DoorDash Restaurant Scraper

Extract restaurant info + complete menus from DoorDash store pages — restaurant name, address, city, cuisine breadcrumbs, FAQ, and full menu sections with item names, descriptions, and prices.

Features

• 16 output fields per restaurant — flat schema with typed defaults (zero nulls)
• Complete menu extraction — every section + item with name, description, and price
• FAQ extraction — questions and answers about the restaurant
• Breadcrumb extraction — cuisine type and neighborhood
• Cloudflare bypass via Patchright Chromium + RESIDENTIAL US proxy (typically clears in 2–15 seconds)
• Hardcoded RESIDENTIAL US proxy — required, applied automatically

Input

Field	Type	Description
storeUrls	Array	DoorDash store URLs (e.g., https://www.doordash.com/store/15034)
maxItems	Integer	Maximum restaurants to scrape (default 10, max 100)

Example Input

{
    "storeUrls": [
        "https://www.doordash.com/store/15034",
        "https://www.doordash.com/store/6422"
    ],
    "maxItems": 10
}

Output

Each restaurant has 16 fields. All fields are always present — empty strings, zero, or empty array as typed defaults, never null.

Identity

Field	Type	Description
storeId	String	DoorDash store ID (from URL)
storeName	String	Restaurant name
storeUrl	String	Store page URL
title	String	Page title
description	String	Meta description

Location

Field	Type	Description
address	String	Street address
city	String	City
state	String	State / region code

Categorization

Field	Type	Description
breadcrumbs	Array	Page breadcrumb names (e.g., ["Home", "Boston", "Burgers", "UBURGER"]) — last is restaurant, second-to-last is cuisine

Menu

Field	Type	Description
menuSections	Array	Section names (e.g., ["Salads", "Burgers", "Drinks"])
menuSectionCount	Integer	Number of menu sections
menuItems	Array	Flat list of items: [{section, name, description, price}, ...]
menuItemCount	Integer	Total items across all sections

FAQ

Field	Type	Description
faq	Array	List of {question, answer} pairs from the FAQ section
faqCount	Integer	Number of FAQ entries

Metadata

Field	Type	Description
scrapedAt	String	ISO 8601 scrape timestamp

Example Output

{
    "storeId": "15034",
    "storeName": "UBURGER",
    "storeUrl": "https://www.doordash.com/store/15034",
    "address": "636 Beacon Street",
    "city": "Boston",
    "state": "MA",
    "breadcrumbs": ["Home", "Boston", "Burgers", "UBURGER"],
    "menuSectionCount": 11,
    "menuItemCount": 37,
    "menuSections": ["Usalads", "Burgers", "Sandwiches", "Sides"],
    "menuItems": [
        {"section": "Usalads", "name": "Greek Salad", "description": "Romaine lettuce, cherry tomatoes, ...", "price": "$8.50"}
    ],
    "faqCount": 3,
    "faq": [
        {"question": "How can I order from UBURGER on DoorDash?", "answer": "..."}
    ],
    "scrapedAt": "2026-04-13T05:55:00+00:00"
}

FAQ

Q: Why doesn't this scrape user reviews? DoorDash removed user-facing reviews from store pages and now only shows aggregate ratings via menu items. The reference actor labeled "DoorDash Reviews Scraper" is misleading — there are no individual reviews to extract. This actor instead extracts everything DoorDash actually exposes: menu, address, FAQ, and breadcrumbs.

Q: Why is a RESIDENTIAL proxy required? DoorDash's www.doordash.com/store/* paths are protected by Cloudflare which blocks Apify datacenter IPs with 403 Just a moment.... A real Chrome browser (Patchright) on a US residential IP solves the Cloudflare challenge automatically in 2–15 seconds. The proxy is hardcoded and applied automatically — no configuration needed.

Q: How long does each restaurant take? Roughly 15–25 seconds per restaurant (Cloudflare solve + page load + JSON-LD parse). Limited by the browser session, not by network throughput.

Q: Can I get prices in dollars (not strings)? Prices are kept as strings ("$8.50") because DoorDash sometimes uses ranges or currency symbols. Cast to float in your downstream pipeline if you need numerics: float(price.replace("$", "").split("-")[0]).

Q: Will this work for all DoorDash stores? Yes for restaurants with a published menu schema. About 40% of DoorDash stores don't expose a Menu JSON-LD on their store page (closed restaurants, brand aggregators like ezCater, certain catering platforms). For those stores the scraper still returns full restaurant info, FAQ, breadcrumbs and address — but menuSections and menuItems come back as empty arrays. Convenience-store / grocery URLs (e.g., /store/cvs, /store/walgreens) have a different page structure and may return fewer fields.

Use Cases

• Restaurant menu intelligence — track competitor pricing across markets
• Cuisine analysis — group restaurants by breadcrumb cuisine tag
• Market research — identify chains operating in specific cities
• Menu engineering — see which items / sections are common across restaurants
• Lead generation — find independent restaurants by city + cuisine