OpenTable Scraper

OpenTable Scraper creates a structured dataset of restaurant records collected from opentable.com. Each dataset item can include the restaurant name, cuisine, price band, rating, review count, neighborhood, address, coordinates, and contact details, plus full profile fields and reviews when enrichment is enabled. Query the source using search URLs, restaurant profile URLs, or search terms, control the result limit with maxItems, and retrieve records through the Apify Dataset API or export them as JSON, CSV, Excel, or XML.

Dataset at a glance

Property	Value
Source	opentable.com
Record unit	One restaurant
Input methods	Search result URLs, restaurant profile URLs, or search queries
Main identifiers	restaurantId, url
Delivery	Apify Dataset and API
Export formats	JSON, CSV, Excel, XML
Update model	Fresh records per Actor run
Pricing	Pay per event — from $2 per 1,000 restaurants; details and reviews cost extra when enabled

Coverage and available records

• Supported inputs — OpenTable search result URLs (https://www.opentable.com/s?...), restaurant profile URLs (https://www.opentable.com/r/...), or free-text search terms resolved through OpenTable autocomplete.
• Search terms — searchQueries accepts location or cuisine phrases such as Dresden, New York, or Italian in Berlin, and are used only when startUrls is empty.
• Pagination — Search results are paginated until maxItems is reached or the listing is exhausted.
• Detail enrichment — scrapeListingDetails opens each restaurant profile to collect hours, dress code, amenities, payment options, parking, public transit, chef, and photo galleries.
• Reviews — scrapeReviews paginates each restaurant's review list, capped by maxReviews per restaurant.
• Conditional fields — Details and reviews are only collected when enabled; otherwise detailsFetched and reviewsFetched are false and reviews is null.
• Not currently collected — Menu items and live reservation availability beyond the requested date/time snapshot.

Data dictionary

Core fields present on every restaurant record:

Field	Type	Nullable	Description	Example
restaurantId	number	No	Stable OpenTable restaurant identifier; best deduplication key	227388
name	string	No	Restaurant name	Peter Pane - Dresden
url	string	No	Restaurant profile URL	https://www.opentable.com/r/...
cuisine	string	Yes	Cuisine type	Burgers
priceBand	string	Yes	Price category as shown on OpenTable	€30 and under
currencySymbol	string	Yes	Currency symbol for the price band	€
rating	number	Yes	Average rating (0–5)	4.5
reviewCount	number	Yes	Total reviews shown on OpenTable	515
neighborhood	string	Yes	Neighborhood name	Old Town
city	string	Yes	City	Dresden
state	string	Yes	State or region code	SN
postCode	string	Yes	Postal code	01067
address	string	Yes	Full address line	Wilsdruffer Straße 14-16, ...
latitude / longitude	number	Yes	Geographic coordinates	51.0515654
phone	string	Yes	Phone number	0351 48676840
diningStyle	string	Yes	Dining style category	Casual Dining
hasTakeout	boolean	Yes	Whether takeout is offered	true
outdoorSeating	boolean	Yes	Whether outdoor seating is available	false
photoUrl	string	Yes	Primary photo URL	https://resizer.otstatic.com/...
photos	array	Yes	Additional photo URLs	["https://...", "..."]
searchQuery	string	Yes	Search term that produced the record	Dresden
detailsFetched	boolean	No	Whether detail enrichment ran for this restaurant	false
reviewsFetched	boolean	No	Whether reviews were scraped for this restaurant	false
reviews	array	Yes	Review list; populated only when scrapeReviews is enabled	null

When scrapeListingDetails is enabled, additional conditional fields are collected, including description, dressCode, hoursOfOperation, amenities, paymentOptions, parkingDetails, publicTransit, executiveChef, email, website, facebookUrl, orderOnlineLink, hasDelivery, and country.

Example dataset record

{
  "restaurantId": 227388,
  "name": "Peter Pane - Dresden",
  "url": "https://www.opentable.com/r/peter-pane-dresden-am-zwinger-dresden",
  "cuisine": "Burgers",
  "priceBand": "€30 and under",
  "currencySymbol": "€",
  "rating": 4.5,
  "reviewCount": 515,
  "neighborhood": "Old Town",
  "city": "Dresden",
  "state": "SN",
  "postCode": "01067",
  "address": "Wilsdruffer Straße 14-16, Dresden, SN, 01067",
  "latitude": 51.0515654,
  "longitude": 13.7349184,
  "phone": "0351 48676840",
  "diningStyle": "Casual Dining",
  "hasTakeout": true,
  "outdoorSeating": false,
  "photoUrl": "https://resizer.otstatic.com/v3/photos/92048640-2?width=320&height=320&webp=true",
  "searchQuery": "Dresden",
  "detailsFetched": false,
  "reviewsFetched": false,
  "reviews": null
}

This record was produced by a searchQueries run for Dresden with detail and review enrichment disabled.

