Tablecheck Restaurants Search Scraper

TableCheck Restaurant Search Scraper: Extract Paris & Global Dining Data

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What Is TableCheck.com?

TableCheck is a restaurant reservation and table management platform operating across Asia, Europe, and beyond. Its search interface surfaces restaurants by location, date, party size, and sorting criteria — making it a rich source of structured dining data. Manually collecting this information is impractical at scale; the TableCheck Restaurant Search Scraper automates extraction from any TableCheck search results page.

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Overview

The TableCheck Search Scraper processes paginated restaurant search result URLs and returns clean, structured records per venue. It is suited for:

• Travel & food platforms aggregating dining options by city
• Market researchers analyzing restaurant density, cuisine diversity, and pricing
• Hospitality analysts benchmarking reservation availability and venue rankings
• Developers building recommendation engines or dining apps

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Input Format

The Actor supports two scraping modes — use only one at a time:

1. Scrape by URLs — scrape data directly from a list of TableCheck search result URLs.

{
  "ignore_url_failures": true,
  "max_items_per_url": 200,
  "urls": [
    "https://www.tablecheck.com/s?dateTime=2026-05-28T19%3A00%3A00&covers=2&latitude=48.86182320277778&longitude=2.336352640046296&searchedLocationName=Paris&shouldUseLatLongSearch=true&originCorrelationId=ec23bce4-acb8-4be7-9e1b-b02a4dbe3f15&corrid=3403c841-8265-4547-99f0-54fa84193d81&metroId=3408&queryUnderstandingType=none&showMap=true&sortBy=web_conversion"
  ]
}

2. Scrape by search filters — scrape data using a keyword and additional filters (region, dates, location, budget, etc.) instead of providing URLs.

{
    "urls": [],
    "include_raw_data": "true",
    "region": "jp",
    "keyword": "tokyo",
    "start_date": "2026-12-04",
    "end_date": "2026-12-07",
    "lat": "35.65860374437126",
    "lng": "139.74541383382513",
    "budget_min": 2000,
    "budget_max": 100000,
    "num_people": 3,
    "sort_by": "relevance",
    "max_items_per_url": 200
}

There is also a General settings group that applies to both modes.

1. Scrape by URLs

Use this mode when you already have specific TableCheck search result URLs. When this mode is used, all options in the "Scrape by search filters" section will be disabled.

Field	Type	Default	Description
urls	array	(required)	One or more TableCheck search result URLs to scrape. Each URL should represent a filtered search (location, date, party size, sort order). You can paste URLs one by one, or use the Bulk edit section to add a prepared list.
ignore_url_failures	boolean	true	If true, the run continues even if individual URLs fail instead of stopping the entire run. Recommended for bulk runs.

Building a Valid Search URL

Navigate to tablecheck.com/s and apply your filters — location, date/time, covers, sort order. Key URL parameters to understand:

Parameter	Meaning
dateTime	Target reservation date/time (ISO 8601, URL-encoded)
covers	Party size
latitude / longitude	Coordinates for geo-based search
searchedLocationName	Human-readable location label (e.g., Paris)
metroId	TableCheck internal metro area identifier
sortBy	Sort order — e.g., web_conversion, distance
showMap	Whether map view is active (does not affect scraper output)

2. Scrape by search filters

Use this mode when you want to search using a keyword and additional filter attributes instead of scraping by URLs. If you use this mode, leave the urls field above empty.

Field	Type	Default	Description
keyword	string	—	The keyword to search for.
region	string (jp, sg)	—	The region to search in: Japan (jp) or Singapore (sg).
sort_by	string (relevance, distance, budget_dinner_avg)	—	Sort results by Relevance, Distance, or Price.
start_date	string (YYYY-MM-DD)	—	Start date of the search range.
end_date	string (YYYY-MM-DD)	—	End date of the search range.
lat	string	—	Latitude used for geo-based search.
lng	string	—	Longitude used for geo-based search.
budget_min	string	—	Minimum budget to filter results by.
budget_max	string	—	Maximum budget to filter results by.
num_people	integer	—	Number of people to filter results by.
page	integer	—	Page number to scrape.

3. General settings

Applies to both scraping modes above.

Field	Type	Default	Description
max_items_per_url	integer	20	Maximum number of restaurant records to collect per URL/search. Set higher (e.g., 200) for comprehensive city-level extraction.
include_raw_data	boolean	false	If true, the scraper will include raw data from website before cleaning in the output.

