Tabelog Scraper — Japan Restaurant Ratings, Reviews & Genres

Tabelog Scraper — Japan Restaurant Ratings, Reviews, Genres & Stations (食べログ)

Scrape Tabelog (食べログ / tabelog.com), Japan's most prestigious and authoritative restaurant discovery directory, and extract structured dining intelligence at enterprise scale. This automated Tabelog scraper systematically captures restaurant names, community star ratings, total review volume, bookmark saves, multi-genre culinary classifications, nearest train stations with walking distances in meters, geographic regions, and food imagery — without requiring login accounts, Japanese phone verifications, or manual scraping workflows.

Export thousands of Japanese culinary venues directly to JSON, CSV, Excel, XML, or integrate via REST API and automated webhooks in minutes.

Engineered for food & beverage analysts, restaurant technology & POS software providers, hospitality booking platforms, culinary travel guides, supply chain logistics, and B2B lead generation specialists who require high-fidelity Japanese dining data without hitting verification walls.

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⚡ At a glance

Feature	Specification
Target Website	Tabelog (Japan's #1 Gourmet & Restaurant Review Platform)
Input Flexibility	Direct search listing URLs (rstLst) by prefecture, ward, city, and culinary cuisine
Paging Scale	1,000+ restaurants across multi-page deep pagination
Data Fields	12+ structured attributes per dining venue
Rating Accuracy	Strict community ratings (1.00–5.00 scale) with engagement metrics
Location Precision	Nearest train/subway station name and parsed walking distance in meters
Cuisine Taxonomy	Parsed array (genres) and comma-separated string (genresText)
Deduplication	100% unique records keyed by stable Tabelog numeric restaurantId
Access Architecture	Stealth anti-detect browser automation paired with Residential IP proxy rotation
Export Formats	JSON, CSV, Excel, XML, HTML Table, Apify Dataset API

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🔎 Why scrape Tabelog?

Tabelog is the undisputed gold standard for gastronomic reputation across Japan. Unlike Western review platforms where average ratings frequently inflate to 4.5 stars, Tabelog enforces a famously rigorous statistical curve:

• Under 3.00: New or unreviewed dining spots.
• 3.00 – 3.49: Solid, reliable everyday local dining venues.
• 3.50 – 3.99: Exceptional culinary destinations in the top 3% of Japan.
• 4.00+: Legendary, Michelin-grade culinary institutions in the top 0.1% nationwide.

This mathematical rigor makes Tabelog ratings the ultimate quality filter for food service intelligence. However, programmatic data acquisition faces significant challenges:

• No public REST API: Tabelog does not offer an open API for commercial dining search and bulk review harvesting.
• Edge protection walls: Automated HTTP requests are challenged by edge security verification checks.
• Complex Japanese DOM markup: Station distances, bookmark counts, and multi-layered cuisine categories are embedded in localized Japanese typography.
• Bandwidth overhead: Heavy high-resolution food photography inflates proxy data costs if not handled properly.

This Tabelog data extractor solves every layer: bypassing edge verification via stealth browser automation, blocking non-essential multimedia assets to reduce data consumption by over 80%, parsing Japanese station strings, and delivering deduplicated rows ready for analysis.

Primary commercial use cases

• Benchmark Regional Dining Markets: Compare average rating curves, review volumes, and bookmark velocity across Tokyo, Osaka, Kyoto, and Fukuoka.
• Generate High-Converting B2B Leads: Supply restaurant technology vendors (POS systems, reservation software, supplier ordering) with curated lists of high-performing venues.
• Station-Level Food Logistics Planning: Calculate culinary density around specific subway and commuter rail stations for delivery hub placement.
• Curate Automated Travel Guides: Populate luxury hospitality applications, concierge booking platforms, and tourism itineraries with top-rated venues.
• Track Culinary Trends: Detect rising cuisine genres (craft beer bars, specialty ramen styles, wagyu dining) before they saturate regional markets.

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✨ Core capabilities

• ✅ Universal Search URL Support: Paste any Tabelog listing URL filtered by prefecture, neighborhood, station, or cuisine (e.g., https://tabelog.com/tokyo/rstLst/ramen/ or https://tabelog.com/osaka/rstLst/sushi/).
• ✅ Deep 1,000+ Restaurant Pagination: Automatically traverses Tabelog's /N/ pagination hierarchy until your target record volume is reached.
• ✅ Strict Rating Extraction: Extracts the true numeric Tabelog score (rating) alongside total user reviews (reviewCount) and community saves (savedCount).
• ✅ Transit & Walking Distance Analytics: Parses the primary train/subway station (station) and isolates the precise walking distance in meters (stationDistanceM).
• ✅ Culinary Taxonomy Categorization: Captures localized Japanese cuisine classifications both as a structured array (genres) and as a normalized string (genresText).
• ✅ Bandwidth Optimized Execution: Intelligently intercepts and blocks resource-heavy images, video, and fonts during navigation, slashing proxy bandwidth by ~80%.
• ✅ Stealth Anti-Detect Automation: Employs advanced headless browser fingerprinting designed to solve complex JavaScript verification challenges seamlessly.
• ✅ Zero Duplicate Assurance: Global in-memory deduplication guarantees that overlapping category queries never produce duplicate records.

