# Tripadvisor Scraper (`one-api/tripadvisor-scraper`) Actor Scrape Tripadvisor.com — hotels, restaurants, attractions, vacation rentals. Autocomplete, free-text location resolver, hotel search (geoId / coords / polygon), details by locationId or URL, and reviews. One row per result with photo, rating, price, Tripadvisor URL and Raw JSON. - **URL**: https://apify.com/one-api/tripadvisor-scraper.md - **Developed by:** [ONE API](https://apify.com/one-api) (community) - **Categories:** Lead generation, Travel - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results 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 ## Tripadvisor Scraper Scrape **Tripadvisor.com** — hotels, restaurants, attractions and vacation rentals — from a single Actor. Six purpose-built endpoints: autocomplete, free-text location resolver, paginated hotel search (geoId / coordinates / polygon), full details by locationId or Tripadvisor URL, and traveler reviews by locationId. Backed by [realtyapi.io](https://realtyapi.io)'s real-time Tripadvisor API. ⭐️ Found this useful? Please leave 5 stars! Issues / requests: 📬 **oneapi.project@gmail.com** --- ### What you can do | Section | What it does | Input format | |---|---|---| | 🔍 **Autocomplete** | Tripadvisor typeahead — resolves any partial query to canonical `locationId` + name + parent geo + photo + URL. Filter by vertical (Hotels, Restaurants, Attractions, Vacation Rentals, Geos, Airports, Cruises). | free text, one per line | | 📍 **Search by Location** | Deterministic free-text resolver: text → `locationId` → enriched detail (lat/lng, parent geo, official URL, photos). | `Paris, France`, `New York City`, etc. | | 🏨 **Search Hotels** | Paginated hotel search with rating, review count, price range, ranking, address, phone, photo, and Tripadvisor URL per row. | `geoId` digits, `lat,lng,radius_mi`, or `lat,lng;lat,lng;...` polygon (≥3 pts) | | ℹ️ **Details by ID** | Full location detail for any Tripadvisor `locationId` (city / hotel / restaurant / attraction / vacation rental). | digits, one per line | | 🔗 **Details by URL** | Same details by parsing the `-dXXXXX` id out of any Tripadvisor `/Hotel_Review-...`, `/Restaurant_Review-...`, `/Attraction_Review-...` or `/VacationRentalReview-...` URL. | full Tripadvisor URL | | ⭐ **Reviews by ID** | Traveler reviews per `locationId` — author, date, rating, title, body, trip type, owner response. | digits + `Reviews entity type` (HOTEL / RESTAURANT / ATTRACTION / VACATION_RENTAL) | Fill any combination of sections in a single run — leave the others empty. --- ### Output Each autocomplete hit / resolved location / hotel / detail / review is pushed as **one dataset row**, flattened to friendly columns plus a `Raw` column with the full upstream JSON for power users. | Mode | Location ID | Name | Rating | ## Reviews | Price | Address | Photos | Tripadvisor URL | |---|---|---|---|---|---|---|---|---| | `autocomplete` | 60763 | New York City | — | — | — | New York, United States | https://media-cdn.tripadvisor.com/... | https://www.tripadvisor.com/Tourism-g60763-... | | `search_bylocation` | 60763 | New York City | — | — | — | New York | https://... | https://www.tripadvisor.com/Tourism-g60763-... | | `search_hotels/geoId` | 671150 | The Empire Hotel | 3.7 | 9615 | from $208 | 44 W 63rd Street, New York City, NY 10023 | https://... | https://www.tripadvisor.com/Hotel_Review-... | | `details_byid` | 671150 | The Empire Hotel | — | — | — | New York City | https://... | https://www.tripadvisor.com/Hotel_Review-... | | `details_byurl` | 671150 | The Empire Hotel | — | — | — | New York City | https://... | https://www.tripadvisor.com/Hotel_Review-... | | `reviews` | 