# Ticketmaster Discovery Scraper (`getdataforme/ticketmaster-discovery-scraper`) Actor Project Cheerio Crawler Typescript is a web scraping tool that uses the Cheerio library to extract structured data efficiently. It offers customizable search queries, date filtering, proxy support, and scalable scraping, delivering results in JSON format for easy integration and analysis. - **URL**: https://apify.com/getdataforme/ticketmaster-discovery-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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 ## Project Cheerio Crawler Typescript Project Cheerio Crawler Typescript is a powerful web scraping tool designed to efficiently extract data from web pages using the Cheerio library. This actor is ideal for users who need to gather structured data from websites for various analytical purposes. With its robust configuration options, it provides a reliable solution for data extraction tasks. ### Features - **Efficient Data Extraction**: Leverages Cheerio for fast and reliable web scraping. - **Customizable Search Queries**: Tailor your data collection with specific search keywords. - **Date Filtering**: Filter results by start and end dates to focus on relevant data. - **Proxy Support**: Utilize Apify's residential proxies to avoid IP bans and ensure anonymity. - **Scalable Scraping**: Capable of handling large volumes of data with ease. - **Structured Output**: Provides data in a clean JSON format for easy integration. - **User-Friendly Interface**: Simple setup and configuration through the Apify platform. ### Input Parameters | Parameter | Type | Required | Description | Example | |---------------------|----------|----------|-----------------------------------------------------------------------------|----------------| | `searchQuery` | String | Yes | Enter the search keywords for the data you wish to scrape. | "wwe" | | `maxItems` | Integer | No | Maximum number of items to scrape. | 100 | | `startDate` | String | No | Filter events starting from this date (YYYY-MM-DD). | "2023-01-01" | | `endDate` | String | No | Filter events ending at this date (YYYY-MM-DD). | "2023-12-31" | | `proxyConfiguration`| Object | No | Specifies proxy servers for scraping to hide origin. | See below | #### Example Proxy Configuration ```json { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } ```` ### Example Usage #### Input ```json { "searchQuery": "wwe", "maxItems": 50, "startDate": "2023-01-01", "endDate": "2023-12-31", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ``` #### Output ```json [ { "title": "WrestleMania 2-Day Combo April 18-19, 2026 - Ticket + Hotel Deals", "id": "1700646ACB7ABE83", "discoveryId": "1AvjZ_kGkl5rlBd", "startDate": "2026-04-19T21:30:00Z", "onsaleDate": "2025-11-28T14:00:00Z", "url": "https://travel.ticketmaster.com/en-US/package-forward/25722313?_uid=e014304e-0962-4dd5-be90-6ed724c31992&utm_source=ticketmaster&utm_medium=referral&utm_campaign=wrestlemania-ticketmaster-adp-shell-sunday", "venue": { "name": "Allegiant Stadium", "city": "Las Vegas", "state": "NV", "country": "US", "address": "3333 Al Davis Way", "latitude": 36.0909, "longitude": -115.1833 }, "timeZone": "America/Los_Angeles", "artists": [ { "name": "WWE WrestleMania", "url": "https://www.ticketmaster.com/wwe-wrestlemania-tickets/artist/853853" }, { "name": "WWE", "url": "https://www.ticketmaster.com/wwe-tickets/artist/807358" } ] } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data on events, products, or services for market insights. - **Competitive Intelligence**: Monitor competitor activities and offerings. - **Price Monitoring**: Track pricing changes over time for strategic decisions. - **Content Aggregation**: Collect and organize content from various sources. - **Academic Research**: Extract data for research projects and studies. - **Business Automation**: Automate data collection processes for efficiency. ### Installation and Usage 1. Search for "Project Cheerio Crawler Typescript" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array where each object contains detailed information about an event, including: - `title`: Event title - `id`: Unique identifier - `startDate`: Event start date - `onsaleDate`: Ticket on-sale date - `url`: Link to the event page - `venue`: Venue details (name, city, state, country, address, coordinates) - `timeZone`: Time zone of the event - `artists`: List of artists with names and URLs ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `searchQuery` (type: `string`): Enter the Search Keywords ## `maxItems` (type: `integer`): Maximum number of items to scrape. ## `startDate` (type: `string`): Filter events starting from this date (YYYY-MM-DD). Leave blank for no filter. ## `endDate` (type: `string`): Filter events ending at this date (YYYY-MM-DD). Leave blank for no filter. ## `proxyConfiguration` (type: `object`): Specifies proxy servers that will be used by the scraper in order to hide its origin. ## Actor input object example ```json { "searchQuery": "wwe", "maxItems": 100, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "searchQuery": "wwe", "startDate": "", "endDate": "", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/ticketmaster-discovery-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 = { "searchQuery": "wwe", "startDate": "", "endDate": "", "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("getdataforme/ticketmaster-discovery-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 '{ "searchQuery": "wwe", "startDate": "", "endDate": "", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call getdataforme/ticketmaster-discovery-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/ticketmaster-discovery-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ticketmaster Discovery Scraper", "description": "Project Cheerio Crawler Typescript is a web scraping tool that uses the Cheerio library to extract structured data efficiently. It offers customizable search queries, date filtering, proxy support, and scalable scraping, delivering results in JSON format for easy integration and analysis.", "version": "0.0", "x-build-id": "h2lLwNr6xTqa81zWY" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~ticketmaster-discovery-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-ticketmaster-discovery-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~ticketmaster-discovery-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-ticketmaster-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~ticketmaster-discovery-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-ticketmaster-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Enter the Search Keywords" }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of items to scrape.", "default": 100 }, "startDate": { "title": "Start Date", "type": "string", "description": "Filter events starting from this date (YYYY-MM-DD). Leave blank for no filter." }, "endDate": { "title": "End Date", "type": "string", "description": "Filter events ending at this date (YYYY-MM-DD). Leave blank for no filter." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Specifies proxy servers that will be used by the scraper in order to hide its origin.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```