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🎯 Practical business applications

1. Restaurant Technology & POS Sales Prospecting

Sales representatives selling modern point-of-sale systems, kitchen display hardware, or mobile ordering solutions can target venues with high bookmark counts (savedCount > 2000) but moderate ratings. These venues handle massive foot traffic and represent prime candidates for operational efficiency software.

2. Food & Beverage Supplier Sourcing

Wholesale meat distributors, sake breweries, and seafood suppliers can scrape specific cuisine categories like yakiniku, sushi, or kaiseki across metropolitan hubs to build prospect directories filtered by neighborhood and station proximity.

3. Food Delivery Hub Placement

Ghost kitchen operators and food delivery aggregators can evaluate dining density around major transit hubs by filtering for venues within 500 meters of key railway stations (stationDistanceM <= 500), identifying underserviced cuisine niches.

4. High-End Tourism & Hospitality Concierge

Travel agencies and luxury credit card concierge desks can maintain automated feeds of restaurants with a Tabelog rating of 3.70+ across major tourist destinations, ensuring VIP clients receive reservations only at verified top-tier establishments.

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📥 Input settings

The actor runs immediately with default settings (demonstrating Tokyo ramen dining), while supporting multi-URL batch crawling.

Parameter	Type	Default	Prefill	Description
startUrls	Array	Tokyo Ramen URL	Tokyo Ramen URL	One or more Tabelog listing/search URLs. Build your filter on tabelog.com and paste the result URL.
maxItems	Integer	0	20	Maximum unique restaurants to scrape across all targets. 0 = unlimited (bounded by pagination).
maxPages	Integer	0	0	Maximum pagination pages per URL (20 restaurants per page). 0 = unlimited deep pagination.
maxConcurrency	Integer	2	2	Number of URLs to process concurrently. Keep low (1–2) for optimal proxy efficiency.
proxyConfiguration	Object	Residential	Residential	Proxy settings. Residential proxies are required for seamless edge challenge resolution.

Sample configurations

1. Quick exploratory test (Prefill default)

{
  "startUrls": [
    { "url": "https://tabelog.com/tokyo/rstLst/ramen/" }
  ],
  "maxItems": 20,
  "maxPages": 0
}

2. Multi-city culinary intelligence (Tokyo Ramen & Osaka Sushi)

{
  "startUrls": [
    { "url": "https://tabelog.com/tokyo/rstLst/ramen/" },
    { "url": "https://tabelog.com/osaka/rstLst/sushi/" }
  ],
  "maxItems": 500,
  "maxPages": 0,
  "maxConcurrency": 2
}

3. Deep neighborhood dive (Ginza Fine Dining)

{
  "startUrls": [
    { "url": "https://tabelog.com/tokyo/A1301/A130101/rstLst/" }
  ],
  "maxItems": 1000,
  "maxPages": 0
}

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📤 Output data structure

Each item in your Apify dataset represents an individual dining establishment.

Data field dictionary

🍽️ Restaurant Identity & Profile

Field	Type	Coverage	Description
restaurantId	String	🟢 High (100%)	Stable numeric Tabelog venue ID (deduplication key).
name	String	🟢 High (100%)	Official restaurant name in Japanese.
url	String	🟢 High (100%)	Canonical URL to the restaurant's Tabelog landing page.
imageUrl	String	🟢 High (98%+)	URL of the primary featured venue or dish photograph.
area	String	🟢 High (100%)	Geographic area identifier derived from the source URL (e.g., tokyo).

⭐ Ratings & Community Engagement

Field	Type	Coverage	Description
rating	Number	🟢 High (98%+)	Official Tabelog score (1.00–5.00). Null for unranked/new listings.
reviewCount	Number	🟢 High (99%+)	Total count of published customer reviews.
savedCount	Number	🟢 High (100%)	Number of users who have saved/bookmarked the venue.

🏷️ Cuisine & Taxonomy

Field	Type	Coverage	Description
genres	Array	🟢 High (100%)	Array of cuisine categories (e.g., ["ラーメン", "つけ麺"]).
genresText	String	🟢 High (100%)	Comma-separated string of all tagged cuisine classifications.

🚉 Transit & Walking Distance

Field	Type	Coverage	Description
station	String	🟢 High (100%)	Raw station description string (e.g., "広尾駅 840m").
stationDistanceM	Number	🟢 High (99%+)	Parsed walking distance from nearest station in meters (840).
scrapedAt	String	🟢 High (100%)	ISO 8601 UTC timestamp of data retrieval.