671150 | Excellent stay | 5 | — | — | Caroline H | — | — | For nested fields (full photo galleries, style tags, parent-geo hierarchy, owner responses, etc.), the complete upstream JSON is in the `Raw` column. --- ### Sample input ```json { "autocomplete_inputs": ["Paris", "Hilton New York"], "autocomplete_searchType": "HOTELS", "autocomplete_resultCount": 10, "search_bylocation_inputs": ["Paris, France", "New York City"], "search_hotels_inputs": [ "60763", "40.7128,-74.0060,5", "40.6,-74.1;40.6,-73.9;40.8,-73.9;40.8,-74.1" ], "search_hotels_resultCount": 30, "search_hotels_pages": 2, "search_hotels_sortOrder": "BEST_VALUE", "search_hotels_priceRange": "min:100,max:500", "search_hotels_minRating": "4", "search_hotels_currency": "USD", "details_byid_inputs": ["60763", "671150"], "details_byurl_inputs": [ "https://www.tripadvisor.com/Hotel_Review-g60763-d671150-Reviews-The_Empire_Hotel-New_York_City_New_York.html" ], "reviews_inputs": ["671150"], "reviews_contentType": "HOTEL", "reviews_resultCount": 50 } ```` *** ### Hotel search filters - **`search_hotels_resultCount`** — hotels per page (1–100, default 30). - **`search_hotels_pages`** — pages per input (1–20). - **`search_hotels_sortOrder`** — `BEST_VALUE`, `PRICE_LOW_TO_HIGH`, `PRICE_HIGH_TO_LOW`, `TRAVELER_RANKING`, `DISTANCE`. - **`search_hotels_priceRange`** — `min:X` | `max:Y` | `min:X,max:Y` in the chosen currency. - **`search_hotels_minRating`** — minimum bubble rating (`4` = 4.0+). - **`search_hotels_currency`** — `USD`, `EUR`, `GBP`, `CAD`, `AUD`, `JPY`, `CNY`, `INR`, `AED`, `BRL`, `MXN`. *** ### Pricing **Pay per result** — $3 per 1,000 items pushed. Failed inputs return a row with `Summary: ERROR: ...` and are billed the same as a successful row. To cap spend, set `Max paid dataset items` on the run page. *** ### Tips - **Don't know a geoId?** Run the **Autocomplete** section first — its `Location ID` column gives you the canonical id to drop into Search Hotels or Details by ID. - **One free-text city → enriched record?** Use **Search by Location** — it does the typeahead resolve + detail fetch in one call. - **Lat/lng circle search?** Format: `40.7128,-74.0060,5` (last value is radius in **miles**). - **Polygon search?** Format: `lat,lng;lat,lng;...` with at least 3 points. - **Got a Tripadvisor URL?** Drop it into **Details by URL** — works on Hotels, Restaurants, Attractions, and Vacation Rentals (any `-dXXXXX` URL). - **Bulk reviews?** Reviews returns up to 100 per `locationId`. Set `reviews_contentType` to the entity vertical (`HOTEL` / `RESTAURANT` / `ATTRACTION` / `VACATION_RENTAL`). # Actor input Schema ## `autocomplete_inputs` (type: `array`): Universal Tripadvisor search-box typeahead. Returns locationId + name + parent geo + photo + URL for each match. Use the searchType filter below to scope by vertical (HOTELS, RESTAURANTS, ATTRACTIONS, VACATION\_RENTALS, GEO, AIRPORTS, ...). Great for resolving a free-text city/POI to a canonical Tripadvisor locationId before calling search\_hotels / details\_byid. ## `autocomplete_searchType` (type: `string`): Limits autocomplete to one Tripadvisor vertical. Defaults to ALL. ## `autocomplete_resultCount` (type: `integer`): How many typeahead matches to return per input. Tripadvisor caps at 25. ## `search_bylocation_inputs` (type: `array`): Deterministic free-text resolver: takes a city/region/POI string (e.g. `Paris`, `New York City`, `Eiffel Tower`), resolves it to the canonical Tripadvisor locationId via typeahead, then enriches it with the full location detail (lat/lng, parent geo, official URL, photos). One dataset row per input. ## `search_hotels_inputs` (type: `array`): Paginated Tripadvisor hotel search. Auto-detects each entry: • `60763` (digits = Tripadvisor geoId — use Autocomplete or Search-by-location to find one) • `40.713,-74.005,5` (lat,lng,radius\_miles — circle search) • `40.6,-74.1;40.6,-73.9;40.8,-73.9;40.8,-74.1` (polygon — `lat,lng;lat,lng;...