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Output Format

Sample output

{
  "id": "5acc1fab590430e6f50007ac",
  "slug": "wakanui-tokyo",
  "venue_url": "https://www.tablecheck.com//en/shops/wakanui-tokyo",
  "name": "WAKANUI Grill Dining Bar Tokyo",
  "name_ja": "ワカヌイグリルダイニングバー東京",
  "headline_en": null,
  "headline_ja": "ニュージーランド産のビーフとラムを東京タワーが見える都会の中心で楽しめる場所",
  "address_en": "3-4-30 Shibakoen, Minato-ku, Tokyo",
  "address_ja": "東京都, 港区, 芝公園3-4-30",
  "area_name_en": "Parc de Shiba",
  "area_name_ja": "芝公園",
  "latitude": 35.658896,
  "longitude": 139.746391,
  "time_zone": "Asia/Tokyo",
  "distance_from_search_km": 0.09417397756805658,
  "nearest_station_name_en": "Kamiyacho",
  "nearest_station_name_ja": "神谷町",
  "nearest_station_lines_en": [
    "Tokyo Metro Hibiya Line"
  ],
  "nearest_station_lines_ja": [
    "東京メトロ日比谷線"
  ],
  "nearest_station_distance_m": 468,
  "nearest_station_walk_minutes": 6,
  "nearest_station_latitude": 35.662964,
  "nearest_station_longitude": 139.745048,
  "nearby_stations": [
    {
      "station_name_en": "Onarimon",
      "station_name_ja": "御成門",
      "line_name_en": "Toei Mita Line",
      "line_name_ja": "都営三田線",
      "line_code": "I",
      "distance_m": 496,
      "walk_minutes": 7
    },
    {
      "station_name_en": "Akabanebashi",
      "station_name_ja": "赤羽橋",
      "line_name_en": "Toei Ōedo Line",
      "line_name_ja": "都営大江戸線",
      "line_code": "E",
      "distance_m": 481,
      "walk_minutes": 7
    },
    {
      "station_name_en": "Kamiyacho",
      "station_name_ja": "神谷町",
      "line_name_en": "Tokyo Metro Hibiya Line",
      "line_name_ja": "東京メトロ日比谷線",
      "line_code": "H",
      "distance_m": 468,
      "walk_minutes": 6
    }
  ],
  "cuisines": [
    "steak",
    "burgers",
    "new_zealand"
  ],
  "features": [
    "business",
    "drinks",
    "groups",
    "quiet",
    "romantic",
    "views"
  ],
  "seating_options": [
    {
      "key": "private_room",
      "bookable": false
    },
    {
      "key": "terrace",
      "bookable": false
    }
  ],
  "smoking": [
    "smokefree"
  ],
  "service_times": [
    "lunch",
    "dinner"
  ],
  "service_modes": [
    "dining"
  ],
  "min_pax": 1,
  "max_pax": 8,
  "currency": "JPY",
  "budget_lunch_avg": 4000.0,
  "budget_dinner_avg": 17500.0,
  "price_band_lunch": "3000-6000",
  "price_band_dinner": "10000-20000",
  "main_image_url": "https://cdn2.tablecheck.com/images/5e49fa9e4b880c7857000579/images/lg/main_1200_900_02.jpg?1581906591",
  "photo_count": 4,
  "photo_urls": [
    "https://cdn2.tablecheck.com/images/5e49fabb4b880c785700057c/images/lg/main_menu_2.jpg?1581906620",
    "https://cdn0.tablecheck.com/images/5e49facc6e731d2160000584/images/lg/main_1200_900_03.jpg?1581906636",
    "https://cdn1.tablecheck.com/images/5e49fada6e731d2101000660/images/lg/menu06.jpg?1581906650",
    "https://cdn3.tablecheck.com/images/5e49fae45f6ddf6b9c000586/images/lg/menu02.jpg?1581906660"
  ],
  "raw_data": {},
  "from_url": "https://www.tablecheck.com/en/japan/search?search_text=tokyo&sort_by=relevance&date_from=2026-12-04&date_to=2026-12-07&geo_latitude=35.65860374437126&geo_longitude=139.74541383382513&budget_dinner_avg_min=2000&budget_dinner_avg_max=100000&num_people=3"
}

Complete Output Structure and Field Definitions

The scraper returns JSON data with each restaurant as an object containing detailed fields. Understanding field purposes enables effective analysis and application.

Venue Identification

ID: Unique identifier of the venue in the TableCheck database. Purpose: Database indexing, tracking specific venues over time, avoiding duplicates.

Slug: URL-friendly venue identifier used in web addresses. Purpose: Constructing direct venue URLs, alternative identifier.

Venue URL: Direct link to the venue page on TableCheck. Purpose: Accessing complete details, making reservations, verification of scraped data.

Name: Venue name in English. Purpose: Display in reports, search and matching venues across datasets.

Name JA: Venue name in Japanese. Purpose: Local-language display, matching with Japanese data sources.

Headline EN: English marketing tagline of the venue (may be null). Purpose: Quick positioning summary when available.

Headline JA: Japanese marketing tagline of the venue. Purpose: Understanding venue positioning, keyword and sentiment analysis.

Location

Address EN: Street address in English. Purpose: Display, geocoding and mailing.

Address JA: Street address in Japanese. Purpose: Local-language navigation and matching with Japanese address data.

Area Name EN: Neighborhood or area name in English. Purpose: Area-level market analysis and venue grouping.

