request:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://item.rakuten.co.jp/example/store/123456789/",
    "schema": {
      "properties": {
        "title": {"type": "string"},
        "price": {"type": "string"},
        "currency": {"type": "string"},
        "sku": {"type": "string"},
        "availability": {"type": "string"},
        "rating": {"type": "string"}
      }
    }
  }'

Both examples return the structured JSON object shown earlier. The SDK handles retries, timeout configuration, and response validation automatically.

Define your schema

AlterLab validates output against your JSON Schema definition. Key benefits:

• Type safety: Fields return as specified types (string/number/boolean)—no casting needed
• Field filtering: Only requested properties appear in output, reducing payload size
• Default values: Use "default": "N/A" for missing fields instead of null
• Format constraints: Add "pattern": "^JPY|USD|EUR$" to currency for validation For Rakuten, we recommend keeping fields as strings since prices may include commas (¥1,980) and SKUs often contain leading zeros. Numbers would lose this formatting. The schema above ensures you get exactly what you defined—no more, no less.

Handle pagination and scale

Rakuten category pages typically show 20-40 items per page. For large-scale extraction:

1. Discover pagination: Extract next-page URL from schema (add "next_page": {"type": "string", "format": "uri"})
2. Batch processing: Use async jobs for 100+ URLs—AlterLab queues requests and returns results when ready
3. Rate control: Stay within limits by adjusting concurrency (default 5 req/sec; contact sales for higher tiers)
4. Cost monitoring: Each extraction costs $0.001-$0.50 based on complexity. View pricing details to estimate monthly spend.

Example async batch job:

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

async def extract_product(url):
    schema = {"type": "object", "properties": {"title": {"type": "string"}, "price": {"type": "string"}}}
    return await client.extract_async(url=url, schema=schema)

urls = [f"https://item.rakuten.co.jp/store/search?page={i}" for i in range(1, 101)]
results = await asyncio.gather(*[extract_product(url) for url in urls])

This processes 100 URLs concurrently while respecting rate limits. Results arrive as a list of validated JSON objects—ready for direct insertion into your data warehouse.

Key takeaways

• Schema-first design: Define your output structure upfront to get typed JSON without parsing
• Public data only: Extract what's visible on logged-out pages; respect robots.txt and ToS
• Zero maintenance: AlterLab handles anti-bot evolution so your pipeline stays stable
• Predictable costs: Pay per extraction with no minimums—ideal for variable workloads
• Production-ready: Async batching and schema validation build reliable data pipelines

Start extracting structured Rakuten data today. Install AlterLab and run your first extraction in minutes. Hit reply if you have questions.