` with ≥3 points) Returns hotels with rating, review count, price range, ranking, address, phone, photo, and tripadvisorUrl per row. Use `search_hotels_resultCount` + `search_hotels_pages` below to control volume. ## `search_hotels_resultCount` (type: `integer`): Hotels returned per page (Tripadvisor caps at 100, defaults to 30). ## `search_hotels_pages` (type: `integer`): Number of pages to fetch per hotel-search input. Page size × pages = total hotels. ## `search_hotels_sortOrder` (type: `string`): How to sort hotel results. `BEST_VALUE` is Tripadvisor's default. ## `search_hotels_priceRange` (type: `string`): `min:100`, `max:500`, or `min:100,max:500`. Filter on nightly price. ## `search_hotels_minRating` (type: `string`): Filter hotels by minimum bubble rating. Empty = no filter. Example: `4` for hotels rated 4.0 or higher. ## `search_hotels_currency` (type: `string`): ISO currency code used for prices in the response. ## `details_byid_inputs` (type: `array`): Pull the full location detail for a Tripadvisor locationId (city, hotel, restaurant, attraction, vacation rental). Returns placeType, lat/lng, parent geo, photos, official URL, contact info. One dataset row per locationId. ## `details_byurl_inputs` (type: `array`): Pull full location detail for a Tripadvisor URL. Works on: • `/Hotel_Review-...-Reviews-...html` • `/Restaurant_Review-...html` • `/Attraction_Review-...html` • `/VacationRentalReview-...html` The `-dXXXXX` segment in the path is parsed as the locationId. ## `reviews_inputs` (type: `array`): Pull traveler reviews for a hotel / restaurant / attraction / vacation rental. Each review (author, date, rating, title, text, trip type, owner response) is pushed as its own dataset row. Use `reviews_contentType` below to set the entity vertical and `reviews_resultCount` to cap items. ## `reviews_contentType` (type: `string`): Tripadvisor splits review storage by entity vertical. Pick the type that matches the locationIds you entered above. ## `reviews_resultCount` (type: `integer`): Cap total reviews fetched per locationId (1–100). ## Actor input object example ```json { "autocomplete_inputs": [ "Paris", "Hilton New York" ], "autocomplete_searchType": "ALL", "autocomplete_resultCount": 10, "search_bylocation_inputs": [ "Paris, France", "New York City" ], "search_hotels_inputs": [ "60763" ], "search_hotels_resultCount": 30, "search_hotels_pages": 1, "search_hotels_sortOrder": "BEST_VALUE", "search_hotels_priceRange": "", "search_hotels_minRating": "", "search_hotels_currency": "USD", "details_byid_inputs": [ "60763", "671150" ], "details_byurl_inputs": [ "https://www.tripadvisor.com/Hotel_Review-g60763-d671150-Reviews-The_Empire_Hotel-New_York_City_New_York.html" ], "reviews_inputs": [ "671150" ], "reviews_contentType": "HOTEL", "reviews_resultCount": 50 } ``` # 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 = { "autocomplete_inputs": [ "Paris", "Hilton New York" ], "search_bylocation_inputs": [ "Paris, France", "New York City" ], "search_hotels_inputs": [ "60763" ], "details_byid_inputs": [ "60763", "671150" ], "details_byurl_inputs": [ "https://www.tripadvisor.com/Hotel_Review-g60763-d671150-Reviews-The_Empire_Hotel-New_York_City_New_York.html" ], "reviews_inputs": [ "671150" ] }; // Run the Actor and wait for it to finish const run = await