# OpenRice Restaurant Scraper (Hong Kong) (`precious_nucleus/openrice-scraper`) Actor Scrape Hong Kong restaurant detail, reviews, and menu photos from OpenRice into a structured dataset. Scheduled batch data feed — polite, reliable crawling over residential proxy. - **URL**: https://apify.com/precious\_nucleus/openrice-scraper.md - **Developed by:** [baby gold](https://apify.com/precious_nucleus) (community) - **Categories:** Travel, E-commerce - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $150.00 / 1,000 restaurant scrapeds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## OpenRice Restaurant Scraper (Hong Kong) Structured restaurant data from [OpenRice](https://www.openrice.com) Hong Kong — full restaurant detail, reviews, and menu photos — delivered to an Apify dataset you can export as JSON, CSV, or Excel, or pull via the API. > ⏱️ **This is a scheduled batch data feed, not a real-time lookup tool.** > OpenRice has no public API and actively rate-limits scrapers, so this actor > crawls **politely and slowly on purpose** (~1 restaurant every 10–15 seconds) to > stay reliable instead of getting blocked. A handful of restaurants takes minutes; > a large crawl runs for **hours**. **Run it on a schedule and collect the dataset** > — don't expect instant results. This trade-off is what keeps the data flowing. --- ### What you get per restaurant Every record is built from OpenRice's own data — **no field is invented; anything the source doesn't provide is left empty rather than guessed.** - **Identity & location** — name (and other-language name), address, district, region, latitude/longitude, phone(s), website - **Ratings & signals** — overall score, smile/cry counts, review count, photo count, bookmark count, price range, open-since, Michelin/award text - **Categories** — cuisine / dish / amenity tags - **Opening hours** - **Reviews** — the most relevant + recent reviews (up to 50 per restaurant), each with rating, text, and photo URLs - **Photos** — door/cover photo and menu photos (CDN image URLs) See the **Output** tab for the full field list and a sample record. ### Input Choose what to crawl. **Filters** combine (e.g. District + Cuisine + Keyword narrow together). If you provide **Restaurant IDs** or **Start URLs**, those win and the filters are ignored. **Filters (combine; leave all empty + tick "Crawl all" for everything):** | Field | Description | |-------|-------------| | **District** | Pick a Hong Kong district (covers all main dining areas — TST, Causeway Bay, Mong Kok, Central, Wan Chai, Kwun Tong, …). | | **Keyword** | Free-text search — restaurant name, cuisine, dish, or area. | | **Cuisine ID** | OpenRice cuisine/category id (advanced — raw numeric id). | | **Newly opened only** | Restrict to newly-opened restaurants (~900). | | **Crawl all of Hong Kong** | Everything (~33,000) when no filter is set, bounded by *Max restaurants*. | **Target specific restaurants instead (overrides filters):** | Field | Description | |-------|-------------| | **Restaurant IDs** | OpenRice POI ids — the number after `-r` in a URL (e.g. `600214`). | | **Start URLs** | OpenRice restaurant detail page URLs. | If you set nothing at all, the actor defaults to **newly opened only**. **Tuning (applies to every mode):** | Field | Default | Description | |-------|---------|-------------| | **Max restaurants** | 500 | Hard cap per run — guards runtime and proxy usage. | | **Reviews per restaurant** | 50 | Most relevant + recent reviews to collect. `0` skips reviews. | | **Include menu photos** | on | Collect user-uploaded menu / price-list photos. Turn off to go faster. | | **Language** | `zh` | `zh` (Chinese) or `en` (English). | ### How to use it (recommended pattern) 1. Pick a **District** (or a **Keyword**), set a sensible **Max restaurants** cap. 2. **Schedule** the actor (e.g. daily or weekly) under *Schedules* so the data stays fresh automatically. 3. Read results from the **dataset** — export to JSON/CSV/Excel or fetch via the Apify API / integrations (Make, Zapier, webhooks). > 💡 A full Hong Kong crawl is tens of thousands of restaurants and runs for hours. > Start with a modest *Max restaurants* cap, confirm the data looks right, then > raise it and run on a schedule. ### Requirements ⚠️ **This actor needs Apify Residential Proxy** (OpenRice HK is geo-gated and defended — datacenter IPs are blocked). Residential proxy is included on Apify's **paid plans (Starter and above)**. On the free plan the crawl will be blocked. The actor runs on **your** account and uses **your** proxy, so usage (proxy + compute, ~tens of US$ per thousand restaurants) is billed to you by Apify. ### Pricing This actor is offered as a **flat monthly rental** — pay once per month for unlimited runs. (Apify platform usage — proxy + compute — is billed separately to your account, as with any actor. See the requirements above.) ### Honest limitations We'd rather you know these up front than be surprised: - **Requires a paid Apify plan** (for Residential proxy — see Requirements). - **Hong Kong only.** OpenRice HK is geo-gated; this actor targets HK restaurants. - **Built for scheduled batch use.** Large crawls take hours by design (see the note above). If you need sub-second single lookups, this isn't the right tool. - **`popular_dishes` is intentionally empty.** OpenRice's current data no longer exposes structured dish-name tags, so we return the real menu photos and leave the dish list empty rather than fabricate groupings from free-text captions. - **Depends on OpenRice's site.