Grab Food Restaurants Scraper

Actor: fatihtahta/grab-food-scraper | Price: $4.00 per 1,000 saved restaurants

This actor scrapes restaurant listings from GrabFood across multiple countries, turning location-based searches into structured datasets. It's designed for market research, competitive analysis, and operational planning.

Overview

The scraper extracts detailed restaurant data from GrabFood's public marketplace. You provide a starting URL (like a city or category page) and a locale, and it paginates through results, outputting clean, deduplicated records ready for export.

Key Features

• Multi-Region Support: Works across all GrabFood markets. Switch locales to scrape data from Indonesia, Singapore, Vietnam, Malaysia, the Philippines, Thailand, Cambodia, and Myanmar.
• Location-Based Scraping: Start from any GrabFood listing page (city, cuisine, collection) to get restaurants relevant to that specific area.
• Comprehensive Data: Captures not just basic info but also promotions, ratings, delivery times, and operational status.
• Analysis-Ready Output: Data is structured and deduplicated, suitable for direct import into BI tools, spreadsheets, or databases.
• Flexible Export: Download results from Apify in JSON, CSV, Excel, or HTML format.

Common Use Cases:
* Mapping all restaurants in a target city for market analysis.
* Tracking competitor promotions and delivery times.
* Identifying coverage gaps for specific cuisines.
* Feeding restaurant metadata into analytics or logistics models.

How to Use

Configure the actor via the Input tab in Apify. The main parameters are:

• startUrls (array, optional): One or more GrabFood listing URLs to begin scraping. If empty, it uses the default marketplace page for your chosen locale.
• locationLanguage (string, optional, default: ID_ID): Sets the region and language. Supported values include ID_EN, ID_ID, PH_EN, SG_EN, VN_EN, VN_VI, MY_EN, KH_EN, MM_EN.
• maxRequestsPerCrawl (number, optional): A hard limit on the number of pagination requests, useful for getting a smaller sample.

Tip: Start with a single city page and a low maxRequestsPerCrawl to test the setup before a full run.

Example Input

{
  "startUrls": [
    "https://food.grab.com/sg/en/restaurants"
  ],
  "locationLanguage": "SG_EN",
  "maxRequestsPerCrawl": 120
}

Input / Output

Input Summary

The actor requires a locale (locationLanguage) and an optional starting URL. The maxRequestsPerCrawl parameter controls the crawl scope.

Output Fields

Each item in the dataset represents one restaurant listing. Key fields include:

• Identifiers & Basics: id, name, address, latitude, longitude
• Service Info: estimatedDeliveryTime, distanceInKm, rating
• Categorization: cuisine[] (array), description
• Promotions & Status: hasPromo, promoDescription, closingSoonText, closedText
• Operations: openHours (structured object), isIntegrated
• Assets: photoHref, photoSmallHref, iconHref
• Source: sourceUrl

Sample Output

{
  "id": "SGFOOD12345",
  "name": "Sushi Express (Downtown)",
  "address": "6 Shenton Way, #02-12",
  "latitude": 1.2795,
  "longitude": 103.8507,
  "estimatedDeliveryTime": 25,
  "cuisine": ["Japanese", "Sushi"],
  "rating": 4.7,
  "distanceInKm": 1.4,
  "description": "Affordable sushi platters delivered fast",
  "hasPromo": true,
  "promoDescription": "20% off platters this week",
  "openHours": {
    "monday": { "open": "10:00", "close": "21:00" }
  },
  "closingSoonText": "",
  "sourceUrl": "https://food.grab.com/sg/en/restaurants"
}