client.actor("one-api/tripadvisor-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 = { "autocomplete_inputs": [ "Paris", "Hilton New York", ], "search_bylocation_inputs": [ "Paris, France", "New York City", ], "search_hotels_inputs": ["60763"], "details_byid_inputs": [ "60763", "671150", ], "details_byurl_inputs": ["https://www.tripadvisor.com/Hotel_Review-g60763-d671150-Reviews-The_Empire_Hotel-New_York_City_New_York.html"], "reviews_inputs": ["671150"], } # Run the Actor and wait for it to finish run = client.actor("one-api/tripadvisor-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 '{ "autocomplete_inputs": [ "Paris", "Hilton New York" ], "search_bylocation_inputs": [ "Paris, France", "New York City" ], "search_hotels_inputs": [ "60763" ], "details_byid_inputs": [ "60763", "671150" ], "details_byurl_inputs": [ "https://www.tripadvisor.com/Hotel_Review-g60763-d671150-Reviews-The_Empire_Hotel-New_York_City_New_York.html" ], "reviews_inputs": [ "671150" ] }' | apify call one-api/tripadvisor-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=one-api/tripadvisor-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tripadvisor Scraper", "description": "Scrape Tripadvisor.com — hotels, restaurants, attractions, vacation rentals. Autocomplete, free-text location resolver, hotel search (geoId / coords / polygon), details by locationId or URL, and reviews. One row per result with photo, rating, price, Tripadvisor URL and Raw JSON.", "version": "1.0", "x-build-id": "P5SJAtzAUme30GoZ1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/one-api~tripadvisor-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-one-api-tripadvisor-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/one-api~tripadvisor-scraper/runs": { "post": { "operationId": "runs-sync-one-api-tripadvisor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/one-api~tripadvisor-scraper/run-sync": { "post": { "operationId": "run-sync-one-api-tripadvisor-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "autocomplete_inputs": { "title": "Autocomplete queries (one per line)", "type": "array", "description": "Universal Tripadvisor search-box typeahead. Returns locationId + name + parent geo + photo + URL for each match. Use the searchType filter below to scope by vertical (HOTELS, RESTAURANTS, ATTRACTIONS, VACATION_RENTALS, GEO, AIRPORTS, ...). Great for resolving a free-text city/POI to a canonical Tripadvisor locationId before calling search_hotels / details_byid.", "items": { "type": "string" } }, "autocomplete_searchType": { "title": "Autocomplete vertical", "enum": [ "ALL", "HOTELS", "RESTAURANTS", "ATTRACTIONS", "VACATION_RENTALS", "GEO", "AIRPORTS", "AIRLINES", "CRUISES" ], "type": "string", "description": "Limits autocomplete to one Tripadvisor vertical. Defaults to ALL.", "default": "ALL" }, "autocomplete_resultCount": { "title": "Autocomplete results per query", "minimum": 1, "maximum": 25, "type": "integer", "description": "How many typeahead matches to return per input. Tripadvisor caps at 25.", "default": 10 }, "search_bylocation_inputs": { "title": "Free-text location to resolve + enrich (one per line)", "type": "array", "description": "Deterministic free-text resolver: takes a city/region/POI string (e.g. `Paris`, `New York City`, `Eiffel Tower`), resolves it to the canonical Tripadvisor locationId via typeahead, then enriches it with the full location detail (lat/lng, parent geo, official URL, photos). One dataset row per input.", "items": { "type": "string" } }, "search_hotels_inputs": { "title": "Hotel search inputs (one per line)", "type": "array", "description": "Paginated Tripadvisor hotel search. Auto-detects each entry:\n• `60763` (digits = Tripadvisor geoId — use Autocomplete or Search-by-location to find one)\n• `40.713,-74.005,5` (lat,lng,radius_miles — circle search)\n• `40.6,-74.1;40.6,-73.9;40.8,-73.9;40.8,-74.1` (polygon — `lat,lng;lat,lng;...