Area Name JA: Neighborhood or area name in Japanese. Purpose: Local-language area grouping.

Latitude: Geographic latitude of the venue. Purpose: Map plotting, distance calculations, spatial analysis.

Longitude: Geographic longitude of the venue. Purpose: Map plotting, distance calculations, spatial analysis.

Time Zone: IANA time zone of the venue. Purpose: Correct interpretation of opening and booking times.

Distance From Search KM: Distance in kilometers from the search coordinates used when scraping. Purpose: Proximity ranking and radius filtering. Note: depends on the search point, not a fixed venue attribute.

Transit Access

Nearest Station Name EN: Closest train station in English. Purpose: Accessibility analysis and visitor guidance.

Nearest Station Name JA: Closest train station in Japanese. Purpose: Local-language navigation.

Nearest Station Lines EN: Train lines serving the nearest station (English). Purpose: Evaluating connectivity and commute options.

Nearest Station Lines JA: Train lines serving the nearest station (Japanese). Purpose: Local-language transit information.

Nearest Station Distance M: Distance to the nearest station in meters. Purpose: Location convenience scoring.

Nearest Station Walk Minutes: Walking time to the nearest station. Purpose: Accessibility comparison between venues.

Nearest Station Latitude: Latitude of the nearest station. Purpose: Route mapping and spatial analysis.

Nearest Station Longitude: Longitude of the nearest station. Purpose: Route mapping and spatial analysis.

Nearby Stations: List of stations within walking distance, one per line, with names, line, line code, distance and walking time. Purpose: Multi-line accessibility analysis and catchment-area studies.

Cuisine and Features

Cuisines: List of cuisine type codes (e.g., steak, burgers). Purpose: Market segmentation and competitor analysis by food type.

Features: List of venue feature tags (e.g., romantic, business, views). Purpose: Matching venues to occasions and customer needs.

Seating Options: List of special seating types (e.g., private_room, terrace) with a flag showing whether each can be booked online. Purpose: Evaluating group and event suitability, online booking availability.

Smoking: Smoking policy codes (e.g., smokefree). Purpose: Filtering by customer preference and regulatory compliance.

Service Times: Meal periods offered (e.g., lunch, dinner). Purpose: Understanding opening patterns and demand windows.

Service Modes: Service types offered (e.g., dining). Purpose: Separating dine-in from takeout or other modes.

Min Pax: Minimum party size accepted for a booking. Purpose: Evaluating suitability for solo diners and small groups.

Max Pax: Maximum party size accepted for a booking. Purpose: Evaluating suitability for groups and events.

Pricing

Currency: Currency of the budget values. Purpose: Correct financial interpretation across markets.

Budget Lunch Avg: Average spend per person at lunch. Purpose: Price benchmarking and market positioning.

Budget Dinner Avg: Average spend per person at dinner. Purpose: Price benchmarking and market positioning.

Price Band Lunch: Lunch price range per person. Purpose: Quick price tier classification.

Price Band Dinner: Dinner price range per person. Purpose: Quick price tier classification.

Media

Main Image URL: Link to the primary venue photo. Purpose: Thumbnails, reports and dashboards.

Photo Count: Total number of additional photos. Purpose: Evaluating listing quality and completeness.

Photo URLs: List of direct links to additional venue photos in original order. Purpose: Downloading images, visual inspection, computer vision analysis.

Raw Data

Raw Data: Complete original JSON object of the venue as scraped, without modification. Purpose: Auditing, debugging, accessing fields not included in the cleaned output (e.g., Korean and Chinese translations, line colors, internal IDs), and reprocessing without re-scraping.

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How to Use

1. Generate your search URL — Go to tablecheck.com/s, set your location, date, party size, and sort preference. Copy the full URL from the browser.
2. Configure input — Paste into urls. Adjust max_items_per_url (up to 200 is typical for a city search).
3. Run the scraper — Start the actor. Progress is visible in the run log.
4. Export — Download results as JSON, CSV, or Excel.

Tips:

• For city-wide coverage, try multiple searches with different sortBy values (web_conversion, distance) and merge results.
• If a URL returns 0 results, verify the dateTime parameter is a future date and covers is valid.
• ignore_url_failures: true is recommended when running multiple URLs simultaneously.

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Use Cases & Business Value

• Dining aggregators: Build up-to-date restaurant directories for any TableCheck-covered city
• Travel apps: Surface bookable restaurants by geo-proximity and cuisine for trip planners
• Price intelligence: Compare average lunch/dinner budgets across neighborhoods or cities
• Availability analytics: Track reservation slot patterns by day, time, or venue type
• Localization research: Use Name Translations and Content Body Translations to study multi-language venue positioning

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Conclusion

The TableCheck Restaurant Search Scraper delivers comprehensive, structured venue data directly from live search results — covering everything from geocoordinates and cuisine tags to real-time availability slots and pricing averages. Whether you're building a dining app or conducting hospitality market research, this scraper turns TableCheck's rich search index into an immediately usable dataset.