** If OpenRice changes their site we patch the actor as fast as we can, but a run during a change may return fewer fields. ### Support Found a broken field or have a request? Use the **Issues** tab on this actor — we monitor it and ship fixes. # Actor input Schema ## `district` (type: `string`): Crawl restaurants in a specific Hong Kong district. Covers the main dining areas; for an area not listed, use Keyword or Restaurant IDs. ## `keyword` (type: `string`): Free-text search (restaurant name, cuisine, dish, area). Combine with District to narrow further. Leave empty to skip. ## `cuisineId` (type: `integer`): Filter by OpenRice cuisine/category id (e.g. 2004). Advanced: there is no public name list, so this takes a raw numeric id. Leave empty to skip. ## `newlyOpenedOnly` (type: `boolean`): Restrict to newly-opened restaurants (~900). Combines with District/Keyword/Cuisine. ## `crawlAll` (type: `boolean`): Crawl across all of HK (~33,000) when no District/Keyword/Cuisine filter is set, bounded by Max restaurants. Takes many hours — keep the cap modest and run on a schedule. ## `restaurantIds` (type: `array`): Specific OpenRice POI ids — the number after -r in a restaurant URL (e.g. 600214). Takes priority over the filters above. ## `startUrls` (type: `array`): Specific OpenRice restaurant detail page URLs. Takes priority over the filters above. ## `maxRestaurants` (type: `integer`): Hard cap on how many restaurants to crawl this run (applies to every mode). ## `reviewsPerRestaurant` (type: `integer`): How many of the most relevant + recent reviews to collect per restaurant. 0 skips reviews. ## `includeMenuPhotos` (type: `boolean`): Also collect user-uploaded menu / price-list photos. Turn off to reduce runtime and proxy usage. ## `language` (type: `string`): Content language for OpenRice. ## Actor input object example ```json { "district": "", "newlyOpenedOnly": false, "crawlAll": false, "restaurantIds": [], "maxRestaurants": 500, "reviewsPerRestaurant": 50, "includeMenuPhotos": true, "language": "zh" } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "restaurantIds": [] }; // Run the Actor and wait for it to finish const run = await client.actor("precious_nucleus/openrice-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "restaurantIds": [] } # Run the Actor and wait for it to finish run = client.actor("precious_nucleus/openrice-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "restaurantIds": [] }' | apify call precious_nucleus/openrice-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=precious_nucleus/openrice-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "OpenRice Restaurant Scraper (Hong Kong)", "description": "Scrape Hong Kong restaurant detail, reviews, and menu photos from OpenRice into a structured dataset. Scheduled batch data feed — polite, reliable crawling over residential proxy.", "version": "0.0", "x-build-id": "cyJwDg225gvNZJf3D" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/precious_nucleus~openrice-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-precious_nucleus-openrice-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/precious_nucleus~openrice-scraper/runs": { "post": { "operationId": "runs-sync-precious_nucleus-openrice-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/precious_nucleus~openrice-scraper/run-sync": { "post": { "operationId": "run-sync-precious_nucleus-openrice-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "district": { "title": "District", "enum": [ "", "2008", "1019", "2010", "1022", "2026", "1003", "3018", "2001", "2029", "2028", "3003", "3007", "1001", "3020", "1004", "2013", "2019", "2016", "3012", "2004", "2005", "2011", "2015", "2022", "3005", "1008", "1026", "2003", "3002", "3015", "1014", "1017", "1018", "1009", "2027", "3006", "3014", "4009", "1011", "1020", "1023", "1025", "2002", "2006", "2020", "2030", "3004", "3017", "3019" ], "type": "string", "description": "Crawl restaurants in a specific Hong Kong district. Covers the main dining areas; for an area not listed, use Keyword or Restaurant IDs.", "default": "" }, "keyword": { "title": "Keyword", "type": "string", "description": "Free-text search (restaurant name, cuisine, dish, area). Combine with District to narrow further. Leave empty to skip." }, "cuisineId": { "title": "Cuisine ID (advanced)", "type": "integer", "description": "Filter by OpenRice cuisine/category id (e.g. 2004). Advanced: there is no public name list, so this takes a raw numeric id. Leave empty to skip." }, "newlyOpenedOnly": { "title": "Newly opened only", "type": "boolean", "description": "Restrict to newly-opened restaurants (~900). Combines with District/Keyword/Cuisine.", "default": false }, "crawlAll": { "title": "Crawl all of Hong Kong", "type": "boolean", "description": "Crawl across all of HK (~33,000) when no District/Keyword/Cuisine filter is set, bounded by Max restaurants. Takes many hours — keep the cap modest and run on a schedule.", "default": false }, "restaurantIds": { "title": "Restaurant IDs", "type": "array", "description": "Specific OpenRice POI ids — the number after -r in a restaurant URL (e.g. 600214). Takes priority over the filters above.", "items": { "type": "string" } }, "startUrls": { "title": "Start URLs", "type": "array", "description": "Specific OpenRice restaurant detail page URLs. Takes priority over the filters above.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxRestaurants": { "title": "Max restaurants", "minimum": 1, "type": "integer", "description": "Hard cap on how many restaurants to crawl this run (applies to every mode).", "default": 500 }, "reviewsPerRestaurant": { "title": "Reviews per restaurant", "minimum": 0, "type": "integer", "description": "How many of the most relevant + recent reviews to collect per restaurant. 0 skips reviews.", "default": 50 }, "includeMenuPhotos": { "title": "Include menu photos", "type": "boolean", "description": "Also collect user-uploaded menu / price-list photos. Turn off to reduce runtime and proxy usage.", "default": true }, "language": { "title": "Language", "enum": [ "zh", "en" ], "type": "string", "description": "Content language for OpenRice.", "default": "zh" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```