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Real JSON output record

{
  "restaurantId": "13306356",
  "name": "THE TOKYO CLUB",
  "rating": 3.16,
  "reviewCount": 22,
  "savedCount": 1974,
  "station": "広尾駅 840m",
  "stationDistanceM": 840,
  "genres": [
    "ダイニングバー",
    "ハンバーガー",
    "ラーメン・つけ麺(担々麺)"
  ],
  "genresText": "ダイニングバー, ハンバーガー, ラーメン・つけ麺(担々麺)",
  "area": "tokyo",
  "url": "https://tabelog.com/tokyo/A1307/A130701/13306356/",
  "imageUrl": "https://tblg.k-img.com/restaurant/images/Rvw/312593/320x320_square_2d0c561f28eeb4c4a0b5095a925f5a5a.jpg",
  "scrapedAt": "2026-11-10T14:40:00.000Z"
}

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🔁 Deep pagination & scale strategy

1. Automatic Route Traversal: Tabelog organizes result pages using trailing /N/ URL path increments (20 establishments per page). The scraper handles URL construction and parameter preservation across all pages.
2. Dynamic 1,000+ Record Scaling: Setting maxItems: 1000 (or maxItems: 0 with maxPages: 0) allows the browser worker to continuously navigate through 50+ consecutive pages per search URL.
3. Smart Session Retention: Once initial edge verification is cleared, the active browser session maintains validated security cookies across pagination requests, maximizing crawling speed.
4. Graceful End-of-Catalog Handling: Navigation terminates cleanly when an empty card grid is encountered or when no new records appear.

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🛠️ Step-by-step tutorial

1. Configure Search on Tabelog: Visit tabelog.com, choose your desired prefecture (e.g., Tokyo, Osaka) and cuisine type, and copy the listing URL from your browser address bar.
2. Add Start URLs: In the Apify Console, paste the copied URL into startUrls. You can paste multiple URLs to collect different cities simultaneously.
3. Define Desired Volume: Set maxItems to 20 for a fast 1-minute test, or scale to 500–1000+ for full market research.
4. Ensure Residential Proxy is Active: Verify that Apify Residential proxy is selected in proxyConfiguration.
5. Run the Actor: Click Start. Live logs will indicate edge clearance and streaming item counts.
6. Export Your Dataset: Download your results in CSV, Excel, or JSON format from the Storage tab.

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🏗️ Architecture & Data Extraction Flow

flowchart TD
    A[Start URLs: Area / Cuisine Search] --> B[Launch Stealth Browser Worker]
    B --> C[Dispatch Request via Residential Proxy]
    C --> D{Edge Verification Challenge?}
    D -->|Yes| E[Automated Turnstile / JS Challenge Clearance]
    E --> F[Retain Verified Session Cookies]
    D -->|No| F
    F --> G[Navigate Listing Page]
    G --> H[Block Heavy Images & Fonts]
    H --> I[Parse HTML Restaurant Cards]
    I --> J[Extract Ratings, Saves, Genres & Transit]
    J --> K[Deduplicate by Restaurant ID]
    K --> L[Stream Items to Apify Dataset]
    L --> M{MaxItems Reached or End of Pages?}
    M -->|No| N[Increment /N/ Page Counter]
    N --> G
    M -->|Yes| O[Close Browser & Finalize Run]

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📊 Sample Code Integrations

Python (apify-client)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Scrape top-rated ramen in Tokyo
run_input = {
    "startUrls": [{"url": "https://tabelog.com/tokyo/rstLst/ramen/"}],
    "maxItems": 40,
    "maxPages": 0,
}

run = client.actor("haketa/tabelog-scraper").call(run_input=run_input, memory_mbytes=4096)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item.get('rating', 'N/A')}] {item['name']} - Station: {item.get('station')} (Saves: {item.get('savedCount')})")

Node.js (apify-client)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('haketa/tabelog-scraper').call({
    startUrls: [{ url: 'https://tabelog.com/osaka/rstLst/sushi/' }],
    maxItems: 40,
    maxPages: 0,
}, { memoryMbytes: 4096 });

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.table(items, ['name', 'rating', 'savedCount', 'genresText', 'station']);

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💡 Pro scraping tips

• Interpreting Tabelog Ratings: In Japan, a rating of 3.50 or higher indicates an outstanding culinary establishment. Filtering your exported CSV for rating >= 3.50 will immediately isolate the top culinary destinations in any city.
• Bookmark Density as a Demand Metric: For newly opened venues that haven't yet gathered enough reviews to score above 3.50, look at savedCount. A high save count (1,000+) is a strong indicator of viral popularity.
• Targeting Specific Neighborhoods: Tabelog allows granular geographic filtering down to specific train stations (e.g., Shibuya, Shinjuku, Ginza). Crafting station-specific URLs gives you exact neighborhood coverage.
• Resource Efficiency: Keep maxConcurrency at 1 or 2. Stealth browser sessions with Turnstile verification operate most reliably with focused concurrency.