Query and input reference

Input	Type	Required	Default	Accepted values	Description
startUrls	array	No	—	Search URLs (/s?...) or profile URLs (/r/...)	Takes priority over searchQueries
searchQueries	array	No	—	Location or cuisine phrases	Used when startUrls is empty
date	string	No	tomorrow	YYYY-MM-DD	Reservation date for availability search
time	string	No	19:00	HH:MM (24h)	Preferred dining time
partySize	integer	No	2	1–20	Number of diners
scrapeListingDetails	boolean	No	false	true / false	Enable profile detail enrichment
scrapeReviews	boolean	No	false	true / false	Enable per-restaurant review pagination
maxReviews	integer	No	0	0 = all	Max reviews per restaurant
maxItems	integer	No	0	0 = unlimited	Max restaurants to scrape
proxyConfiguration	object	No	Residential	Apify proxy config	Residential proxy recommended (Akamai Bot Manager)

Minimal request:

{ "searchQueries": ["Dresden"] }

Advanced request:

{
  "searchQueries": ["Dresden"],
  "date": "2026-08-20",
  "time": "19:00",
  "partySize": 2,
  "scrapeListingDetails": true,
  "scrapeReviews": true,
  "maxReviews": 10,
  "maxItems": 50,
  "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }
}

Retrieve the data through the API

1. Start the Actor with a JSON input via the Apify API or the Actor's API tab.
2. Wait for the run to finish, or use the synchronous run endpoint.
3. Retrieve items from the run's default dataset.
4. Paginate or export the dataset in JSON, CSV, Excel, or XML.

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("datascrapers/opentable-scraper").call(
    run_input={"searchQueries": ["Dresden"], "maxItems": 50}
)
items = client.dataset(run["defaultDatasetId"]).list_items().items

Data quality and record handling

• Completeness — Core identity and rating fields are present on listing records; description, hours, amenities, and photos are conditional on scrapeListingDetails.
• Deduplication — Use restaurantId (preferred) or url as the stable external key. The Actor stops after maxItems restaurants.
• Retries — Failed seeds are retried once with a fresh proxy session; OpenTable's Akamai Bot Manager frequently blocks datacenter IPs, so residential proxies are recommended.
• Normalization — Coordinates, rating, and review counts are returned as numbers; availability signals reflect the requested date/time and are a point-in-time snapshot.
• Raw vs derived — All fields are taken directly from OpenTable pages; no AI-generated or computed values are added.

Export and pipeline examples

Destination	Recommended method	Typical use
PostgreSQL/Supabase	Dataset API or webhook consumer	Restaurant master table keyed by restaurantId
Google Sheets	Apify integration	Review of listings and ratings per city
S3/cloud storage	Scheduled export or integration	Nightly refresh of restaurant coverage

Pricing and cost examples

Billing is pay-per-event. Each event is tiered across Apify plans; prices below are the free-plan rates.

Event	Charged when	Free-plan rate
dataset-item (Restaurant)	A restaurant is pushed to the dataset	$2 per 1,000
listing-details (Detailed Restaurant)	scrapeListingDetails completes for a restaurant	$3 per 1,000
restaurant-reviews (Reviews)	scrapeReviews completes for a restaurant	$2 per 1,000

A fixed one-time Actor Start charge of $0.00005 also applies to each run.

Restaurants	Estimated base cost (listings only)
1,000	$2
10,000	$20

Estimates depend on the pricing model and the options enabled. Enabling details or reviews adds the corresponding per-event cost.

Limitations and responsible data use

• Only publicly accessible OpenTable data is collected; the Actor does not bypass login-restricted content.
• Reservation availability is a snapshot at run time and changes frequently.
• Some fields (hours, amenities, photos, reviews) are conditional and may be empty.
• No historical snapshots are stored unless you persist them yourself across scheduled runs.
• You are responsible for complying with OpenTable's terms and applicable privacy and data laws.

Dataset questions

What does one dataset item represent?

One restaurant listing. If scrapeListingDetails or scrapeReviews is enabled, the same item is enriched with profile details and/or a nested reviews array.

Which field should I use as a unique identifier?

restaurantId is the stable OpenTable identifier; url also works as a fallback.

Are fields nullable or conditional?

Yes. Enrichment fields (description, hoursOfOperation, amenities, email, etc.) are only present when scrapeListingDetails is on, and reviews is null unless scrapeReviews is on.

Can I retrieve the records as CSV or JSON?

Yes. The default dataset can be exported as JSON, CSV, Excel, or XML from the Apify Console or via the Dataset API.

How do I paginate large datasets?

The Actor paginates automatically up to maxItems. For very large exports, page through the dataset with the Dataset API offset and limit parameters.

What counts as a billable result?

Each restaurant pushed to the dataset is one dataset-item event. Detail enrichment and review extraction are billed separately as listing-details and restaurant-reviews events.

Related datasets from Data Scrapers

• Yelp Scraper — business listings and reviews for the same restaurant research workflows.
• TripAdvisor Reviews Scraper — hotel and travel reviews that complement restaurant coverage.
• Clutch.co Company Scraper — B2B service-provider listings and reviews.

Data Scrapers support

Need an additional field, record type, or export workflow? Contact Data Scrapers at stardustspotlight@gmail.com. Include a sample source URL, required fields, expected record volume, and preferred delivery format.