` with ≥3 points)\n\nReturns hotels with rating, review count, price range, ranking, address, phone, photo, and tripadvisorUrl per row. Use `search_hotels_resultCount` + `search_hotels_pages` below to control volume.", "items": { "type": "string" } }, "search_hotels_resultCount": { "title": "Hotels per page", "minimum": 1, "maximum": 100, "type": "integer", "description": "Hotels returned per page (Tripadvisor caps at 100, defaults to 30).", "default": 30 }, "search_hotels_pages": { "title": "Hotel-search pages per input", "minimum": 1, "maximum": 20, "type": "integer", "description": "Number of pages to fetch per hotel-search input. Page size × pages = total hotels.", "default": 1 }, "search_hotels_sortOrder": { "title": "Hotel sort order", "enum": [ "BEST_VALUE", "PRICE_LOW_TO_HIGH", "PRICE_HIGH_TO_LOW", "TRAVELER_RANKING", "DISTANCE" ], "type": "string", "description": "How to sort hotel results. `BEST_VALUE` is Tripadvisor's default.", "default": "BEST_VALUE" }, "search_hotels_priceRange": { "title": "Hotel price range (USD per night)", "type": "string", "description": "`min:100`, `max:500`, or `min:100,max:500`. Filter on nightly price.", "default": "" }, "search_hotels_minRating": { "title": "Minimum traveler rating (0–5)", "type": "string", "description": "Filter hotels by minimum bubble rating. Empty = no filter. Example: `4` for hotels rated 4.0 or higher.", "default": "" }, "search_hotels_currency": { "title": "Currency", "enum": [ "USD", "EUR", "GBP", "CAD", "AUD", "JPY", "CNY", "INR", "AED", "BRL", "MXN" ], "type": "string", "description": "ISO currency code used for prices in the response.", "default": "USD" }, "details_byid_inputs": { "title": "Tripadvisor locationIds (one per line)", "type": "array", "description": "Pull the full location detail for a Tripadvisor locationId (city, hotel, restaurant, attraction, vacation rental). Returns placeType, lat/lng, parent geo, photos, official URL, contact info. One dataset row per locationId.", "items": { "type": "string" } }, "details_byurl_inputs": { "title": "Tripadvisor URLs (one per line)", "type": "array", "description": "Pull full location detail for a Tripadvisor URL. Works on:\n• `/Hotel_Review-...-Reviews-...html`\n• `/Restaurant_Review-...html`\n• `/Attraction_Review-...html`\n• `/VacationRentalReview-...html`\nThe `-dXXXXX` segment in the path is parsed as the locationId.", "items": { "type": "string" } }, "reviews_inputs": { "title": "locationIds for reviews (one per line)", "type": "array", "description": "Pull traveler reviews for a hotel / restaurant / attraction / vacation rental. Each review (author, date, rating, title, text, trip type, owner response) is pushed as its own dataset row. Use `reviews_contentType` below to set the entity vertical and `reviews_resultCount` to cap items.", "items": { "type": "string" } }, "reviews_contentType": { "title": "Reviews entity type", "enum": [ "HOTEL", "RESTAURANT", "ATTRACTION", "VACATION_RENTAL" ], "type": "string", "description": "Tripadvisor splits review storage by entity vertical. Pick the type that matches the locationIds you entered above.", "default": "HOTEL" }, "reviews_resultCount": { "title": "Max reviews per locationId", "minimum": 1, "maximum": 100, "type": "integer", "description": "Cap total reviews fetched per locationId (1–100).", "default": 50 } } }, "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 } } } } } } } } } } ```