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❓ Frequently Asked Questions

Does this actor require a Tabelog account or login?
No. The scraper accesses only public listing pages and does not require user accounts, passwords, or personal credentials.

Why are residential proxies required for Tabelog?
Tabelog deploys strict edge security filters that automatically block traffic from commercial datacenter IP ranges. Residential proxies ensure that requests originate from standard residential network pools, allowing verification challenges to clear smoothly.

Why are ratings null for a small number of venues?
Brand-new restaurants or venues that have not yet received the minimum threshold of customer reviews do not have an official Tabelog rating assigned yet. A null rating accurately reflects the absence of a public score on the site.

Can I extract detailed menus, business hours, and phone numbers?
This actor extracts listing-level data across thousands of restaurants. Deep page details (such as operating hours, table counts, and telephone numbers) require visiting individual restaurant profile pages.

What is the recommended memory allocation?
Because the scraper runs an anti-detect stealth browser, allocating 2,048 MB to 4,096 MB of memory ensures smooth browser execution without memory constraints.

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🔗 Related Japan & hospitality actors

• SUUMO Scraper — Japan's #1 real estate portal for rental apartments and properties.
• Rakuten Scraper — E-commerce product search, pricing, and reviews on Rakuten Ichiba.
• Mercari Scraper — C2C marketplace listings, second-hand prices, and resale comps in Japan.

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🗾 Comparison: Tabelog vs. Other Japan Dining Sources

Attribute	Tabelog (食べログ)	Google Maps Japan	Hot Pepper Gourmet	Gurunavi
Coverage	800,000+ venues nationwide	Comprehensive POIs	High chain/izakaya focus	Moderate dining coverage
Rating Calibration	Strict (3.5+ = Top 3%)	Inflated (mostly 4.0–4.5)	Sponsored/booking biased	Booking focused
Bookmark Analytics	Saves count (savedCount)	Star bookmarks (private)	Points collected	Coupons collected
Transit Accuracy	Exact meters to station	Geographic coordinate only	Station text description	Nearest exit text
Cuisine Granularity	Deep (e.g., Niboshi Ramen)	Generic (e.g., Ramen Restaurant)	General broad categories	Broad categories
Scraping Accessibility	Automated via this Actor	Heavy dynamic DOM restrictions	Limited listing depths	Pagination limits

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📈 Advanced Scraping & Workflow Automation

Scheduled Dining Intelligence

Automate recurring monitoring runs using Apify Schedule configurations:

• Weekly Trend Monitoring: Run maxItems: 100 every Monday to capture rating shifts and new customer reviews across key dining hubs.
• Monthly Expansion Discovery: Set startUrls across emerging business districts (e.g., Toranomon, Nihonbashi) to discover newly opened restaurants right as they get indexed.

Enterprise Webhook Pipelines

Stream live results into PostgreSQL, BigQuery, or Snowflake through Apify Webhook events (ACTOR.RUN.SUCCEEDED). Pipe restaurant payloads directly into sales CRM platforms (HubSpot, Salesforce) or notification channels (Slack, Discord) whenever a restaurant with rating >= 3.80 is detected.

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Can I combine multiple prefectures in one extraction job?
Yes. You can supply an arbitrary number of listing URLs inside startUrls (e.g., Tokyo ramen, Osaka okonomiyaki, Kyoto kaiseki). The actor will systematically process each target sequentially while enforcing global deduplication across the unified dataset.

How does walking distance parsing handle complex Japanese station descriptions?
Tabelog displays station proximity with formats like "Shibuya Station 350m" or "Ginza Station Exit A2 120m". The scraper uses targeted regular expressions to isolate the numeric meter distance into stationDistanceM while preserving the full text description in station.

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📋 Changelog

v1.2 (2026-11)

• Unlocked Deep 1,000+ Pagination: Removed restrictive 60-page caps, enabling comprehensive catalog scraping across thousands of listings governed by maxItems.
• Fast Prefill Optimization: Standardized prefill run volume (20 items) for lightning-fast trial runs under 90 seconds.
• Enhanced Schema Normalization: Aligned input and output schemas with strict Apify specifications.
• Optimized Anti-Detect Flow: Upgraded session clearance routines and retry logic for continuous multi-page traversals.

v1.1 (2026-11)

• Added structured walking distance parsing in meters (stationDistanceM).
• Implemented aggressive media asset blocking to reduce proxy bandwidth consumption by 80%.

v1.0 (2026-10)

• Initial production release supporting multi-URL ingestion, rating extraction, and automated deduplication.